NCEE0

NATIONAL CENTER FOR
ENVIRONMENTAL ECONOMICS

A Hedonic Analysis of the Impact of LUST Sites on
House Prices in Frederick, Baltimore, and
Baltimore City Counties

Jeffrey Zabel and Dennis Guignet

Working Paper Series

Working Paper# 10-01
January, 2010



stA}.^ U.S. Environmental Protection Agency

National Center for Environmental Economics
1200 Pennsylvania Avenue, NW(MC 1809)

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A Hedonic Analysis of the Impact of LUST Sites on
House Prices in Frederick, Baltimore, and Baltimore

City Counties

Jeffrey Zabel and Dennis Guignet

NCEE Working Paper Series
Working Paper # 10-01
January, 2010

DISCLAIMER

The views expressed in this paper are those of the author(s) and do not necessarily represent
those of the U.S. Environmental Protection Agency. In addition, although the research described
in this paper may have been funded entirely or in part by the U.S. Environmental Protection
Agency, it has not been subjected to the Agency's required peer and policy review. No official
Agency endorsement should be inferred.


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A Hedonic Analysis of the Impact of LUST Sites on House Prices
in Frederick, Baltimore, and Baltimore City Counties

Jeffrey Zabel
Economics Department
Tufts University

and

Dennis Guignet
Department of Agricultural and Resource Economics
University of Maryland

KEYWORDS: LUST, Hedonic Analysis, Groundwater Contamination, Remediation
Benefits

Abstract

Petroleum from leaking underground storage tanks (LUSTs) can contaminate local soil,
and surface and groundwater. In some cases this can pose health risks to the surrounding
population. Focusing on single family home sales from 1996-2007 in three Maryland
counties, we use a hedonic house price model to estimate the willingness to pay to live
father away from LUST sites. Particular attention is given to how property values are
affected by leak and cleanup activity at a LUST site, the severity of contamination, the
presence of a primary exposure path (i.e., private groundwater wells), and publicity
surrounding a LUST site. The results suggest that although the typical LUST site may
not significantly affect nearby property values, more publicized (and more contaminated
sites) can impact surrounding home values by more than 10%.

We would like to thank the National Center for Smart Growth at the University of
Maryland for providing us with the housing transaction data. We also would like to
thank Anna Alberini, Chip Paterson, and particularly Kelly Maguire for useful comments.

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1. INTRODUCTION

Petroleum products are used in many industrial activities, and some products
(such as motor fuel) are sold to consumers at commercial facilities. Such facilities are
widespread, and often store petroleum products onsite in underground storage tanks
(USTs). For example, USTs are commonly used at gas stations to store gasoline, diesel,
and other petroleum products.

Over time leaks may occur as a result of corrosion and rusting, cracks, defective
piping, and because of spills during refilling and maintenance activities. Petroleum from
leaking underground storage tanks (LUSTs) contaminates the surrounding soil and can
percolate into local groundwater aquifers. Oil contamination can migrate via surface run-
off or local groundwater flows, and could potentially contaminate the surrounding
environment and nearby water bodies. As of March 2009, there were over 482,166
known UST releases throughout the United States.1

In addition to environmental contamination, LUSTs can pose potential adverse
health risks. Vapors can travel upwards into nearby homes and other structures. This
poses several acute health risks such as headaches, nausea, and even potential
explosions.2 Exposure to petroleum products over long periods of time increases the risk
of some chronic diseases. Consumption of contaminated groundwater is the primary
exposure path of concern. Petroleum products break down to several carcinogens and
other contaminants that can affect the kidneys, liver, and nervous system. As a result,

1	US Environmental Protection Agency (EPA) htto://www. epa. gov/oust/faas/faa9a.htm. Accessed July 16,
2009.

2	Maryland Department of Environment (MDE),

http://www.mde.state.md.us/assets/document/LRP%20Vapor%20Intrusion%20Guidance(6').pdf. accessed
July 16, 2009.

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concentration levels of these petroleum constituents in drinking water are regulated by
the US Environmental Protection Agency (EPA).3

Due to the potential environmental costs and health risks, LUSTs may adversely
affect the welfare of nearby residents. If so, the cleanup of contamination from a LUST
should result in some benefit to residents. We attempt to measure the benefits of cleaning
up LUSTs, as reflected in residential property values. A hedonic property value model is
estimated using single family home sales from 1996-2007 in three Maryland counties:
Baltimore City, Baltimore, and Frederick. Careful attention is given to how property
values are affected by leak and cleanup activity at LUST sites, the severity of
contamination, the presence of a primary exposure path (i.e., private groundwater wells),
and publicity surrounding a LUST site. The main conclusion from this analysis is that
the average LUST site is unlikely to have a significant impact on house prices. However,
the results suggest that the most publicized (and more contaminated sites) LUST sites can
significantly impact nearby property values by more than 10%.

