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EPA 600/5-73-005
October 1973
Socioeconomic Environmental Studies Series
Benefit of Water Pollution Control
On Property Values
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55
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C3
Office of Research and Development
U.S. Environmental Protection Agency
Washington, O.C. 20460
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and
Monitoring, Environmental Protection Agency, have
been grouped into five series. These five bread
categories were established to facilitate further
development and application of environmental
technology. Elimination of traditional grouping
was consciously planned to foster technology
transfer and a maximum interface in related
fields. The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the SOCIOECONOMIC
ENVIRONMENTAL STUDIES series. This series
describes research on the socioeconomic impact of
environmental problems. This covers recycling and
other recovery operations with emphasis on
monetary incentives. The non-scientific realms of
legal systems, cultural values, and business
systems are also involved. Because of their
interdisciplinary scope, system evaluations and
environmental management reports are included in
this series.
EPA REVIEW NOTICE
This report has been reviewed by the Office ot Research and
Development, EPA, and approved for publication. Approval
does not signify that the contents necessarily reflect the
views and policies of the Environmental Protection Agency,
nor does mention of trade names or commercial products consti-
tute endorsement or recommendation for use.
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EPA-600/5-73-005
October 1973
Benefit of
Water Pollution Control
on Property Values
By
David M. Dornbusch
Stephen M. Barrager
Contract No. 68-01-0753
Project 01AAB-07
Program Element ] H1094
Project Officer
Fred H. Abel, Ph.D.
Economic Analysis Branch
Implementation Research Division
Washington, D.C. 20460
Prepared for
OFFICE OF RESEARCH AND MONITORING
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
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Abstract
This study was undertaken to determine the current state-of-knowledge con-
cerning the measurement of the potential benefit of water pollution control
on property values, and to analyze the relationship between water quality
parameters and property values at several sites where water pollution has
been substantially reduced in recent years. Multiple-regression analysis
and an interview technique were employed to study the relationship between
residential and recreational property values and water quality components.
Study sites were located on San Diego Bay and the Kanawha, Ohio, and Willa-
mette Rivers. It was found that effective pollution abatement on badly poll
ted water bodies can increase the value of single-family homes situated on
waterfront lots by 8 to 25 percent, and that these water quality improvement
can affect property values up to 4000 feet away from the water's edge. It
was also found that the measurable water quality parameters which have the
greatest influence on property values are dissolved oxygen concentration,
fecal coliform concentrations, clarity, visual pollutants (trash and debris)
toxic chemicals, and pH.
Case study results were combined with a 1971 Environmental Protection Agency
water pollution survey to estimate the national benefit expressed in increas
residential, recreational and rural waterfront property values, to be gained
from water pollution abatement. The estimated capital value of the benefit
ranges from .6 to 3.1 billion dollars, with a most likely benefit of 1.3
billion dollars.
This report was submitted in fulfillment of Contract number 68-01-0753 under
the sponsorship of the Office of Research and Development, Environmental
Protection Agency, by David M. Dornbusch and Company, Inc., San Francisco,
California.
ii
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Contents
Section Page
I Conclusions 1
II Introduction 3
III Site Selection and Case Study Methodology 6
IV Site Descriptions and Case Study Results 16
V Water Quality 42
VI National Benefit of Water Pollution Control on Property Values 51
VII Acknowledgments 77
VIII References 78
IX Appendices 80
in
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Figures
No.
\ Site Locations • 10
2 The Linear and Reciprocal Form of Water Quality Influence 15
3 Map of Coronado and San Diego Bay, California 24
4 Map of Clackamas County, Oregon - Urban and Rural Sites 26
5 Map of Charleston and Dunbar, West Virginia on the Kanawha River .... 27
6 Map of Beaver, Pennsylvania Site on the Ohio River 29
7 Benefit of Pollution Abatement Expressed as Percentage of Residential Property
Value 38
8 Benefit of Pollution Abatement Expressed as Dollar Increase Per Single-Family
Residence 39
9 Relative Values of Water Quality Aspects and Important Water Parameters
Which Determine the Suitability for Each Purpose 48
10 Major Water System Boundaries 55
11 Minor Basins with DI Index Greater Than .2 56
12 Estimated Relationship Between Pollution Intensity and Maximum Property
Value Increase Obtainable by Pollution Abatement 58
13 Relationship Between Benefit and Water Distance Used for Medium National
Benefit Calculation 63
14 Relation Between Town Population and Maximum Pollution Abatement Benefit 65
IV
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Tables
No. Page
1 Residents' Interview Responses - Water Quality Change 21
2 Residents' Interview Responses - Water Quality Change 22
3 Residents' Interview Responses - Water Quality Change 23
4 Important Correlation Coefficients and Property Value Statistics 30
5 Gross Taxable Value of Locally Assessed Real Property 32
6 Regression Equations for Percent Changes in Property Values 34
7 Regression Equations for Absolute Changes in Property Values 35
8 Pollution Abatement Benefits Calculated from Percent and Absolute Change
Regression Equations 36
9 Water Quality Aspect Value Assessment 47
10 Area of Residential Property and Waterfront Parks Affected by Water Pollution
in Metropolitan Areas of More Than One Million Population 62
11 Expected Benefit in Towns Outside Large Metropolitan Areas 67
12 Miles of Polluted Rural Waterfront Measured for Each Major Basin 69
13 Estimated Value of Rural Land Affected by Water Pollution 70
14 Expected Residential and Recreational Property Value Increase Obtainable by
Water Pollution Abatement 73
15 National Benefit of Pollution Abatement on Property Values 75
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Section I
Conclusions
1. A substantial water quality improvement in a badly polluted water body will
increase the value of nearby urban residential property. The pollution abatement
benefit for each residence can be expressed as a percentage of the original property
value. The maximum percentage increase occurs at the waterfront and the benefit
decreases inversely with distance from the water. The extent of the water quality
influence depends on the presence or absence of obstacles between residences and
the water, but benefits can be obtained up to 4000 feet from the water body. This
study measured pollution abatement benefits of from 3 to 25 percent for single-
family waterfront residences.
2. The value of rural land suitable for development near a large water body is also
increased by water pollution abatement. This study observed increases attributable
to pollution abatement of from 65 to 100 percent for waterfront land on the
Willamette River near Portland, Oregon.
3. This study found that residential property owners generally value the wildlife
support capacity of natural water resources more than either aesthetics or boating
and swimming potential. The measurable water quality parameters which have the
greatest influence on property values were found to be dissolved oxygen, fecal
coliforms, clarity, trash and debris (visual pollutants), toxic chemicals, and pH. The
feasibility of correcting those pollution components which are of greatest impor-
tance to residential property owners, and therefore of realizing benefits in property
value increase, is very good.
4. Our results indicated that abatement of pollution in all waters in the nation to levels
which are not inhibiting to desirable life forms or practical uses and which are
aesthetically agreeable would increase the total capital value of existing residential
and recreational property from .6 to 3.1 billion dollars. The most likely increase
would be 1.3 billion dollars. The annualized value of 1.3 billion dollars is 76 million
dollars, using a 6 percent discount rate. About 59 percent of the total increase
would occur in towns of from 1000 to 1,000,000 population; 31 percent would
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accrue to large metropolitan areas; and property value increases in rural areas would
account for the remaining 10 percent.
Although 40 percent of the nation's people live in metropolitan areas of greater than
one million population where property values are highest, these areas receive only 31
percent of the benefit. This is explained in part by the fact that industry and
commerce, rather than homes and parks, occupy most of the land adjacent to badly
polluted water in these areas.
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Section II
Introduction
Increases in the value of properties adjacent to rivers, lakes, and bays represent one of
the benefits of water pollution abatement. Our objective in this study has been to
measure this benefit. In the course of achieving this objective, we accomplished tasks
which are described in detail in this report:
1) We completed a review of the literature on the current state-of-knowledge
concerning the potential benefit of water pollution control on property values.
2) We undertook specific case study analyses in several areas where a significant
change in water quality had taken place in order to determine the magnitude of
the property value impact, the importance of specific components of water
quality in people's valuation of water resources, and the relation between
property values and water quality. We focussed on residential and recreational
properties in metropolitan areas and towns, and on all waterfront properties in
rural areas.
3) Using national figures for the amount of stream and lake shoreline exposed to
polluted water, and the magnitude of the impact measured at our case study
sites, we estimated the benefit to the nation in terms of increased residential
and recreational property values which would result from nationwide water
pollution abatement.
Property value is a valid and important reflector of the value of improvements to a
natural resource such as water quality improvements. If people consider a stream or lake
to be an amenity, they will be willing to pay more to live near it. The amount people are
willing to pay will thus be reflected in the market value of houses and land located near
the water. However, if the water is badly polluted, it ceases to represent an amenity to
nearby property owners, and consequently the value of properties near the water will
decrease. As the water quality subsequently improves, nearby property values will
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increase accordingly to reflect the water body's value as an amenity. The water pollution
abatement will produce the greatest benefit to owners of properties located at the water's
edge, while at some distance away, the water quality improvements will have no
measurable effect.
Multiple-regression analysis was used to isolate the influence of the change in water
quality from all of the other influences affecting property value changes during the time
period studied at the case sites. Multiple-regression analysis offers a proven and widely
accepted method for apportioning the total variation in a variable to the. various
influences which combine to produce that variation. The explanation of property value
changes is one frequent application of the multiple-regression technique. Since the
influence of water quality improvements on surrounding property values varies with the
properties' distances from the water body, we were able to refine the technique to isolate
that portion of total property value change which is associated with changes in water
quality by including distance from the water in our regression equations. In the absence
of other influences which vary with distance from the water resource, it is reasonable to
attribute to water quality changes that portion of the variation in property value which is
highly correlated with distance from the water. With this method, we can determine how
rapidly .water quality improvement effects decrease with distance from the water as well
as the magnitude of the impacts. This refinement allowed us to estimate Jhe national
property value benefit attainable from water pollution abatement with a greater degree of
precision than would have been possible had we not been able to assess the decrease of
benefits impact with distance from the water.
When applying the regression technique to isolate water quality influences, particular care
must be taken to account for any other property value influence which might vary
colinearly with distance to the water body and thus be confused with the water quality
influence. A new waterfront park would constitute an influence of this type. We
deliberately selected our case study sites to avoid such colinear influences.
Our final selection of case study sites was the product of a nationwide search for water
bodies with documented water quality changes. Five major water bodies were located
which had experienced significant and well-documented water quality, improvements
between ,1960 and 1970. The water bodies were San Diego Bay, the Willamette River in
Oregon, the Kanawha River in West Virginia, the Ohio River in Pennsylvania, and Lake
Washington in Seattle, Washington. Seven areas adjacent to these water bodies were
selected as case study sites to measure the influence which recent water pollution
abatement had on surrounding property values. 'Six of these areas'• were urban or
suburban, while one was rural.'
Sales prices and calibrated local tax assessment values were used to measure the changes
in the value of single-family residences and in recreational and rural waterfront land
which occurred during the same time period as the water pollution .abatement. We
conducted our own sales ratio studies to compare local tax assessments with actual sales
prices in order to validate the accuracies, of assessed values as surrogate data where actual
sales prices were not available for both of the years bounding the period of water quality
change. We used only those assessed values which compared closely to actual sales prices.
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We also designed a personal interview for administration to residential property owners in
our study areas, to learn if they perceived the change in water quality, and how they
ranked the relative importance of the wildlife support capacity, recreational potential and
aesthetic aspects of all water resources. We also queried the relative importance of
different measurable aesthetic water components in their total valuation of water quality.
The interviews were conducted at the study sites by an experienced opinion research
firm.
Although we did not develop a new water quality index, we did use the tabulated
interview results to determine which water quality components - and hence, which
pollutants - are the primary determinants of the property value impact. This result in
turn applies to an estimation of which water pollution abatement efforts will produce the
greatest property value benefit.
Our estimation of the national property value benefit obtainable through water pollution
abatement efforts proceeded in several steps. First, we used the results of a 1971 United
States Environmental Protection Agency water pollution survey using a pollution
duration-intensity (DI) index, to locate polluted water reaches throughout the contiguous
United States, and to establish the severity of the pollution of each. The amount and
types of all discernible recreational and residential property adjacent to polluted water
was measured on United States Geological Survey topographic maps.
i
Then we established a relationship between pollution levels as measured by the DI index
and the potential increases in property values which would accrue from pollution
abatement. The estimation of the relationship was based primarily on the case study
results and our experience in other phases of the study.
Finally, we calculated" the benefit in terms of an increase in property values nationwide
which would be obtained if abatement efforts reduced the pollution level of all national
waters to a DI factor of zero. (A DI level of zero implies pollution levels which are not
inhibiting to desirable life forms and practical uses, and which are aesthetically agreeable.)
We calculated low, medium, and high estimates of the national benefit to account for the
relative conservativeness of the assumptions upon which our measurements were made for
different kinds of areas, the differing extent of inclusiveness of properties for which value
changes were measured, and the different degrees of confidence supporting the ranges
within which the actual national benefit can be expected to lie.
The body of this report consists of four main sections. Section II describes the method-
ology used to measure water quality influences on property values. Section III discusses
the characteristics of the sites chosen for intensive study and the results of the regression
analysis. Water quality and the results of the personal interviews to determine the
evaluation of water quality aspects are discussed in Section IV. Section V describes the
methodology and results of the national benefit calculation. The annotated bibliography
which represents a review of the current state-of-knowledge on the potential benefits of
water pollution control is contained in Appendix I.
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Section III
Site Selection and
Case Study Methodology
SITE SELECTION
Our first project step was the location of potential sites for intensive study. In order to
maximize the effectiveness of our property benefits assessment methodology, the case
study sites must meet several selection criteria. First, the study site should be located on
a water body which has experienced a significant and well-documented change in water
quality between 1960 and 1970. Any change in water quality which would be apparent
to people living near the water or using the water for recreational purposes is considered
significant. Good documentation is integral to meaningful study results, as well as useful
in estimating the nationwide benefits of pollution abatement. The time period, 1960 to
1970, was selected to coincide with the collection of national census data. United States
Census Bureau statistics were used to estimate coincidental changes in some socio-
economic variables. A ten-year span was considered adequate for water quality influences
to be reflected in property values.
In addition to water quality changes, the potential site should have a stable, relatively
homogeneous area of residential and recreational property running from the water's edge
back for a distance of at least 4,000 feet. The distance to the water from a particular
property plays a major role in our regression analysis, and it is important to have as
much variation in this variable as possible. However, our earlier studies have indicated
that the impact of a water resource on property values is generally not significant beyond
4,000 feet from the shoreline [10].
It is also desirable for the potential sites to be clear of major obstructions, such as
freeways or railways, between the water body and surrounding property. Such obstruc-
tions interfere with the resource's impact, and property value responses beyond these
obstructions are usually non-existent.
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Another desirable site characteristic is the presence of some water-oriented recreation
property within or near the site. The presence of recreation property at the site enables
us to study water quality influences on recreational land value as well as the role of
recreational areas as interfaces between the water body and private property.
To estimate changes in property values over time requires recorded market values for the
same property in or around the two years bounding the period of the change under
study. Actual sales prices under stable conditions are the best reflection of owners'
valuations of their properties. However, only a small fraction of all properties are sold in
any one year. An even smaller fraction of all properties would have been sold both in the
years around I960, and then again in the years around 1970. Thus it is extremely
unlikely that a sufficient number of properties at any one site would have been sold in
both of those years (even allowing a year or two on each side of both years) to provide
for valid results. To circumvent this primary data problem, assessed value data can be
used instead of actual sales prices, to estimate the market value for one of the base years.
Clearly, assessed values only represent an assessor's judgment of the property's market
value. However, we have found that in some cases, the assessors' estimations reflect actual
sales prices with remarkable accuracy. This is no coincidence, since frequently the
assessors' formula is carefully developed on the basis of actual sales records and period-
ically checked to keep it up to date. Furthermore, assessed values can be verified and
adjusted by using those sales prices which are available. Since it is integral that a good
correlation exist between assessed property values and actual sales prices at any- site
where we intend to use assessed values to represent market prices, we compared assessed
values with actual sales prices at our study sites prior to their use. The results of the sales
price-assessed value correlation analysis for selected sites are included in Appendices A
through F.
Bays, lakes, and river reaches where there might have been a significant change in water
quality between 1960 and 1970, were first located through library research and telephone
conversations with water resource researchers and managers. The U.S. Army Corps of
Engineers, the U.S. Geological Survey, the U.S. Department of the Interior Office of
Water Resources Research, and the Environmental Protection Agency (EPA), as well as
many authorities in state governments and river basin commissions were contacted for
potential site listings. Systematic coverage of the contiguous United States was accom-
plished by contacting the chief of the Surveillance and Analysis Branch of the EPA
Office of Water Programs in each of the ten regional EPA offices. This effort yielded the
names of over forty water bodies where there may have been significant pollution
changes.
Persons in government or water management most familiar with the water quality at a
potential site were located and contacted to verify the water quality changes, to
determine the history of the changes, and to establish the location, quantity, and quality
of the data documenting the changes. If water quality changes could be verified, the
distribution and condition of real estate was then determined as completely as possible
by telephone contacts and map study; The status and accessibility of local real estate
sales and tax assessment data were also established as completely as possible by tele-
phone.
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Finally, trips were made to several water quality surveillance offices to examine actual
water quality data in order to verify changes in pollution levels. Where water quality
changes were significant and well-documented, field surveys were made to determine the
distribution of private and public property, to examine the relationship between proper-
ties and the water body, and to gather preliminary sales and tax data.
A list of all of the water bodies which were investigated and then rejected as study sites
follows this report in Appendix H. An examination of this list will reveal the extent of
the site search.
Our investigation yielded seven sites situated on five different water bodies where there
have been substantial, well-documented water quality changes since 1960, and'where
property characteristics and distribution meet our study criteria. The sites included two
rivers in the eastern United States, and a river, an ocean bay, and a lake in the western
states. Six sites are urban, and one is rural.
The water bodies finally selected for study were San Diego Bay in Southern California;
the Willamette River in Oregon; Lake Washington in Seattle, Washington; the Ohio River
in the vicinity of Pittsburgh, Pennsylvania; and the Kanawha River in West Virginia.
Deliberate efforts to control pollution have measurably improved water quality in each of
these water bodies since 1960. The nature of the improvements is discussed in Section
III, and the water quality data is included in Appendices A through G.
The property value impact of water pollution abatement is a function both of the size
and type of water body and of the type of development on its banks or shore. That is,
the property value benefit may be greater for large lakes surrounded by recreation
property, than for rivers, where residential properties are not the sole development. At
the same time, the size and type of water body may influence the type of adjacent real
estate development, and this relationship may differ for stream and river bank as opposed
to lake and bay shore or ocean beach. (For example, it would seem inadvisable to
develop massive industry on the shore of a small lake but very convenient to locate it
along the banks of a large river). This relationship will even differ considerably between
water bodies of the same type where local circumstances vary. Obvious factors which
influence the relationship are differences in suitability for development, potential for
alternative uses, local economic circumstances, and recreation opportunities.
In addition, where there are no developments or topographic obstructions, a tendency
exists for higher-priced properties to locate on the shoreline, whatever the water body.
Lower-priced properties adjacent to these may then realize a certain spill-over of benefits
from the higher-priced homes. This effect can allow for the property value benefits from
improved water quality to be felt by properties at greater distances from the water's edge
than would otherwise be the case.
In selecting our case study sites, we tried to achieve as much variation as possible along
water body types (including rivers, lakes, and bays) and geographic locations so that our
results and the experience gained would be useful in extrapolating to estimate the
national benefits of pollution abatement in all types of water bodies. Our nationwide
survey was exhaustive for sites on water resources which could meet our selection
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criteria. The seven sites adjacent to five water bodies reported here are the only
candidates nationwide which met our criteria for water quality conditions and documen-
tation and for the physical developments at the site. It thus seems highly unlikely that a
study sample large enough to represent all water body types and site development
criteria, as well as all water quality changes could be found to rigorously determine the
differing impacts of different types of water resources. However, the preponderance of
controllable water pollution nationwide occurs in rivers; thus the inclusion of three rivers
in our sample renders our study particularly valuable for extrapolating the national
property value benefit attainable through water pollution abatement.
On the basis of the criteria discussed above, seven sites adjacent to the water bodies were
selected for intensive study. San Diego Bay, the Ohio River, the Willamette River, and
Lake Washington each had one residential study area, while the Kanawha River had two.
An additional area in the Willamette River Valley near Portland, Oregon, was selected to
study the influence of water quality on rural land values. The sites are plotted on the
map of Figure 1, while the detailed characteristics of each site are treated in Section III
and Appendices A through G.
Occupying the entire west side of Lake Washington, Seattle, was a very appealing site
because it offered enough residential properties which sold both in years near I960 and
again near 1970 for us to use only actual sales price data in the regression analysis. The
dramatic water changes experienced in Lake Washington and the fact that this was the
only lake site available, also rendered it a highly desirable site, in spite of the serious
economic. recession which upset the Seattle housing market in the late 1960's. Our
attempts to correct for the effects of the recession on the housing market proved
unsuccessful, and after poor preliminary results we rejected the Seattle site.
METHODOLOGY
The same general methodology, was applied at every site. Initially, the study area
boundaries were defined and all those physical factors within or near the site were
identified which might affect property values differentially across the area colinearly with
the water quality improvement. These factors included shopping centers, schools, major
employers, growing commercial areas, new highways or bridges, and parks. The impact of
any of these stationary influences on surrounding property values tends to depend on
their line of sight distance or shortest access route from the property to the influence
object.
Changes in non-stationary variables which might affect property values were also investi-
gated, including zoning changes, changes in city services, such as sewers or water, changes
in housing density, and changes in racial composition.
Zoning changes were determined from maps in city or county planning offices. We
controlled for the effects of rezoning by eliminating from our study any area or parcel of
land which had been rezoned since 1960. No major rezoning programs had been
undertaken at any of the study sites. There were also no changes in city services or major
street improvements within the boundaries of any of our case sites during the period
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Lake Washington Site
I
Willamette River Urban
and Rural Sites
San Diego Bay
Coronado Site
Figure 1
SITE LOCATIONS
Ohio River Beaver Site
Kanawha River Dunbar
and Charleston Sites
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studied. This kind of stability is one major advantage of selecting sites in well established,
developed neighborhoods.
As mentioned earlier, the years 1960 and 1970 were chosen as base years to study
property value changes. Water quality improvements at all sites took place during the
1960's, and the time span selected allowed Census data to be used to measure changes in
housing density, population, and racial composition.
Sales prices were used at all sites to estimate market value at one extreme of the time
span, while tax assessment valuations were used at the other (with the single exception of
the Seattle site). Thus, the change in market value of any particular property was
generally approximated by the difference between a sales price and an assessed value.
The local tax assessments used in this study were all tested by comparing a large sample
of house sales prices with the assessed value of the same property at the time of the sale.
Only data from good assessments were used as substitutes for market value and a
correction factor based on actual sales prices was always applied to assessed values to
correct any consistent bias.
Sales price data was collected from public tax records or deeds. We made every effort to
select only those sales which might represent the true market value of a particular piece
of property. Sales between members of the same family or sales of estate property were
discarded, as were all sales where there was a large discrepancy between a sales price and
the assessed value of the property at the time of sale. In some cases the sales price
appearing on a deed or other public record is misleading, and an accurate interpretation
of the recorded sales price figure requires information about such things as financing
arrangements, and other property transactions between buyer and seller. However,
misrepresentative sales prices can usually be detected by reading the terminology of the
deed or by comparing sales price to assessed value at the time of the sale. We were able
to detect and discard misleading data by following these procedures.
The property value change for the" period studied was calculated by adding the capitalized
value of property tax changes to the estimated change in sales or market value. The
purchaser of a parcel of property assumes, in addition to his property rights, the
responsibility for paying property taxes into the indefinite future. Thus, the real market
value of the property, what the buyer really pays, is the total sales price of the property
plus an indefinite number of tax payments. These taxes must be accounted for in our
analysis because they directly affect the sales prices we observe, while increased tax
revenues constitute another benefit attributable to improved water quality [4].
A discount rate of 10 percent was used to calculate the capitalized or present value of
the tax change. All 1960 values, sales prices and taxes, were inflated to 1970 dollar values
using the Consumer Price Index. Capitalized values of taxes, using the 10 percent
discount rate, averaged about 20 percent of sales prices.
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REGRESSION MODELS
Two models were developed to explain temporal changes in residential property value.
One model expresses value changes in absolute terms, whereas the other expresses change
in percentage terms. Both models have the following format:
CHANGE IN PROPERTY VALUE = aj x POPULATION DENSITY CHANGE
+ a2 x HOUSING DENSITY CHANGE
+ a3 x RACIAL COMPOSITION CHANGE
+ a4 x ZONING CHANGE
+ as x INITIAL PROPERTY VALUE
+ b, x DISTANCE! x CHANGE IN INFLUENCE,
+ • • • +
+ bj x DISTANCE x CHANGE IN INFLUENCE;
+ bj+, x DISTANCE-TO-WATER FUNCTION
+ CONSTANT + RANDOM ERROR
The left side of the equation is the change in property value. Value change for the
absolute model is calculated as follows:
Change in Value, AV = V70 + CT70 - (V60 + CT60) x PI
where V70 = Sales price in 1970.
CT70 = Capitalized value of real property taxes in 1970.
V60 = Sales price in 1960.
CT60 = Capitalized value of real property taxes in 1960.
PI = Rate used to inflate 1960 values to 1970 dollar values.
In the "percentage change" model, change is expressed as:
AV
PI x V60
We analyzed both absolute and percentage value change models for two reasons. First, it
was uncertain whether an amenity such as a proximate water resource raises all property
values by a fixed amount, as in the absolute value model, or whether the increase in
value depends on the original value of the property, as in the percentage change model.
The results from both models were consistent, and indicated that the increase in value
due to pollution abatement does depend on the initial property value ,as implied by the
percentage change model for single-family residences. That is, the benefit to the owner of
a 100,000-dollar home might be a 10,000-dollar increase in its value, whereas the benefit
to the owner of a 10,000-dollar home next door might be only 1000 dollars. (Both
increases are 10 percent of the initial property value, but the absolute value model would
attribute the same benefit to both properties.)
The second reason for analyzing percentage change as well as absolute change is more
subtle. If properties near the water tend to be more expensive as is frequently the case, a
colinearity will exist between distance to the water and property value. The colinearity
12
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can lead to difficulties in separating property value changes due to inflation and increases
in the demand for all property from changes due to pollution abatement. This ambiguity
is avoided to some degree by using the percentage change model.
One explanation for the better results of the percentage increase model is that the
amount people are willing to pay for an amenity such as a water resource depends upon
their income and higher income families, who buy higher priced homes, are willing to pay
more for improved water quality.
The terms on the right side of the model equation are explained in the following
paragraphs.
POPULATION DENSITY CHANGE, HOUSING DENSITY CHANGE, and RACIAL
COMPOSITION CHANGE are all important variables, expressing changes in community
character which may affect property values. U.S. Census data for 1960 and 1970 were
used to measure the changes in these variables by Census tract. Sites were selected for
study only where the changes in these variables were so small that they could have no
appreciable affect on changes in property values and hence could be eliminated from the
regression.
ZONING CHANGE can be treated as a dummy variable with its value either one or zero,
depending on whether the property was or was not within an area rezoned between 1960
and 1970. Such zoning changes can have important effects on property values, principally
by affecting expectations. Zoning changes can be determined from maps in the offices of
local planning authorities. The number of parcels of land rezoned within our sites was so
small that we removed them from the sample thus eliminating the zoning variable from
further consideration.
INITIAL PROPERTY VALUE is the 1960 market value of the property. This term is
particularly important in the absolute value change model because it captures the change
in value due to simple capital appre9iation, that is, properties of greater value increase in
value in the same proportion as do parcels of lesser value. This term becomes important
in the percentage change form of the model if a great variation in property values exists
at the study site and market demand is substantially different for high- and low-valued
properties.
CHANGE IN INFLUENCE is any change in the area, such as construction of a school,
shopping center, or highway access, which may have an impact on property values. It can
also represent major changes in these types of influences, such as expansion of a school
or improvement of local shopping facilities. An implicit assumption in the form of this
term- is that the magnitude of the impact of any of such influence on the value of any
particular property will be proportional to the distance between the property and the
influence. Our earlier studies have shown that the influence of these nuisances and
amenities on the value of properties near them is best represented by an expression which
depends directly upon the distance between each property and the influencing factor.
The most appropriate expression in each particular instance will be discussed with each
specific equation.
13
-------
DISTANCE-TO-WATER FUNCTION expresses the form of the relation between property
appreciation due to water quality improvement and the distance from the water. Two
functional forms were tested, a linear function of distance and a function inversely
proportional to distance. Both forms are diagrammed in Figure 2. On the basis of our
earlier results, we initially assumed in both cases that the influence of the river or lake
was negligible beyond 4000 feet. In some cases it was found that the limit of influence
approximated 2000 feet. The inverse form of the distance function provided the best
statistical results in most cases.
The CONSTANT TERM accounts for any effects which the other terms of the equation
have not specifically accounted for but which exert a predominant influence in increasing
or decreasing property values over the period of analysis. Such effects as an increase in
air pollution will be accounted for by this term.
The RANDOM ERROR term accounts for all of the random effects which may have
exerted an influence on the change in value but which did not produce a predominant
inflation or deflation of property values.
In selecting independent variables for inclusion in the property change model it is integral
to include all those factors which might be colinear or confused with a water pollution
abatement impact. For example, if a new park were created along the water's edge during
the same period that water quality improved, the regression analysis would be. unable to
distinguish the benefits due to the park from the benefits due to improved water; they
act colinearly. We selected sites carefully to avoid including simultaneous improvements,
while any influence which was suspected of colinearity with the water quality improve-
ment was included in the model so that the magnitude of its effect could be identified.
Some variables which are significant determinants of property value changes can conve-
niently be ignored if their absence does not affect measurement of the water impact. The
value of improvements which have been added to each property during the period of
analysis is one such variable. For established, well-maintained residential areas such as our
study sites, home improvements will tend to be relatively small and random. Thus the
errors introduced by ignoring this variable can be expected to be small and random and
in no way confusable in the final analyses with effects due to pollution abatement. The
object of our model is to isolate property value changes attributable to water quality
changes, rather than to explain completely why a property value changes.
14
-------
W
>
O
a
:«
w
H
o
H
t-H
w
w
Linear Form
Reciprocal
Form
DISTANCE FROM WATER BODY
Figure Z
THE LINEAR-AND RECIPROCAL FORM
OF WATER QUALITY INFLUENCE
15
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Section IV
Site Descriptions and
Case Study Results
Property value changes were analyzed for six urban residential sites and one rural site.
This section describes the water quality change, property characteristics, and regression
results for each study site. Each subject, such as water quality or property characteristics,
will be discussed for all the sites together rather than treating the three subjects on a
site-by-site basis. This organization will serve to highlight the differences between sites
and avoid repetition. Water quality data, specifics about data used in the value change
calculation, and a map of influences, are included in Appendices A through F. The
regression results are summarized in Tables 6, 7, and 8. Raw data and correlation
coefficients for all sites are included in Appendix K.
WATER QUALITY CHANGES
Five major water resources were located where significant, well-documented water quality
changes had taken place between 1960 and 1970. Each of these water bodies has been
the focus of deliberate municipal and industrial clean-up action after it reached or was
approaching a highly polluted condition.
1) San Diego Bay — Located on the Pacific Coast in Southern California, the Bay
is an important recreational resource as well as a military and commercial
fishing port.
-2) Willamette River - This river runs generally northward through western Oregon
to its confluence with the Columbia River at Portland. Two-thirds of all
Oregonians live in the Willamette River Valley, where the principal economic
activities are agriculture, lumber, wood pulp and related industries.
16
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3) Ohio River (Pennsylvania) — The Ohio River, America's major industrial river,
is formed by the confluence of the Allegheny and Monongahela Rivers at
Pittsburgh, running through six states and joining the Mississippi at Cairo,
Illinois.
4) Kanawha River — The Kanawha is a tributary of the Ohio, lying wholly within
West Virginia.
5) Lake Washington - This large lake is located within the Seattle metropolitan
area. The history of water quality changes on Lake Washington is discussed in
Appendix G.
In 1955, pollution in San Diego Bay was characterized by frequent algae blooms, low
dissolved oxygen concentrations, and high concentrations of bacteria associated with
sewage treatment plant discharges (fecal coliforms). The bay. was unfit for swimming,
unhealthy for wildlife, and unsightly. Sludge deposits were accumulating on the bottom
of the Bay and floating oil and debris were frequently visible on the water's surface. The
primary cause of the pollution was the discharge into the Bay of large quantities of
insufficiently treated municipal and industrial wastes from San Diego and surrounding
communities. These Biochemical Oxygen Demand (BOD) loads depleted the Bay's dis-
solved oxygen, thereby inhibiting fish populations, and enriched the water with nitrates
and phosphates, enabling large undesirable algae populations to flourish.
The principal corrective actions, taken in the early 1960's, were the consolidation of
waste treatment plants, improvement of treatment, and the diversion of waste discharges
to a Pacific Ocean outfall. Recovery of water quality was rapid after the waste diversion.
Although San Diego is still in the process of decreasing its waste discharges to the Bay,
the major water pollution control benefits have already been realized. Today, the
dissolved oxygen concentrations throughout the Bay remain above five parts per million,
fecal coliform levels are low, sludge deposits are disappearing, and the water is now fit
for people's swimming and indigenous fish species.
Another dramatic improvement in water quality has been realized on the lower
Willamette River. In the 1940's this portion of the river was popularly referred to as "an
open sewer." Inadequately treated, oxygen-demanding municipal wastes and industrial
wastes produced principally by wood pulp processing plants, had badly polluted the river.
The worst conditions occurred during the summer and early fall seasons, when water
flows were at very low levels, and dissolved oxygen levels approached or reached zero,
while fecal coliform counts greatly exceeded health standards. Sludge had accumulated on
the river bottom and debris from logging operations and sewage treatment plant overflow
cluttered the surface.
About thirty years ago, water quality restoration efforts were begun in the Willamette
Valley, with the result that by 1970 the Willamette River
-------
the heavy spring run-off is released in the summer and fall to supplement the lighter
run-off to maintain a sufficient flow to dilute waste loads. To complement low flow
augmentation, withdrawals of water by industry and agriculture are limited during critical
periods. The major portion of this water quality improvement took place between 1960
and 1970, the period of our study.
The large Ohio River has a highly industrialized basin. Water quality has improved at
several points within the Ohio River Valley since the early 1950's, when a compact
formed between eight principal states created the Ohio River Valley Sanitation Commis-
sion (ORSANCO) to set standards and reduce pollution. Excellent water quality data
from ORSANCO indicates that in the river reach just downstream of the Pittsburgh
metropolitan area and its giant steel-making complex, the minimum monthly Average
dissolved oxygen has increased twenty percent during the period 1963 to 1970, to over
six milligrams per liter. Average specific conductivity (a rough measure of industrial
chemical pollution) has decreased twenty percent, while minimum average pH is up
approximately ten percent. The rising pH is in part the result of efforts to limit acid
mine drainage into the tributary streams of the Ohio. A major new consolidated
municipal waste treatment plant which began operations at Pittsburgh in 1960 has also
had its effect. In 1967 a major dam project was completed on the Alleghany River.
Releases of water from behind this dam have augmented Ohio River flows during the
critical summer months, reducing pollution concentrations by dilution. However, the
measured improvements on .this reach of the Ohio are modest compared to those in San
Diego Bay or the Willamette River, and we were not certain at the outset of this study
that we could measure an impact on residential property values. Our doubts have proven
to be justified, as inconclusive results indicate that the impact was apparently small.
The Kanawha River is relatively small, with an average flow of about 9000 cubic feet per
second compared to 94,000 for the Ohio at Cincinnati. However, it is burdened with the
waste discharges of one of the largest petro-chemical industrial complexes in the United
States. In 1960, the Kanawha was grossly polluted. The lower reaches of the river were in
a septic condition (zero dissolved oxygen) during a third of each year.
A phased municipal and industrial clean-up program was implemented in the Kanawha
Valley in 1958. Removalof visual pollutants, a 40 percent reduction in the BOD
(Biological Oxygen Demand) wastes from industrial sources, and primary treatment of
sewage by all towns was accomplished by 1964. A program initiated in 1964 requiring a
50 percent reduction of remaining wastes as well as secondary sewage treatment was well
underway by 1968. On the lower portions of the Kanawha the changes in water quality
have been large. Changes in the vicinity of Charleston where our study sites were located
are measurable but not dramatic. The most significant change at Charleston has probably
been the undocumented decrease in visual pollutants.
PERCEPTION OF WATER QUALITY CHANGES
Significant changes can occur in the condition of a water body without their being
readily apparent to people. If water quality improvements are to change peoples' valua-
tion of a water resource and their valuation of surrounding property in turn, the people
18
-------
must be aware that the changes have taken place. In order to determine to what extent
people actually were aware of the water quality changes described above, we interviewed
people at five of the study sites located on three of the water bodies.
The five interview sites were selected where preliminary analysis indicated positive
impacts of pollution abatement on property values. Our sample included 160 people (80
men and 80 women) who own and live in homes located within 4000 feet of three of the
water bodies. None of the persons interviewed owned property in common with other
members of the sample. Forty persons were interviewed at the urban site and forty at the
rural site on the Willamette River; forty were interviewed at the San Diego Bay site (the
City of Coronado) as well as a total of forty at two sites on the Kanawha River near
Charleston, West Virginia.
The questions asked each of the residents are listed below in the order they were
presented.
1) Do you think there has been any change in the quality of the water of the
(name of river, bay) since 1960?
. If the respondent answered "yes" to question 1 then questions 2 and 3 were asked.
2) Would you say the water quality is better or worse than it was then?
3) Would you say much, somewhat, or only slightly (better or worse)?
4) Would you say the water of the (name of river, bay) nearest to where you live
looks different now than it did say, 10 or 15 years ago?
If the respondent answered "yes" on question 4, then questions 5 and 6 were asked.
5) How would you describe the difference?
6) Do you agree or disagree with the following statements?
(Agree completely, agree somewhat, neither agree nor disagree, disagree
somewhat, disagree completely).
a) The water is clearer now than it was.
b) There is less floating debris and refuse than there was.
c) The water smells better.
d) There seems to be more wildlife now.
e) There are fewer dead fish now than there were.
0 The color of the water is better now.
19
-------
7) Compared to I960, do you think there is more, less or about the same amount
; of boating on the (name of river, bay)?
8) Would you say there is more, less or about the same amount of swimming in
the (name of river, bay) as there was in 1960?
9) Would you say there are more, less or about the same number of fish in the
(name of river, bay) than there were in 1960?
10) Do you think there are more, less or about the same number of water birds
now as there were 10 or 15 years ago?
The general consensus of the Willamette River and San Diego Bay respondents was that
the water had definitely improved since 1960; they thought the water was clearer and
smelled better, the color was improved and there was less floating debris and oil, and
fewer dead fish than there was before. The respondents seemed to be divided only on
whether or not there was more wildlife now than in 1960.
A greater difference of opinion was.found at the Kanawha River sites. There, 39 percent
of the respondents said they thought the water was worse than in 1960, while 27 percent
thought it was better, and 34 percent thought there was no change or had no opinion.
Nonetheless, the responses to question 6 seem to indicate that there is some awareness of
an improvement in the decrease of floating debris and numbers of dead fish (questions 6b
and 6e). This response correlates with the clean-up of visual pollution which was
accomplished on the Kanawha by 1964. The results of the interviews are summarized
graphically in Tables 1, 2 and 3.
The interview results definitely support the results of the regression results reported later.
Where people perceived large water quality improvements, substantial impacts on
property values were measured (on the Willamette River and San Diego Bay sites). On the
Kanawha River, where people perceived little or no water improvement the regression
analysis showed small impacts on property values.
SITE CHARACTERISTICS
The residential and rural sites where water quality impacts were measured in this study
are described below.
San Diego Bay (Coronado) - Coronado is a residential community about one and
one-half miles square, located on a peninsula directly across the Bay from downtown
San Diego (see map of Figure 3). Coronado is connected to San Diego by a toll
bridge which was completed in 1969. Coronado is bounded on the north and east
by San Diego Bay, on the west by a Navy base, and on the south by the Ocean;
there are no significant barriers between residences and the waterfront. On the north
side private property extends up to the water, and there is a municipal golf course
and marina on the east side with public access.
20
-------
WILLAMETTE RIVER
- URBAN -
WILLAMETTE RIVER
- RURAL -
SAN 01 EGO BAY
KANAWHA. RIVER
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TABLE I - RESIDENTS1 INTERVIEW RESPONSES
WATER QUALITY CHANCE
21
-------
•
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- RURAL -
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TA8LE 2 - RESIDENTS' INTERVIEW BESPONSES
KfTER QU'LITY CHANGE
22
-------
WILLAMETTE RIVER
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- RURAL -
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TABLE 3 - RESIDENTS' INTERVIEW RESPONSES
WATER QUALITY CHANGE
Z3
-------
San Diego
Figure 3
CORONADO SITE
(SAN DIEGO BAY)
24
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Sales prices and assessed values were collected for all of that portion of Coronado
which lies within 4000 feet of the Bay (about three-fourths of the city).
Willamette River (Clackamas County, Oregon - Residential) - Residential property
extends along both banks of the Willamette River between Portland and Oregon
City. One area was particularly well-suited for investigation because of its size (about
two miles long and one mile deep) and lack of obstructions. The area is in an
unincorporated portion of Clackamas County known as Oak Grove and Jennings
Lodge. The map of Figure 4 shows the relationship between the site, the river, and
other towns. Single-family houses over 15 years old dominate the site. Private
property extends to the river banks, although there are several access points for
fishing or boat launching. The bank opposite the site is uncluttered and scenic.
Willamette River (Clackamas County, Oregon — Rural land) — Between Oregon City
and Salem, Oregon the Willamette is bordered by predominately rural tracts on both
banks. Data was collected for small rural land parcels which sold in the years
between 1968 and 1972, and which had not changed in size or shape since 1960.
The study site was defined as both banks of the river upstream of Oregon City for
about eighteen miles. This area includes portions of Clackamas, Yamhill, and Marion
Counties, but because of availability of data most tracts included in the sample are
in Clackamas County. See Figure 4 for a map of the rural area. The original sample
included unimproved land parcels as well as land parcels with buildings. Our
preliminary analysis revealed no correlation between land value and value of
improvements (buildings), so only land values were studied further.
The demand for land in this area is substantial because of its proximity to rapidly
growing metropolitan Portland. The rural area is atypical in this sense and study
results should be interpreted as representative of rural land near a growing popula-
tion concentration rather than general agricultural land.
Water clean-up has renewed interest in the Willamette River to the extent that the
State of Oregon is presently acquiring as much of the river banks as possible
(possibly 200 miles) for parks and greenways.
Kanawha River (Charleston, West Virginia) - This site consists of a dense residential
area 10,000 feet by 3,000 feet in the Kanawha City section of Charleston. It is on
the south bank of the Kanawha River almost directly across from the West Virginia
state capital (see Figure 5). The settlement is primarily single-family residential with
some multi-family properties and one major commercial avenue. There is no public
recreational property within the site. The river bank itself is privately owned, with
very limited public access.
The major development at this site took place before 1955.
Kanawha River (Dunbar, West Virginia) - Dunbar is a town of 9000 people
immediately downstream of Charleston, West Virginia on the north bank of the
Kanawha River (see Figure 5). The residential area studied (5000 by 2000 feet) lies
25
-------
0
Oregon City
Figure 4
CLACKAMAS COUNTY:
URBAN AND RURAL SITES
(WILLAMETTE RIVER)
26
-------
miles
Figure 5
CHARLESTON AND DUNBAR SITES
(KANAWHA RIVER)
27
-------
in an unobstructed area between the river and a railroad line. There is no public
recreational property within the site, and the river bank is occupied by private
residences. The primary difference between the Charleston and Dunbar sites is the
average value of single-family homes. The average value in Dunbar is about $17,000,
whereas the average value of houses in the Charleston site is $27,700.
Ohio River (Beaver, Pennsylvania) - This site includes the entire borough of Beaver,
Pennsylvania (population 6000). Beaver is located on a high bank (60 feet above
water) overlooking the Ohio River twenty miles downstream from Pittsburgh (see
Figure 6). The development is primarily older single-family dwellings, with some
concentrated commercial property.
There is a narrow linear park on the crest of the bank overlooking a scenic stretch
of the Ohio for most of the length of Beaver. Immediate actual access to the water
is limited by the high bank.
We collected, processed, and analyzed data for all of the above sites. We first analyzed
data samples to identify important property value influences and significant correlations
between important variables. Two quantitative variables are said to be correlated if there
is an association between them. If the value of one does not depend on the value of the
other, then the correlation coefficient of the two is zero and they are called independent.
If the value of one variable does depend on the other the magnitude of the correlation
coefficient will approach one as the dependence increases. For example, the value of
homes and homeowners' incomes should be highly correlated; whereas, the value of the
home and the last digit of the owner's telephone number would have a low correlation
coefficient because there is no apparent association between the variables.
Table 4 lists the important correlation coefficients and property statistics for each of our
study sites. All values are for single-family residences except the entries for the rural area
(row 3), which are expressed in terms of value per acre of land. Average 1970 property
values for the samples of single-family residences varied from $16,412 at the Dunbar,
West Virginia site to $49,062 dollars at the Coronado, California site. Lot size or
property area ranged from 6,088 square feet at Coronado to 22,630 square feet at the
urban site in Oregon. Lots at all the other residential sites were closer to the size of those
in Coronado.
It is important to observe that at the residential sites, all the correlation coefficients
between 1960 property value and distance from the property to the water are negative
and small in magnitude (Table 4). In other words, there is a slight tendency for
higher-priced properties to be closer to the water, and this tendency supports the
hypothesis that property values within the residential sites are positively influenced by
the water resources. If the water bodies were a nuisance, or represented a flood hazard,
as on some river banks, this relationship could not be expected to hold.
It is further notable (referring still to Table 4) that the correlation between 1960
property value (V60) and the "percent" change in property value between 1960 and
AV
1970 (,, ), is also generally negative. This implies that the values of higher-priced
V e.n
'60
28
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Beaver Falls
ORSANCO
Monitor ing
Station
Beaver
Site
South Heights
ORSANCO
Monitoring
Station
Figure 6
BEAVER SITE
(OHIO RIVER)
Mo nongahe la
River
Pittsburgh
-------
Table 4. IMPORTANT CORRELATION COEFFICIENTS AND PROPERTY VALUE STATISTICS
Location
Coronado, Calif.
(San Diego Bay)
Clackamas County
Oregon"
Residential
(Willamette River)
Clackamas County
Oregon
Rural Land
(Willamette River)
Charleston, W. Va.
(Kanawha River)
Dunbar, W. Va.
(Kanawha River)
Beaver-, Penn.
(Ohio River)
Sample Average
V70
$ per res.
49,062
25,844
8,688$/
acre
26, 241
16,412
22: 511
AV
$ per res.
13, 855
3, 725
4103$/acre
-1809
-2
2, 322
AV
V60
.435
. 168
1. 90
-. 05
. 11
. 14
V
A
8. 29$/ft2
1. 28$/ft2
8688$/acre
3. 95$/ft2
3. 20$/ft2
4. 03$/ft2
A
6,088ft2
22,630ft2
7. 16 acre
7, 878ft2
6, 185ft2
6,460ft2
Correlation Coefficients
V6o-dw
-.03
. -. 37
. 19
-. 18
-. 28
-.36
V60-AV
. 75
. 13
. 33
-. 32
-.64
-. 27
v,.-Av
V60- V60
. 11
-. 30
-, 25
-. 23
-. 55
-. 34
V60-A
.47
. 25
.51
.61
.44
. 54
u>
o
V£,Q - Value in I960 (no taxes); AV = Change in value; A = Area; res - residence; dw -Water distance
-------
properties within the sample were inflated slightly less by demand pressures over the
ten-year period than the values of lower-priced properties.
Negative values in Table 4 for the average change in value at Charleston and Dunbar
mean that the change in housing values at these sites did not keep up with the changes in
the national. Consumer Price Index, used to inflate 1960 housing prices to 1970 dollar
values. The change in the Consumer Price Index was 31 percent [8].
We originally thought that it might be desirable to use change in value per unit area as a
measure of water resource impact because of its simple interpretation. However, we
eventually rejected this measure for the residential sites because of the lack of a strong
correlation between property value and lot size; that is, many expensive houses are built
on small lots and vice versa.
We collected all available sales data which met the study requirements at each site. For
example, at the Willamette River residential site all houses located within 4000 feet of
the river which had been sold in 1969, 1970, or 1971 were plotted and their actual sales
prices recorded. We then examined tax records for 1960 to determine the 1960 assessed
values of the property, and to eliminate from the samples any properties which changed
in size or number of buildings between 1960 and the year of sale. Given the site
boundaries and available sales data, we maintained the largest sample sizes possible.
• We also collected data for vacant lots, but since samples of sufficient size for meaningful
multiple-regression analysis were obtainable for single-family residential properties alone,
vacant lots had to eventually be excluded from analysis. This is not a serious deficiency
since single-family residences account for an average 83 percent of the total value of
taxable residential property, nationally, while total taxable value of vacant lots consti-
tutes less than 3 percent of the total value of residential properties [7] (see Table 5 for a
breakdown of gross national property value by type of property). Therefore, by measur-
ing the pollution abatement impact on the value of single-family homes, we are analyzing
that property type to which most of the benefits will accrue within metropolitan areas.
Finally, although it was not used for regression analysis, the vacant land value data we
obtained is valuable for imputing a value to park and recreation land in the national
benefit part of this study.
REGRESSION RESULTS
As discussed in Section III, two models of property value increase attributable to
pollution abatement were analyzed using multiple-regression analysis. One model
expressed value change in percentages and the other in absolute value change. The use of
two models allows us to check back and forth between them for consistency.
Property value changes attributable to water quality improvement were found to be
substantial and statistically significant for the residential and rural sites on the Willamette
River and for the San Diego Bay site (Coronado). Results for the Charleston site on the
Kanawha River were significant but indicated a smaller water quality impact. Results for
the Dunbar site on the Kanawha River and for the Beaver, Pennsylvania site on the Ohio
31
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Table 5. GROSS TAXABLE VALUE OF LOCALLY ASSESSED REAL PROPERTY
Type of real property
Residential (non-farm) . .
Single-family houses* -
Commercial and Indus-
other and unallo cable • • -
Gross assessed
value
Amount
(billions
dollars)
393.2
236.3
196.7
43,4
10.2
97.2
60.0
37.1
6.0
Percent
100.0
60. 1
50.0
11.0
2.6
24.7
15.3
9.4
1.5
Properties
Number
(thou-
sands)
74,832
42,329
40,436
14,085
14,250
2,487
2,112
376
1,679
Percent
100.0
56.6
54.0
18.8
19.0
3.3
2.8
0.5
2.2
Source: 1967 Census of Governments [T|
32
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River were statistically inconclusive.
Actual final regression, equations are listed in Tables 6 and 7. Table 6 reports the results
of the percentage change model while Table 7 reports the results of the absolute value
change model.
The first term on the right-hand side of* each equation in Tables 6 and 7 is that portion
of the change which the multiple regression computation attributes to the change in
water quality. .The leading coefficient of this term is determined by the regression
computation. This coefficient depends upon the data sample being regressed, the correla-
tion between independent variables, and also upon the form of the remainder of this
term (the distance-to-water function). The distance-to-water function (in parenthesis)
expresses how the property value benefit of pollution abatement changes with distance
from the water.
The form of this function is hypothesized and then verified experimentally. See Figure 2
for a graphical comparison of a linear and a reciprocal distance function. Reciprocal
functions of distance usually yielded better results than linear distance terms, except for
the Coronado site. The constants appearing in the water distance function are determined
by the rate at which benefits decrease as distance from the water increases and also by
the maximum distance at which a benefit is realized (2000 or 4000 feet).
Standard errors of the coefficients, degrees of freedom (dof), and multiple correlation
coefficients (R2) are also included in the tables. The standard error together with the
degrees of freedom indicate with what degree of confidence we can assert that the
regression coefficient is not equal to zero. The results for which the probability is greater
than .95 that the distance-to-water coefficient is not equal to zero, based on our sample,
are marked with a dagger.
R2 is a measure of the fraction of the total variation in property value changes which is
explained by the regression equation or model. The R2 factor varies from values of . 10
for the Charleston site to .72 at Coronado. This range of R2 values is acceptable for our
study since we were not attempting to explain all of the variation in property values, but
were only concerned with isolating the property value change attributable to water
quality changes. Therefore, while any influence that might interfere with the isolation of
the water impact must be taken into account, factors which do not influence values
colinearly with distance to the water can reasonably be neglected. Thus,. although high
R2 values are generally desirable, they are not necessary in this application.
Data inputs to the regression equations, and correlation coefficients are listed site by site
in Appendix K.
The computed values of the water quality benefit are listed in Table 8 for residences
located 100, 500, 1000, and 2000 feet from the water's edge. These values can be
interpreted as the best estimate of the capitalized benefit per residence or acre of rural
land of the pollution abatement which occurred.
33
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T4&1« 6. REGRESSION EQUATIONS FOR PERCENT CHANGES IN rr IPERTY VALUES
Coronado, Call/.
(San Dl«go Bay)
, , / \ fa. bridge! f<), Orange Avenue -~| ft, Wavy BaM~| [tot Area -"I
[ /M ) I'00/ - 0-206 x 10'? ("000 - d, water) • 0.16S x 10*2 I access - fl.?19 K 10'? I Coonereial St. t 0.673 x 10'2 access » 0.131 x 10'2 I sq. ft
X V**' (0.180 K 10-J> (0.766 x 10'? J (0.310 x 10'?)U J (0.150 x 10-?>L J (0.10S x lO-*)
» 0,?3 dof. • 106
uhor« d • distance In fe*t
- 0.901 x 10-" (Property V«lua.9&u) « IS.68
(1.131 K 10-*)
Cl*Ocaaa* County,
Oregon
Residential
(Willamette River)
/V ) (K>O) « 0.3S» x 10* t 1 . o.OOO?) t 0.00776 (d, p»rk») - 0.0171 (d. naar*«t) - 0.001S& (d. »Sopplnj center) * 0.00^53 (d, Portland) * 0.000637 (lot ar*a
*5 * ' (0.196 x 10s) •• "*t*r * ld°° (0.00782) (0.00«0) «chool (0.00376) (0-00?18) (0.0001S5) iq. ft.
i 0.30 dof. -- 90
where d « distance in feet
- 0.00738 (Property v«Jue,..t) * 25.01
(0.00051) ""
Clacxaauc County,
Oregon
Rural Land
(Vlllaamte River)
(fl% ) (I0°) ' 0-»W * 10* < 1 - .00077) - 33.0 (d, nearest boat raep) » S.98 (d. nn«r«»t bridje acce») t 10.1 (d. near«Jt tovn) . 7-*.29
V *° (0-"59 x 10S) *• M*r*r 4 so° (3i.6) •!)«« (8.35) allea ( 9.7) alias
R? « 0.1? dof.
where d • distance In feet
Charleston, V. V*.
(Kanawha River)
-C ) (lOo) . 303 U—I - .0005) - O.OOOW7 (d, brld£« icceit) . 0 01X1503 ( Properly v,lu«...o) . 1J.17
V«0' V / t, w.t.r (0.000659) (0.000716)
0.10 oof.
vh«r« d • diataix;* in feet
Dunbar, U. V*.
(Kanawha River)
) (lOo) » - 1250 (J — water *
0/ v ' (1001) '
0.00777 (d, nearest schoo
[lot «r«j -1
>1) - 0.00138 (d. n«« hiitiw.y (Proporty v.lu«H70) • 0.«
(O.OKW63)
The coefficient of the distance to "ater terai is significant at the
95 percent level of confidence.
The terete in parenthetic below the coef f Lcient* are the standard error*
of the coefficients.
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Table 7. REGRESS I OK EQUATIONS FOR ABSOLUTE CHANGES IN PROPERTY VALUES
Coronado, Calif.
(Sao DlegO B*y)
[«, Drldt«1 fa, Or»n6« lw«nu« -1 ft, »«"y «..«1 fUt Ar«« -T
• 1.07 ("000 - d. »«l«r) • 0.161 «ce««l - 0.593 Co»nl«l Ss. ' J.«« «cce»s « 0-J'O Itq. ft. t 0.«'« (Prep«i
(0 S«) (0.»5«)L J (1.017)1 J (0.«4)L J (0.3«S)<- J (0.0«7)
erty Valuo19<) - 10,937
R7 » 0.72 dof. * 106 Mh«p« d : dlsi«nc« in
Clackanas County,
Oregon
(tfillaaette River)
. o.VflS x 10T (5 Vat'er t 1000 - 0.000?) » 0.3S9 (d, p«r-k) - 3.17 (d, n«ar«it school) - 0>flO * O.Si* (d, PortJ«nd) » O.i*9 (Lot
(0.772 x 107) * (0.39?) (O.S6) (0.573) (0.302) (O.OJ?) «q.
O.t*l dof.
d • di»ianc« In (*«t
- 0.060 (Property
(0.071)
u>
Clackaaat County,
Or«gon
• 0.3S" x lo!J (a—w.t,r f
(0.070 x JO') '
- .000??) - «7u (d, n«are*'. boat rup)
(S«6) alias
fd, n««reat brldcol
- 761 I «e distance in feel
Beav«p, Penn.
(Ohio Rlvtr)
- 0 ««93 x 106 (. 1 • -COOS)
(1*136 x 106) 4- M'tep
R7 » 0.20 dof. • "6
fd. State Street -1 fI9 (d. Higl
.2«.)L J (1.337) (l.M6)L J 12.630)
:h Softool) » 0.221 (Proparty
(o.oee)
where d * dl»tane« in feet
The coefficient of the distance to water term It significant at the
95 percent level of confidence.
The tertt* In parenthesis belov th« coefficients are th« standard error*
of the coefficients.
6V includes capitalized value of change In taxes.
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Table 8. POLLUTION ABATEMENT BENEFITS CALCULATED FROM
PERCENT AND ABSOLUTE CHANGE REGRESSION EQUATIONS
Location
Coronado, Calif.
(San Diego Bay)
Clackamas County,
Oregon
Residential
(Willamette River)
Clackamas County
Oregon
Rural Land
(Willamette River)
Charleston, W. Va.
- (Kanawha River)
Dunbar, W. Va.
(Kanawha River)
Beaver, Perm.
(Ohio River)
Percent Benefit Per Residence
at Various Distances from the Water
100 feet
8. 2
24. 9
65.4
2. 88
500 feet
7.4
16.7
39.7
.45
r!000 feet
6.3
10.7
20.3
. 15
2000 feet
4. 2
4.6
8. 1
0
Absolute Benefit Per Residence
at Various Distances from Water
(Dollars per Residence)
100 feet
4173
3395
5075*
894
500 feet
3745
2280
3080*
141
1000 feet
3210
1455
1575*
47
2000 feet
2140
630
630*
0
Inconclusive Regression Results
Inconclusive Regression Results
*Dollars/Acre of land only.
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RESULTS FOR RESIDENTIAL PROPERTY
The pollution abatement benefits are displayed graphically in Figures 7 and 8 for the
three residential sites where the benefits were measurable. The benefits at San Diego Bay
and the Willamette River are comparable. This comparability could be expected since
there have been major changes in water quality at both sites, and interview results
verified that property owners were aware of the water quality improvements. Moreover,
the water quality changes themselves were similar, including increased dissolved oxygen,
decreased fecal coliform counts, and a decrease in visual pollutants such as floating
debris, scum, bottom sludge, and algae.
Results indicate that an existing parcel of residential property on the shore of San Diego
Bay at Coronado experienced an 8.2 percent increase in value due to pollution abate-
ment, while a house on the banks of the Willamette River near Portland probably
experienced a 16 to 25 percent increase. The benefits decreased more rapidly with
distance from the water at the Willamette River site than at Coronado. At 2000 feet
from the water the benefit was 4 percent at the San Diego Bay site and about the same
at the Willamette site. The difference in the size of the water bodies may explain the
different rates of decrease in benefits with distance from the water. The sizable San
Diego Bay dominates the Coronado site more than the Willamette River does the
Clackamas County site.
If benefits are expressed in absolute terms as derived from the absolute value change
model, the results are very nearly the same for San Diego Bay and the Willamette River
(see Figure 8). For a residence 100 feet from the water, the benefit is $4,173 at San
Diego Bay, and $3,395 on the Willamette River. If these absolute changes are converted
to percentage changes using the value of an average home from the respective study
samples, the results for both the bay and the river are nearly the same as those reported
in Figure 7. In this sense, the results of our study were consistent.
On the Kanawha River at Charleston where water quality changes have been moderate
and interview respondents were not in general agreement whether water quality had
improved or worsened, the regression results indicated a significant but much smaller
benefit. The pollution abatement raised river bank property values by about three percent
and the impact decreased rapidly to zero at 2000 feet. Regressions for property value
changes at Dunbar, about eight miles downstream from the Charleston site, were not
statistically significant enough to justify drawing any conclusions about benefits from
pollution abatement. Our inability to obtain significant results is certainly due in part to
the moderate water quality change but may also be attributed to the way in which value
changes were measured. Assessed values were used to calculate 1968 property values,
which were in turn inflated to 1970 values. Actual sales records were used for 1960 sales
prices. Although the assessments from which the 1968 property values were derived were
very good, it is conceivable that more time is needed before the water quality improve-
ments are reflected in assessed values, than for the impact to be felt on sales prices. Since
assessors use sales data to compute their assessments, sales prices must increase before
assessed values can increase. For the more successful sites, we carried out calculations
using assessed values to estimate property value before the water quality improvement
and actual sales prices for 1970 values. Inconsistencies in 1960 tax assessments at the
37
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25 H
w ?n J
U 20 i
W
a
w
W
&
W
15-
w
w 10
o
E-i
I
U
«
W
0.
Willamette River, Oregon
San Diego Bay,
California
Kanawha River at Charleston,
West Virginia
1000
2000
30150
DISTANCE OF RESIDENCE FROM WATER, feet
Figure 7.
BENEFIT OF POLLUTION ABATEMENT EXPRESSED AS
PERCENTAGE OF RESIDENTIAL PROPERTY VALUE
38
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01
t-t
JS 4000'
,— «
o
T3
w"
u
£
w
Q 3000
I-H
w
W
W
A
H
n
h
W
z
W
CQ
2000
1000
San Diego Bay, California
Willamette River,
Oregon
Kanawha River at
Charleston, West Virginia
500
1000 1500
2000 2500
3000
DISTANCE OF.RESIDENCE FROM WATER, feet
Figure 8
BENEFIT OF POLLUTION ABATEMENT EXPRESSED AS
DOLLAR INCREASE PER SINGLE-FAMILY RESIDENCE
39
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Kanawha River sites precluded our using this method there.
Results of the analysis for the Beaver site were also inconclusive, probably because the
water improvement impacts were too small to be detected. Moreover, there is no direct
river access, and the residential properties studied are all situated at the top of a steep,
60-foot-high bank. The location is a scenic non-industrial part of the Ohio River, and
residents' enjoyment of the river is primarily aesthetic. Although the people know the
river has improved, the chemical changes in the water (higher dissolved oxygen, higher
pH, and lower specific conductivity) are not readily apparent and therefore have little or
no tangible effect on property values on the high bank. Finally, the moderate changes in
water quality affect the quality of the river view at Beaver very little.
We may be able to draw one conclusion about the different influences of rivers, bays, or
lakes on the basis of our site studies and extensive observation. A comparison of the
Willamette River and San Diego Bay, where-water quality changes were similar, indicates
that while the property value benefits to properties adjacent to the water are larger for
the river than for the bay, the benefits from the bay decrease less rapidly with increasing
distance from the water's edge. In other words, because the bay is a dominant geographic
factor, its quality changes seem to have a smaller but more extensive influence on
property values.
RURAL SITE
If rural land has potential uses which are affected by water quality such as for homesites
or recreation sites, then pollution abatement can be expected to raise the market value of
this land. If the only uses are agricultural, then water quality improvement will raise land
values only if agricultural productivity depends on water quality (irrigation, for example).
A substantial benefit was measured for the rural land along the Willamette River near
Portland. For the sample tested, the percent change model attributed a 65.4 percent
increase in value to water-proximate land, while the absolute value model attributed a
gain of $5,075 per acre on land with an average value of $4,585 per acre in 1960
(equivalent to a 110 percent increase). Both of these increases apply only to land within
100 feet of the water. The benefit decreases rapidly with distance from the water,
reaching zero at 4000 feet.
In the rural Willamette Valley site, there is definitely an increasing demand for vacant
land for homesites and parks. All land values in the sample almost doubled from 1960 to
1970. Since both rural and urban respondents in the Valley are moreover aware of the
Willamette's improved condition, the benefit calculations seem reasonable. These calcula-
tions say in effect that, based on average 1960 prices, the expected 1970 benefit from
pollution abatement was a 65 to 110 percent increase in value for waterfront land. The
first figure derives from the percent value change model and the second from the
absolute change model. The higher absolute change model results were more significant
statistically.
Although the analysis showed that impact of water quality changes on rural land values
40
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can be expressed as a continuous function of distance from the water, a very high
correlation was also found using only a waterfront dummy variable with no distance
term. This indicates that benefit is greatly dependent on the land offering actual water
access in addition to proximity.
In the last part of this study, the results of the case studies were used as guidelines to
calculate the national benefit of pollution abatement on property values. How the
calculation was accomplished is described in Section VI.
41
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Section V
Water Quality
The first portion of this study measured the increase in property values attributable to
improvements in water quality. Very little was said about the factors which contribute to
people's perception of water quality, or the physical parameters which determine that
quality.
Quantification of water quality changes is not a necessary prerequisite to measuring the
impacts of the changes at each of our study sites, because the water quality change is the
same for all properties within each study site.
It was our original intention to conduct personal surveys of peoples' attitudes towards
the various aspects of water quality and to combine the results with technical knowledge
of water properties to create an index relating changes in property value to changes in
the most frequently measured water quality parameters. Such an index would allow
comparison between sites and prediction of the property value benefits of various degrees
of pollution abatement on the basis of recorded data on presently polluted water
throughout the country. However, we did not develop a new index; this effort had to be
abandoned when it became clear that the technical data necessary to define and use such
an index was lacking. Measurements for the many parameters required to derive a
meaningful index are not collected and recorded systematically or at enough places for us
to do a useful analysis.
We did use the results of our interview to verify the relationship between peoples'
awareness of water quality changes at each site and tangible impacts on property values
which we measured using regression analysis. The results of this effort were described in
Section IV, under "Perception of Water Quality Changes." We also accomplished the
important task of isolating the principal utility aspects of residential property owners'
valuation of the quality of a proximate water body. We can judge which directly
measured water parameters are important by knowing what-.measurable properties of
water are the important determinants of suitability for each use. For example, if
42
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swimming -is a very important water use and bacteria concentrations (fecal coliform
counts) are the primary determinant of whether or not water is safe to swim in, then we
can conclude that fecal coliform counts are an important determinant of "water quality."
In the remainder of this section we will explain which principal uses determine resident
valuations of a water body, which directly measured water parameters determine the
suitability of water for each use, and how we measured the relative importance of each
use to property owners.
From the standpoint of a residential property owner, there are basically three preceptible
aspects or utilities for a proximate water resource's quality or value: aesthetics, wildlife
support capacity, and recreational potential. The aesthetic value of a river or lake is a
measure of how pleasing the water body is to look at or be near. It is important to note
in passing that the aesthetic value of a river or lake is determined as much or more by
the condition of the bank or shoreline as by the quality of the water [2]. For example,
the Hudson River Valley may be most beautiful in the fall when the trees are changing
color although water conditions are the worst at this time of year due to low flows and
high temperatures. The combination of water quality and the quality or character of the
interface between land and water accounts for the total aesthetic value of a water
resource.
However, in-, this study our concern is with the quality of the water. All the study sites
were deliberately selected in places where the area around the water is scenic,
uncluttered, and non-industrial, so that water quality was the major determinant of
changes in water resource impacts. The aesthetic value of the water alone is a product of
its color, clarity, odor, the amount of debris floating on its surface or visible on the
bottom and shore, and any floating oil, scum, foam, or sludge.
Pure water is colorless. Whatever color water appears to have is due to dissolved
impurities, suspended solids, bottom coloration, or reflected light. Usually, if the colora-
tion is due to human activities, it is the product of suspended solids producing muddy,
turbid water or an overabundance of green, brown, or red algae. Both affect the clarity
or turbidity of the water as well as the color. Therefore, when referring to water
pollution levels, clarity and water color are highly correlated.
Water clarity can be measured. A popular and meaningful measure of water clarity is the
Secchi disk method: an eight-inch diameter white disk is lowered into the water and by a
controlled procedure, the maximum depth at which it is visible is measured.
Odor is another important determinant of aesthetic value. Numerous impurities impart
odor to water; some odors result from waste discharges while others may be natural.
Most commonly, persistent disagreeable odors are due to anaerobic biological activity
which takes place when dissolved oxygen concentrations are depleted by oxygen
demanding municipal and industrial waste loads. Although odor seems to be a difficult
parameter to measure in any objective manner because of individual variations in tastes
and sensitivity, there is at least one widely accepted, straightforward measure of odor
intensity. This is the threshold odor number. The threshold odor number of a water
sample is- equal to 2N where N is the number of times the sample must be diluted (with
odorless water) before it has no detectable odor. While this test does not account for the
43
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nature of the odor or its cause, it is sensitive to the tester's olfactory senses as well as
inexpensive to perform and easily interpreted.
Visible pollutants such as cans, bottles, or paper on the water surface or bottom as well
as unnatural oil, scum, and sludge are also important determinants of water appearance
and aesthetic value but, unfortunately, standard objective measures of these nuisances are
not in common usage and remain badly needed for meaningful measurement of pollution
trends [12).
In addition to aesthetics, water bodies of all types are also valuable by virtue of the fact
that they constitute the natural habitat of numerous species of fish, birds, and other
living things. These aquatic creatures provide man with food and recreation, as well as
psychic pleasure. Needless to say, water quality is an important factor in the wildlife
support capacity of a water body. We know of only one effort to relate water quality
parameters to the water's fitness for wildlife. Research was conducted with the National
Sanitation Foundation to develop a water quality index specifically for fish and wildlife
[3]. The index (called the FAWL Index) is based on the judgment of a large group of
professional water quality managers. Using a modified Delphi (interview) technique, nine
water quality parameters were selected and weighted to indicate how healthy a fresh
water body is for all life forms. The nine water parameters in order of their importance
in the judgment of these experts are dissolved oxygen, temperature, pH, phenols,
turbidity, ammonia, dissolved solids, nitrates, and phosphates. The weightings of the
parameters are the following (the weights have a sum of unity):
Parameter Weight
Dissolved Oxygen .206
Temperature . 169
pH .142
Phenols .099
Turbidity .088
Ammonia .084
Dissolved Solids .074
Nitrate .074
Phosphate .064
People also value water for its recreational potential, such as boating, fishing, and
swimming. Although the recreational potential of any water body also depends on access
and on facilities such as boat ramps or beaches with life guards, it is determined largely
by water quality. Water quality is most critical for swimming. The Committee on Water
Quality Criteria recommended that fecal coliform count should be used as the indicator
organism for evaluating the microbiological suitability of recreational waters [1].
For primary contact recreation (activity where there is a significant risk of water
ingestion) the Committee recommended that fecal coliform counts shall neither exceed a
log mean of 200/100 ml, nor shall more than 10 percent of total samples during any 30
day period exceed 400/100 ml. The Committee also recommended that the pH should be
within the range of 6.5 to 8.3 except when due to natural causes, and that in no case
shall it be less than 5.0 or more than 9.0. In addition, the Committee suggested that the
44
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clarity of primary contact waters should be such that a Secchi disk is visible at a
minimum depth of 4 feet, and that the maximum water temperature should not exceed
85 degrees F. For other than primary contact recreation, such as boating or fishing, the
Committee recommended that fecal coliform content should neither exceed a log mean
of 1000/100 ml, nor exceed 2000/100 ml in more than 10 percent of the samples, and
that for fishing to be a suitable activity, conditions which are healthy for fish and
wildlife prevail. In summary then, fecal coliforms and pH along with the parameters
which determine suitability of water for wildlife, are the important water quality
determinants of recreation potential.
Aesthetics, wildlife support capacity, and recreational potential are by no means indepen-
dent attributes of a water body. It is difficult to conceive of conditions where a natural
body of water is aesthetically pleasing and good for boating and swimming, yet still unfit
as a wildlife habitat. Nonetheless, the three attributes are independent and recognizable
enough that it is useful and convenient to think of the total value of a water resource as
the sum of its aesthetic value, wildlife support value, and recreation value.
Some of the people who bought homes at our study sites valued the water enough to pay
more to live near it, and we have measured the value they place on the total change in
water quality in terms of changes in property values. We employed an interview tech-
nique to determine further the relative importance of each of the three aspects of water
utility described above to the owners of nearby residential properties.
We interviewed a random sample of 160 residential property owners (40 at each of 4
locations) at their homes. The text of the personal interview is contained in Appendix I.
Questions 1 through 9 deal with value assessments. The sample of property owners was
selected from within the urban and rural study sites on the Willamette River in Oregon, the
site on San Diego Bay, and the Kanawha River sites. Within the site boundaries the
samples are distributed randomly with respect to distance to the water, that is, some
respondents live at the water's edge and others live as far as 4000 feet away from the
water. The sample was divided evenly between males and females. All respondents said
they had participated in the decision to buy their home, but no two respondents lived at
the same address.
Each respondent was asked to distribute 100 votes between three categories of water
capability, aesthetics, wildlife support, and recreation opportunity, in a manner which
would reflect their personal feelings about the relative value of each. Subsequently,
respondents were asked to distribute another 100 votes within the aesthetics category
between water clarity, color, odor, and floating debris or oil, in terms of their importance
as aspects of water appearance and* attractiveness.
Before voting, respondents were asked to imagine themselves in a hypothetical situation
where the water attributes were mutually exclusive, and make a pair-by-pair choice
between the three water attributes. Within this hypothetical framework most respondents
said they preferred improvement of wildlife support capacity to both recreation oppor-
tunity and appearance. The choice between water appearance and recreation was more
difficult, but the majority of the respondents preferred measures to improve appearance.
The hypothetical situations were posed prior to vote casting to help define the attributes
45
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and give respondents an opportunity to think about the ordering of their personal
preferences.
The results of the vote casting are presented site by site in Table 9. The cumulative result
of the value assessment is presented more graphically in the pie chart depicted in Figure
9. Wildlife support accounted for 49.3 percent of the total value of a water resource.
Since fish are wildlife, respondents were asked to use their votes in the wildlife category
to include their evaluation of fishing (46 percent of respondents said they had been
fishing within the last two years). Of the remaining votes, appearance (aesthetics),
swimming, and boating accounted for 26, 6, 13.9, and 10.2 percent, respectively. The
outcome of the voting seems to indicate that property owners are most concerned with
making water fit for wildlife. If boating and swimming are lumped together under the
label, recreation, then people weigh the importance of recreation and appearance about
equally.
In designing our survey questionnaire, we sought to render the voting categories as
mutually independent as possible in order to preclude ambiguous responses. Therefore,
although a given water capacity might have relevance to more than one utility category,
it was necessary to assign it to a single one. Fishing and picnicking are both forms of
recreation; however, the possibility for their enjoyment is primarily determined by water
quality conditions under the wildlife support capacity and aesthetic categories. That is,
fishing is impossible where there are no fish. Similarly, picnicking is not feasible where
aesthetic deterioration has rendered the water body unpleasant. Therefore, our respon-
dents were asked to evaluate fishing under the wildlife support category, and picnicking
as a facet of aesthetics. Boating and swimming remained as the primary recreational
activities whose feasibility was separable from the other two categories.
The cumulative result of the distribution of 100 votes among the aesthetic or appearance
aspects of water was 36.6 percent weight on the absence of floating debris and oil, 25.7
percent weight on odor, 27.7 percent on clarity, and 10 percent on color. Thus, of the
factors influencing aesthetics, trash and debris take precedence over odor and water
clarity or color.
There is no substantial difference among average responses-for different sites. All are
remarkably similar in their ordering and weighting of water attributes. Responses for men
and women were also remarkably similar with no clearly recognizable differences. The
average age of respondents was between 45 and 60 years, with only eleven percent under
30.
When asked, 75 percent of the people interviewed replied that the voting system did let
them accurately express their feelings about the various aspects of water attractiveness
and appearance. When respondents were dissatisfied with the voting system, the reasons
most frequently offered were: "the choices are not specific enough or are too limited;"
"thet choices overlap, or are not mutually exclusive;" "color and clearness of water are
the same thing;" "my feelings depend upon whether the water condition was natural or
affected by man." Some people simply needed more time than our interview offered to
respond. While these criticisms are certainly valid in the abstract, given the complexity of
interdependencies between water attributes and the administrative ease aimed at in our
46
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WILIA*TTC RIVER
- URBAN -
WILLAMETTE RIVER
- RURAL -
SAN 01 EGO BAY
KAHAWHA RIVER
CUMULATIVE
•M
1
I
-,
100
90
60
40
20
0
100
80
60
40
20
0
100
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
9. II
If yov tied 100 votes, how woufd
you distribute then among these
categories of water quality In
»
1 ; i i
.1..
19 55 II 15
1 1 . I
31 39 12 18
. 1 . .
26 51 9 14
1 1 . .
31 52 9 8
1 1 . .
27 49 tO H
Q. 12
If you baa 100 votes, how uovltt
you distribute the* acong these
aspects of watar appaaranc* and
portance to yov?
-
8 °
I i S i
.III
15 25 -25 35
.III
8 29 26 37
.III
10 20 32 38
.III
7 29 32 38
.III
10 26 28 36
TABLE » _ WATER QUALITY ASPECT
VALUE ASSESSMENT
47
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FECAL COLPFORMS
FECAL COL 1 FORMS,
PH, CLARITY,
TEMPERATURE
DISSOLVED OXYGEN
Figaro 9 - THE RELATIVE VALUES OF WATER QUALITY ASPECTS AND
IMPORTANT WATER PARAMETERS WHICH DETERMINE THE
SUITABILITY FOR EACH PURPOSE.
48
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questionnaire, the results are remarkably consistent and yield considerable insight into
what determines the value of a major water body for nearby property owners.
CONCLUSIONS
Figure 9 displays the relative importance of the different water quality aspects together
with the important water parameters which determine each aspect. An examination of
this figure reveals which water parameters are most important in determining how
property owners perceive water quality and how water quality affects property values in
turn. As we have seen, the most important water aspect for the property owners sampled
is wildlife support capacity and the primary determinants of this capacity are toxic
chemicals, dissolved oxygen, temperature, and pH. The suitability of a water body for
swimming and boating activities is determined by fecal coliform concentration, pH,
clarity, and temperature. The aesthetic value of water depends largely on clarity, absence
of floating debris and oil, and odor (usually a function of dissolved oxygen concentra-
tion). Thus, the most important direct measures of water quality as it is perceived by
nearby property owners are toxic chemicals, dissolved oxygen, fecal coliforms, clarity,
trash and debris, and pH. The list is roughly in order of importance. These measurable
components of water quality are the principal determinants of how residential property
owners at our case study sites rate the value of their resource.
We drew no direct quantitative link between people's perception and the measures of
water quality and the results of our regression analyses at the six sites where water
quality has changed. The interview results and regression results are nonetheless mutually
supportive. At San Diego Bay and the Willamette River, where there were substantial
changes in dissolved oxygen concentrations, fecal coliform concentrations, water clarity,
and visual pollutants, we also measured substantial benefits. In contrast, on the Kanawha
River where the only perceptible change was in the amount of visual pollutants (oil and
debris), measured benefits were small at the Charleston site while the regression results at
Dunbar were inconclusive. Regression results were also inconclusive at Beaver, Pennsyl-
vania, where measured changes in the dissolved oxygen, specific conductivity, and pH of
the Ohio River produced no major changes in recreation usage, appearance, or wildlife
support capacity. Together, these results support the hypothesis that significant changes
in the water parameters which our interview results determined to be of primary
importance do produce large changes in property values.
The possibility of correcting those pollution components which are of greatest impor-
tance to residential property owners, and therefore of realizing benefits in property value
increases, is very good. Pollution manifested as low oxygen, high fecal coliform counts, or
high concentrations of toxic chemicals is correctable by improving municipal and indus-
trial waste treatment. Trash, debris, and other visual pollutants are also controllable.
Since floating debris and other visual pollutants comprise a full third of the detriment to
a water body's aesthetic value according to our interview responses, cleaning up trash and
debris on a badly littered river reach alone might increase property values measurably.
Lack of water clarity due to algae blooms is correctable if the algae concentrations are
due to over-enrichment of the water by waste discharges. Poor clarity or equivalently
high turbidity due to rapid water run-off and soil erosion are much more difficult to
49
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control, as is low pH due to acidic drainage from abandoned coal mines, a condition
prevalent in the Ohio River Basin.
During the course of this project several shortcomings in present pollution trends analysis
have become apparent. To measure progress in water pollution abatement as it is
perceived by the public, the important parameters listed earlier should be measured
primarily at places and during periods when problems are known to exist, such as
downstream of population and industrial concentrations during low flow summer months.
Historically, however, water monitoring has been done upstream of waste discharges at
municipal water intakes and at dam sites or water works where it is convenient. Water
body flow characteristics and waste discharge patterns should be given more consideration
in the design of water monitoring programs. While the parameters most widely and
routinely monitored are those which are easy to measure or important to public water
suppliers or geologists, more attention should be given to those which reflect the
condition of the water as it is perceived by the public (dissolved oxygen, fecal coliforms,
clarity, floating debris and oil, and toxic chemicals). Good dissolved oxygen, fecal
coliform, and clarity measurements are far too scarce. The number of water samples
taken for each measurement and the number or frequency of measurements should be no
fewer than the minimum required to achieve some reasonable degree of statistical
significance. Data taken only once a month, for example, is virtually worthless for
observing pollution trends because of variations in readings due to routine measurement
error and daily and yearly fluctuations in discharges and weather. It also is helpful to
interpretation to have the data presented as cumulative frequency distributions, that is, in
terms of the percentage of measurements which exceeded.critical quality criteria.
50
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Section VI
National Benefit of
Water Pollution Control
on Property Values
We can now estimate the national benefit of water pollution control using the results of
the case studies as guidelines and data on the incidence of water pollution in the
contiguous United States. We will make low, medium, and high estimates of the total
property value increase attainable by bringing all waters in the nation to a condition
which will support desirable life forms, permit desired practical water uses, and which is
aesthetically pleasant. The high and low estimates are necessarily based on extreme
assumptions and are intended to define the range within which the actual benefit can
reasonably be expected to be. The medium value represents our best estimate of what the
benefit will be on the basis of all available information.
The estimated benefit is the expected increase in the values of existing residential and
recreational property which will result from pollution abatement. Potential increases in
the values of land now occupied by industry, highways, or railroads, and the value of
future developments which might become feasible after pollution abatement are not
considered.
Extrapolating from a limited number of successful case study results to an estimate at the
national level is beset with many problems. One major difficulty stems from the fact that
data limitations or on-site characteristics .constrained us to a very narrow sample of
geographic areas, types of water bodies, and water quality changes upon which to base
our estimate. Consequently we have to rely on subjective assessments of many important
relationships and parameters. One such fundamental relationship is that between potential
property value increases and the pollution level measures which are available nationally.
In addition, the poor quality of information about the location, character, and intensity
of water pollution makes a great deal of guesswork necessary to reconstruct a nationwide
51
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picture of water poiiunon ana mis in turn introduces a nigh level, of uncertainty. Many
simplifying assumptions are necessary due to the great magnitude of the task of inven-
torying property affected by water pollution. In the description that follows we have
explained our methodology and assumptions as clearly as possible in order to make our
estimate useful as a base for future work. We have also included whatever unequivocal
supportive evidence exists and related the sensitivity of our national benefit estimate to
variations in those assumptions for which there is the weakest support.
LOCATION OF POLLUTED WATERS AND INTENSITY OF WATER POLLUTION
The first step in measuring the national benefit of pollution abatement is to determine
the locations of all polluted water bodies which are large enough to influence property
values and also to determine the intensity and duration of the pollution at each location.
The Environmental Protection Agency has conducted two national water pollution inven-
tories (the 1970 and 1971 PD1 surveys) to measure the prevalence, duration, and
intensity of pollution within each of the 241 minor drainage basins within the fifteen
major drainage basins. A river reach or shoreline was classified as polluted and labelled a
"pollution zone" for inventory purposes if it was consistently or recurrently out of
compliance with one or more of the legal water quality criteria. For each pollution zone
the annual duration of pollution was measured in terms of the number of quarter-year
periods or seasons in which it occurs. Values from 0 to 1 were assigned to a duration
index, D, as follows (10]:
0.4 for violations occurring within 91 consecutive days.
0.6 for violations occurring within a period greater than or equal to 92 consecutive
days, but less than or equal to 183 consecutive days.
0.8 for violations occurring within a period greater than 183 consecutive days, but
less than or equal to 274 consecutive days.
1.0 for violations occurring in all four quarters within.a period greater than 274
consecutive days.
The intensity of pollution in a specific pollution zone was measured- in terms of its
effects rather than in terms of water quality parameters. An intensity index, 1, was
assigned to each pollution zone. The value of I ranges from 0 to 1 and represents the
simple addition of the values assigned to three component measures which classify
impacts according to ecological, utilitarian, and aesthetic considerations. Ecological
impacts include the effects of pollution on the existence or the potential for existence of
desirable life forms, including man. Pollution effects causing reductions in the economic
or resource utilization values of the water (including boating and swimming) are grouped
under utilitarian impacts. Lastly, pollution effects disagreeable to the senses are included
in the category of aesthetics. The value scale for each of these components follows [11]:
Ecological
0.1 = conditions that threaten stress of life forms (including sanitary aspects not
related to any verifiable instances of contagions).;
0.2 = conditions that produce stress on indigenous life forms.
52
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0.3 - conditions that reduce productivity of indigenous life forms.
0.4 = conditions that inhibit normal life processes or threaten elimination of
indigenous life forms.
0.5 = conditions that eliminate one or more indigenous life forms.
Utilitarian
0.1 = conditions that require costs above the norm to realise legally defined
(i.e., in water quality standards) uses.
0.2 = conditions that intermittently inhibit realization of some desired and
practical uses or necessitate use of an alternate source.
0.3 = conditions which frequently or continually prevent the realization of
desired and practical uses or cause physical damage to facilities.
Aesthetic
0.1 = visually unpleasant.
0.2 = visually unpleasant with association of unpleasant tastes or odors.
The maximum weightings for each impact category agree well with the weightings we
determined through personal interviews for our categories of wildlife support capacity
(.49), recreational potential (.24), and aesthetics (.27). The correlation between our
wildlife support capacity value and the EPA "ecological" value is almost exact, while the
correlations between the weightings our respondents gave to our recreation potential and
aesthetics categories and the relative weightings of the EPA "utilitarian" and "aesthetic"
categories are very close. Since our interviews aimed at determining the relative impor-
tance of the different aspects to the single-family home owner's total valuation of water
quality, the agreement between our respondents' weightings and those of the EPA
intensity index effect categories indicates that the intensity index, I, is a good measure of
pollution as it affects residential property values.
The duration, D, and intensity, I, indices for different localities were assigned by teams
of EPA staff members familiar with legally-established water quality criteria and uses,
water quality data, and local ecological patterns. Results of local pollution assessments
were summarized by the EPA and an average duration-intensity index (DI) was computed
using the following formula:
DI =
where
Pi = Pollution prevalence, i.e., length in miles of pollution zone i. (River
miles or shoreline miles)
Dj = Duration index for pollution zone i.
Ij = Intensity of pollution for pollution zone i.
n = Number of pollution zones in a basin.
53
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Some interesting patterns were apparent when the EPA summarized duration and
intensity indices and pollution for the nation. There are 260,324 stream miles in the
contiguous United States. The 1971 PDI Survey indicated that 76,299 miles, or 29
percent, are polluted and of these polluted miles, 57,741, or 22 percent, lie in minor
drainage basins, where the average DI factor is .2 or greater. Three major drainage
systems (the Ohio River, the South Atlantic watershed, and the Great Lakes Basin)
contain 23.9 percent of the nation's stream miles, but 48.9 percent of the polluted
stream miles. Extensive pollution and high DI factors are generally limited to the Ohio,
Great Lakes, Tennessee and North and South Atlantic watersheds. The boundaries of the
major water systems are shown on the map of Figure 10. Data on total stream'miles,
number of polluted stream miles, average duration-intensity factor, and percentage contri-
bution of major pollution sources (municipal, industrial, and federal installations, agricul-
tural and rural wastes, mining wastes, water resources development, and transportation)
are available for all 241 minor drainage areas from the 1971 DPI Survey. This summary
data has been compiled and published [11]. Although we sought more disaggregated data
for all polluted minor basins, we were able to obtain the raw data from which the
summaries were compiled (maps plotting pollution zones, and tables recording exact
polluted river miles) for only one important water system, the Great Lakes Basin. For
this basin, we were able to determine exactly which river reaches and shorelines were
considered polluted, and we could also determine their estimated DI factors.
For other minor basins, we relied on the PDI Survey summary data, our experience from
mapping the Great Lakes data, water quality data from a variety of sources, and our own
judgment to locate polluted water bodies. Water run-off patterns and areas of concen-
trated population and industry were easily discernible on large-scale topographic maps.
Given the amount of stream miles that were considered polluted (from PDI summary
data) and the sources of the pollution, it was possible for us to select and mark the
waters which are most likely polluted. Most pollutants significant to determining property
benefits emanate from fixed discharge points, such as municipal sewage treatment plants
or industrial waste discharges, and the most probable locations of these sources can be
deduced from the basin topography and the distribution of population and transportation
systems. The average DI factor for each minor basin was assumed to apply uniformly to
all polluted water within the basin unless other information justified assigning pollution
intensity levels more selectively. We gained considerable insight into the general distribu-
tion of water pollution throughout the country, as well as some detailed information
about specific water bodies in the early phases of this study, when we were searching for
sites where water quality had changed. We put much of this experience and information
to good use reconstructing the PDI survey in several areas. Our basic approach in locating
and marking polluted reaches was conservative. No water body was marked as polluted
unless there was some evidence to indicate that it was. Small tributary streams considered
to have little positive effect on property values, were generally neglected, although there
was occasionally reason to believe that some of these small tributaries were polluted,
particularly in coal mining areas where acid drainage is a problem. Minor basins with an
average DI factor of .2 or less were neglected because these moderate pollution levels
would have only small impacts on property values, and therefore their contribution to
accuracy of the national benefit estimate did not justify their measurement costs. Minor
basins where the average DI factor was greater than .2 and upon which benefit measure-
ments were based are shaded on the map of Figure 11.
54
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01
m
r \
FIGURE 10-MAJOR WATER SYSTEMS
-------
Figure 11
MINOR BASINS WITH AVERAGE PI INDEX
GREATER THAN , 2 (SHADED AREA)
-------
Using the methods and assumptions just described we marked all polluted or "most likely
polluted" river reaches and shorelines on U.S. Geological Survey topographic maps
(l:250,000-scale series, a scale of approximately four miles to the inch). These maps
which are available for the entire country except Alaska, show the extent of urban areas,
political boundaries, roads, railroads, surface water, swampy areas, levees, and sometimes
ground cover, as well as topography (200-foot contour interval). Mountainous areas, flood
plains of major rivers, and general land use are discernible on these maps. This set of
maps upon which we marked polluted water bodies and pollution intensity formed the
base from which the national property benefit of pollution abatement was measured.
RELATIONSHIP BETWEEN BENEFIT AND THE DURATION-INTENSITY FACTOR
Before we can calculate how much existing residential, recreational, and rural property
values will increase if water pollution is reduced to levels which will support desirable life
forms and practical water uses and where the water is aesthetically agreeable (that is,
where the DI factor approaches zero), we must establish an appropriate relationship
between the pollution duration-intensity factor (DI) and benefits.
Our interview results indicated that the I factor was a good measure of pollution as it
affects residential property values. On the basis of our case studies, however, we have no
way of similarly validating the D factor. Nonetheless, the pollution inventory results are
only available in terms of the DI index. The D factor is relatively less important than the
I because in most cases intense pollution conditions do occur during the summer months,
when people in most parts of the nation are most aware of water quality. Since
multiplying the intensity factor, I, by the duration factor, D, to compute the DI index
attenuates the intensity factor, we can only guess that the resultant DI index will
underestimate the pollution level perceived by property owners. This understatement will
in turn render our national property value benefits estimate conservative. The heavy solid
curve (Figure 12) represents our best estimate of the correct relationship between urban
residential property benefit and the DI factor, with benefit expressed as the maximum
percentage increase in an urban residential property located 100 feet from the water's
edge. This curve applies to what might be considered as average local circumstances. The
results of many more than five case studies would be necessary to give this curve precise
meaning. However, we can base our selection of this curve on the insight we have gained
from our case results into the relationship between the DI factor and the benefit impact.
As we have drawn it, the curve has two important characteristics: (1) the curve increases
rapidly with increasing DI ratings, and (2) rather than continuing to increase rapidly
above DI ratings of about .5, the curve levels off to an approximate maximum 18 percent
change in property value. Our earlier assumption that improvements to water with a DI
of less than .2 would produce little property value benefit is also consistent with the
behavior of this curve. We performed all our calculations of the national benefit from
pollution abatement on the basis of this curve's shape. We moved or scaled the curve so
that its maximum benefit point coincided with a 30 percent or 10 percent increase, in
order to obtain the "high" and "low" benefit estimates discussed later. The dotted curves
in Figure 12 represent the "high" and "low" estimate relations. The relationship between
the maximum height of this curve and the metropolitan area and town components of
the national benefit estimate is linear (changes in these components of the benefit
57
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01
oo
VJ
a
25"
5-
- HIGH
Willamette River
MEDIUM
___ __ — LOW
San Diego Bay
.2 .4 .6 .8 1.0
POLLUTION DURATION-INTENSITY FACTOR (DI)
Figure 12
RELATIONSHIP BETWEEN POLLUTION INTENSITY AND
MAXIMUM PROPERTY VALUE INCREASE OBTAINABLE
BY POLLUTION ABATEMENT
-------
estimate are directly proportional to changes in the height of the curve).
Among our case studies are two urban sites where pollution started at high levels as
measured by the DI factor, and was reduced to DI levels near zero. These are Cbronado
(San Diego Bay) and the residential site on the Willamette River in Oregon. On San Diego
Bay serious pollution prevailed year-round, with 1960 pre-clean-up levels which would
have merited a DI factor rating of .8 to 1.0, if computed. Critical water quality
conditions on the lower Willamette River prevailed only during the low-flow summer
months, so the appropriate duration index before clean-up was probably .4 or .6, with an
intensity index of .8 to 1.0. Thus the approximated DI ratings (the product of D and I)
on the Willamette River changed from somewhere in the range of .32 to .6 in 1960, to
zero by 1970.
The results for Coronado and the Willamette River urban site (percent change models) are
plotted in their estimated position in Figure 12. These are the only results that can
meaningfully be plotted on this graph because these are the only sites where the latest
water quality levels approach a DI of zero. The two points plotted are consistent with
our speculations about the relationship of the results measured at the study sites and the
quality changes in their respective water bodies, and their comparison with typical
residential property near other polluted water bodies throughout the nation. Notice that
if the horizontal axis were pollution intensity, I, only, the point for the Willamette River
results would be well within the area bounded by the "high" and "low" curves.
Multiplication by the duration factor, D, has the effect of moving the point to the left.
As we have stated we believe that the intensity factor (I) alone rather than the product
of the duration factor and the intensity factor, is more representative of the effect of
pollution on property values. However, as already mentioned, the pollution inventory
results are only available in terms of the average DI index for each of the 241 minor
drainage basins.
To check the sensitivity of the benefit calculation to changes in the shape of the curve,
we also calculated benefits using the dotted straight line relationship between property
value and DI changes shown in Figure 12. The results obtained using the curve were
about 19 percent higher than those obtained using the straight line. This moderate
difference in benefits calculated on the basis of such large differences in the assumed
shape of the relationship between property value and DI changes implies that the national
benefit estimate is relatively insensitive to the exact shape of the curve.
BENEFIT CALCULATION
We performed the national pollution abatement calculation in three separate parts and
then summed the parts to derive the total benefit. The three separate parts are the
following:
1) Metropolitan benefit — This is the expected increase in the value of existing
residential property and parks in all metropolitan areas of more than one
million people, plus thirteen other large metropolitan areas with known water
pollution.
59
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2) Town benefit — This is the estimated benefit to property values in all towns of
more than one thousand people outside of the metropolitan areas..
3) Rural land benefit — This is the estimated total increase in rural waterfront
land values.
In the metropolitan and town estimates we considered the benefit of pollution abatement
that will be realized as increases in the market value of existing residential and park
property. Increases in value which might be attributed to land occupied by industry or
transportation were ignored because although in some instances the value of the land
could increase, the benefit would not be immediately realized by the public.
A factor to account for the capital value which taxes add to real property values was
included in the benefit calculation. In effect, an average property tax of 2 percent [7]
capitalized at ten percent per annum was added to the market value of all properties.
This effectively raises the value of all properties by 22 percent. Property value estimates
were also inflated wherever necessary to Fall, 1972 dollar values, using the Consumer
Price Index [8].
METROPOLITAN AREAS
Our case study results indicate that potential water pollution abatement benefits
measured as increases in property values can be expressed as a percent of the value of
existing residences and land within 4000 feet of polluted water. The percent increase or
benefit depends on the distance of the property from the water and the duration and
iritensity of the water pollution. To calculate the benefit within metropolitan areas we
measured the area of all densely developed residential property and park lands which
would be affected by pollution abatement. Each area was classified according to its
distance from the water and the duration and intensity of the water pollution. National
averages for the value per unit area of densely developed residential areas and the average
value of vacant land can be used to convert area measurements to dollar values.
Given the property values, distribution of property with respect to distance from the
polluted water body, the intensity of the pollution, and the curve of Figure 12, the
expected benefit of pollution abatement can be estimated. The benefit is the sum of all
the individual property benefits computed as a percentage of their original value..
We began the estimation of the metropolitan benefit by using U.S. Geological Survey
7!/2-minute series topographic maps to locate and measure the area of all waterfront parks
and densely developed residential areas located within 4000 feet of a water body with a
pollution duration-intensity factor of greater than .2. The 7!/i-minute series of maps has a
scale of 2000 feet per inch and provides such detail that land use patterns are clearly
discernible. Streets, railroad lines, factories, storage tanks, and in many cases individual
houses, are included on these maps.
The amount of dense residential area in each of six categories (depending on distance
from a polluted water body) was measured using the appropriate maps for each metro-
60
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politan area, and keyed to the appropriate DI factor. The length and depth (up to 4000
feet) of all parks directly adjacent to polluted water bodies were also measured and
keyed to appropriate pollution levels. The height of the river bank adjacent to the
property was noted if it was over 100 feet, as was the number of train tracks between
the property and the water body if there were more than one and less than four sets of
tracks. Property obstructed from the water by industry, commercial buildings, more than
three train tracks, a major highway, or. a river bank of over 500 feet in height was not
measured since it was felt that these factors would reduce the positive effect of water
pollution abatement to negligible levels. The area occupied by dense residential develop-
ment within four thousand feet of polluted water measured by the preceding criteria in
all metropolitan areas of over a million persons is summarized by distance category in
Table 10. The numbers are rather small because land along river banks and harbors is
dominated by industry and transportation, since industry originally developed in close
proximity to water channels providing transportation, power, and waste disposal.
It was arbitrarily assumed that pollution abatement impacts on property values are
decreased 50 percent by two lines of railroad track at the water's edge and 90 percent by
three to compensate for the obstructing effects which these rights-of-way undoubtedly
produce. It was further assumed for computational simplicity that the effects of water
quality improvements would decrease in a linear fashion as the height of the river bank
increases from 100 to 500 feet (that is, no benefit would be felt above 500 feet). We
estimated that neglecting residential property obstructed from the water by more than
one line of tracks or a bank over 100 feet high would decrease the total benefit estimate
by less than 6 percent.
Given our case study results, it seems reasonable to assert that the benefits of improved
water quality to unobstructed residential and park property generally decreases propor-
tionally as the reciprocal of the distance to the water, and that the effects are negligible
beyond four thousand feet. The functional relation between percent increase in property
value and distance to the water which provided the best results at the Willamette River
site was used in the metropolitan benefit calculation. This is appropriate because most of
the residential areas affected by water pollution are adjacent to rivers. The function
which is graphed in Figure 13, has the following form:
n * i (1200 - .3 x Distance to water) D . ,nn f +
Percent Increase =J|—pp— :—, ,nnn—' x Percent increase at 100 feet
Distance to water + 1000
The value per unit area of densely developed residential property was estimated using the
19,000-dollar U.S. 1970 median value of a single-family residence [6], and 7770 square
feet as the average house lot size corrected to include area occupied by streets. We used
data from our study sites to estimate the average lot size.
A value of 100,000 dollars per acre was used for park land in metropolitan areas. This
figure is based on the value of vacant land in San Francisco, Berkeley, and San Diego,
California; Charleston, West Virginia; Portland, Oregon; and Seattle, Washington. Increases
in land value were the only pollution abatement benefits assigned to parks in the national
benefit estimate, although many other park benefits may result from cleaner water, such
as, increased enjoyment by park visitors. In .this sense we underestimate the park benefit
for the sake of computational ease.
61
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Table 10. AREA OF RESIDENTIAL PROPERTY AND WATERFRONT
PARKS AFFECTED BY WATER POLLUTION IN METRO-
POLITAN AREAS OF MORE THAN ONE MILLION POPU-
LATION
Type of Property
Dense Residential
Waterfront Park
Distance from
Polluted Water
Body (in feet)
0 to 500
500 to 1000
1000 to 1500
1500 to 2000
2000 to 3000
3000 to 4000
0 to 4000
0 to 4000
Area
(in Square Miles)
6.23
8.04
5.65
6.67
7.70
6.02
40. 31.
133.61
62
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1000 2000 3000
DISTANCE FROM WATER, feet
4000
Figure 13
RELATIONSHIP BETWEEN BENEFIT (IN PERCENTAGE TERMS)
AND WATER DISTANCE USED FOR MEDIUM NATIONAL BENEFIT
CALCULATION
63
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The land value increase is the most straightforward portion of the benefit to define and
measure. Only parks or portions of parks with direct unobstructed water contact were
measured. We designed and wrote a computer program to calculate the expected total
increase in property values from measured areas, distances, and pollution levels. Assuming
a maximum 18 percent increase in property value (corresponding to a DI change from 1
to 0) 100 feet from the water body and the functional relationship between percent
increase and distance to the water plotted in Figure 13, the estimated potential benefit
for all metropolitan areas of over a million population plus 13 other large metropolitan
areas with known water pollution, was a 307 million dollar increase in existing residential
property values, and a 208 million dollar increase in the value of land now occupied by
waterfront parks.
TOWNS
There are many towns with populations from 100 to 1,000,000 located adjacent to large
polluted water bodies. To simplify the calculation of the benefit to these towns, we
assumed a relationship between the population of a town and the maximum potential
benefit achievable from pollution abatement. The maximum potential benefit is defined
here as the total increase possible in the sum of community property values if all
adjacent water was initially polluted to a level equal to a DI factor of one, and then
improved to a level equal to a DI of zero.
Town of over 1000 population affected by pollution were identified from the large scale
U.S. Geological Survey maps upon which we had marked polluted water and then
classified according to their pollution level and orientation to the water. Towns were
ignored if their developed area was not in direct contact with the water as shown on the
maps. Thus, towns merely located near a polluted water body are not included in this
estimate. We inferred that if a town was built near but not in direct.contact with a river
or lake, there was a reason for avoiding the water, such as flood hazard or swamps, and
that these obstacles would tend to render the impact of pollution abatement on property
values negligible.
We developed two functions relating population and maximum potential benefit: one for
towns adjacent to water (single-bank towns) and another for towns straddling rivers
(double-bank towns). To estimate the forms of the functions, a random sample of 30 of
each type of town was drawn from those affected by pollution and the method used in
the metropolitan area calculation was used to measure the maximum potential benefits to
each of the 60 towns as accurately as possible. Then, using a least-squares numerical
technique we selected a linear function to approximate the relation between maximum
potential benefit and population. The two functions are graphed in Figure 14 for the case
of a 10 percent increase in value of 100 feet from the water.
Quadratic functions were also tested for fit to sample data, but the linear function
proved to be the most reasonable. It should be noted that we neglected towns of less
than 1000 population. Although many such towns exist, their development tends to be
very scattered and it was impossible for us to determine their location relationship to the
water on the l:250,000-scale maps. The properties affected by pollution abatement near
64
-------
O
Q
fc
O
CO
5 J
3 -
1 -
Double Bank
Single Bank
100
200 300 400
TOWN POPULATION, thousands
500
Figure 14
RELATION BETWEEN TOWN POPULATION AND
MAXIMUM POLLUTION ABATEMENT BENEFIT
65
-------
these towns were included in the rural benefit measurement.
The pollution abatement benefit for each town of over 1000 population was computed
by multiplying the appropriate maximum potential benefit, as determined from the
functional relation between population and maximum water quality impact, times the
percent of maximum benefit expected, as determined from the average minor basin
pollution duration-intensity index and the curve of Figure 12. The national town'benefit
is the sum of the individual town benefits for 853 towns with an average population of
24,864. The medium estimate of the town benefit is summarized by major water system
in Table 11.
RURAL AREAS
The values of rural land adjacent to water will also be increased by pollution abatement.
This will be particularly true where there is a strong demand for rural home and
recreation sites, and where the number of sites adjacent to unpolluted water is limited.
We calculated the potential benefit of pollution abatement along each polluted rural river
reach or shoreline as a percent increase in the estimated value of the waterfront land. The
appropriate percent increase for each river bank is determined by the duration-intensity
of the pollution. The national benefit was computed as the simple sum of the benefits
for all of the pollution zones.
Using a percentage change regression model, we estimated that rural waterfront land near
Portland, Oregon increased 65 percent as a result of pollution abatement on the
Willamette River. This is a rather special case, in that the Portland metropolitan area is
growing rapidly and the demand for all land in the Willamette Valley is increasing. In
addition, the land has good access, most is suitable for building, and the river has
considerable recreation and aesthetic value.
Because of these special circumstances in the Willamette Valley, the 65 percent increase
in waterfront land there probably represents the maximum benefit which can be realized
by rural land through water pollution abatement. It would be more reasonable to expect
a smaller property value increase in other regions of the country where the special
circumstances of the Willamette situation do not pertain. Therefore, we reasoned that
rural waterfront land in these other regions, which is suitable for building and located on
a badly polluted lake or river with water access and low flood hazard, might show a
maximum value increase of between 10 and 65 percent as the result of pollution
abatement. The 65 percent maximum increase of this range is equivalent to the
Willamette River results, while the low maximum increase of 10 percent is our own most
conservative estimate of the increase in the value of rural waterfront land attainable from
a dramatic improvement in water quality.
Miles of rural waterfront land adjacent to polluted water were measured on the U.S.
Geological Survey 1:250,000 topographic maps. We recorded mileages, pollution levels,
and the county and state of location. Waterfront land was classified in the following five
categories: preferred river bank, shoreline, marshy, mountainous, and highway or railroad.
Waterfront land which appeared to be suitable for buijding (that is, accessible, dry, and
66
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Table 11. EXPECTED BENEFIT IN TOWNS OUTSIDE LARGE
METROPOLITAN AREAS (MEDIUM ESTIMATE)
Number
Major Water System of
Towns
North Atlantic
Middle Atlantic
South Atlantic
Tennessee
Ohio
Lake Erie
Upper Mississippi
Superior- Michigan-Huron
Missouri
Lower Mississippi
Colorado
Texas-Rio Grande
Columbia- North Pacific
California
187
104
87
10
201
33
85
105
11
10
13
5
2
Average
Population
(thousands)
24. 1
22.5
37.2
30.7
17.0
21.4
23.0
19.2
36.1
135.7
.
51.5
40.8
37.8
Average
DI
.7
.6
.6
.6
.5
.8
.7
.6
.4
.4
.6
.4
.4
Benefit
Estimate
(millions$)
192.0
100.9
100.3
12.1
157.0
27.3
78.3
89.6
7.7
13.5
13. 3
4.5
1.6
Great Basin
Total
853
24.9
798.1
67
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not too steep) was classified as preferred river bank or shoreline. The value of this land
was assumed to be 2.6 times the average value of all farm land in the county where it
was located. The factor 2.6 is a national average ratio of the value of land adjacent to
water to the value of land without water access [9]. The average value of all farm land
by county is recorded in the 1969 Census of Agriculture [5]. By using the average value
of local land, we are in effect adjusting the benefit calculation for regional differences in
the demand for land.
Marshy and swampy areas are generally marked as,such on the one to 250,000-scale
topographic maps. Land in this classification was arbitrarily assumed to be worth only
half the value of preferred land in the same county. Mountainous areas are clearly
distinguishable on the topographic maps, and this land was also arbitrarily valued at half
the value of preferred land because of its inaccessibility and the scarcity of building sites
close to the water. Ignoring swampy or mountainous miles completely would have
decreased our benefit estimates less than one percent. Many of the overland transporta-
tion routes in the U.S. follow closely along riverbanks and shorelines. About one third of
all polluted waterfront miles were traversed by highways or railroad tracks or both.
Highways and railways can either raise land values above the local average by providing
increased access, or- depress land values below the local average to the extent that they
constitute a nuisance. Where highways or railroads are immediately adjacent to the water,
the land has virtually no recreation potential. It was assumed here th-at the average net
effects of highways or railroads was to depress land values 20 percent below the values of
"preferred land." No economic land value studies were found which either support or
reject this assumption. It was based on our own observations and intuition. However, the
benefit estimates are relatively insensitive to the assumed 20 percent. The total national
benefit would decrease by only about 2.5 percent if the highway and railroad miles were
neglected completely. The total polluted miles measured in each category are summarized
by major river basin in Ta'ble 12, and the estimated value of rural property affected by
water pollution is summarized in Table 13.
In rural areas, pollution abatement was assumed to affect only the value of waterfront
property, defined as that land from 120 feet distant to the water's edge. This definition
seems reasonable since the depth of an average city lot is about 100 feet and a typical
waterfront lot might be about 20 feet longer. According to this definition, there are 14.6
acres of waterfront land in a mile of shoreline.
The curve of Figure 12 was used to relate the pollution duration-intensity factor (DI) to
the maximum percentage increase in land value due to pollution abatement. Maximum
percentage increases of 10, 30, and 65 percent were used to calculate the low, medium
and high national rural land benefits. The maximum percentage increases express the
average waterfront land value increase which could be expected if the water quality was
to improve from extremely bad (a DI factor of one) to extremely good (a DI factor of
zero). Based on our urban and rural case study results, a ten percent increase is a very
conservative estimate, while a 65 percent increase seems optimistic.
68
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Table 12. MILES OF POLLUTED RURAL WATERFRONT MEASURED FOR EACH MAJOR BASIN
Major Water System
North Atlantic
Middle Atlantic-
South Atlantic
Tennessee .
Ohio
Lake Erie
Upper Mississippi
Superior- Michigan-Huron
Missouri
Lower Mississippi
Colorado
Texas-Rio Grande
Columbia- North Pacific
Calif ornia
Great Basin
Total
Swamp
or
Marsh
10
105
1760
14
34
10
356
8
13.
496
0.
871
6
20
0
3704
Mountain-.
- ous
0
116
44
67
808
14
0
9
87
18
89
104
44
0
0
1400
Highway .
or
Railroad
2745
1240
921
285
4469
283
1216
473
568
986
155
430
585
243
99
14700
Preferred
River
Front
926
526
2853
795
5705
732
2412
11:94
1944
2858
363
2696
324
78
73
23,480
Preferred
Shoreline
558
0
231
75
84
37
25
287
5
362
50
141
0
9
96
I960
Total
4239
1987
5809
1236
11100
1063
4023
1961
2540
4790
586
4228
1013
380
288
45,244
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Table 13. ESTIMATED VALUE OF RURAL LAND AFFJECTED BY WATER POLLUTION (IN MILLIONS OF DOLLARS)
Major Water System
North Atlantic
Middle Atlantic
South Atlantic
Tennessee
Ohio
Lake Erie
Upper Mississippi
Superior- Michigan- Huron
Missouri
Lower Mississippi
Colorado
Texas-Rio Grande
Columbia -North Pacific
California
Great Basin
Total
Swamp
or
Marsh
.02
.38
6.38
.06
.08
.07
.97
.02
.01
1.67
.00
3.18
.00
.02
.05
12.91
Mountain-
ous
.00
.52
.16
• 19
1.74
.00
.06
.00
.02
.17
.05
.24
.16
.08
.00
3.39
Highway
- or
Railroad
25.84
6.92
6.28
2.16
22. 77
2.98
8.17
5.23
3.19
7.24
1.66
2.97
2.30
1.42
.44
99.57
Preferred
River
Front
15.32
5.38
21.46
7.63
52.27
9.27
17.48
12.17
14.10
16.18
3.72
15.87
1.44
.61
.41
193.31
Preferred
Shoreline
5.48
.00
2.03
.59
.63
.59
.78
1.35
.00
2.35
.59
1.73
.00
.10
1. 17
17.39
Total
46.66
13.20
36.31
10.63
77.49
12.91
27.46
18.77
17. 32
27.61
6.02
23.99
3.90
2.23
2.07
326.57
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COMPLETENESS OF MEASUREMENT INVENTORY
To the best of our judgment, our measurements have accounted for about 80 percent of
the property affected by water pollution. This 80 percent completeness is the product of
conservative assumptions, the procedures described above, and our intuitive "feel" for the
inclusiveness of our measurement inventory, based on our experience with property value
impact assessment and water pollution data. Our medium estimate of the national benefit
is inflated to account for this assumption that we have accounted for 80 percent of all
properties affected by water pollution.
We have adjusted the low and high benefit calculations to demonstrate the sensitivity of
the benefit estimate to the extremes of variation in the degree of completeness assumed.
The range of the variation reflects our degree of confidence in our measurement
procedure. The low estimate assumes we have accounted for 100 percent of all property
affected by water pollution, while the high estimate assumes a 60 percent completeness.
(The high benefit estimate was inflated accordingly). We are confident that the actual
completeness of our measurement inventory Jies somewhere in the range of 60 to 100
percent.
The accuracy of our affected property measurements was limited by our ability to locate
polluted water and to determine land use patterns from U.S. Geological Survey topo-
graphic maps. We know, for example, from the difference between the mileage of
polluted waterfront we measured and the total mileage considered to be polluted in the
original PDI Survey, that we did not plot every polluted water body with a Dl index of
.2 or greater. However, those polluted waters we overlooked would tend to be on small
tributary streams in remote areas away from population and industrial centers, where
pollutants are less concentrated and abatement benefits measured as property value
increases would consequently be small.
In metropolitan areas the accuracy of our estimate is more dependent on locating all
affected property. And some omissions were inevitable. Where residential properties are
sparce, or where the residential development has occurred since the last update of the
U.S. Geological Survey Maps in the 1960's, some properties would be overlooked. Similar
omissions of new waterfront parks or parks not labelled as such on the maps would also
occur. In general, however, the unobstructed dense residential and park property within
4000 feet of polluted water in metropolitan areas has been carefully inventoried on very
detailed maps. The total area actually measured is perhaps smaller than expected, because
within most metropolitan areas a large share of waterfront land is occupied by factories,
warehouses, highways, and railroads. We deliberately did not attribute any benefit to
lands devoted to these activities.
We have systematically attempted to measure the towns and rural areas most likely to be
affected by water pollution abatement within each minor drainage basin which has an
average DI factor greater than .2. Our^own judgment was necessary to locate some of
these polluted water bodies on topographic maps, but we feel we have considered nearly
all which are located in towns of populations greater than 1000 and those which have the
greatest effect on rural land values.
71
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LOW, MEDIUM, AND HIGH ESTIMATE RESULTS
Three estimates (a low, a medium, and a high) were calculated for the potential national
increase in residential and recreational property value which can be expected to result
from water pollution abatement.
The medium estimate gives the most likely value of the national benefit, based on the
findings of this report. It is also based on the best estimates available for the magnitude
and extent of the effects of a dramatic change in water quality on proximate residential
and rural property; on our own best estimate of the relationship between the EPA's
pollution duration-intensity factor (DI) and property value increases; and on our estima-
tion of how completely we included the value changes for all property affected by
polluted water.
The major assumptions for this best estimation are the following:
Assumption 1:
A change in water quality from a badly polluted condition (DI factor of one) to a
condition which will sustain desirable life forms and desired and practical water uses
and which is aesthetically pleasant (DI factor of zero), will increase by 18 percent
the value of an unobstructed single-family residence 100 feet from .the water.
Furthermore, the impact of pollution abatement on property values is assumed to
decrease as an inverse function of distance from the water body, approaching zero at
four thousand feet. These assumptions are based on the results of our case studies
on the Willamette River, Kanawha River, and San Diego Bay.
Assumption 2:
The same water quality change described under Assumption 1 will increase the value
of rural waterfront land by thirty percent.
Assumption 3:
It is assumed that our measurements included 80 percent of all properties affected
by water pollution.
The results of the computation for the medium estimate are summarized by major water
system in Table 14. The total national benefit according to this estimate is 1.35 billion
dollars. About 59 percent of this benefit will accrue to towns, 31 percent to metro-
politan areas (with population greater than one million), and only 10 percent to rural
river bank and lakeshore.
The "low" estimate reflects the national benefit obtainable under very conservative
assumptions. In our opinion, there is a .85 probability that the national benefit is
actually greater than this estimate. This is a subjective probability assessment which
reflects our own judgment. It is based on confidence levels derived from our case studies,
our belief in the validity of our assumptions and approximations, and the sensitivity of
our final estimates to variations in uncertain variables.
The assumptions incorporated in the low estimate are the following:
72
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Table 14. EXPECTED RESIDENTIAL AND RECREATIONAL PROPER-
TY VALUE INCREASE (MEDIUM ESTIMATE) OBTAINABLE
BY WATER POLLUTION ABATEMENT (MILLIONS OF $)
Major Water System
North Atlantic
Middle Atlantic
South Atlantic
Tenne.ssee
Ohio
Lake Erie
Upper Mississippi
Superior-Michigan-Huron
Missouri
Lower Mississippi
Colorado
Texas-Rio Grande
Columbia-North Pacific
California
Great Basin
Total
Metro
Areas
131.6
22.5
40.8
29.0
15.0
26.8
79.5
18.4
5.9
50.0
2.6
422. 1
Towns
192.0
100.9
100.3
12.1
157.0
27.3
78.4
89.5
7.7
13.5
13.3
4.5
1.6
798. 1
Rural
20.7
5.5
17.7
5.5
24.8
4.4
10.9
7.1
5.6
9.3
2.2
10.4
1.4
.7
1. 1
127.3
Total
344.3
128.9
158.8
17.6
210.8
46.7
116. 1
176.1
31.7
28.7
2.2
73.7
5.9
4.9
1. 1
1347.5
73
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Assumption •!:
A water quality change from a DI factor of one to zero will increase the value of a
single-family residence 100 feet from the water by 10 percent.
Assumption 2:
Rural waterfront land values will be increased a maximum of 10 percent by
pollution abatement.
Assumption 3:
We have accounted for all properties affected by water pollution.
The "high" estimate is the benefit calculated for very optimistic assumptions. We feel
that the probability is about .85 that the actual national benefit is less than this high
estimate. The assumptions incorporated in the high estimate are the following:
Assumption 1:
The maximum increase in residential property values 100 feet from the water is 30
percent.
Assumption 2:
Rural waterfront land will realize a maximum 65 percent increase frojn pollution
abatement.
Assumption 3:
We have accounted for only 60 percent of all properties affected by water pollution.
The results of the three estimates are summarized in Table 15. The total capital value of
the low estimate is approximately .6 billion dollars, the medium is about 1.35 billion,
and the high is nearly 3.1. The comparative magnitude of these values is perhaps clearer
when we note that the median estimate is equivalent to about one-half of one percent of
the taxable value of all non-commercial and non-industrial property in the nation. The
annualized value of the medium estimate is 76 million dollars per year when calculated
by standard accounting procedures, using a discount rate of six percent and an infinite
time horizon.
We cannot overemphasize the precise extent and limitations of these benefit estimates.
The estimates reflect the expected increase in existing residential and recreational
property values if pollution levels in all water bodies in the contiguous United States are
reduced to conditions which are not inhibiting to desirable life forms or desired practical
water uses, and which are aesthetically agreeable. This benefit would be realized whether
or not cities and states took any positive action to acquire waterfront land or to provide
parks or make efforts to change land use patterns. Concerted water clean-up efforts
together with active programs to develop residences, commerce, and recreation facilities
near safe water bodies would substantially increase the benefits calculated in this study,
particularly in metropolitan areas. In San Diego Bay, for example, hotels, restaurants,
marinas, and other recreation facilities are being built on man-made islands where such
developments would be nonsensical if the bay remained polluted.
74
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Table 15. NATIONAL BENEFIT OF POLLUTION ABATEMENT ON PROPERTY VALUES
(Millions of Dollars)
Estimate
Low
Medium
(most likely)
High
Metropolitan Areas
Residential
112
252
560
Parks
75
170
379
Towns
355
798
1774
Rural
34
127
368
Total
576
1347
3081
Total Annualized Value
using 6% discount rate
33
76
175
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Some additional comments will place this benefit estimate in theoretical perspective.
First, we have estimated the value of a complete water clean-up rather than the marginal
benefit of incremental improvements in pollution levels. This seemed the logical way to
produce meaningful results, given the limited number of case studies and limited avail-
ability of information about the characteristics, intensity, and distribution of water
pollution. Another point is that due to supply and demand interactions, the market price
of most economic goods such as property near clean water, will decrease as more of it
becomes available. We have not made any adjustments in our estimates to compensate for
this effect. However, the change in price should theoretically be small, and therefore so
should the effect on our benefit estimate. The fraction of total housing and land which is
affected by water pollution is small in any given region, while the total housing market
offers a large number of substitutes for an amenity such as proximity to clean water (for
example, a view or additional space). The large number of close substitute commodities
implies that the price of any single commodity (in this case, property near clean water)
will be relatively insensitive to small changes in its supply.
The actuaJ total of tangible and intangible benefits which could be realized from water
pollution abatement would be much greater than what we have estimated in this study.
Our principal concern was to estimate only the benefit reflected in increased property
values, and that increase only for existing residential and park property. Future changes
in land use to high-yield uses rendered possible by water pollution control'rep resent a
potentially very large benefit which remains uncounted in our estimate. Perhaps the
greatest benefit of unpolluted water will be the satisfaction derived by all people,
property owners and non-property owners alike, from their assurance that the nation's
water bodies are a continuing healthful resource and fit habitat for all forms of life.
76
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Section VII
Acknowledgments
Members of the staff of David M. Dornbusch and Company, Inc., are acknowledged with
special credit to Marcy Avrin, James Crawford, Patricia Gelb, Michael Heumann, Patricia
Hoskinson, Neil Mayer, and William Ziefle.
We express our appreciation to our project officer, Dr. Fred Abel, and Drs. Mark Sidell
and Dennis Tihansky of the Environmental Protection Agency for providing technical and
administrative support during the conduct of this research. We thank the Opinion
Research Corporation for their help in performing on-site interviews, and we thank all of
those persons in water quality organizations and assessors' offices who assisted us fully in
obtaining records and information. Particularly notable for their spirit of cooperation
are: Dr. Nina I. McClelland of the National Sanitation Foundation, David A. Dunsmore
of ORSANCO, A. Ben Clymer of the Ohio State Health Department, Mr. Davies of the
West Virginia Department of Natural Resources, Bob Fergerson of the Kanawha County
Assessor's Office, and Charles W. Dougherty of the E.P.A., Region V. Finally we wish to
acknowledge the following persons who labored diligently and without whom the project
could not have been completed: Thorne Barrager, Alan Carpenter, Jim Gantt, Elinor
Gordon, Philip Groody, Vicky Sperry, John Montague, Barbara Warren, and Celeste Woo.
77
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Section VIII
References
1. Federal Water Pollution Control Administration, Water Quality Criteria, Report of
the National Technical Advisory Committee to the Secretary of the Interior,
Washington, D.C., U.S. Government Printing Office, 1968.
2. Litton, R. Burton, Jr., et al., "An Aesthetic Overview of the Role of Water in the
Landscape," Springfield, Virginia, National Technical Information Service, 1971.
3. O'Connor, Michael F., "The Application of Multi-Attribute Scaling Procedures to the
Development of Indices of Value," Technical Report, Engineering Psychology
Laboratory, Ann Arbor, University of Michigan, 1972.
4. Rothenburg, Jerome, Economic Evaluation of Urban Renewal, Washington, D.C.,
1967.
5. U.S. Department of Commerce, Bureau of the Census, "Area Reports," 1969 Census
of Agriculture, Vol. I, Washington, D.C., U.S. Government Printing Office.
6. U.S. Department of Commerce, Bureau of the Census, "Metropolitan Housing
Characteristics," 1970 Census of Housing, Washington, D.C., U.S. Government
Printing Office, 1971.
7. U.S. Department of Commerce, Bureau of the Census, "Taxable Property Values,"
1967 Census of Governments, Vol. II, Washington, D.C., U.S. Government Printing
Office, 1968.
8. U.S. Department of Commerce, Bureau of Labor Statistics, Handbook of Labor
Statistics, Washington, D.C., U.S. Government Printing Office, 1971.
9. U.S. Department of the Interior, Bureau of Outdoor Recreation, Recreation Land
Price Escalation, Washington, D.C., U.S. Government Printing Office, 1967.
78
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10. U.S. Department of the Interior, Office of Water Resources Research, Methodology
to Evaluate Socio-Economic Benefits of Urban Water Resources, Washington, D.C.,
U.S. Government Printing Office, 1971.
11. U.S. Environmental Protection Agency, Economics of Clean Water, Vol. II, Washing-
ton, D.C., U.S. Government Printing Office, 1972.
12. Wolman, M. Gordon, "The Nation's Rivers," Science, Vol. 174, 1971, pp. 905-918.
Appendix References
1. Gleeson, George W., "The Return of a River: the Willamette River, Oregon,"
Corvallis, Oregon State University, June 1972.
2. State of Oregon, Department of Environmental Quality, "Water Quality Control in
Oregon," Portland, Oregon, December 1970.
3. Stone, Ralph, and Company, Inc., Engineers, "Estuarine-Oriented Community
Planning for San Diego Bay," prepared for Federal Water Pollution Control Admin-
istration, June 30, 1969.
79
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Section IX
Appendices
Page No.
A. Coronado (San Diego Bay) 82
Table A-l: • San Diego Bay -Water Quality: Before and After Cleanup . . 83
Figure A-l: Coronado (San Diego Bay) •.•:.... 86
B. Clackamas County: Residential (Willamette River) 88
Figure B-l: Dissolved Oxygen Levels — Lower Willamette River, Low
Flow Months - June-October 89
Figure B-2: Major BOD Discharges . . •...'. 90
Figure B-3: Dissolved Oxygen Levels for Selected Years 91
Figure B-4: Fecal Coliform for 1962 and 1970 92
Figure B-5: Clackamas County: Residential (Willamette River) ...<.. 94
C. Clackamas County: Rural Land (Willamette River) .......... 95
Figure C-l: Clackamas County: Rural Land (Willamette River) .... 97
D. Charleston, West Virginia (Kanawha River) . 98
Figure D-l: Average Monthly Dissolved Oxygen Levels . 99
Figure D-2: Average Monthly Hydrogen Ion Concentrations 99
Figure D-3: Average Monthly Odor Levels 100
Figure D-4: Average Monthly Ammonia Concentrations . 100
Figure D-5: Charleston, West Virginia (Kanawha River) ........ 102
E. Dunbar, West Virginia (Kanawha River) • . 103
i
Figure E-l: Average Monthly Dissolved Oxygen Levels 104
Figure E-2: Average Monthly Ammonia Concentrations 104
Figure E-3: Average Monthly Odor Levels 105
Figure E-4: Dunbar, West Virginia (Kanawha River) 107
F. Beaver, Pennsylvania (Ohio River) 108
80
-------
Figure F-l: Maximum Monthly Average Specific Conductivity (Beaver
Falls Station) 109
Figure F-2: Minimum Monthly Average Dissolved Oxygen (Beaver Falls
Station) 109
Figure F-3: Minimum Monthly Average Hydrogen Ion Concentration
(Beaver Falls Station) 109
Figure F-4: Maximum Monthly Average Specific Conductivity (South
Heights Station) 110
Figure F-5: Minimum Monthly Average Dissolved Oxygen (South Heights
Station) 110
Figure F-6: Minimum Monthly Average Hydrogen Ion Concentration
(South Heights Station) 110
Figure F-7: Beaver, Pennsylvania (Ohio River) 112
G. Seattle, Washington (Lake Washington) 113
H. Rejected Sites 114
I. Public Opinion Survey 117
J. Annotated Bibliography . 128
K. Case Study Data and Correlation Coefficients 134
Table K-l: Correlation Coefficients - Coronado Site 135
Table K-2: Correlation Coefficients - Clackamas County Urban Site . . 136
Table K-3: Correlation Coefficients - Clackamas County Rural Site . . 137
Table K-4: Correlation Coefficients - Charleston Site 138
Table K-5: Correlation Coefficients - Dunbar Site . . 139
Table K-6: Correlation Coefficients - Beaver Site 140
Table K-7: Sample Data - Coronado 141
Table K-8: Sample Data - Clackamas County Urban 143
Table K-9: Sample Data - Clackamas County Rural 145
Table K-10: Sample Data - Charleston 146
Table K-l 1: Sample Data - Dunbar 147
Table K-l2: Sample Data - Beaver ' . 148
81
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APPENDIX A
CORONADO
(SAN DIEGO BAY)
Coronado, located just across from San Diego, is bordered on the northwest by the Naval
Air Station, on the southwest by the Pacific Ocean, and on the east by the San Diego
Bay. The residences enjoy relatively flat topography and close proximity to water
oriented activities.
WATER QUALITY CHANGES
In 1960, San Diego voters passed a bond issue for construction of the San Diego
Metropolitan Sewerage System. Construction of the system began in 1961, the system
became operational in 1963, and by the end of 1964, all shore-based sewage discharges to
San Diego Bay were ended. The project led to a significant improvement in wa'ter quality
(see Table A-l).
By 1960, almost two-thirds of the entire bay had dissolved oxygen (DO) levels below
4 ppm while in central bay areas DO levels dropped below 1 ppm [3]. Between 1960 and
1963, (before the sewerage system began operations) biological oxygen demand (BOD)
increased at a rate of about five percent annually, which probably caused DO levels to
deteriorate proportionally. Many areas of the bay developed anaerobic conditions
producing offensive odors and an unsightly appearance. By 1965 however, as a result of
the sewerage project, DO levels had increased to over 5ppm in most of the bay (including
off-shore Coronado), allowing many desirable fish species to return.
Fecal coliform density, measured by a most probable number (MPN) index, is generally
accepted as a reasonably valid statistical analysis of the bacteriological quality of a
particular water sample [3]. The level of MPN is used to indicate the proportion of
human and animal waste pollution. The California State Board of Public Health requires
that at a public beach the MPN shall not exceed 1000 per 100 ml.
Prior to operation of the new sewerage system, ooliform densities adjacent to Coronado
exceeded the State's public health standard, and the beaches were quarantined. Since the
cessation of domestic sewerage outfall into the bay there have been no public beach
restrictions on water contact activities.
Before 1964, floating solids of sewage origin collected in "rafts" on the bay surface and
along the shoreline, producing both public health hazards and aesthetic deterioration.
Now, Coronado and other community residences enjoy a clean and pleasing bay [3].
82
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Table A-l. ^ SAN DIEGO BAY WATER QUALITY.
BEFORE AND AFTER CLEANUP [3]
Characteristic
1960
Before Cleanup
1965
After Cleanup
Chemical
Dissolved Oxygen Concentrations
(by percent of Bay surface)
more than 4 mg/1
more than 5 mg/1
more than 6 mg/1
more than 7 mg/1
Coliform Bacteria
F. Coli Concentrations
(by percent of Bay surface)
MPN always less than 1000/100 ml
MPN usually less than 1000/100 ml
MPN often in excess of 1000/100 ml
MPN usually in excess of 1000/100
Physical
Clarity
(Secchi Disc readings, depth ft)
Sludge deposits
Thickness (ft)
Areal Extent (percent decrease)
Floating Debris (storm drainage)
Floating Refuse from Vessels
Foam of Waste Origin
Perlite
Grease
Oil Slicks (primarily from ships)
Plankton Growths
Color
Number of Major Algal Blooms
Odors
26
13
4
0
(1963)
49
18
33
100
97
81
6
9
91
less than 10 greater than average
(1951)
3-7
less than I
70
No measurable Change
Extensive
Extensive
Extensive
Moderate
None
Moderate
Widespread
None
No change, but more perceptible in
clean Bay.
Yellow-Brown Light Green
&Red
Many
Noxious
One, as a result of a
Dredging Project
\
Normal salt water
83
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Table A-l (continued)
Characteristics
1960
Before Cleanup
1965
After Cleanup
Bay Water Color
Fish
Brownish Blue-green
Loss of Return of Desirable
Desirable Species Including:
Species Bonito
Black Sea Bass
Sole
Halibut
Sculpin
Sand Bass
Octopus
Bonefish
Striped Bass
Steelhead Trout
Silver Salmon
Yellowtail
Barracuda
Angle Shark
Source: San Diego Regional Water Quality Control Board.
84
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PROPERTY VALUE CHANGES
Property values reflecting conditions before water quality improved, were calculated using
1964 assessed values. (Coronado was physically appraised in 1963). We converted the
assessed values into proxies for sales prices on the basis of a calculated sales ratio of 24.1
percent. (This ratio is the average of'four sales ratios for the years 1961 through 1964,
for single-family residential properties in San Diego. The State Equalization Board
provided the ratios to us). We then inflated each estimate using the Consumer Price Index
(CPI), in order to account for inflation between 1964 and 1971.
In order to estimate the consistency (degree of deviation from the average) of assessed
values to sales prices we performed our own sales ratio study. We collected data on 32
sales between 1968 and 1970. The results showed reasonable consistency:
mean of the ratio of assessed value
to sales price (100%): 0.81
standard deviation: 0.102
(standard deviation / mean) x 100: 12.5%
We calculated property values reflecting conditions after water quality improved, using
transaction taxes levied on 1971 sales. In Coronado, the transaction tax equals $1.10 per
$ 1000 of the selling price of the property.
We estimated the change in market value of a particular piece of property by subtracting
the 1963 estimate from the 1971 value. However, the real economic value of real estate
includes not only the market price, but: also .the property .taxes as weuVIn order to make
annual tax payments commensurate with a present market value, we discounted the 1964
and 1971 property taxes using a 10 percent discount rate and an infinite time horizon.
(1964 taxes were adjusted using the CPI to account for inflationary differences). The
change in the real economic value of a single-family residence was then estimated by
adding the change in market value to the change in discounted property tax payments for
the respective years.
One hundred thirteen (113) observations comprised the final sample of properties used in
the regression analysis. We attempted to eliminate all of those properties that had
undergone major improvements or similar changes that might have hampered the relia-
bility of results.
See Figure A-l for the location of important influences within the site.
Various linear and non-linear functional forms for the water quality term were tested.
The linear form proved to give the best fit for both absolute and percent value changes
with distance from the bay.
Population changes, housing density changes, and racial composition changes were left
out of the regressions because no major shifts in these factors were indicated by the
85
-------
00
<*<
Figure A-l
CORONADO (SAN DIEGO BAY)
San Diego
Bay
SAMPLE POINT
NAVAL AIR STATION
ACCESS
SCHOOL
ROAD
CORONADO
BOUNDARY
miles
-------
Census data, and their inclusion would not have improved the reliability of the water
quality term. A concentrated area of multi-family government housing was razed within
census tract 110 in 1969. The effects of this change should be accounted for by the
"distance to bridge access" variable in the regression equation.
Census Data
Total Non-White No. Single-Family
Population Population Housing Units
Tract 1960 1970 1960 1970 1960 1970
107 1500 1471 18 35 355 346
108 2700 2621 35 122 710 739
109 2036 1960 15 41 703 786
110 4307 1908 703 1.02 570 773
111 3714 3603 39 110 1214 1480
87
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APPENDIX B
CLACKAMAS COUNTY: RESIDENTIAL
(WILLAMETTE RIVER)
Located just north of Oregon City along the east side of the Willamette River, the
Clackamas County site provides approximately three miles of homogeneous residential
real estate. The terrain is relatively flat except for the river bank which is steep in some
places. Residences in the area enjoy easy access to the water and those living adjacent to
the river enjoy a beautiful view as well.
WATER QUALITY CHANGES
A strong Water Quality Control program under the Oregon Department of Environmental
Quality has brought about a significant improvement in the water quality of the
Willamette River, particularly within the last ten to fifteen years.
Figures B-l and B-2 indicate the extent to which industrial and municipal pollution has
been reduced. Industrial waste discharges were reduced 86 percent, while municipal waste
discharges (and their industrial components) declined eight-nine percent, overall [2].
Between 1957 and 1970, major biological oxygen demand (BOD) discharges from both
industrial and municipal sources decreased from about 20 million population equivalents
to about one-half million (Figure B-2). Figure B-l shows improvement in dissolved
oxygen (DO) levels for Portland Harbor during the critical summer months between 1957
and 1970. In addition, sludge deposits and slime growth have steadily declined over the
years.
Figure B-3 shows the DO profile along the Willamette River for selected years [ 1 ]. Also
indicated is the relative location of the two case study areas (residential and rural). Levels
of DO at the residential site during the low flow period have almost doubled between
1956 and 1970. Figure B-4 shows the improvement in the fecal coliform count for the
months of August and September between 1962 and 1970 [1]. Both study areas now
reside well within desirable water quality standards.
PROPERTY VALUE CHANGES
We estimated property values, reflecting earlier conditions, by using 1963 assessments.
(The site was physically appraised in 1962 for the 1963 tax roll). We converted
assessments to sales estimates using an assessed value to sales price ratio of 24.4 percent.
(The Clackamas County estimate of the average assessed value to sales price ratio for
1963). Finally, we inflated these estimates into 1970 dollars using the Consumer Price
Index (CPI).
88
-------
5ppm
DO Standard
Figure B-l
Dissolved Oxygen Levels - Lower Willamette River
Low Flow Months - June-October[zJ
89
-------
Total
Industrial
Municipal
U7A I
1957
1970
Figure B-2
Major BOD Discharges
90
-------
10
g
gen
T3
8
7
6
5
4
3
2
1
0
(*L_
"Tb-
Average 1968, 69,7()
Urban
Site
Rural Site
a) Standard Salem to New-
berg
b) Standard Willamette Falls
to Newberg
All 1956 points from faired
data.
20 30 40 50 60 70
River Miles from Mouth
80
90
Figure B-3
Dissolved Oxygen Levels for Selected Years \\\
91
-------
o
o
o
o
o
o
U
a
(H
O
.r4
r—4
O
O
co
o
USPHS Data
Aug.-Sept. 1962
D.E.Q. Data
Aug.-Sept. 1970
Urban Site Rural Site
0.08
0.06
0. 04
0. 04
100
River Miles from Mouth
Figure B-4
Fecal Coliform for 1962 and 1970 [l]
92
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We attempted to measure the consistency with which assessed values reflect true market
values (or rather a fixed proportion of true market prices) by conducting our own
sales-ratio study. We collected data on fifty-nine (59) properties that had sold during
1969 and that had been physically appraised during the previous year. The results
indicated that appraisers demonstrate reasonable consistency:
mean of the ratio of sales price to assessed value (100%): 1.301
standard deviation: 0.110
(standard deviation / mean) x 100: 10.0%
Clackamas County provided actual sales price information on single-family residences that
sold between 1969 and 1971 within the study area. We adjusted these values using the
CPI to reflect 1970 prices.
We estimated the change in market value of each property by subtracting the 1963
adjusted assessment from the more recent sales price. The change in the real economic
value of each property was then approximated by adding the change (adjusted for
increases in the CPI) in discounted tax payments over the period of observation to the
change in estimated market value. We used a discount rate of 10 percent and an infinite
time horizon to calculate the present capital value of taxes.
See Figure B-5 for the location of important elements within the study area.
Population changes, housing density changes, and racial composition changes were not
included in the regressions because no major changes which would interfere with measure-
ment of water pollution effects were evident from the census data, and therefore their
inclusion would not improve the reliability of the water term.
Census Data
Total Non-White No. Single-Family
Population Population Housing Units
Tract 1960 1970 1960 1970 1960 1970
212 2282 3135 — — 647 559
213 3407 4599 — .2 1077 1286
217 2902 4077 .1 .1 960 1024
HISTORY OF THE ANALYSIS
Ninety-eight (98) observations comprised the sample of properties used in the regression.
We sought to include only those properties which had not experienced major improve-
ments or similar changes that might have interfered with the reliability of results.
Both linear and non-linear functional forms of the water quality term were tested. The
non-linear form (reciprocal of the distance to the river) appeared to give the best
description of both absolute and percentage value changes.
93
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94
-------
APPENDIX C
CLACKAMAS COUNTY: RURAL LAND
(WILLAMETTE RIVER)
The rural site extends south along the Willamette River from just below Oregon City in
Clackamas County to just above Wheatland Ferry Landing (near Hopewell in Yamhill
County). While the study area covers almost forty miles of river, most of the properties
are concentrated in the first fifteen miles south of Oregon City.
WATER QUALITY CHANGES
See Appendix B for a description of water quality changes along the Willamette River.
PROPERTY VALUE CHANGES
We used assessment data from the years around 1960 to estimate the per acre market
value of each property. Clackamas and Marion Counties provided the sales ratios (assessed
value to market value) for rural land, 18.1 and 23.2 percent tespeclively, which we used
to convert the data into reliable market estimates.
We measured the consistency of rural land assessments for both counties from the range
of ratios for a sample of known sales. We collected data on 28 properties in Clackamas
County and 48 in Marion County. The results of the study indicated the Clackamas
County assessments to be the more consistent of the two, as is evident from the
following comparison:
Clackamas County:
mean of the ratio of sales price
to assessed value (100%): 1.062
standard deviation: 0.122
(standard deviation / mean) x 100: 11.5%
Marion County:
mean of sales price to assessed
value (100%): 1.313
standard deviation: 0.225
(standard deviation / mean) x 100: 17.1%
95
-------
Clackamas, Marion, and Yamhill Counties provided actual price information on recent
sales within the study area (1968-1972). We adjusted these values using the Consumer
Price Index (CPI) to 1970 dollar values.
We estimated the per acre change in the market value of each property by subtracting the
adjusted 1960 assessment from the 1970 sales price.
The change in the real economic value of rural land was then approximated by adding
this value change to the change (adjusted for inflation) in discounted tax payments over
the period of observation. We used a discount rate of ten percent and an infinite time
horizon to calculate the present capital value of taxes..
See Figure C-l for the location of important elements within the site.
HISTORY OF THE ANALYSIS
Thirty-four (34) observations comprised the sample of properties used in the regression.
There is a low correlation between land area and improvement value so we eliminated all
properties with improvements in order to only capture the value changes of land itself.
We tested both linear and reciprocal forms of the water quality term, and found that the
reciprocal form gave the best description of property value changes as a function of
distance from the Willamette River.
96
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Figure C-l
CLACKAMAS COUNTY: RURAL LAND
Newberg
Dundee
(WILLAMETTE RIVER)
Wi 1sonvi1le
SAMPLE POINT
BOAT ACCESS
WILLAMETTE
RIVER
-------
APPENDIX D
CHARLESTON, WEST VIRGINIA
(KANAWHA RIVER)
A section of Charleston called Kanawha City, the study area, provides almost two miles
of dense residential development along the Kanawha River. The area is flat with a steep,
twenty-foot river bank. Residences adjacent to the Kanawha enjoy both private river
access and a scenic view.
WATER QUALITY CHANGES
Figures D-l through D-4 report the extent to which pollution has been reduced over the
study period with respect to four parameters: ammonia concentration (NH3), hydrogen
ion concentration (pH), dissolved oxygen (DO), and odor (data provided by the West
Virginia Department of Natural Resources). Each parameter shows a marked improve-
ment. According to the Department, readings were taken four times daily from the
Kanawha City Bridge during the low flow period of the summer months. The graphs
depicted represent average monthly conditions for the years 1960 through 1962, and for
1966 through 1968. In addition to abatement of pollutants reported above, all visual
pollution was also removed by 1964.
PROPERTY VALUE CHANGES
Because of inconsistencies in assessment data prior to 1967, we used sales prices to
reflect market conditions before the water quality change. We collected data on sales
between 1959 and 1961, and adjusted the prices to 1970 dollar values using the
Consumer Price Index (CPI).
We used 1968 assessed values to estimate market prices after the water quality of the
Kanawha River had improved. The assessments were converted to proxies for market
values using an estimated sales ratio of 84.8 percent (our estimate, based on a recent
study; see below). These data were then inflated into 1970 dollar values using the CPI.
We measured the consistency of the county appraisers' assessments by conducting our
own sales ratio study. We collected data on forty recent sales in Kanawha City, and
determined that consistency was adequate for our purposes, as is shown below.
mean of the ratio of assessed value (100%)
to actual sales price: 0.848
standard deviation: 0.096
(standard deviation / mean) x 100: 11.3%
98
-------
9.0 ,
6.0 -
(X
a,
3. 0
0
60 - '62
June July Aug, Sept. Oct.
Figure D-l Average Monthly Dissolved Oxygen Levels
7. 8-,
7.6-
ffi
a
7.4-
7. 2
•60 - '62
June July Aug. Sept. Oct.
Figure D-2 Average Monthly Hydrogen Ion Concentrations
99
-------
4 J
fl
O
T)
2 -
une
•60 - '62
'66 - '68
July
Aug. Sept.
Figure D-3 Average. Monthly O^or Levels
Oct.
3.0
2.0
g
a
a
1.0
0
'60 - '62
June July Aug. Sept. Oct.
Figure D-4 Average Monthly Ammonia Concentrations
100
-------
We calculated the estimated change in the market value of each property by subtracting
the earlier sales price from the later market estimate. The change in the real economic
value of residential property was then approximated by adding this value change to the
change (adjusted for inflation) in discounted tax payments over the period of observa-
tion. As before, we used a discount rate of ten percent and an infinite time horizon to
calculate the present capital value of the change in taxes.
See Figure D-5 for the location of important elements within the site.
Socio-economic factors were not included in the analysis because the census data for
1960 were found to be incomplete. It should be noted that while these factors would
undoubtedly increase the explanatory power of the equation (R2), there is no evidence
that they would improve the reliability of the water quality term.
HISTORY OF THE ANALYSIS
Sixty-five (65) observations constitute the sample of properties used in the final analysis.
Observation points located on the north side (river side) of MacCorkle Avenue (the
commercial street) were eliminated after preliminary computations, because the effects of
water quality changes apparently did not extend beyond this barrier.
We tested both linear and reciprocal functional forms of the water term. The reciprocal
form as shown in Tables 6 and 7 gave the best description of value changes attributable
to water quality improvement.
101
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Figure D-5
CHARLESTON, WEST VIRGINIA
(KANAWHA RIVER)
Kanawha
River
0 SAMPLE POINT
A BRIDGE ACCESS
® SCHOOL
ROAD
RAILROAD
SCALE: I inch = 2000 feet
-------
APPENDIX E
DUNBAR, WEST VIRGINIA
(KANAWHA RIVER)
Located about four miles west of Charleston on the north side of the Kanawha River, the
town of Dunbar provides more than a mile of residential real estate for study. Like
Charleston, the site terrain is flat except for the steep river bank. Residences adjacent to
the Kanawha enjoy both private access and a scenic view of the river.
WATER QUALITY CHANGES
Figures E-l through E-3 are derived from data provided by the West Virginia Department
of Natural Resources; they indicate the degree of improvement in water quality over the
study period. While ammonia concentrations show almost no change, dissolved oxygen
(DO) and odor improved moderately. As in the Kanawha City site, readings were taken
four times daily from the Dunbar Bridge adjacent to the study area during the summer
months. The graphs depicted represent average monthly pollution levels for the years
1960 through 1962, and for 1966 through 1968. In addition to the improvement in DO
and odor, "visual pollutants" were removed by 1964.
PROPERTY VALUE CHANGES
Because of inconsistencies in the asessment data for Dunbar prior to 1967, we used sales
prices to reflect market conditions around 1960. We collected data on sales between
1959 and 1961, and adjusted these to 1970 dollar values using the Consumer Price Index
(CPI).
Property values from a 1968 property appraisal provided the only usable assessment data.
Later years, after the water had improved more, would have been preferable but property
has not been systematically reappraised since 1968. We converted the assessments into
proxies for market values using an estimated sales ratio of 90.1 percent (our estimate,
based on a recent study; see below). These data were then inflated into 1970 dollars
using the CPI.
We measured the consistency of appraisers' assessments of residential property by doing a
sales ratio study. We collected data on forty-five (45) recent sales in Dunbar. The results
reported below indicate reasonable consistency:
mean of the ratio of assessed (100%) to sales price: 0.901
standard deviation: 0.130
(standard deviation / mean) x 100: 14.4%
103
-------
9.0
6.0
S
a,
a.
3.0
0
'60 - '62
June July Aug. Sept. Oct.
Figure E-l Average Monthly Dissolved Oxygen Levels
2.0 n
1.0 -
60 - '62
June
July
Aug. Sept.
Oct.
Figure E-2 Average Monthly Ammonia Concentrations
104
-------
7 -
X
4)
T)
M
O
O 6 .
'60 -
June
July
Aug.
Sept.
Oct.
Figure E-3
Average Monthly Odor Levels
105
-------
We calculated the estimated change in the market value of each property by subtracting
the earlier sales price from the later market estimate. The change in the real economic
value of residential property was then estimated by adding to this market value change,
the change (adjusted for CPI increases) in discounted tax payments over the period of
observation. As with our other sites, we assumed a discount rate of ten percent and an
infinite time horizon.
See Figure E-4 for the location of important elements within the study area.
Socio-economic factors could not be included in the regression analysis because, as in
Kanawha City, appropriate data for 1960 was not available. Similarly, while these
influences would undoubtedly have increased the explanatory power of the equations
(the R2 s), there was no evidence of changes which would influence the reliability of the
water quality term.
HISTORY OF THE ANALYSIS
Twenty-nine (29) observations constituted the final sample of properties used in the
analysis. Many observations were eliminated at Dunbar because of irregularities in
property values as measured from tax records.
Observation points located on the far side of the railroad tracks were also eliminated
after preliminary computations which indicated that the effects of the water quality
changes did not extend beyond this barrier.
We tested both linear and reciprocal forms of the water quality term. The reciprocal form
(reciprocal of the distance to the Kanawha River) appeared to give the best description of
value changes, even though it too proved to be inconclusive.
106
-------
O
-J
Figure E-4
DUNBAR, WEST VIRGINIA
(KANAWHA RIVER)
O SAMPLE POINT
0 HIGHWAY ACCESS
Y BRIDGE ACCESS
ROAD
I [ RAILROAD
COMMERCIAL
H SCHOOL
— N
SCALE:
I inch « 800
feet
Kanawha
River
-------
APPENDIX F
BEAVER, PENNSYLVANIA
(OHIO RIVER)
Located about twenty miles downstream and northwest of Pittsburgh, the town of Beaver
provides over a mile of homogeneous residential real estate along the Ohio River. The
terrain is flat except for the river bank which is generally steep and about 60 feet high.
WATER QUALITY CHANGES
Figures F-l through F-6, derived from data provided by the Ohio River Valley Water
Sanitation Commission (ORSANCO), show the extent to which pollution has been
reduced. The graphs depicted illustrate noticeable improvements in three measured
parameters: dissolved oxygen (DO), specific conductivity, and hydrogen ion concentration
(pH). Two continuous robot monitoring stations, one located about four miles upstream
on the Beaver River near Beaver Falls and the other about ten miles upstream on the
Ohio River between Beaver and Pittsburgh, performed the measurements. The graphs
report the minimum or maximum monthly average readings taken at each station for the
years between 1962 and 1970.
PROPERTY VALUE CHANGES
We estimated property values, reflecting earlier conditions using 1960 assessment data.
(The site was physically appraised just prior to 1960). We converted assessments to sales
estimates assuming an assessed value to sales ratio of 33.3 percent. (The State Equaliza-
tion Board provided this ratio, on the basis of a 1962 county wide study). Finally, we
inflated these estimates into 1970 dollars using the Consumer Price Index (CPI).
We measured the consistency with which assessed values reflect market values (or rather,
a fixed proportion of true market prices) by conducting our own sales ratio study. We
collected information on thirty-one (31) residential properties. The results reported below
indicated moderate consistency.
mean of assessed value (100%) to sales price: 1.006
standard deviation: 0.205
(standard deviation / mean) x 100: 20.5%
Beaver County provided actual sales price information on single-family residences that
sold between 1969 and 1971 within the study site. These values were adjusted, using the
CPI, to 1970 dollars.
108
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o
•X)
W
o
0.8-
0.7-
0.6-
0.5.
0.4-
0. 3
'63 - '65
Years
Figure F-l
Maximum Monthly Aver-
age Specific Conductivity.
(Beaver Falls Station)
7. OL
GO
5.
4.0-
3. a
2. 0
'68 -
'63 - '65
Years
Figure F-2
Minimum Monthly Aver-
age Dissolved Oxygen.
(Beaver Falls Station)
7. 0-
6.0-1
5. 0.
'68 - '70
'63 - '65
Years
Figure F-3
Minimum Monthly Aver-
age Hydrogen Ion Concen-
tration.
(Beaver Falls Station)
-------
U)
o
0.8
0.7
0.6 -
0.5 -
0.4.
0. 3
•62 - '64
'68 - '70
~i 1
Years
Figure F-4
Maximum Monthly Aver-
age Specific Conductivity.
(South Heights Station)
7.0-
bO
5. 0.
4. 0-
3.0-
2. 0
'68 - '70.
'63 - '65
1 1 r
Years
Figure F-5
Minimum Monthly Aver-
age Dissolved Oxygen.
(South Heights Station)
7.0 -
ffi 6.0-
5. 0_
'68 - '70
'62 - '64
Years
Figure F-6
Minimum Monthly Aver-
age Hydrogen Ion Concen-
tration.
(South Heights Station)
-------
We estimated the change in market value of each property by subtracting the 1960 value
from the more recent sales price. The change in the real economic value of each property
was then approximated by adding the change (adjusted for increases in the CPI) in
discounted tax payments over the period of observation to the change in estimated
market value. As with the rest of our study sites, we used a discount rate of ten percent
and an infinite time horizon in calculating the present capital value of the change in
property taxes.
See Figure F-7 for the location of important elements within the study area.
Socio-economic variables could not be included because the sample points covered only
one Census Tract and there have been no significant changes in population, housing
density, or racial composition within that tract.
Census Data
Total Non-White No. Single-Family
Population Population Housing Units
Tract 1960 1970 1960 1970 1960 1970
6023 3303 3242 7 22 976 696
6024 2857 2858 16 17 865 780
HISTORY OF THE ANALYSIS
Fifty-three (53) observations constitute the final sample used in the analysis. Observations
located further than 2000 feet from the river were eliminated after preliminary computa-
tions, because it was felt that the effects of water quality changes did not extend beyond
this distance.
We tested both linear and reciprocal functional forms of the water term. The reciprocal
form of the distance to the Ohio River appeared to give the best description of value
changes, but even it yielded inconclusive results.
Ill
-------
Figure F-7
BEAVER, PENNSYLVANIA
(OHIO RIVER)
O
High School
SAMPLE POINT
-------
APPENDIX G
SEATTLE, WASHINGTON
(LAKE WASHINGTON)
Water conditions in Lake Washington were satisfactory prior to 1955. In 1955, the water
quality in the lake started to worsen due to increasing discharges of municipal wastes and
the accumulation of plant nutrients to critical levels. By the summer of 1962 the lake
was severely degraded. Heavy blooms of brown algae and high coliform counts rendered
the lake unsightly and unfit for recreation. Effluent discharge into Lake Washington was
drastically curtailed subsequently in 1962, when the Seattle Metropolitan Council com-
pleted new sewage trunk lines and treatment plants and major waste outfalls were
diverted out of the lake. Algae continued to make the water very turbid or cloudy until
the period between 1966 and 1968, when the water quality improved markedly. By
1970, the water conditions had been restored to pre-1955 quality.
The temporary nature of the water pollution at Lake Washington distinguished it from
other sites. Water quality worsened rapidly, was very bad for about four summers, and
then improved rapidly.
113
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APPENDIX H
REJECTED SITES
The following is a list of all water bodies which were investigated and then rejected as
study sites:
1. Chain of Lakes
(in Madison, Wisconsin)
2. Upper Mississippi River
(downstream of St. Paul, Minnesota)
3. Lake Minnetonka
(Minnesota)
4. Fairmont Chain of Lakes
(Southern Minnesota)
5. Clarks Fork River
(Montana)
6. South Platte River
(Denver, Colorado)
7. Animas River
(Durango, Colorado)
8. Lake Tahoe
(California)
9. San Antonio River
(San Antonio, Texas)
10. Sabine River
(Eastern Texas)
11. BigPapillon Creek
(Omaha, Nebraska)
12. Little Blue River
(Kansas City, Missouri)
13. Wilson Creek
(Springfield, Missouri)
114
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14. Fox River
(St. Charles, Illinois)
15. St. Joseph River
(South Bend, Indiana)
16. Huron River
(Ann Arbor, Michigan)
17. Ohio River
(at Cincinnati, Ohio)
18. Mahoning River
(Youngstown, Ohio)
19. Miami River
(above Toledo, Ohio)
20. Muskingham River
(Zanesville, Ohio)
21. Ottawa River
(Ottawa, Ohio)
22. Little Walnut Creek
(Columbus, Ohio)
23. Schuylkill River
(Philadelphia, Pennsylvania)
24. Upper Brandywine Creek
(Southeastern Pennsylvania)
25. Lake Erie
(Sandusky, Ohio)
26. Lake Erie
(Geneva-on-the-Lake, Ohio)
27. Lake Erie
(Erie, Pennsylvania)
28. Upper Hudson River
(New York)
29. Long Island Beaches
(New York)
115
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30. Whippany River
(Newark, New Jersey)
31. Lake Champlain
(New York and Vermont)
32. Hoosic River
(Southern Vermont)
33. Pemigewasset River
(New Hampshire)
34. Delaware River Estuary
(Delaware)
35. Potomac River
(Maryland)
36. James River
(Richmond, Virginia)
37. Roanoke River, Smith Mountain
Reservoir (Virginia)
38. St. Johns River
(Jacksonville, Florida)
39. Perdido Bay and Mobile Bay
(Alabama)
116
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APPENDIX I
PUBLIC OPINION SURVEY
117
-------
PUBLIC OPINION SURVEY
Hello. I'm with Opinion Research Corporation (west). We're talking
with people in your neighborhood about water resources. May I speak with the
(MALE) (FEMALE) head of your household?
1. First, do you own your home or do you rent here?
Own 1 (CONTINUE)
Rent a (THANK RESPONDENT AUD TERMINATE)
2. Please try to imagine yourself in the particular situation described on this
card. Read the description of the situation with me first and when we've
finished, feel free to go back over any details which aay not have been
completely clear. (HAND RESPONDENT CARD A) Please read along with me on
this card.
Imagine that you are given a house overlooking a large lake.
The water of the lake ia clear, has a pleasing color, and
has no odor.
Although the water is perfectly safe, you are not allowed to
use the lake for swimming or boating.
Because of a chemical in the water there are no fish, birds,
or other wildlife in the lake. (The chemical has no effect
on people).
In this situation, you are given a choice between the two
following alternatives. YOU CANNOT HAVE BOTH. Which one.
would you choose?
You Have a Choice Of
Recreation Permit — You would be given a recreation permit
which allows you to use the lake for swimming and boating.
Treatment for Wildlife^ — A treatment would be applied to get
rid of the chemical vhich keeps fish, birds and other wildlife
from living in the lake. If the treatment were applied, wild-
life would live in and around the lake in a very short time.
Which one, would you choose — the Recreation Permit or the Treatment for
Wildlife?
Recreation Permit 1
Treatment for Wildlife 2
118
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2a. Was this a hard choice or an easy choice?
Hard Choice 1
Easy Choice 2
3. (HAND RESPONDENT CARD B).
Again, you are given a houae overlooking a large lake. This time, the
lake is cloudy, not a very pleasing color, and has an unpleasant odor.
Just as in the first case, there are no fish, birds or other wildlife
in the lake because of a chemical in the water.
In this situation, you are given a choice between the two following
alternatives. YOU CANNOT HAVE BOTH. Which ^ne would you choose?
You Have a Choice Of
Treatment for Appearance — A treatment would be applied to make
the vater clear, greatly Improve the color, and remove any dis-
agreeable odors. If the treatment were applied, the appearance
and odor of the lake would greatly improve in a very short time.
Treatment for Wildlife — A treatment would be applied to get
rid of the chemical which keeps fish, birds and other wildlife
from living in the lake. If th» treatment were applied, wild-
life would live in and around the lake in a very short time.
Which one would you choose — the Treatment for Appearance or the Treatment
for Wildlife?
Treatment for Appearance 1
Treatment for Wildlife 2
3a. Was this a hard choice or an easy choice?
Hard Choice 1
Easy Choice 2
1*. (HAND RESPONDENT CARD C).
Once again, you are given a house overlooking a large lake. The water is
cloudy, not a very pleasing color, and has an unpleasant odor.
Although the water is perfectly safe, you are not allowed to use the lake
for swimming or boating.
In this situation you are given a choice between the two following alterna-
tives. YOU CANNOT HAVE BOTH. Which one would you choose?
You Have a Choice Of
Treatment for Appearance — A treatment would be applied to make
the water clear, greatly improve the color, and remove any dis-
agreeable odors. If the treatment were applied, the appearance
and odor of the lake would greatly improve in a very short time.
119
-------
Recreation Permit — You would be given a recreation permit
which allows you to use the lake for swimming and boating.
Which one would you choose — the Treatment for Appearance or the Recreation
Permit?
Treatment for Appearance 1
Recreation Permit 2
Ua. Was this a hard choice or an easy choice?
Hard Choice I
Easy Choice 2
5. In describing these three situations we talked about several things that
affect the quality of a body of water — things like the way it looks and
smells, whether there are fish and other wildlife there, and the ability
to use the water for recreation.
(HAND RESPONDENT Q.5 ANSWER SHEET).
This page lists four categories of water quality. Suppose you have a total
of 100 votes to distribute among these categories. How you place your
votes shows how important it would be to have each item in a river, bay, or
other body of water located near where you live.
The more important you feel any item is, the more votes you should give to
that item. The less important you feel an item is, the fewer votes you
should give that item.
Remember, you have a total of 100 votes altogether.
Are there any questions?
Please read the categories carefully and write in the number of votes you
want to assign each category in the space below it.
6. (HAND RESPONDENT CARD D).
The appearance and attractiveness of a (RIVER, BAY) depends on the color
of the water, the odor, the clearness or cloudiness of the water, and the
amount of floating debris or oil. Which ONE of these things about a
(RIVER, BAY.y would be most important to you? (READ CATEGORIES. WRITE
IN "1" NEXT TO THE ITEM NAMED MOST IMPORTANT. RECORD BELOW).
7. Which would be next most important to you? (WRITE IN "2" NEXT TO ITEM
NAMED AS NEXT MOST IMPORTANT).
Color of the water
Odor of the water
Clearness of the water
Absence of floating debris or oil on the water
120
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8. If you had 100 votes, how would you distribute them among these aspects of
water appearance and attractiveness in terms of importance to you?
Color of the water
Odor of the water
Clearness of the water
Absence of floating debris or oil on the water
(TAKE BACK CARD D).
9. Does this voting system let you accurately explain your feelings about the
various aspects of water attractiveness and appearance?
Yes 1 (GO ON TO Q.10)
L't Know 3HASK Q'9a)
9a. Why is that? Any other reasons you say that?
-------
10. Do you do any boating?
Yes 1 (ASK Q.ll)
No 2 (SKIP TO Q.12)
11. Do you do any boating on the (HAME OF RIVER, BAY)?
Yes 1
(ASK Q.lla)
Ho 2
(ASK ft.lib)
lla.Where on the (NAME OP RIVER, BAY) is that?
lib.Would you do any boating on the (HAME OF RIVER. BAY)?
Yes 1 (ASK Q.llc) Ho 2 (ASK Q.lld)
lie. Where on the (NAME OF RIVER,
BAY) would that be?
lid. Why is that? PROBE: Any other
reasons you wouldn't do any boat-
ing on the (NAME OF RIVER, BAY)?
12. Compared to I960, do you think there is more, less or about the same amount
of boating on the (NAME OF RIVER, BAY)? (IF RESPONDENT IN AREA LESS THAN
13 YEARS: Just your impression.)
More 1
Less 2
About the same 3
Don't Know, U
122
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Yes 1 (ASK Q.l>»)
No 2 (SKIP TO Q.15)
1U. Do you swim In the (NAME OF RIVER. BAY)T
Yea 1
(ASK Q.lUa)
No 2
(ASK Q.
Wa. Where in the (NAME OF RIVER. BAY) is that?
lUb. Would you go swinming in the (NAME OF RIVER, BAY)?
Yes 1 (ASK Q.ll»c) No 2 (ASK Q.lUd)
Where on the (NAME OF RIVER. BAY)
would that be?
Why is that? PROBE: Any other
reasons you wouldn't do any
swimming in the (NAME OF RIVER,
BAY)?
15. Would you say there is more, less or about the same amount of swimming in
the (NAME OF RIVER. BAY) as there vas in 1960?
More 1
Less 2
About the same 3
Don't Know k
123
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16". Do you do any fishing7
Yes 1 (ASK Q.17)
Ho 2 (SKIP TO Q.18)
17. Do you do any fishing on the (NAME OF RIVER, BAY)?
Yes 1
(ASK. Q.17a)
No 2
(ASK Q.lTb)
Ifa.Where on the (NAME OF RIVER. BAY)
is that?
17b. Would you do any fishing on the (NAME OF RIVER, BAY)_?
No 2 (ASK Q.17d)
Yes 1 (ASK Q.17c)
r
17c~. Where on the (NAME OF RIVER, BAY)
vould that be?
17d. Why is that? PROBE: Any
other reasons you wouldn't
do any fishing on the (NAME
OF RIVER, BAY)?
18. Would you say there are more, less or about the same amount of fish in the
(NAME OF RIVER. BAY) than there vere in I960?
More fish 1
Less fish 2
About the same 3
Don't Know U
124
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19.
Do you think there are more, less or about the same number of water birds
here nov as there were 10 or 15 years ago?
More 1
Less 2
About the same 3
Don't know 1»
20. How long have you lived in the
Less than one year
One year to less than 2 years
2 years to less than 3 years
3 years to less than 1» years
I* years to less than 5 years
5 years to less than 6 years
6 years to less than 7 years
7 years to less than 8 years
area?
1
2
3
I)
5
6
7
8
8 years to less than 9 years
9 years to leas than 10'years
10 years to less than 15 years
15 years to less than 20 years
20 years or more
9
10
11
12
13
21. Do you think there has been any change in the quality of the water of the
(NAME OF RIVER. BAY) since 1960? (IF EESPONDEHT IN AKEA LESS THAN 13 YEARS)
"We are interested in your impression of whether there have been any changes,
even if you weren't here then.
Yes, change
No, no change
Don't Know
(ASK Q.22 AND H.23)
(SKIP TO Q.2lt)
22. Would you say the water quality is better or worse than it
was then?
Better 1
Worse 2
23. Would you say much, somewhat or only slightly (ANSWER IH Q.22)?
Much better 1
Somewhat better 2
Only slightly better 3
Only slightly worse U
Somewhat worse 5
Much worse 6
2l(. Would you say the water of the (NAME OP RIVER, BAY) nearest to whore you
live looks different now than it did say, 10 or 15 years ago? We are
interested in your impression of whether or not the water looks different
now than it did then.
Yes
n° .+ v
Don't Know
(ASK Q.25 AND Q.26)
T0 ft'2T)
125
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25. Hov would you describe this difference? How else?
26. (HAND RESPONDED? CARD E).
I'll read you a few statements about hov the water of the (NAME OF RIVER,
BAY) looks now compared with 10 or 15 years ago. Looking at this card,
please tell me vhether you agree or disagree with each statement and how
strongly you agree or disagree. Remember, we are interested in your im-
pressions, even if you didn't live here at that time.
Neither
Agree
Agree Agree Nor Disagree Disagree
Completely Somewhat Disagree Somewhat Completely
The water is clearer
now than it waa 1 2 3 !* 5
There is less float-
ing debris and refuse
than there was 1 2 3 1* 5
The water smells
better 1 2 3 !» 5
There seems to be
more wildlife now 1 2 3 !» 5
There are fewer dead
fish now than there
were 1 2 3 & 5
The color of the
water is better now 1 2 3 1* 5
126
-------
27. What ore you usually doing when you get your best look at the water of
the (NAME OF RIVER, BAY)? (DO HOT REAP CATEGORIES).
Looking out window of home 1
Walking by 2
Driving by 3
Fishing U
Other (SPECIFY) 5
28. How often do you get close enough to the (HAME OF RIVER. BAY) to see Into
the water:
More than 10 times a year 1
Five to ten times a year 2
Three to under five times a year 3
Once or twice a year U
Less than once a year 5
.29. Just a few background questions for statistical purposes and then we'll be
through. Did you participate in the decision to buy your home or was the
decision made entirely by someone elsef
Respondent participated in decision 1
Decision entirely by someone else 2
30. (HAND RESPONDENT CARD F) Which of the categories on this card includes
your age? (CIRCLE CODE LETTER)
127
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APPENDIX J
ANNOTATED BIBLIOGRAPHY
INTRODUCTION
We have surveyed and listed the most important sources concerning the demand for
property near the water, the effect of water pollution on property values, and the
demand for water-based recreation. While some provide background and perspective,
others treat the respective topics specifically. Particularly significant are the empirical
works on the determination of property values. Each title is briefly annotated, and we
have confined our introductory remarks to a few very significant works.
The price that a piece of property will command in the market depends not only on its
own characteristics but also on those of the neighborhood in which it is located. Three
papers explore the effect of the various physical and demographic variables upon
property values: "Effects of Race and other Demographic Factors on the Values of Single
Family Homes," by Martin Bailey; "The Determinants of Residential Land Values," by
Eugene Brigham; and "Land Value and Land Development Influence Factors: An
Analytical Approach for Examining Policy Alternatives," by S.F. Weiss.
Other references concern the relationship between the existence and quality of water
resources and the value of adjacent properties. These are: "Water Quality and the Value
of Homesites on the Rockaway River, N.J.," by J. Beyer; "Lakeshore Property Values: A
Guide to Public Investment in Recreation," by Elizabeth David; "The Influence of
Reservoir Projects on Land Values," by Jack Knetsch; and "Estuarine Clean Water
Cost-Benefit Studies," by R. Stone. Of special interest are the studies by Elizabeth David
and Jack Knetsch whose substantiations of the effect of water on property values offer
an excellent point of departure for our study. Both papers employ essentially cross-
sectional techniques, which we supplement by providing the needed time dimension with
a before-and-after approach.
BIBLIOGRAPHY
Alonso, William, Location and Land Use, Cambridge, Harvard University Press, 1964.
This book treats the the theoretical foundations of rent and related topics, and
provides good background information for a study of land value. Included are
discussions and some applications of the economics of urban land, household
equilibrium, and residential bid price curves.
Bailey, Martin J., "Effects of Race and Other Demographic Factors on-the Values of
Single Family Homes," Land Economics, Vol. 42, May 1966, pp. 215-220.
Discussion and multiple regression analysis of the effect demographic and physical
factors have on the value of residential home. The study demonstrates the impor-
tance of considering "community" variables in a land value regression equation.
128
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Beardsley, Wendell, "Bias and Noncomparability in Recreation Evaluation Models," Land
Economics, Vol. 47, May 1971, pp. 175-81.
Discussion of the sources of bias in. estimating recreation demand by the travel cost
method. The article supplements Clawson's work on this subject, and provides good
background information.
Beyer, J., "Water Quality and the Value of Homesites on the Rockaway River, N.J." New
Brunswick, Water Resources Research Institute, Rutgers-The State University, 1969.
An exploratory investigation of the relationship between the river and real estate
values .on the Rockaway River. No statistical correlation analysis was performed;
only questionnaires were given to a few realtors and residents. The paper concludes
that a relationship does exist and suggests that a regression analysis would be a good
method of investigation.
Biniek, Joseph P., "Economics of Water Pollution," Washington, B.C., U.S. Department
of Agriculture,, 1969.
This paper provides good background information on water pollution, public
concern, externalities, costs .of pollution control, and agricultural pollution.
Brigham, Eugene F.,- "The Determinants of Residential Land Values,". Land Economics,
Vol. 41, November 196.5, pp. 325-330.
Presenting the findings-of a land value study Of Los Angeles County, this article
serves as a good reference on the important variables for a land value regression
equation.
Brodsky, Harold,, "Residential Improvement Values: Centra;! City," Land Economics, Vol.
46, August 1970, pp. 229-246.
This article gives a good theoretical survey of some of the relevant variables in
determining rents. Multiple regression is used to demonstrate the relationship
between land values, improvement values and distance to the central business
district. Good reference for selecting.variables for a regression analysis.
Clawson, Marion, and J.L. Knetsch, Economics of Outdoor Recreation, Resources for the
Future, Inc., Baltimore, The Johns Hopkins Press, 1966.
This book provides an intensive discussion and good background information on the
demand for outdoor recreation, emphasizing the travel cost method of estimation.
Crocker, Thomas D., "Urban Air Pollution Damage Functions: Theory and Measure-
ment," Environmental Protection Agency, Office of Air Programs, June 1971.
This cross-sectional study uses FHA value assessment data to test relationships
between pollution levels and property values. Local tax assessments were found to
be poor proxies for actual market sales, prices, while FHA assessments were good
proxies. The hypothesis that land values are more sensitive to air pollution dosages
than are the values of land improvements was also supported by the results.
David, Elizabeth L., "The Exploding Demand for Recreational Property," Land
Economics, Vol. 45, May 1969, pp. 206-217.
An analysis of the trend in recreational property values over time (1952, 1957,
129
-------
1962), this paper shows the substantial gains accrued to private owners of water-
front property. These results lend support to the contention that water quality
changes should affect property values.
David, Elizabeth L., "Lakeshore Property Values: A Guide to Public Investment in
Recreation," Water Resources Research, Vol. 4, August 1968, pp. 697-707.
A study of lakeshore property values using multiple regression analysis. Significant
variables include: characteristics of shore area, proximity to population centers,
presence of other lakes in the area, and water quality. Results support the
hypothesis that changes in water quality affect nearby property values.
Dee, Norbert, et al., "Environmental Evaluation System for Water Resource Planning,"
Report for the Bureau of Reclamation, Batelle Columbus Laboratories, January 1972.
The report includes a section on assigning an environmental importance weight to
the following water quality parameters: dissolved oxygen, temperature, fecal coli-
forms, turbidity, pH, BOD, nitrates, phosphates, and total solids. Parameter weights
are based on the judgment of experts.
Dougal, Merwin D., E. Robert Baumann, and John F. Timmons, "Physical and Economic
Factors Associated with the Establishment of Stream Water Quality Standards,"
Volume I, Iowa State Water Resources Research Institute, March 1970.
An historical review of water pollution problems, this study includes identification
and effects of potential pollutants, application of water quality standards, mathe-
matical models of stream behavior and economic aspects. The paper gives a general
background to the economic dimension of water pollution problems including the
benefits and costs of improving water quality.
Hammer, Thomas R., et al., "The Effect of Large Urban Park on Real Estate Value,"
RSRI Discussion Paper Series No. 51, September 1971.
This cross-sectional study develops a logarithmic regression equation in an attempt
to measure the value of Pennypack Park in Philadelphia. A significant relationship
exists between residential sales prices and access to the park, substantiating the
hypothesis that public parks have a positive effect on adjacent land values.
Jarrett, Henry, ed., Environmental Quality In a Growing Economy, Baltimore, Johns
Hopkins Press.
Contents include: resources development, environment and health, externalities,
research problems, welfare economics, public attitude, policies, along with good
background information on environmental economics.
Keiper, Joseph, and others, Theory and Measurement of Rent, Philadelphia, Chilton Co.,
1961.
This book discusses various aspects of rent theory, including the important variables
in land valuation. In addition, the book covers the problems and techniques of
measuring property and land values, which are integral to formulating a land value
regression equation.
Kitchen, James W., "Land Values Adjacent to an Urban Neighborhood Park," Land
130
-------
Economics, Vol. 43, No.'3, August 1967, pp. 357-360.
This paper tests the hypothesis that the value of adjacent properties diminishes with
distance from neighborhood parks. No significant relationship was found, using
either assessed values (land plus improvements) or sales prices; however, a significant
negative- correlation was established between assessed land values (assessed improve-
ment values were excluded) and distance from the park.
Kneese, Allen V., ed., Water Research, Resources for the Future, Inc., Baltimore, Johns
Hopkins Press, 1965.
This study includes discussions of the problems of discounting and public investment
criteria, social valuation of water recreational facilities, and comparisons of methods
of recreation evaluation, as well as of public goods, externalities, and regression
analysis, as they relate to recreation.
Knetsch, Jack L., "The Influence of Reservoir Projects on Land Values," Journal of Farm
Economics, V,pl. 46, February 1964, pp. 520-538.
In this study of the relation between property values and the presence of nearby
lakes, significant differences in values were found to be attributable to the lakes.
Several characteristics of the lakes and sites also seemed to influence property
values. The study is also a useful reference on the relevant variables for a land
value-water quality regression analysis.
Knetsch, Jack L., "Land Values and Parks in Urban Fringe Areas," Journal of Farm
Economics, Vol. 44, pp. 1718-1729.
This article discusses the method of using land-value surplus on property adjacent to
urban parks for estimating the social benefits of these parks. It also serves as a good
supporting reference.
Little, Arthur D., Inc., Tourism and Recreation: A State-of-the-Art Study, Washington,
B.C., U.S. Department of Commerce, 1967.
The study provides background on current techniques of economic development and
planning as related to tourism and recreation. It should prove useful as a partial
check-list of relevant considerations in connection with recreation demand.
McClellan, Keith, and Elliott A. Medrich, "Outdoor Recreation: Economic Considerations
for Optimal Site Selection and Development," Land Economics, Vol. 45, May 1969, pp.
174-182.
This article includes a general review of methods currently used to estimate demand
for outdoor recreation, and proposes a more "systematic" method for dealing with
location and development of recreation facilities. It provides good background
information on the demand for water-based recreation.
Milliman, J.W., "Land Values as Measures of Primary Irrigation Benefits," Journal of
Farm Economics, Vol. 41, No. 2, May 1959, pp. 234-243.
This' analysis compares the "budget" method (i.e., discounting future net benefits)
with the land value approach for estimating irrigation benefits. The author gives a
concise presentation of the major issues, concluding that since both methods involve
a number of theoretical and empirical problems, the best choice depends on the
particular circumstances surrounding the case.
131
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Nemerow, Nelson E., "Benefits of Water Quality Enhancement," Washington, D.C.,
Environmental Protection Agency, Water Quality Office, December 1970.
The paper proposes a method for the development arid application of a pollution
index. It also discusses various methods for measuring the total dollar'benefits from
water pollution control, including'the increase in adjacent property values.
Perloff, Harvey S., ed., The Quality of the Urban Environment, Resources for the Future,
Inc., Baltimore, The Johns Hopkins Press, 1969.
Chapter 2, entitled "Pollution and Environmental Quality," discusses pollution from
a materials balance viewpoint. Chapter 7, "The Value of Urban Land," gives the
theoretical and empirical basis of urban land values. Both chapters provide a useful
framework in which to view the respective topics.
Ridker, Ronald G., and John A. Henning, "The Determinants of Residential Property
Values with Special Reference to Air Pollution," Review of Economics and Statistics,
May 1967, pp. 246-257.
The study uses multiple regression analysis in an attempt to isolate the effects of air
pollution, property characteristics, and other factors on property values. It points
out some of the important variables that should be considered for a land value
regression equation.
Sickler, David W., "On the Uses and Abuses of Economic Science in Evaluating Public
Outdoor Recreation," Land Economics, Vol. 42, November 1966, pp. 485-494.
This is a critique of the travel cost method of estimating demand for outdoor
recreation. It points out the limitations and assumptions of the basic model,
emphasizing the problems of income distribution effects. The article supplements
Clawson's work on recreation demand.
Stone, R., and H. Friedland, "Estuarine Clean Water Cost-Benefit Studies," Fifth Inter-
national Water Pollution Research Conference, San Francisco, 1970.
A socio-economic cost-benefit analysis was made of the beneficial uses of San Diego
Bay in relation to the improvement of the Bay's water quality in the 1960's.
Changes in assessed valuations of comparable residential land parcels were analyzed
based on their distance from the Bay. Results showed a positive correlation between
water quality and land values.
Thueson, Gerald J., A Study of Public Attitudes and Multiple Objective Decision Criteria
for Water Pollution Control Projects, OWRR Project No. A-028-GA, Georgia Institute of
Technology, October 1971.
This report presents a method for assigning non-economic values to changes in water
quality parameters for different types of water use (recreation, water supply,
effluent disposal, etc.). Only discussion and no empirical results are given.
Walker, William R., ed., Economics of Air and Water Pollution, Blacksburg, Water
Resources Research Center, Bulletin 26.
The seventeen papers, including "The Measurement of Economic Losses from
Uncompensated Externalities," by T.D. Crocker, give a good overall treatment of
economic side effects.
132
-------
Weiss, S.F., T.G. Donnelly, and EJ. Kaiser, -"Land Value and Land Development
Influence Factors: An Analytical Approach for Examining Policy Alternatives," Land
Economics, Vol. 42, May 1966, pp. 230-232.
The authors examine fourteen variables affecting land values, using the multiple
regression technique. The study provides useful information concerning the selection
of important variables influencing land values for a regression analysis.
133
-------
APPENDIX K
CASE STUDY DATA AND CORRELATION COEFFICIENTS
134
-------
Table K-l. CORRELATION COEFFICIENTS - CORONADO SITE
V64
AV
AV/V64
4000 - dw
Distance to
Bridge access
Distance to
Orange Ave.
Distance to
Navy access
Lot Area
Distance to
nearest School
vO
1
>
.81
1
vO
*
. 10
.63
1
-1
i
o
o
o
.03
.16
.18
1
SO
PQ
•4-i
• t-4
Q
.14
-.05
-.28
-.41
1
u
rt
O
4J
TO
• t-t
Q
-.06
-.16
. 19
-. 27
.76
1
nJ
•
4-1
CO
3
.17
.37
.46
.21
-.58
-.36
1
ri
^
.47
.35
.01
-. 13
.27
.27
-. 14
1
o
o
X
o
w
4->
CO
Q
.16
. 27
. 17
.64
-;03
.00
.30
.02
1
135
-------
Table K-2. CORRELATION COEFFICIENTS - CLACKAMAS COUNTY URBAN
V^o Assessed
. AV
AV/v63
l/(dw+1000)-.0002
Distance to Water
Distance to Park
Distance to
nearest School
Distance to
Shopping Center
Distance to
Highway 99E
Distance to Portland
Lot Area
W
CO
cu
CO
CO
CO
>
1
>
<1
.90
1
CO
J
<
-.30
.08
1
o
o
0 CM
q? o
- •
.53
.48
-.04
1
1-4
V
n)
4J
to
• r-t
Q
-.37
-.34
-.01
-.91
1
i-^
rt
P,
*
4-1
CQ
• r-4
Q
-.02
-.03
-.04
.20
-.22
1
o
o
.A
o
CO
CO
• 1-1
Q
.18
.04
-.16
.45
-.42
.03
1
c
Q
a
0
CO
CO
Q
.22
. 17
-.01
.64
-.75
.21
.57
1
W
o
K
to
• Q
.30
. 23
-.11
.70
-.75
. 14
.19
.32
1
fi
rt
4-J
^
O
p.
ca
Q
. 21
. 15
-.01
.36
-. 36
-.53
.59
.49
.35
1
ri
*o
.25
.43
.24
.10
-.03
-.00
.05
.06
-.03
-.03
1
136
-------
Table K-3. CORRELATION COEFFICIENTS - CLACKAMAS COUNTY RURAL SITE
V60
AV/acre
AV/V^Q
Distance to Water
l/(dw+500)-. 00022
Distance to nearest
Boat Ramp
Distance to nearest
Bridge
Distance to Portland
Distance to Salem
Distance to nearest
Town
Waterfront footage
Lot Area
o
>
1
0)
M
o
nJ
^
^
-. 14
1
o
vO
p
<>
-.23
. 22
1
*-!
f4
Q
-.01
-.47
.04
.04
-.27
.33
1
TJ
§
c— 1
4-1
^
0
Pn
"^
4-1
03
• r4
Q
-.08
-.38
.10
-. 03
-.20
.28
.94
1
H
^j
rt
W
*
4J
(0
Q
. 14
.37
-.06
,04
. 21
-.22
-.80
-.92
1
rt
j£
o
H
4->
CO
• r-1
Q
-.11
-.09
. 23
.05
-.08
-.18
.07
.30
-.55
1
Q)
flj
4J
O
o
^
fi
0
h
^
tfl
^
.32
.21
.41
-.38
.52
. 12
.17
.22
-.17
.06
1
at
a)
^
o
.50
-.51
. 17
.59
-.50
.15
.47
.45
-.37
. 17
.19
1
137
-------
Table K-4. CORRELATION COEFFICIENTS - CHARLESTON SITE
Av
AV/v60
V60
(l/dw) - .0005
Distance to Water
Distance to
nearest School
Distance to Mac
Corkle Ave.
Distance to
Bridge access
Waterfront Dum-
my Variable
Lot Area
^
<
1
o
^o
>
*""*-"•
^.
<
.90
1
o
^o
>
-.26
-.23
1
o
o
o
*
1
^-K
rrl
^^*
.25
. 15
. 25
1
0)
oj
*v^.
4J
CO
Q
-.23
-.16
-.18
-.61
1
i— i
o
o
A
o
•^^
•4-1
• r-<
Q
.01
-.04
-.32
.17
-.13
1
O
-j
.
^
•^^
.
U)
>^4
Q
.22
.22
.33
.64
-.85
.02
1
0)
00
T)
t-i
ffl
.
to
Q
.06
.00
-. 11
.16
-. 11
.74
.19
1
^>
rt
0
FH
m
0)
flj
. ^
. 25
. 15
. 24
.99
-.57
.16
.61
.16
1
0)
k
-------
Table K-5. CORRELATION COEFFICIENTS - DUNBAR SITE
V60
AV
AV/V60
Distance to Water
(l/dw) - .0005
Distance to
nearest School
Distance to Central
Business District
Distance to
Bridge access
Distance to
Highway access
Distance to Railroad
Lot Area
o
1
§
-.60
1
o
vO
<
-.55
.93
1
/Water
4J
(0
Q
-.28
. 25
. 26
1
in
o
o
o
i
f— <
. 37
-. 20
-. 14
-.73
1
/School
w
••-i
P
-.06
.13
.20
.04
.22
1
Q
cq*
•
O
-M
[Q
• f-l
p
.02
-.03
-. 13
-. 34
. 11
-.65
1
• *-*
tt
4J
CO
Q
.04
-.02
-.11
-.32
.13
-.66
.99
1
!
-*J
10
••-1
Q
.02
-.02
-. 12
-.34
. 12
-.66
.99
.99
1
•
•1-3
CO
.1-4
p
.19
-. 06
.06
-. 17
.28
.59
-.68
-.61
-.66
1
0)
t)
.44
-.10
-.02
-.58
.77
.06
. 20
.24
. 21
.31
1
139
-------
Table K-6. CORRELATION COEFFICIENTS - BEAVER SITE
AV
AV/V60
V70
Distance to Water
(l/dw)-.0005
Distance to
State Street
Distance to
Agnew Sq.
Distance to
Railroad Stn
Distance to
High School
Distance to near-
est corner Park
Lot Area
>
<
1
o
;>
<
.88
1
o
>
.32
.18
1
M
4)
a
c
&o
to
Q
.20
-.00
.31
-.44
.42
.05
1
«
-------
Table K-7. SAMPLE DATA - CORONADO SITE
Obser-
vation
Number
1
2
3
4
s
6
7
8
9
10
11
12
13
1ft
IS
16
IT
18
19
20
21
22
23
2".
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
1.0
41
42
43
44
4S
46
47
48
49
SO
51
52
S3
S4
55
56
V64
.2795ZE-15
.245936-05
.17768E*OS
.2I180E.05
.31364E*OS
.30714E*OS
.42307E*05
.45015E*05
.23185E*OS
.23185E-05
.54603E*05
.388946*05
.163596*05
.?7382E*OS-
.19772E*OS
.30010E-OS
.34B31E"15
.23997E-05
.24539E*05
.24732E-OS
.18472E*')S
.18418E*OS
.23239E.05
.21126E-OS
.280066*05
.3/5406*05
.39S44g.cS
.32719E-OS
.24268E*05
.23185E*OS
.260S6E-OS
.27952E*OS
.211806-05
.S0486E*05
.2B656E.OS
.37S40E.OS
.36186E05
.80000E-OS
.73000E-U5
.31SOOE-OS
.37SOOE-05
.63000E-OS
.52000E.OS
.19000E-OS
. JOOOOE'OS
.22SOOE-OS
.41500E*05
.S3500e»OS
.i«500E'OS
,J2SOOE«OS
.32000F-OS
.22000E>05
,2SS30r-OS
.3SOOOE-OS
•29POOE-05
.27000f .05
.3SOOOE-05
.40000E'OS
.37000E'OS
.16000E'OS
,30000r«0b
.34SOOF«OS
,37000E«05
.31000F.05
.68500E*OS
.36500E-OS
,46000E'OS
.36000E>OS
,4noOOE>»5
.29500E'OS
.29500E-05
,33SOOE«OS
.47000E-05
,21000E'U5
.33000E«05
.".OSOOE'05
.!9000E>«5
.36SOOE-OS
.30000E>05
,27000E'OS
.25SOO£«05 '
.27SOOE«OS
.26000E*OS
.16500E.OS
.11000E>05
.21SOOE*OS
.*9000E«05
T72
.66iicE'ni
,So«87E»03
.5>242hf.«o3
,4017(,E'03
.701396.03
.70V70E»03
,9863t>E>03
.1009SE*04
.649S5E«03
.9262)E-01
.1443<«E*04
.7<*294E'03
.383Oll*C">3
.36C96E«03
,74b78E»03
.769it4E'4E>03
.4Sftl3E«03
.46d64E«03
,360H66«03
.«3J03t«03
.62S»9E»03
.SOS21E-01
.649S5E«03
.8776?E»03
.8179SE-03
.72172E-C3
.52426E'03
.5I676E-03
.52926£«03
.60144E*03
.S2926£'03
.13107E-04
.60144E-03
,10104£<04
,93824£«03
.79390E>03
.420S3E«03
,46364E>03
.5523f-F.'03
.72172E-03
.33610E«01
,'i6b64E«03
.64859E«03
.«S769E-03
,69767E'03
.6a4S3E'03
.52926E-03
.*'>'.S8E-0.1
.50S21E«03
,4S709E»03
.33430E-03
. 637526*03
.39647E-03
.86607E«03
Distance to
Bridge
acceai (ft.)
.3fcOOPE*04
.<>25e«E>0<>
.43100E«0<>
.47«OOE«0'.
. 34700E-0',
.33700E>0<>
.28700E<04
.26200E>04
.22900E«04
.19300E.04
.Z0700E*04
.46700E«04
.46700E>04
.J6*09£-0*
.31200E-04
.30<>OOE>04-
.26600E<04
.24900£»04
.24000E<04
,22300E<0<.
.!<><>OOE<04
.12900E*04
.17200E«0<.
.89000E>03
.43400E>04
.<.3400E»(K
.43«00£*04
.38200E»04
,32200E>0<.
.a8800E>0<>
.10lOOE*04
.10300E-0<.
,10100E'0<.
.8«OOOE«03
.58000E-03
.32000E-03
.47900E-04
.SISOOE'O'.
,43SOOE>04
.4000»E>04
.36*OOE'0<.
.302SOE»04
.26600E>04
.24800E-0'.
. 173006*04
.!31«OE-04
,10100E*04
.41200E>04
.389COE«04
.37006E-04
.37000E-04
.37000E.O-.
.33000E-04
.33000E>04
.33000E>0<>
.35SOOE-04
Distance to
Orange Ave.
(ft.)
.22700E-0*
.28100E-0'-
.29000E«0'>
.32300E«0'>
.20'?50E»0*
• 20800E*0<>
.17200E-0'-
.14900E«04
. 12800E-0*
.13200E>0<>
.16400£>0«
.31400F«0<>
.31400E»0«
.P)JffOE«0«
.17300E-04
.16200E.04
.13300E>04
• 12700E<0't
.10800E>04
.92SOOE»03
.76000E*03
. li.SOOE-0*
. 16200E-0*
.17200E-04
.282006>0<* .
.28200E.O'-
.26200E-0*
.22300E-04
. 16300E-0*
-13750E«04
.81000E-03
•83000E*03
.91000E-03
.10300E-04
.140006*04
.17100E«04
.32300E>00«
.203006*04
.144006*04
.102SO£*04
.970006*03
.14000E*03
.380006*03
.79000E*03
.257006*04
.232006*04
.21500E*04
.215006*04
.215006*04
.17500E*04
.17500E*04
.17SOOE*04
*20600E*04
Distance to
nearest
School (ft.)
.34000E*04
.23800E*04
.25SOOE«04
.20900E-04
.26900E«04
.29JOOE«04
.27400E*04
.255006*04
.23700E»04
.190006*04
.18200E.04
.18800E*(I4
.182006*04
.206006*04
.26600E*04
.23900E*04
,22400E*04
.23200E*04
.207006*04
,19700E*04
,17300E*04
.11400E*04
.58HOOE*03 .
.550006*03
,11400E*04
.11700E*04
.12000E-04
.16600E*04
.18800E«04
.192506*04
.142006*04
.14000E*04
.133006*04
.116006*04
.775006*03
.600006*03
.41000E*03
.46000E>03
.10100E'04
.126006*04
.187006*04
.137506*04 :
.11 1006*94
.870006*03 .'
.'122006*04
.118006*04
.140006*04
.114006*04
.14200E*04
.188006*04
.188006*04
,18800E*04
.125006*04
.125006*04
.125006*04
.130006*04
Lot Area
(•«.«.)
.700006*04
.700006*04
.700006*04
,35000E*04
.70000E-04
,70000E"04
.lOSOOE'OS
.700006*04
.507506*04
.525006*04 .
. .lOSOOE'OS
.700006*04
.350006*04
.45JOO£«04
.350006*04
,70000E*04
.700006*04
.560006*04
.420006*04
.56000E*.04
.600006*04
.22500E«04
.700006*04
,35l/OOE*04
.700006*04
.700006*04
.700006*04
.70000E«04
,43000E*04
.42500E*04
'.700006*04
.675006*04
.450006*04
.95000E*04
.520006*04 '
.880006*04
.101306*05
.144506*05
.450006*04
.S6000f>0«
.525006*04
.700006*04
.350006*04
.300006*04
.630006*04
.350006*04
.700006*04
.700006*04
.560006*04
.560006*04
.560006*04
.560006*04
.350006*04
.350006*04
.337506*04
.910006*04
Distance to
Navy access
(ft.)
'. 26400E-0-
.14900E«04
.16600E*04
.11900E-04
.!9200E>04
.225006*04
.266006*04
.27200E-04
.30800E*04
.3S600E*04
.379006*04
.980006*03
.920006*03
.13400F*04
.19400E*04
.21200E«"4
.24600E*04
.26800E*04
.26300E*04
,294006*04
.355006*04
.422506*04
.468006*04
.475006*04
.400006*03
.410006*03
.420006*03
.103006*04
.161006*04
.199006*04
.390006*04
.390006*04
.396006*04
.412506*04
.4530'0£*04
.492006*04
.660006*03
.520006*03
.49000E*03
.fl«000£'fl.1
.1220'06*04
.182006*04
.218006*04
..24600E*04
.307SOE«04
.352006*04
.393006*04
.130006*04
.150006*04
.147006*04
.150006*04
.153006*04
.177006*04
.17700E-04
.177006*04
.1H200E*04
Distance to
Water (ft.)
.210UOF>iU
,52000E*03
.32000E-03
.580006-03
80000F.03
.530006-03
.49000E.03
,1300«6-03
.940UOE.03
.100006*04
,70000I>03
.«20006*03
.««onOE«03
.126006.04
.11300E-04
.10800F..04
.12000F.04
.108006-04
.132006*04
.14000E-04
.165006.04
.118006*04
.160006*04
. 140906*04
.172006.04
•16800F.04
. .64006*04
.16200E-04
.175006*04
.155006.04
.187006-04
.182006*04
.176006*04
.1B0006*04
.178006*04
.193006*04
.24500F.04
.224006*04
.192006*04
.30100£.0«
. .218006-04
.22ROOE-04
.2.T/90E.04
.261006-04
.231006*04
.258006-04
.270006-04
.277006-04
.286006-04
.261006-04
.2661)06-04
.272006*04
.275006*04
.270006*04
.765006*04
.29H006.04
-------
Table K-7. SAMPLE tXATA - CORONADO SITE (CONTINUED)
Ob0er-
vation
Number
57
58
59
60
61
6!
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
1C
81
82
S3
84
«S
86
97
98
09
90
91
92
93
90
95
96
97
98
99
100
101
102
103
10*
105
106
107
108
109
110
in
112
113
Mean:
Standard
Deviation:
V64
.30064E-05
.27302E-05
.Z318SE-OS
.22S3SE-OS
.36836E-OS
.3959ae.es
.21668E-OS
.24756F-OS
.34669E-05
.34669E-05
.34127E.HS
.34777E-P5
.34127E-OS
.37269E-05
.3526SE-OS
.41603E-05
.33477E-05
.40SS3E.05
.S4S49E.05
.32773E-05
.40302E-OS
.34127E-05
.4371SE-05
.11S92F-05
.23239E-05
.8943SE-OS
.10942E-OS
.2S947E.OS
.30173E-05
.27302E.05
.27302F.-OS
.3S481E-OS
.15709E-OS
.1565SE-05
.43661£.OS
.539S3E-OS
.21S60E-05
.22S3SE-05
.36186E-OS
.38244E.05
.15904F.-06
.3«777£. OS
.23889E-OS
.43661E-OS
.23997E-OS
.24702E-05
.S5958E-OS
.S3141E-05
.14301E-05
.45719E-OS
.56608E-05
.20476E-OS
.24593E-OS
.1S709E-05
.30714E-OS
.17768E-05
.19122E-OS
31.055
16,899
T64
.40196E-03
.36502E-03
.3099BE-03
.30129E-03
,«92E»03
.72932E.03
.*3817E>03
.5388<.E'03
.45528E-03
.58*<.7E-03
,15«99E«03
.31070E-03
.11957E»0'.
.14630E'03
.3*692E>03
.40341F-03
.36S02E«03
.36S03E»03
.47439E-03
.21003E-03
.20931E-03
.S83Y5E>n3
.72136E-03
.28825E»03
.30129E-03
,<>83aOE*03
.S1132E-03
.2126<>E«04
.468000E>05
.<.2000F.'OS
.33000t«OS
.46000O05
.<.2SOOE'OS
.".OOOOf-05
,<.1000E'OS
.«SSOOE«OS
.SOOOOE'05
.650005-05
.90000E»U5
.4SSOOE-05
.SOOOOE'OS
.38000E.OS
.SOOOOE'OS
,17SOOE«OS
.27000E-OS
.12700E-06
.18000E<05
.•.COOOE-OS
,*5000E*OS
,<>OOOOE>05
.35000E-OS
,«6000E'OS
.25SOOE«05
,2«OOOE*05
.78000E-05
.86500F..OS
.29000E-OS
.33000E-05
.54000r'05
.63500E-05
.22300E-06
.<.7000E»OS
,32000E»OS
.65000E»05
.32000E-05
.3".("OOF.«OS
.7SOOOE-05
.82500E-05
.17500E-OS
.SOOOOE'OS
.83000E-05
.34500E-05
.32SOOE-OS
.22500E-05
.»8000E»05
,26500E>05
.25000E-05
41,70*
25,120
T?2
.BlTS^E'O-l
.67361E-OJ
.58B93E"13
.SS337E-03
.81026E*03
.9«979E-03
,*9270E»03
.SS332E-03
.8179SE-03
.7698".E»03
.77«65E'03
.75733E'03
.76503E-03
.B6607E-03
.890I3E-03
.91418E-03
.79390E-03
.85067E-03
.12t>2bf.«0«.
.90168E-0!
.90036E-03
,7<.S7HE»03
.9382«.E'03
.336SOE-03
.S7738E-OJ
.21171E'04
.31275E-0.1
.72172E-.0-S
.73616E»03
.7699«.E«03
.67361E-03
.89U13E-03
,50S21E'03
,<>0898E'03
.12029E-0'.
.l^ftSe-Ci.
.SOS21E-03
.«S709E»03
.81795E-03
.lOld'.E'O'.
.38*92E«04
.805«5E«03
.6370<-E'03
.9016BE'03
.<.6M64E-03
.SfcOBlE'03
.14434Eo04
.14309E>0«
,33685E»03
.96230E-03
.13232E«0«.
.SOS2IE»03
.61299E-03
.OS709E-03
.67361E«03
.36086E>03
.S2926E-03
736
412
Distance to
Bridge
ar-«0<>
.96000r>03
.10<>OOE>04
. I1300E-0'.
.58<.OOE»0«.
.'.6500E-0'.
.4S200E.04
.18300E.04
.6««OaC>04
.S9SOOE>04
.57000E«04
.56500E-04
.55600E-04
.62000E-04
.62700E-04
.S5000E»04
.49000E-04
.49000E-04
.46ZOOE*04
.4S500E*04
,35400E>04
.34708E-04
.36300C-04
.32600E-04
.32600E«04
.28700E-0'
.30200E>04
.2Z200E«04
.22500E«04
.18300E-04
.27SOOE«04
.29200C<04
.23SOOE.04
.27200E*04
.31?OOE«04
.34200E>04
,27400E»04
.34300E-04
.29SOOE>04
.32700E-04
.42300E-04
.46300E*04
.26800E«04
.40700E<04
.34600E-04
.23300E>04
.21300E-04
.20100E*04
.16300E-04
.\5EOOE«04
.12300E«04
.87000E-03
.36300E>04
.71000E-03
.54040E-03
.43700E.04
.4*18»E.04
3140
1443
Distance to
Orange Ave.
(ft.)
.11704
.21000E-04
.22200E-04
.43200E-04
.31800E-04
.30600E>04
.26000E.04
.4530«E«84
.44700E*04
.42000E<04
.39000E»04
.37000E-04
.41000E-04
.40000E-04
.33200E404
.28000E>04
.28000E>04
.2B200E«04
.2S800E-04
.14300E.04
.13000E.04
.13000E-04
.87000E-03
.87000E-03
.30000E-03
.S7000E«03
.52000E»03
.10700E*04
.15800E«04
.22300E-04
.2l900e«04
,28200E«04
.31500E-04
.28300E-04
.30300E-04
.68000E-03
.87000E-03
.12700E-04
.14500E>04
.250006.03
.31800E-04
.17200E-04
.25400E«04
.19SOOE-04
.11400E»04
.10700E-04
.10700E»0»
.11000E-04
.11900E«04
.86000E-03
.IS200E«0*
.20700E«04
.12100E<04
.20000E-04
.27600E-04
.28200E-04
1986
1008
Distance to
nearest
School (ft.)
.11700E-04
.1«600E«04
.18300E«04
.U500E»04
,60000E'03
•96000E-03
.11600E>04
,27000t'04
.J0200E-04
.10500E«04
,10000E>04
.70000E»03
.98000E«03
.15200E'04
.18200E-04
,14700E«04
.14000E>04
.14000E-04
.12500E-04
.12800E*04
,«5000E'03
.72000E-03
,63000E'«3
.SeoOOE'03
.SfiOOOE-03
.S3000E«03
,45000E>03
.10000E-04
,16SOOE'04
,20300E'04
.28500E-04
.29200E«04
.28700E»04
.31800E>04
.34500E>94
•37200E-04
.14600E-04
,21SOOE»0*
.21300E-04
,25200E«04
,31800E«04
.23800E-0'.
,26000E«04
,18600E>04
.22300E*04
.22700E'04
.22800E-04
.21SOOE»04
.16900E-04
.15008E-04,-
.14600E*04
.72000E«03
.16200E-04
.94000E-03
,40000E>03
.aeoooE-03
.92000E-03
1631
7S4
Lot Area
(•q.ft.)
.70UCOE>0<.
.62SOOE*04
.60000E-04
.32uOOE*04
,60000E*04
.70000E>04
.35oOOE*04
.56000F.*04
.75000E«04
.75000E'0<,
.7SOOOE.04
.7SOOOE'0*
.7SOOOE'04
.75000E>04
.80aoOE>04
.10SOOE-05
.70000E-04
.70000E>04
.10SOOE*05
.60000E>04
.70000E»04
.7ooot>e>04
. 70000 f.<04
.20880E-04
..70000E'04
.70000E*04
.15000E*04
.56000E-04
.63000E-04
.*1600E'04
.60000E>04
.60000e*04
.56000E«04
.26S70E»04
.78090E>94
.78dOOE'04
.70000E-04
.30000E*04
.63000E-04
.SOOOOE*04
.6SOOOE-04
.70000E-04
.40000E-04
.70000E>04
.S6000E-04
.63000E«04
.70000E<04
.10500E-OS
.33000E*04
.70000E«04
.70000E<04
,30000E*04
.70000E»04
.30000E*04
.40000E«04
.35000E«04
.50000E-04
6068
Z098
Diatenee to
Navy accea*
(ft.)
.4025lt .0*
.42000F -04
.52«OOE«04
.S3200F-04
.17300E.-04
.17300F*ft*
.18700E-04
.S7300E-04
.22IOOE-04
.22400E-04
.20700E-04
.187SOE-04
.21400E-04
.26flOOC*04
.30200F.-04
,25600E*04
.2300"f:»04
.23000E-04
.21400E-04
.20500F-04
.26500F-04
.28300i:'04
.30000E«n4
-32SOOE-04
.32SOOt'04
.37200E»04
.34700F.-04
.*2700E«04
,49000E*04
.S1000F-04
.61000F-04
.62000F..04
.64000E-04
.6BOOOF-04
.67600E>0<.
,70600E«04
.47300F«04
,54000E*04
.S4SOOE-04
.58000E-04
.61700E-04
.14800E*04
.31300E-04
,10300E*04
.15100F.-04
.28400E'Oft
.30300E«0*
. 32000/T-0<.
,36600E<04
,36<.OOF.'04
.38000E-04
.46200f '04
.1 1700C«04
.43400E*04
.53ROOE*04
.55000E>03
.45000E*03
2950
1645
Distance to
Water (ft.)
.3P700E«(14
.PP010F-04
,1«?OOE'04
. loOOOF -04
.342uqf 'Q4
.344i)nc*0<*
.35100F-04
.)<,700F..l)4
.3«5flO£-04
.40000E-04
.JOSOOE-04
.36500E'04
,39400E'04
.447nQE>04
.40000E-04
.42800E>04
,40300E>n4
.39700E-04
.3S200F-04
.370-JOE-04
.3X700F-04
.30400E.P4
,3e400t»'|4
.35000F-D4
.riSOOOE-fl".
,?9flOOF-04
.32200E-04
.24ROOE'04
.I9400F-04
.10<>OOE«04
.°7000£.OJ
.nnoOOE-03
,14500E>04
.12?OOE«04
.700«OE«03
.650006-03
•19500E-04
.14000E-04
,12600E*0'i
.91000E-03
•S90006-03
.33000F.-03
.47000E-03
.11200E-04
•13000E-04
.11100E-04
.11900E-04
.11200E-04
.12500E-04
.12600F.-04
.16600C-04
.14700E-04
. 1 6400E-04
.17BOOC-04
.156006-04
.?OflOOF«04
.19SOOE-04
Z027
110Z
-------
Table K-8. SAMPLE DATA - CLACKAMAS COUNTY URBAN SITE
Number
\
2
3
4
s
ft
7
ft
9
10
11
12
13
14
15
16
17
IB
}(»
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
4 1
42
43
44
45
46
47
48
49
50
SI
52
S3
54
55
56
V63
•33350E*OS
.204l5£»05
.108S7E-05
. 16779C*OS
• 18078E»OS
•19584E«05
• 1631 1E»OS ~*
.125716*05
.28363E-OS
.18909E«05
.12571E«05
•12760E»05
.23428E>05
•10026E*05
• I0009£*OS
,S8700E«04
.12571£«OS
.19116E»OS
• 17143E«05
.1828SE»05
•36155E»OS
. 89868E-04
.2S3SOE-OS
. 39480E«0*
• 15732E«05
.12104£»OS
•85193E«04
.11896E<05
,24987E«05
•18181E«OS
. 10909£*05
.21 194E*o5
•S7|42£»04
•70648E«04
.37142E*05
•92466E*04
,22129E»OS
. 14233E*OS
,23013£«OS
.78440E'04
.93505E«04
. 10BOSE»05
,S4S44E«05
•11324E>OS
,18233E>OS
.69609E-04
.17922E-OS
.20«39E<05
.10701E-05
.28675E«OS
.228S7E.OS
.18337E.OS
.19168E-OS
.96621E-04
. 12623E-05
.2280SE-05
V70
,47425E*05
.22SOOE«05
.M018E-OS
• 20S99E »05
•24000E*05
•27004E*05
• 19062E *05
• 10200E *05
•50302E*05
.2SSOOE*OS
.15620E-OS
• 14296E*05
.31770E«05
.11750E*OS
. 13767E*05
•85000C*04
,132SOE«OS
,19591£*05
•26000C*05
,23721E»05
•37126E»05
.11000E-05
,24621E*OS
*63540E*04
. 17550E»05
,13892E«05
.11736E«05
.17000E-05
.31617E«05
.16267E*OS
. 10000E*OS
.20950E-05
.10778E«05
.91010E*04
.37065E«05
•88620E*04
•20800£*05
•17500E-05
.21000E-05
.11976E«OS
.11497E«05
. 16096E*OS
.S7486E«OS
.10060E-05
.16239E*OS
,9SOOOE«04
.17367E-OS
.23473E«05
.12178E»05
.31617E-OS
.2515IE-05
.17897E«OS
,19449E«05
.10SOOE-05
.I73t7£«05
.33<>0«E«OS
T
'63
,76»47E«03
.46626E-03
.24833E«03
.38?63E>03
.41304E*03
,44725E'03
•37250E*03
.28634r>03
.64744E>03
.43ZOSE-03
.28634E-03
.32S62E-03
.S3467E»03
.22933E-03
.22806E-03
.13430E-03
.28634E>03
.43S8SE-03
.391SOE»03
.41684E«03
.82482E>03
,20S25E«03
,S7902E«03
.89957E*02
.40>64E*03
.'.7621E-03
. 19385E-03
.27U4E*03
.S7015E-03
.41431£<03
.24833E«03
.4839«E«03
.13050E-03
.16041E-03
.84762E«03
.21I59E*03
.SOS53E«03
.3243SE-03
.S2454E«03
.1786SE-03
.21286E*03
,24707E«03
.12442E*04
.2S847E-03
.41S58E-03
.1S837E-03
.40924E«03
.51187E»03
.244S3E»03
.6S377E-03
.52074E-03
.4181IE-03
.43711F.«03
.22046E-03
.28761E.03
.S2074E«03
T
T70
.79200E«03
.S7800E*03
.20600E-03
.45100E-03
.S1800E-03
.S2SOOE«03
.41000E«03
.27100E>03
.11970E«04
.S7300E»03
.32200E«03
•33300E-03
.61900E-03
.27000E-03
,31600E«03
,22300E«03
.30400E-03
.32200E*03
.SBSOOE-03
.64300E-03
.72600E-03
.29SOOE«03
.68100E-03
.18IOOE-03
.37SOOE-03
.28100E-03
.2?.900E«03
,34000E>03
,57SOO£>03
,38400E«03
.26700E-03
,46600E<03
.22SOOE-03
.12700E«03
.84700£«03
.21000E>03
,S1900E>03
.36300E-03
.S5200E«03
,24000E<03
.24000E»03
.28200E-03
.13070E«0»
.29300E«03
.43200E*03
.21500E-03
.39900E-03
.S3800E.03
.32100E>03
.81700E-03
.S0700E-03
.43300E-03
,*2300E«03
.2B200E-03
.39200E-03
.58600E«03
Distance to
Water (it. )
0.
.17000E>04
. 18800E-04
. 19600E-04
.84000E*03
.11200E>04
.13600E>04
.1H400E*04
.28000E>04
.28800E«04
.31800E-04
.24000E*04
.76000E-03
.10800E<04
.10800E-0".
.17200E-04
,19200E«04
.I2400E<04
.14»OOE*04
.38000E<04
0.
.92000E<03
,40000E>03
,16000£*04
.38800E-04
.30800E-04
.33?OOE«04
.34400E-04
0.
.20000E-03
.40000E-03
.68000E-03
.96000E-03
.13600E*04
0.
.10600E-04
.13600E>04
.)S20C£'04
. 1S200E-CX.
.16400E*04
.16800E>04
.28000E-04
.30800E-04
.21200E-04
.24800E-04
.24000E-04
.26400E*04
.30800E-04
.32800E-04
0.
.24000E-03
.S2000E»03
.72000E-03
.21200E>04
.22000E-04
.18000E-04
Distance to
Park (ft. )
.76800E-04
.70800E>04
.69200E>04
.70400E-04
.68000E*04
.66000E<04
.6S200E*04
.65600E>04
.64000E<04
.60800E-04
,61200E>04
.50400E-04
.64000C*04
,61600E*04
.60800E-04
.S8400E*04
.S9200E-0'.
,S7200E«04
.S6800E*04
.47200E«04
.S9600E-04
.SS600E*04
.56000E*04
.S2400E-04
.47200E»04
.44800E<04
.48000E-04
.48000E-04
.S4000E>04
,52800E>04
.52400E-04
,S2800£*04
.48000E-04
.4S600E-04
.S1200E-04
,45600E*04
.42400E<04
.41600E-04
-41200E-04
.41600E«04
.40800E-04
.35800£*04
.34800E-04
, 36400E-04
.31200E-04
.38000£>04
•30000E-04
.30400E>04
.27200E>04
.46400E<04
.42800E-04
.42800E-04
.38800E-04
.31600E-04
.30000E<04
.25200E-04
Diatance to
neareat
School (ft. )
.20800E>04
.72000E-03
.72000E»03
.72000E-03
.20400E>04
.17200E»04
.1S?OOE»04
.40000E-03
.70000E-03
.60COOE-03
. IOOOOE'0".
.10800E-04
.24800E*04
.22000E-04
.22000E>04
.16000E-04
.14000E-04
. 19600E-0".
. ISSOOE-O".
.13600E>04
.28000E>04
.20400E-04
.24400E-04
•14400E-04
.76000E-03
.60000E-03
.I0800E-04
.11?OOE*04
.27200E«04
.27200E-04
.24000E*04
.21200E«04
.18400E*04
.14400E-04
.32800E-04
. 18800E-04
.16BOOE-04
.1S?.OOE*04
.1S200E-04
,12800E«04
.13200E-04
.48000E-03
.48000E<03
-I1600E-04
.11200E-04
.S6000E-03
.13600E>04
.96000E-03
.12400E*04
.42800E-04
.41600E«04
.39200E-04
.36600E-04
.20400E-04
.22000E-04
.26400E-04
Diatance to
Shopping
Center (ft. )
,61000E<04
.48COOE.04
.46000E-04
.46000E>04
.S6800E>04
,53200E<04
.52000E*04
.46000E*04
.37200E«04
.35600E-04
.33600E-04
,39400E*04
,S6800E*04
,53200E>04
.S2800E«04
.47200E-04
.46000E*04
.50400E-04
.49200E*04
.2S600E*04
,60200E'04
.52400E-04
.56800E>0»
.4S600E-04
.33200E-04
.31200E-04
.30000E-04
.29200E-04
,S9200C>04
.59200E.04
.56000E-04
.S3600E-04
.S0800E>04
.46800E>04
.63600E-04
.S0400E>04
,48400E*04
.46400E-04
.46400E-04
.44400E<04
.44400E-04
.33800E-04
.29200E-04
,40800E«04
.36000E-04
,36800E>04
.38000E*04
.J8000E-04
.30800E-04
.S6000E-03
.67200E-04
.6S600E-04
.62000E-04
.45600E*04
.46000E-04
.46800E-04
Diatance to
Highway
QQF* Itt 1
77fc 1*1. )
.42800E-04
.23200E«04
.20400E-04
.20400E-04
.41600E-04
.38400E>04
.36400E*04
.26000E-04
.14000E-04
. 16000E-04
.12000C-04
.30000E-04
.46000E>04
.42800E-04
.42800E>04
.36000E-04
.34000E«04
.41200E<04
.39600E-04
.16000E-04
,S3200E>04
.44800E<04
.50000E-04
.38000E-04
,Z5200E'04
.23800E-04
•20800E-04
.20000E>04
.S4000O04
.54000E«04
.50800E-0".
.47200E-04
,45200E»04
,41200E»04
.58800E*04
.45200E*04
.43200E«04
.41600E-04
.41600E>04
.39600E«04
.39600E-04
.JB400E-04
.24800E>04
.36400E>04
.34000E-04
.31600E-04
.34000E-04
,27600E«04
.2h800E-04
.64800E-04
.6S200E>04
.61«OOE>04
.58000E-04
.41600E-04
.42400E«04
.43?0(>E»0<.
Distance to
Portland
fft 1
I*1- 1
.45600E-04
.32000E-04
.30000E>04
.30000E-04
.SOOOOE.04
.48000E>04
,4680«E>04
,37200E»04
.31JOOE-04
.35600E*04
,34000£>04
.S2400E.04
.S7200E*04
.55200E-04
.S5600E.04
.S1200E-04
.48800E.04
.56800E>04
.56000E-0*
.48000E>04
.68400E>04
.62000E-04
.67600E-04
.S7600E-04
.S2000E-04
.S3200E>04
,48800E>04
.48800E.04
,74000E*04
.75200E>04
.72000E-04
.66800E-0".
.68800E-04
.66BOOE-04
.84000E-0".
.71800E-04
.720«OE>04
.7080«E>04
.71200E.04
.68400E-04
.69600E'0<.
.64400E-04
.63200E-04
.7«OOOE«04
.70400E-04
.64400E>04
.72800E»04
.64800E-04
.66800E.04
.99290£«04
.99200E-04
.96«OOE*04
.80400E«04
.79600E«04
.8160»E>04
.86000E-04
Lot Area
f«o ft )
(BtJ. H. I
.39600E-05
.12000E>05
.16000E-05
.12000E-05
.742SOE>04
.14700E-OS
.1417SE«OS
.18000E-05
. 14B05E-06
.56300E-OS
.499SOE-05
.M700E-05
.137SOE-05
.23200E-05
.1402SE>05
.91000E-0<-
.65000E-04
.26?SOE»05
.262SOE-05
.747SOE-04
.16200E-05
.91000E-04
.13050E-OS
.76500E-04
.80000E-04
.12SOOE«OS
.lOOOOE'OS
.a5oooE>o4
.1912SE-OS
.72000E>04
.32300E-OS
.26100E-05
.203SOE-OS
.23100E.05
.142SOE-05
.68250E-04
.34850E-OS
.12BOOE-05
.10400E>05
.142SOE«OS
.1187S£»05
-SSOOOE-04
.18000E-05
,34000E>05
.90000E-0'.
.58SOOE-04
.123SOE-OS
.12008E-05
.72000E-04
.2137SE.OS
.1890«E>05
.22400E-OS
.22600E-05
.202SOE-OS
.20250£>OS
.76875E«OS
-------
Table K-8. SAMPLE DATA - CLACKAMAS COUNTY URBAN SITE (CONTINUED)
Observation.
Number
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
Mean:
Standard
Deviation!
V63
.1781B£«05
.43843E»05
.24S19£-oS
.24363E-OS
.32882005
.1S688E>05
.143B9E'05
. 1S961E-OS
.23272E.OS
.12727E*OS
.1SS32E-05
.1241SE»05
.10649E.05
.45817E.05
.23740E«05
.94430E*04
.19428E*05
.18441E*05
.30805E«05
.16727E.OS
.19792E*05
.12623E-05
.202S9E*05
.12363E*05
.108S7E*OS
.14441E.05
.10S97E*05
.15844E-05
.134S4E*05
-19740E«OS
.25870E.OS
.23064E*05
.16987E-05
.12675E.05
.30181C*OS
•27324E*05
.36467E.OS
.21818E*04
.15013E*05
.14337E.OS
.29350E*05
.96102E-04
18, 162
9168
V70
.19162E»05
.476SSE«05
.29652E*05
.21180E-05
.21000E*05
.17676t*05
.148SOE-OS
.•27219£*05
.27004E.05
.16191£*05
,21709E*OS
.14773E*OS
•>13237£*05
.61H41£*05
.25945E«05
.239S2E*05
.19641£*05
.22946E*05
.34012£»05
,37844£*05
.30000E*05
.13978E-05
.21000£*05
.13413E'OS
.11497E*OS
.13892E»05
.12SOOE-05
,13413E*OS
.12800£*05
.23473E-05
.27004E*05
.23473E-05
.179506*05
.13SOOE-OS
.33S33E«OS
.2SOOOE*05
,4077!E*OS
.40000E*04
.180006*05
.14371E*OS
i3ll38E-OS
.19697E*OS
21,144
10,755
T63
.»0671E»03
•.8S817E*03
.49666E*03
.49a86E«03
.6664*E*03
,31802E*03
• 29141O03
,38390£*03
.*7132E*03
.2S720E*03
.3I422E*03
.2S087E«03
.21539E*03
.92744E-03
.49S40E»03
.21539E*03
.4434SE«03
,37376E*03
.62336E-03
.33829E*03
.40037E*03
.25593E*03
,41051E*03
.2S087E*03
.22046E*03
.29268E*03
.21412E*03
,320S5E*03
,27240E*03
.39910E-03
,52327E*03
.47S12E«03
,34336E*03
,25720E*03
.61069£*03
.562SSE*03
,73866£«03
.49413E«02
.30915E-03
.29521£*03
.60436E-03
.21S39E*03
396
197
T70
.48600E*03
.93000E*03
.68400E*03
.7S800C«03
,85700E«03
.3S200E«03
.34400E*03
•4fl200£*03
.58400E*03
.33500E*03
.44800E*03
.29500E*03
.28900E*03
.11100E*04
.54600E*03
.38100E*03
.50600E.03
,42400E«03
.8S100E.03
.43100E*03
,S5000£-*03
.30700E*03
.45200£»03
.32900E*03
.31400E-03
.34800E-03
.28200E.03
.40700E«03
.31700E«03
.54600E*03
,56400E*03
.57000E.03
.40100E*03
,32100E*03
.92200£«03
.74800E«03
.80400E*03
.16200E*03
.40700E-03
.39700E.03
.65900E*03
,35500E*03
470
226
Distance to
Water (£t. )
,31600E*04
0.
0.
.60000E-03
,13600E*04
.I9200E«04
.20000E«04
.I8400E*04
.24000E*04
.26800E*04
.26800E-04
,26800E*04
.30400E*04
0.
,40000£*03
.92000E*03
.19600E*04
. 2.5?OOE*04
.12000E*03
.11600E*04
t!3600E*04
.32000£*04
.12800£»04
.13600E*04
.1S600E*04
.16400E-04
.25200£*04
,33600£»04
.328006*04
.44000E«03
,19200E»04
. 10800E-04
.28*OOE*04
.29200E*04
0.
0.
.32000E*03
.40000E*03
.64000E-03
.64000E*03
.84000E-03
.84000E*03
1546
1056
Distance to
Park (ft. )
.25200E*04
.32400E*04
.32000E*04
.28800£*04
•2?600£*04
,18400E*04
.6*000£*03
.44000E*03
.52000E-03
.88000E-03
,88000E*03
.9ZOOOE*03
.1Z800E-04
.19200E*04
.16800E*04
.92000E*03
.64000E-03
.12400C*04
. 17600E-04
.10400E-04
.13600£*04
.23200E*04
.22400E«04
.22400E*04
.20400E*04
,25200E*04
,29200E*04
.36000E-04
,27200£»04
,29200£>04
.2SOOOE*04
.37200£*04
.34800E«04
.3S200E*04
.41200E*04
.42400E*04
.48000E«04
,49200E*04
•44400E-04
.46000E*04
.48400E-04
.49600£»04
3941
1788
Distance to
nearest
School (ft. )
.15200E*04
,30800E*04
.30000E*04
.29200E»04
.31200E»04
.252006*04
.16800E*04
.13200E*04
.17600E«04
.20800E*04
.20800E*04
.20800E*04
.24000E*04
.14400E-04
.96000E-03
.400006*03
.13200E*04
* 196QOE*04
.60000£*03
.60000E-03
.10400E-04
,28400E*04
,16400E*04
.17200E*04
.16800E*04
.21600£*04
.28800£*04
.38800E-04
.30800E*04
,20000C*04
.E4ROOE.04
.31200E*04
,35600E*04
.36400E>04
,32800E*04
,34000E*04
.40800E-04
.42400E*04
.38000E*04
.40000E>04
.42400E*04
,44000E*04
2048
1071
Distance to
Shopping
Center (ft. )
.32800£*04
^62800E*04
,62800£*04
.SR400E*04
.S2000E«04
.41600E-04
.38800E*04
.43200E-04
.35200E*04
.32400E-04
,32800E*04
.33200E-04
.29600£*04
.S7200£*04
,S6400E*04
.50000£«04
.40800E*04
.37200E-04
.5BOOOE*04
.4S800E»04
.48800E*04
.39200£*04
.552006*04
.54800t«04
,52000E*04
.S4400E*04
,50800E*04
.48400C*04
,42400E*04
.64000E*04
.54400E.04
,64800E»04
.51200E*04
.51200E*04
.72800E*04
.73200E»04
,7S600E*04
.76000E*04
.71200E*04
.72800£*04
.72800£»04
,73200E»04
4877
1304
Distance to
Highway
99 E (ft. )
.29200E*04
.58400E-04
.S9000E»04
.54000E*04
.<.8000E-Oi.
.37200E*04
.32000E*04
.33600E'04
.2S200E*04
.21600E.04
•21600E-04
.21600E*04
.17600E<04
,SOOOOE*04
.47200E*04
.38400E-04
.26800£*04
.20400E.04
.45600E.04
.34000E*04
,30800E*04
.12400E*04
.30800E*04
.29200E*04
.28000E*04
.26000E*04
,16400E*04
.52000E*03
.10SOOE-04
.37200E»04
v2?400C*04
,25600E*04
.10000E*04
.8ROOO£*03
.34000E>04
.33600£*04
.28400E*04
.26600E*04
.26000E*04
.25600E*04
.21600E*04
.212006*04
3417
1353
Distance to
Portland
(ft.)
.73200E*04
«1Q240£*OS
•)0240E*05
.98QOOE<04
.90400E«04
.85200£*04
.88800E*04
.92000£*04
•90400E*04
•88800£*04
•89600£*04
*90000£*04
.87600E*04
.10480E*05
•10680E*05
,10280E*05
•96800E«04
•94800£*04
* 10960£*05
.10400E*05
.98000E*04
.11280E*05
.11 120E*05
.10970E.OS
. 1 1200E*05
•92000E*03
. 106ao£*05
. 1 024QE.Q5
.12080E*05
.H280E*05
• 12320E*05
.11120E*05
•11 120E*05
*13080E*05
.13120E*05
. 13480£*05
.13S20E*OS
.13000E.OS
. 13200E*05
• 13200E*05
.13280E.OS
8013
2872
Lot Area
(»q. ft. )
.2392SE*05
.57500E*05
.57500£*05
.13200E*05
. l 1200E»05
.24000E.05
.14400E*05
.12600e*05
.20900£*05
. iaOQO£*05
•25500E*05
.2SSOOE*OS
.3S6SOE*OS
•41000E*05
.99000£*04
,20000E*05
.1487SE-OS
•1SOOO£*05
>24000£*05
.25SOO£*05
•87750£»OS
.75000E*04
*12000£*05
. 12825E*OS
,21250E*05
. l 1400£*05
•64000£*05
.14SOOE*05
*30000£*05
.21000E*OS
.11000E*05
•81000E*04
.2422S£«OS
,24225E*05
.46600£*05
«2a900E«05
.25000E*05
.18400£*OS
.67500£*04
. 10800E*05
.43750E*05
.437505*05
22,630
20, 071
-------
Table K-9. SAMPLE DATA - CLACKAMAS COUNTY RURAL SITE
Obaervation
Number
1
,2
3
4
5
ft
7
8
9
10
11
12
13
1*
15
16
17
18
19
20
21
22
23
24
as
26
27
23
29
30
31
32
33
34
V60
.11001E-OS
.78168E-04
.72377E-04
.90469E-04
.11S80E-0*
.27497E-0*
.17S87E-OS
.73819E-04
.S7902E-04
.54286E-04
.S4286E-04
.11073E-OS
.11797E.05
.142456-05
.27379E-04
.23161E-04
-11797E-OS
.94805E-04
.27497E-04
.3*7*1E-0*
.26776E-04
.12449E-05
.14692E-OS
.12301E-04
.24602E-04
.62238E-0*
. 166SOE-04
.129SSE-OS
.S0003E-0*
.36497E-0*
.6S998E-04
.23331E-04
.S5714E.04
.19742E-04
T60
.20429E-03
.14S16E-03
.13441E-03
.16801E-03
.1S61SE-02
.51 077C-02
.32661E-03
.99S47E-02
-78076E-02
.7270SE-02
.7270SE-02
.21S06E-03
.24S98E-03
.24S98E-03
.S3409E-02
.31021E-02
.1S801E-03
.12699E-03
.36837E«02
.46S31E-02
.35868E-02
'.24I77E-03
.2728SE-03
• 16480E-02
.32960E-02
-83366E-02
.199S1E-02
.27875E-03
.I1332E-03
.82713E-02
.149S8E-03
.27287E-02
.6S762E-02
.23082E-02
V70
.21174E-OS
.243SOE-OS
.71392E-0*
.13174E.OS
.26772E-04
.14267E-05
.36254E-OS
.10060E'OS
.79402E«0*
.13386E-05
.95283E-04
.18204E>05
.16732E-OS
.11155E.OS
•38324E-04
.65000E-04
.22310E'OS
.27999E-OS
.IISOOE'OS
.11SOOE-OS
. 10S39E-05
.23872E-OS
.22000E-OS
.S577SE-04
• 12440E-05
.36113E-OS
.25000E-04
.80000E-05
.66930E-04
.33465E-04
.32000E-OS
.22310E-04
.10481E-OS
.49821E-04
T70
.46422E«03
.44392C-03
•241 82E*03
.2-.182E-03
.108S7E«03
.33998E-03
.8S3SOE-03
.21714E.03
. 19646E-03
.20S17E«03
.2S583E-03
.60240E-03
.4J168E-03
.4t698E-03
.10542E-03
.577SOE-OZ
.44860E-03
.S4S94E-03
.12412E-03
• 1241 2E-03
.1S071E-03
.632S2E-03
.69702E-03
. 10874E-03
.2SSS8E-03
.7B303E-03
.49430E-02
.Z6372E-03
. 13984E-03
•9&990E-02
• 12356E-03 *
.49810E-02
«24fr05E-03
.I1322E-03
Water (ft. )
•£SOOOE>04
.25000E-04
.SOOOOE-02
.50000E-02
. 12000E>0»
.39000E-04
. 17SOOE-04
.SOOOOE-02
.SOOOOE-02
.SOOOOE-02
.SOOOOE-02
.50000E-02
. SOOOOE-02
.SOOOOE-02
.11500E-04
.10000E-04
.46000E-04
.SOOOOE-02
.SOOOOE-02
. SOOOOE-02
.SOOOOE-02
.SOOOOE-02
.61000E-04
.SOOOOE-02
.SOOOOE-02
.SOOOOE-02
.16000E-04
.SOOOOE-02
.12000E-04
.70000E-03
.13000E-04
.13000E-04
-16SOOC-04
-3SOOOE-03
Dtatance to
Dearest
Boat Ramp
(milea)
.12000E«01
.I1000E-01
.aooooe-oo
.90000E-00
.20000E-01
.80000E-00
.40000E-00
.30000E-00
.60000E-00
.19000E.01
.21000E-01
.16000E-01
.ISOOOE'Ol
.13000E-01
.SOOOOE-00
.40000E-00
.10000E-01
.10000E-00
.20000E-00
.30000C-00
.30000E-00
.60000E-00
.20000E-01
.10000E-01
.90000E-00
.20000E-00
.46000E-01
.30000E-01
.leoooE-oi
.18000E-01
.30000E-00
,12000E«Ol
.10000E-01
.10000E-01
Diatance to
neareat
Bridge
(milea)
.46.000E-01
.47000E-01
.48000E-01
.49000E-01
.53000E-01
.44000E-01
.39000E-01
.3SOOOE-01
.32000E-01
.22000E-01
.19000E-01
.13000E-01
-llOOOE-0.1
.10000E-01
.40000E-00
.30000E-00
.90000E-00
.40000E-00
.60000E-00
•70000E-00
.70000E-00
.10000E-01
.24000E-01
.21000E«01
.22000E-01
.29000E-01
-41000E-01
.13400E-02
.14000E-02
. UOOOE'02
.12800E-02
.40000E-01
.37000E-01
.38000E.01
Portland
(milea)
.18500E-02
.18600E-02
.19400E*02
.19500E-02
.20600E-02
.18200E-02
.18200E-02
.18700E-02
.18700E-02
.17700E-02
.17700E.02
.17700E-02
.17700E-02
.17700E-02
.17900E-02
.18600E-02
.19200E-02
.18400E-02
.18400E-02
!l8400C-02
.18400E-02
.18400E-02
. 19500E-02
.19600E-02
.20000E.02
.21900E-02
.33600E-02
.33800E-02
.33800E>02
.35200E-02
.21400E.02
.21100E-02
.21100E-02
Diatance to
Town
(milea)
.46000E-01
.47000E-01
.60000E-01
.60000E-01
.20000E-01
.stoooe-oi
.S6000E-01
.24000E-01
.23000E-01
.32000E-01
.33000E-01
.79000E-01
.BOOOOE-01
.eiooOE-oi
.92000E-01
.96000E-01
.10200E-02
.97000E-01
.9800«E-01
!99000E-01
.10100E-02
.H200E-02
.11300E-02
.11400E-02
.12200E-02
.62000E-01
.92000E-01
. IOSOOE'02
.10500E-02
.11400E-02
.13300E-02
.13000E-02
.13100E-02
Waterfront
footage
0.
0.
.10000E-03
.15000E-03
0.
0.
0.
.15000E-03
.10000E-03
.1SOOOE-03
.1SOOOE-03
.10000E-03
.1SOOOE-03
.1SOOOE-03
0.
0.
0.
.45000E-03
.10000E-03
.10000E-03
.10000E-03
.30000E-03
0.
.10000E-03
.SOOOOE-02
.45000E-03
0.
-10SOOE-04
0.
0.
0.
0.
0.
0.
Lot Area
(acrea)
.13100E-02
.13000E-02
.60000E-00
.80000E-00
.10000E-01
.8SOOOE-01
.26600E-02
.70000E*00
.SOOOOE-00
.10000E-01
.looooe-oi
.14000E-01
.BOOOOE-00
.11000E-01
.SOOOOE-00
.Z4000E-01
.3S700E-02
.31000E-01
.90000E-00
.90000E-00
.70000E-00
.ISOOOE'Ol
.20000E-02
.SOOOOE-00
.30000E-01
.12100E-02
.29000E-01
.30000E-02
.20000E-02
-10000E-02
.17400E-02
.19000E-01
.66000E-01
.33000E-01
Standard
Deviation:
89.6
16,013
14.847
992
1459
0.99
3.85
3.91
5.07
8.26
3.36
115
7.16
9.54
-------
Table K-10. SAMPLE DATA - CHARLESTON SITE
Observation
Number
1
I
3
4
5
6
7
6
9
10
11
12
13
14
15
16
17
18
19
2H
21
22
23
24
25
26
27
28
^9
30
31
32
33
3* .
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
SO
SI
52
S3
54
55
56
57
SB
59
60
61
6Z
63
64
65
V60
.3007SE»OS
.1B646E-OS
.3428SE-OS
-2S864E.OS
.3*060E»05
.27669E»05
-637S9E-OS
-222SSE'OS
.30676E.OS
.19248E-05
-33684E-OS
-1022SE»05
.33VS8E'OS
.601SOE-05
-2213SE-05
.228S7.E.OS
.228S7E.OS
.23940E.OS
.34J8SE-OS
-27669E.05
•19970E»OS
-24060E-OS
-25864E.05
• U030E-05
.ZS263E-OS
.30676E-05
.2I052E-05
.21052E>05
.162*OE»OS
.27669E»05
-222SSE-05
•22616E-OS
.•16842E«OS
.28270E.OS
.30676E-OS
.28872E-05
.48120E-05
.37293E«05
.31278E«OS
.210SZE-OS
.2S864E-OS
-49323E»OS
-28872E«OS
-222SSE»05
.24060E-05
.36090E-05
•23458£«OS
•16240E»OS
-2989SE«05
.42105E»OS
.21052E-OS
-2S263E»OS
.36090E»05
-27669E»05
.S4135E.05
.32481E»05
-SS338E-05
.27067E-OS
. 198<.9E'OS
.3J481E-05
.25864E.OS
•1S037E«05
.20<>51E02
,1037»E»03
,9S09SE«02
.86»SOE«02
,9S09SE»02
.2S935E»03
.62244E-02
.86450E-0?
.9S09SE»02
.14697E»03
.864SOE>02
.69160E>02
,2S93SE»03
.77805E-02
,10374E»03
,SS328E>02
.7780SE«02
,1S561E'03
,8472JE«02
.77BOSE«0?
.864SOE«02
.82992E«02
.41496E-02
.7780SE»02
,I0374E»03
.69160E>02
.69I60E<02
.36309E>02
,89908E«02
.10720E-03
.86450E*02
,S1870E«02
.12968E-03
.10720E«03
.17290E«03
^5»35E»03
.31641E«03
,77805E«02
.864SOE«02
.7780SE>02
,2S93S£«03
.7780SE«02
.7780SE»02
.89908E*02
.11238E>03
.62244E-02
.14697E>03
,77805E«02
,17290E«03
.7780SE»02
.96824E>02
.12103E>03
,77805E»02
,4322SE»03
,10374E«03
,17290E«03
.86450E<03
.S1870E-03
.19884E>03
. 11238E-03
,S1870E*02
,77805E«02
.13832E-03
,17290E«03
V70
.47022E«OS
.19092E>OS
.30641E-OS
.2S4S6E>05
.22745E-OS
.26163E>OS
,5S389E«OS
.208596-05
.29462E«05
.I7913E-05
.29462E-OS
.1944SE-OS
. 19S63E-05
,S9279E»OS
.2S338E-05
.25102E«05
.20388E«OS
.20B59E«05
,36180E'OS
.24395E-05
.17560E-05
,234S2E«05
.21095E>OS
.14849E*05
.17795E»OS
.26398E>05
.19327E«OS
,Z0034£'OS
.1S320E»OS
.24041E«OS
.18620E-05
.22391E«OS
.15085E-05
.29)09E>05
.28991E<05
.20034E>OS
,46197E«OS
.33469E>05
.2S691E>05
.18S02E-05
.2)331E«OS
.47847E»OS
.28I66E«OS
.19799E-05
.27341E-OS
.32V73E»05
.15910E-05
.16381E-OS
.27223E»05
.40069E-05
.19681E>05
.21331E'OS
.31937E»OS
.22I56E-05
,S1383E«OS
.29934E«05
.48554E-05
.2439SE>OS
. 18738E-OS
,25927E«05
.24748E-05
.14613E>OS
.22627E-OS
-188S6E-OS
.21802E.05
T70
.3702SE-I03
.1S033E>03
.24127E<03
.20044E<03
.17909E-03
.20601E-03
.43614E>03
. 164J5E-03
.231»9E«03
.14105E->03
,23199E«03
.1531IE-03
.1S404£«03
.46676C-03
.19951E-03
.1976SE»03
.16054E.03
.1642SC>03
.284BBE*03
.19209E>03
.13826E-03
.18466E«03
.16610E>Oi
.JJ69a£.OJ
.14012E-03
,20786E'03
.1S218E-03
.1S77SE«03
.12063E«03
• .18930E-03
.14662E>03
.17631E«03
.11878E-03
.22920E*03
.22828E-03
.1S77SE«03
.36376E-03
.2635»E>03
.202J9E-09
.14S69E*03
.16796E«03
.37675E«03
.22178E«03
.1SS90E-03
.21S28E«03
.25333E-03
.12527E»03
.12899E-03
.21436E«D3
.31S50E>03
.1S497E-03
.16796E>03
.2Sl»7E«03
.174i.SE«03
.40*S9E«03
.23S70E>03
,38232E«03
. 19209E'03
. 1475»E«03
.20415E<03
.19487E»03
.1I507E»03
.17817E-03
.14847E<03
.17167E«03
Distance to
Water (ft.)
.20000E-02
,85000E«03
.10000E.04
.90000E-03
.72000E«03
.70000E-03
.S7000E«03
.70000E-03
,4SOOOE>03
,42000E>03
.89000E-03
.98000E-03
,12400E>04
.20000E>02
.12600E»04
.64000E*03
.SOOOOE»03
,83000E«03
, I7900E-0*
.30000E-03
. J2400E-04
.24000E>03
,30000E'03
.96000E*03
. 13900E-04
.11800E-04
.66000E«03
. llftOOE'O".
,1I200E«04
.70000E-03
.200006-02
.60000E-03
.6SOOOE-03
.87000E-03
.98000E>«3
.99000E«03
.8S<)OOE««3
.20000E-02
.13200E-0*
.9SOOOE-03
,17700E»04
.20000E-02
.S2000E-03
.98000E.03
,71000E«03
.10700E»0*
.20800E-04
.5SOOOE-03
,10700E*04
.86000E.03
,94000E*03
.33000E-03
.S4000E<03
.12000E*04
.37000E-03
,4SOOOE*03
. 108DOE.«4
.13200E.04
.48000E-P3
.89000E-03
.S6000E03
.10200E>04
.84000E«03
.20000E-02
.85000E-03
Diatance to
nearest
School (ft.)
.16900E*04
.38700E-0*
-51000E.03
.68000E.03
.86000E.03
.S3000E«03
.86000E-03
.33200E-0*
.1910«E<04
.11600E>04
.43000E.03
.S7000E.03
.94000E-03
.11ZOOE.04
.16500E-04
.30000E*03
.399006.0*
.21400E.04
.21000E-03
.53000E.03
. 10200E.04
.36600E«04
.52000E-03
.40600E«04
.10400E«04
.31100E.O<.
.23200E>04
. 13900E.04
.39800E-04
.49000E*03
.2060»E*04
.3420«E>04
.41300E404
.94000£<03
.32000E.03
.97000E-03
.50000E-03
.38100E-04
.12200E.04
.26700E«04
.11000E>04
.23700E.04
.13000E-04
.36400E-04
.11300E-04
.70000E.03
. 33000E«04
.37700E-04
.43000E*03
.66000E.03
.27000E-04
.SOOOOE.03
M760CE«04 •-
. 10300E.04
.11700E«04
. 19500E«04
.11261SE.04
.19100E«04
.12200E-0*
. 16500E.04
.82000E-03
-0.
.33000E-03
.38900E-04
.35000E-04
Distance to
Mac Corkle
Ave. (ft.)
. 14600E-04
.S7000E-03
.7SOOOE-03
.54000E-03
.74000E'03
•74000E«03
. 12000E'0*
.73000E-03
.10SOOE-04
.10300E-04
.S4000E-03
.10400E-04
.SSOOOE-03
. 15400E-04
.24000E-03
.73000E-03
.88000E>03
.73000E-03
.23000E-03
.10600E-04
.41000E-03
.12100E-04
.10600E'04
•40000E-03
.24000E«03
.18000E'03
.87000E«03
.23000E-03
.2SOOOE-03
. 74000E-03
.12900E'04
.60000E'03
.72000E-03
.89000E'03
.40000E-03
.40000E-03
-90000E-03
.14200E>04
.2SOOOE«03
.S600CE-03
.25000E<03
.14200E-04
.73000E'03
.SSOOOE-03
.73000E-03
.67000E-03
-S7000E-03
.88000E-03
.S3000E-03
.72000E-03
.S7000E-03
.10700E-04
-10SOOE-04
.25000E-03
.11800E-04
.92000F.03
. 74664 E. 4 3
.2SOOOE-03
.12300E-04
.56000E-03
•89000E-03
.54000E'03
.53000E-03
.135006-04
.35000E-03
Dlatance to
Bridge
acce>i (ft. )
.74000E-04
.10100E-05
.6S200E-0*
. 14000E-04
.16400E-04
.13600E-04
.S8300E-04
.94200E-04
.27400E-04
.19600E-04
.12400E-04
.10BOOE-04
,47900E«04
.63SOOE--04
.22300E-04
.76000E-03
.10250E-05
.81800E-04
.38000E-03
. 13200E-04
.71700E«0*
.97300E-04
.12600E-04
.10070E-05
.62000E-03
.26000E-04
.83500E-04
.20200E-04
.loioor-os
. 12900E-04
. 19400E-0*
.31000E-OI.
.I01SOE-05
.46200E-04
.10600E-04
.67000E-03
.61000E-04
.88500E-04
.26500E-04
.87200E>04
.67000E-03
.31000E-04
.21000E-04
.97000E-04
.19000E-04
.67SOOE»04
.94500E«04
.98500E-04
. 38800E-0".
.37000E-04
.87700E-04
.11900E-04
.78000E-04
.16500E-04
.37700£«04
.17300E-04
.49SOOE-04
.79000E-04
.7I400E-04
.23SOOE>fl4
. 16400E-VI4
-0.
.11000E-04
.99300E-04
.30700E-04
Waterfront
Dummy
Variable
.10000E--01
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
.10000E-0)
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
.10000E-01
0.
0.
0.
0.
0.
0.
.10000E-01
0.
0.
0.
.10000E.01
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
.10000E-01
0.
Lot Area
(•9- ft.)
.2488SE-OS
.60000E-04
.90000E-04
.60000E-04
.60000E-04
.tOOOOE-0*
.12000E-OS
.60000E-04
.60000E»04
-60000E-04
.60000E-04
.60000E-04
.60000E-04
.43000E-OS
.60000E-04
.60000E-04
.4SOOOE-04
.72000E-04
.80000E-04
.60000E-04
.40000E«04
.60000E-04
.60000E-04
.48000E-04
.600006-04
.60000E*04
.60000E-04
.60000E-04
.48000E-04
.60000E-04
.10000E-05
.750006-04
.48000E-04
.80000E-04
.60000E-04
.60000E-04
.80000E-04
.I3500E-OS
.60000E-04
.60000E-04
.50000E-04
.20000E-05
.60000E-04
.60000E-04
.60000E-04
.900006-04
.40000E-04
.48000E-04
.600006-04
.90000E-04
.60000E-04
.60000E-04
.96000E-04
.60000E-04
.12000E-05
.60000E-04
.15000E-OS
•7SOOOE-04
• 10800E*OS
•60000E-04
•60000E-04
•30000E-04
•S4000E-04
•12000E-05
.50000E-04
Mean
28,049
Standard
Deviation: 10,722
71.3
26,241
10, 178
80.1
792
434
1690
1234
3SS
0.09
0. 29
5671
-------
Table K-ll. SAMPLE DATA - DUNBAR SITE
Obser-
vation
Number
1
2
3
5
b
7
B
9
10
11
12
13
14
15
16 -
17
IB
19
20
21
22
23
24
2S
26
27
28
29
Mean:
Standard
Deviation
V60
.22616E-OS
,23*58£«OS
.14616E-OS
. 198*9E-OS
.96240E>04
.18045E-OS
-23*S8E«OS
.2S263E>05
.222S5E«OS
.13233E>OS
.13233E-«OS
. 17443E-OS
.962»OE'0*
.1363*E-OS
.16842E*OS
.ZS864E.OS
.53654E«04
. 16842E-05
.1S639E»OS
. 11428E-OS
.16361E«OS
.31879E.OS
.14436E>05
.216S4E«OS
.24060E-OS
.13834£.OS
.72180E-0*
.90225E«04
.210S2E>OS
17.174
: 6282
T60
.29393E»02
.10028E>03
.S1870E»02
,63973E«02
.4322SE*02
,10374£«03
.7780SE«02
.65702E»02
.60S15E-02.
.20748E-02
,51870E»02
.69160E-02
.4322SE>02
.63973E«02
.69160E>02
.69160E>02
.34S80E>02
.69160E>02
.SS328E-02
.13832E>02
.63973E»02
.10374E«03
,S1870E«02
.70889E>02
Isi870E»02
.29393E-02
.72618E-02
.8*72IE»02
61.1
23.0
V70
. 16546E>05
.?1322E'05
. 1*881 E« OS
. 19767E-OS
. lllOSE'OS
15S47E«OS
•89095E»OS
•23320E'OS
•17102E«OS
.10661E'05
.12104E-OS
. 15S47E-05
. lllOSE'OS
.U881E-05
.14S48E»OS
.22321E«OS
•10106E'05
.13326E-05
•12882E«OS
.11882E-05
. 15769E«OS
.25986E«05
.UeeiE'OS
•17102E«OS
.IS991E-05
-15103E»OS
.2S541E.OS
• 13548E*OS
. 13992E«05
16.412
4904
T70
. 13826E-03
.17816E-03
.12434E-03
. 14S17E-03
.92793E«02
.12991E«03
.24312E«03
.19»87E»03
. 14290E-03
.890B2E>02
.10114E*03
.12991E«03
.92793E«02
.12434E-03
.12156E-03
.186S1E-03
.844»2E«02
-1113SE.03
.99289E-02
.13177E«03
.2171*E>03
•12434E*03
.14290E.03
.13362E-03
•12620E*03
.21342E<03
-11321E.03
. 11A92E.03
137
4J.O
Distance to
Water (it. )
.20000E-02
.20000E-02
.10SOOE«04
.80000E«03
.12000E-04
.6SOOOE-03
.ISOOOE'04
.10000E«04
.18000E>04
.19000E-04
.70000E-03
.11000E»04
. 16000E-04
. 17000E-04
,20000E»04
.20000E«02
.I7SOOE>04
.16SOOE*04
,70000E>03
.lflSOOE«04
. 1SOOOE'04
,19SOOE»04-
. 14SOOE-04
.14000E>04
.14000E-04
.16000E*04 •
. 19000E-04
.70000E>03
.11000E-04
1242
591
Distance to
nearest
School (ft.)
.29000E'0*
. 18000E-04
. 12000E>0*
.80000E.03
.36000E>0<>
.21000E«04
.4«OOOE>03
.3200«E>04
,2SOOOE«04
.3SOOOE-04
.I300«E>04
.12SOOE«04
-90000E-03
,6SOOOE'03
.12000E»04
.36000E-04
.210»«E«04
.90000E-03
.85«»OE.03
.31000E-04
.29000C-04
.21000E«04
.32«OOE«04
.14JOOE-04
.17000E-04
.46««OE>04
.34S»«E»04
.9S«»OE>03
2047
1120
Distance to
Central
Business
District (ft.)
.94000E-04
.14SOOE«05
.12SOOE-05
.ICSOOE'OS
.1S300E-05
.10900E-05
.2SOOOE>04
.SOOOOE'O*
. ISSOOE'O*
.10200E-OS
.14200E-05
. 12SOOE»OS
.IIOSOE'OS
.60000E>04
.4SSO«E«04
.11000E-OS
.I0500E.05
.80500E-04
.S3«««E>04
.88««OE«04
.83000E'04
.96000E-04
.3600«E<84
.7000«E<04
.S9000E»C*
.>43«»E>«S
.9seeeE.o4
8810
3852
Distance to
Bridge
access (ft.)
.104SOE-05
. 15500E-05
. 13800E-OS
.117SOE»OS
.BOOOOE'03
.14700E>OS
.12300E-05
.20SOOE-0*
.SeOOOE'O*
.30000E-03
.IISOOE'OS
.ISSOOE'OS
.13700E'OS
,11800E»OS
.I2600E>OS
.76000E«0*
,52000E»04
,12600E»OS
.ueooE-os
.92S»OE«04
.60000E-0*
.10050E-05
.93000E-0*
.10900E-OS
!80000E«04
IlS980E05
9693
4349
Distance to
Highway
access (ft.)
.106SOE>OS
. 1SSOOE-05
. 12800E«OS
.lUOOE'OS
,38000E»0*
.13SOOE-05
.II&OOE'OS
.49000E»04
,67000E>04
.31500E>04
. 11300E'OS
. 14SOOE-05
!ll200E»05
. 11600E'OS
.64000E.O*
.63000E-04
.11700E«OS
. 11500E.05
.92000E-04
.70000E>04
.97000E-0*
. 105SOE.OS
.SS500E.04
.84SOOE.04
.74000E<04
•14900E»05
.»07SOE»OS
9807
3308
Diatance to
Rallroad(ft.)
.37500E-04
.IOOOOE'0*
.ISOOOE'O*
.2SOOOE>04
.24000£*04
.90000E*03
. 16000E-0*
.34000E-0*
.30000E>04
.17SOOE-04
.29000C«04
.5SOOOE>03
.14000E-04
. IOOOOE'04
.44000E«04
.29000E-04
.13SOOE-04
.27000E>04
.24500E-0*
.32500E-04
.20000E«04
.28500E-04
.22000E'04
.31000E«04
,30000E'04
.29000E>04
.10000E>04
.26000E-0*
22S7
980
Lot Area
(sq. «. )
.16000£«OS
.10*00£.05
.40000E'04
,90000E'04
.28800E-04
.SOOOOE'O*
.7SOOO£«04
.40000E>04
.60000E>04
,30000E>04
!72000E»04
.65000E-04
.50000E-0*
.40000E*04
.127SOE*OS
.36000E«04
.*OOOOE'0*
.4SOOOE-04
.36000E>04
.60000E-04
.60000E-04
.72000E-0*
.67SOOE«04
.42000E-0*
.36000E-0*
.82000E-0*
.40000E«04
.10000E>OS
6166
3079
-------
Table K-12. SAMPLE DATA - BEAVER SITE
1
•v
30
3
z
o
o
•n
2
a
(•9
2
OO
Obser-
vation
Nujnber
t
Z
3
4
5
6
7
8
9
10
11
1Z
13
1*
15
16
17
18
1»
20
Zl
ZZ
Z3
24
zs
Z6
37
za
29
30
31
3Z
33
34
35
36
37
38
39
40
41
<>z
o
4*
45
46
47
48
49
SO
51
S3
53
Mean:
Standard
Deviation:
V60
.2U75E.05
.19626E-OS
.1°S22E»05
.374SOE-05
. 16708E-05
-17672E-05
.23171E-OS
.26393E«OS
. 199Z9E-05
.20695E-05
.26381E-05
.20834E-05
-JZ661E-05
-11312E.05
-17917E-OS
.15208E-05
.20S33E«05
.12649E-05
. 19243E-05
-222S4E-05
.10S4SE*05
.36380E-OS
.S95S3E-05
-190SSE-OS
.21463E-05
.218706-05
-11312E-OS
.206386-05
.179S1E-OS
.201836-05
-I3696E-OS
.23266E-05
-13*05E«OS
.7SS74E.04
.215686-05
.21416E-OS
-23207E-05
-180S7E«05
.22660E-OS
.24172E-OS
.17881E-OS
.30532E-05
.18Z89E-OS
. I1080E-05
.ZOZ9ZE-OS
. 18881E-05
.2298SE-OS
.12917E-OS
.14800E«OS
.Z1056E-OS
. 13894E-OS
. U662E-05
.23171E-OS
20.189
7959
T60
.31S71E«03
.288ZOE<03
.28689E-03
.5SOZOE«03
.24497E»03
.ZS938E-03
.34Q60E*03
.38776E-03
.2934*E»03
.3039ZE*03
.38776E«03
.30&S4E-03
.1860ZE«03
.16637E-03
,26331E«03
.ZZ»01E»03
.30130E-03
. 1860ZE-03
.28J96E-03
.327SOE«03
.15<.S8E«03
,S3448E*03
.87S08E-03
.28034E<03
.31S71E«03
.3J095E-03
.16637E-03
.3039?E»03
,26331E«03
.29606E>03
.Z0174E*03
.34191E»03
.19650E»03
.1113SE-03
.3170JE-03
. 3K.40E-03
.34060E>03
.Z6S93E«03
.33274E>03
.3SS01E>03
.Z6331E>03
.44«33E«03
.268S5E-03
.16244E-03
.29868E»03
,Z777ZE«03
.33798E»03
.1899SE-03
.Z1746E*03
.30916£«03
.20436E-03
.J161SE-03
.34060E-03
297
117
V70
.2340*E>05
.17897E-05
.36300E-05
.29652E»05
.Z0118E-05
.165006-05
.Z3000E>05
-36006E-05
.23298E«05
,28740E«OS
.32299E>OS
.238Z7E*OS
.1SZ3ZE«OS
.17500E>05
,Z3827E«OS
.12646E«05
.Z6475E-OS
. lsee5E«os
.14370E«OS
.2S416E>05
.95800E>04
.383ZOE>OS
.43800E«OS
,23471E>05
.26000E-05
.Z3000E*OS
.10590E-OS
.18ZOZE*05
.Z6475E-OS
.18873E-05
.22000E<05
.Z7S34E>05
. 1S885E-OS
.1Z178E>05
.Z4870E«05
.Z6824E>05
.30656£>OS
.18000E>OS
.19000E-OS
.27Z69E.OS
,ZZ99ZE«05
.3706SE-OS
.18200E-05
. 148J6E-05
.28593E>05
.Z1500E>05
.23950E-OS
.11735E»OS
.14Z96E«OS
.Z1076E05
.Z7S34E«OS
22,511
7323
T70
.51800E«03
.47300E-03
.47000E-03
.90300E«03
.40300E*03
.42600E*03
.55900E*03
.63700E-03
.48100E<03
.49900E-03
.63600E-03
.50300E-03
.30SOOE>03
.27300E«03
.43200E-03
.36700E-03
.49SOOE*03
.30SOOE«03
.46400E-03
.53700E-03
.2S40*OE»03
.87800E-03
.'14360E»04
.46000E*03
.51800E-03
.52800E-03
.Z7300E*03
.49800E»03
.43300E*03
.48700E-03
.33000E>03
.S6100E-03
.32300E>03
.18200E-03
.S2000E-03
.51700E-03
.56000E-03
.43600E»03
.54700E>03
.58300E-03
.43100E-03
.73600E-03
.44100E*03
.26700E*03
.48900E«03
,45500E'03
.S5«.OOE-03
.31200E*03
.35700E«03
,50800E'03
.33500E-03
.35400E-03
.5S900E-03
487
192
Diatance to
Water (ft. )
.90000E-03
.70000E-03
.5SOOOE-03
.95000E-03
.11000E>04
. 10000E-0*
.90000E-03
.70000E-03
.20000E*04
.9SOOOE-03
.30000E-03
.IOSOOE'0".
•70000E-03
.1SOOOE-04
.55000E*03
.11000E«04
.16SOOE>04
. 19000E-0".
.80000E-03
.8SOOOE-03
.10000E>04
.80000E-03
.25000E-03
.18000E>04
.25000E-03
.12000E*04
.20000E«04
.HOOOE-04
.11000E*04
.70000E-03
. 12500E-0".
.95000E«03
.60000E-03
.11000E»0<.
.16500E-04
.20000E«04
. 19SOOE-04
.11000E>04
.85000E-03
.25000E-03
.6SOOOE*03
.40000E-03
.90000E-03
.I9000E-04
. 14SOOE-0*
.25000E«03
.16000E-04
. 12000E-0*
.90000E«03
.IOOOOE-04
.19500E-0*
.SOOOOE'03
.30000E-03
(048
518
DL>t&nc« to
State Street
(ft.)
0.
. IOOOOE-04
. 10000E-0*
.SOOOOE'03
.SOOOOE-03
.40000E>03
.2SOOOE.03
.<.SOOOE»03
.".OOOOE-03
.50000E.03
.95000E-03
.40000E«03
.SOOOOE-03
0.
.90000E-03
0.
.10000E-03
.30000E-03
.60000E»03
.30000E-03
.2SOOOE-03
.6SOOOE>03
,13SOOE>04
.4SOOOC«03
.SOOOOE'03
0.
.45000E-03
,40000E«03
0.
.75000E-03
0.
.SOOOOE-03
,9SOOOE»03
.3SOOOE-03
0.
.40000E*03
.35000E«03
.SOOOOE-03
.6SOOOE-03
.95000E-03
.SOOOOE-03
.80000E-03
.30000E-03
.30000E-03
.20000E-03
.12000E-04
.60000E-03
.40000E>03
.60000E>03
.<.5000E»03
.40000E«03
.65000E«03
.85000E-03
492
321
Diitaace to
Agncm Sq.
(ft.)
.26000E*04
. 11000E-04
,1S500E>04
.19000E-04
.50000E-03
,20000E*04
.30000E-04
.40000E-0*
.60000E-03
.21000E-04
.27000E«04
.18000E-04
.26000E-0*
.SOOOOE'03
.21000E-04
.26SOOE-0*
.14SOOE»04
. 12000E-04
.16000E>04
.32000E«04
.24000E-04
.20000E-04
.15000E-04
.24000E-04
.39000E-04
.26000E«0»
.17500E«04
.16000E>04
.27000E-04
. 13500E-04
.22000E-04
.17SOOE«04
.10000E»04
. 13500E-04
. 14500E-04
.16000E-04
.15000E«04
.8SOOOE-03
.18000E«04
.Z8000E«04
.26SOOE-04
.29SOOE-04
.33000E-04
.13SOOE»04
.90000E-03
.23500E-04
.28000E-04
.6SOOOE-03
. 18000E-04
. 19000E'04
. 1ZOOOE-04
. 18000E-04
.32000E-04
1972
832
Dlitance to
RaUroad
Station (ft.)
.40000E'03
.28000E-04
.4JOOOE-04
. U300E-0*
.29000E-04
.S5000E-0*
.60000E«04
.70000E-04
.3SOOOE>04
.IOOOOE'04
.90000E-03
.47000E-04
.60000E-03
.36000E-04
. 13000E-04
.56000E-04
.44000E>04
. 19SOOE-04
.46000E-04
.62000E-04
.54000E«04
. 12000E-04
.26000E>04
.90000E>03
.68000E>0<.
.SSOOOE'Oi
.14SOOE-04
.1SOOOE-04
.56000E*04
.41000E-0*.
.52000E-04
.47000E-04
.38500E-04
.43000E-04
.15000E-04
.1S500E-04
. 15000E-04
.37000E-04
.13500E-0*
.80000E*03
.40000E-03
.580«OE«04
.S2SOOE«04
.17000E.04
.Z0500E-04
. 13SOOE-04
.8SOOOE-03
•35000E-04
.470»CE«94
.48500E.04
.41SOOE*04
-47000E-04
.60000E-04
3312
1957
Distance to
High
School (ft.)
.56000E-04
.40500E-04
.31500E-04
.SOOOOE-04
.36000E-04
.24000E»04
.23000E-04
.28000E-04
.26300E-04
.S1000E-04
.S8000E-04
.24000E-04
.56000E-04
.28000E-04
.52000E-04
.20000E-04
.20SOOE-04
.40000E-04
.27000E-04
.23000E-04
.Z2000E-04
.51000E-0*
.46000E-04
.S1000E<04
.31000E-04
.20000E-0*
.45000E-04
.47000E«04
.20000E-04
.30000E-04
.20000E«04
.2SOOOE-04
.40000E>04
.2SSOOE«0*
.44000E-04
.43500E*04
.43000E-04
.30000E-0*
.49SDOE»«4
.59000E-04
.57000E«04
.a7500E«0*
.23000E-04
.42000E-04
.40000E-04
.55000E-04
.S4000E-0*
.30500E<04
.25SOOE.04
.23500E-04
.20000E-04
.26SOOE-04
.28SO«E»04
3604
1279
Distance to
nearest
corner
Park (ft.)
. 14000E-0".
.11SOOE-04
.30000E-03
.800006-03
.15000E'04
.90000E-03
. 18000E-0*
.265006-0".
.12500E-04
.90000E-03
.HOOOE-04
.90000E-03
.13000E-04
,14000E'04
.50000E-03
.16SOOE»04
.IOSOOE«04
. 15500E-04
.60000E-03
. 19300E-04
.13000E>04
.70000E-03
,7SOOOE»03
.12SOOE«04
.24000E-04
. 16000E-0".
. .800006-03
•80000E-03
.160006-0".
.SOOOOE-03
.13500E-04
.80000E-03
.12000E-04
.80000E-03
.110006-0".
.7SOOOE-03
.80000E-03
.IOOOOE-04
.6SOOOE-03
•12500E-04
.140006-04
. 14500E-0".
.210006-04
.900006-03
.11500E-04
.600006-03
. 15000E-04
.12000E-04
.70000E-03
.90000E-03
.80000E-03
.65000E-03
.17000E-04
1152
482
Lot Area
(sq. ft.)
.92400E-04
.S37SOE«04
.8Z800E«04
.142SOE-05
.324SOE-04
.67200E-04
.70000E-04
.61600E-04
.46200E-04
.520006-04
.80010E-04
.75000E-04
.72000E-04
.436806-04
.690006-04
.510006-04
.16000E-OS
.23400E-04
.582406-04
.7S600E-04
.350006-04
.190006-04
-1S720E-OS
.62000E-04
.68000E-04
.510006-04
.564006-04
•86400E-04
.JSOOOE-04
.S9400E-04
.465006-04
.675006-04
.562506-04
.300006-04
.29380E«0«
.55000E-04
.825006-04
.625006-04
.43000E-04
.803106-04
.666506-04
.SOOOOE-04
.700006-04
.39000E-04
.79ZOOE«04
.440006-04
.61200E-04
.400006-04
.56ZSOE-0*
.840006-04
.336006-04
.S7000E-04
.92000E-04
6460
2969
-------
SELECTED WATER
RESOURCES ABSTRACTS
INPUT TRANSACTION FORM
,'AV
w
Benefit of Water Pollution Control on Property Values S,
^^ 8. P> -formic Orgsi' -ttioa
D. M. Dornbusch and S. M. Barrager
David M. Dornbusch and Company
San Francisco, CA
OlAAB-07
It- Sponsoring Orjaa/z,-.
Environmental Protection Agency report number,
EPA-600/5-73-OQ5, October 1973,
68-01-0753
13. Type , " Repot. -aid
Period Covered
This study was undertaken to determine the current state-of-knowledge concerning
the measurement of the potential benefit of water pollution control on property values,
and to analyze the relationship between water quality parameters and property values at
several sites where water pollution has been substantially reduced in recent years.
Multiple-regression analysis and an interview technique were employed to study the
relationship between residential and recreational property values and water quality com •
ponents. Study sites were located on San Diego Bay and the Kanawha, Ohio, and Willamet
Rivers. It was found that effective pollution abatement on badly polluted water bodies
can increase the value of single-family homes situated on waterfront lots by 8 to 25
percent, and that these water quality improvements can affect property values up to
4000 feet away from the water's edge. It was also found that the measurable water
quality parameters which have the greatest influence on property values are dissolved
oxygen concentration, fecal coliform concentrations, clarity, visual pollutants (trash
and debris), toxic chemicals, and pH.
Case study results were combined with a 1971 EPA water pollution survey to estimati!
the national benefit expressed in increased residential, recreational and rural water-
front property values, to be gained from water pollution abatement. The estimated capi-
tal value of the benefit ranges from ,6 to 3.1 billion dollars, with a most likely
benefit of 1.3 billion dollars.
:e
i ."a. i'-
*Water Quality, *Water Quality Control, *Economics, *Benefits, *Property Values
J9. S' urityr -ss,
2V. Sfcui. /
.of
price
Send To:
WATER RESOURCES SCIENTIFIC INFORMATION CENTER
U S DEPARTMENT OF THE INTERIOR
WASHINGTON, D. C. 2O24O
Dennis P. Tihansky
Environmental Protection Agency
-------
ENVIRONMENTAL PROTECTION ASENCY
Forms and Publications Center
Route 8, Box 116, Hwy. 7O. West
Rttieifih, North Carolina 27612
povTAaE AND nmm
ENVIRONMENTAL.
A4WNCT
Official Business
EPA-336
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$
s*<#
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%
«*/
If your addrrt* fa incorrect, pkue cfean^e on the above label;
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If you do not Of an to con tinac receiving till* (ec&okkl *cjp««t
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------- |