This paper proceeds as follows. In Section 2, we provide a literature review. In
Section 3, we discuss the data that we will use to estimate the hedonic model. This
includes the property transaction data we were able to obtain from the National Center for
Smart Growth and the LUST data that we obtained from the Maryland Department of the
Environment. In Section 4, we lay out the framework for using the hedonic model. We
pay particular attention to measuring the baseline impact of living near the UST site prior
to discovery of the leak so that the impact of the LUST site is measured with respect to
this baseline. In Section 5, we present the results, followed by some concluding remarks
in Section 6.

3 US EPA, http://www.epa.gOv/safewater/contaminants/index.html#listmcl. accessed July 16, 2009.

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2. LITERATURE REVIEW

There is a large literature that provides evidence that hazardous waste sites
adversely affect the prices of nearby residences. Boyle and Kiel (2001) provide a recent
survey of the literature. A significant portion of the hedonic residential property value
studies focus on Superfund sites. Comparing across these studies Farber (1998) finds that
surrounding residential property values increase, on average, by $3,500 for each
additional mile away from a hazardous site. Boyle and Kiel (2001) find significant
variation in this premium across studies ranging from $190 to $11,450.

The relatively small literature on the impact of contamination on the value of non-
residential properties is surveyed in Jackson (2001). This includes the impact on the
values of nearby commercial and industrial properties and on the contaminated property
itself. Jackson analyzes seven studies (Dotzour (1997), Guntermann (1995), Page and
Rabinowitz (1993), Patchin (1994), Sementelli and Simons (1997), Simons and
Sementelli (1997), and Simons, Bowen and Sementelli (1999). Jackson reports that all of
these studies that estimate the impact of contamination on the sales prices of commercial
and industrial properties find significantly negative effects.

Some studies focus on the value of the contaminated property itself. McGrath
(2000) estimates a hedonic equation of sales prices of industrial properties in Chicago
that includes the probability of contamination, PROBCON. The estimated coefficient for
PROBCON is negative and significant. The impact is a 76% median unit discount or
approximately a one million dollar ($1995) per acre decrease in parcel value. This is a
particularly large impact and McGrath speculates that "investors are perhaps either

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overestimating the financial liability or that the discounts incorporate the present value of
required legal costs certain to be part of any site redevelopment." (page 440). Jackson
(2002) estimates the impact of current or previous contamination on prices using a
hedonic equation applied to sales of industrial properties in Southern California. He finds
that contaminated industrial properties sell for a discount of approximately thirty percent,
on average. Alberini (2007) examines the "contamination discount" of selected
contaminated properties in Colorado and finds that the contaminated property appreciates
in value after participation in the Colorado Voluntary Cleanup Program.

In the remainder of this literature review, we first focus on studies relevant to our
analysis of the impact of LUST sites/groundwater contamination on property values.
Initially we consider impacts on nearby properties. We then review the literature on the
impacts on the LUST sites, themselves. Second, we consider the impact of property
contamination on the likelihood that such properties will be redeveloped. Third, we look
at three studies that use data from our study area, Maryland.

2.1 The Impact of LUST Sites/Groundwater Contamination on Property Values;
Nearby Sites

One study that looked specifically at LUST sites is Simons, Bowen and
Sementelli (1997; henceforth SBS97). SBS97 analyze the impact of USTs on 16,990
residential sales in Cuyahoga County, Ohio in 1992 (this includes the city of Cleveland).
They considered three types of USTs: non-leaking tanks registered with the State of
Ohio, and registered and unregistered LUSTs. There were 2,513 tank sites; 1,151 non-
leaking, 835 leaking but unregistered, and 527 leaking and registered. SBS97 cite a study

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by Bowen, Sailing, Haynes, and Cyran (1995) that developed a ranking of the toxicity of
noxious environmental releases. Based on their analysis, LUSTs are expected to have a
very localized impact. SBS97 interpreted this to mean being within sight distance or
within a city block (300 feet). Hence, they generated indicator variables for units within
this distance of the three types of USTs. There were 83 sales within the required
distance of an UST; 42 near non-leaking USTs, 24 near leaking but unregistered USTs,
and 17 near leaking and registered USTs. The only indicator that was marginally
significant (at 5% but not at 1%) was for leaking and registered USTs. The estimated
coefficient indicated that houses near a registered UST that is known to have leaked sold
for a discount of $15,152 or 17% of the average sales price in 1992. This result should be
viewed with caution since it is based on a small number of sales (17) and the model does
not control for other potential LULUs (locally undesirable land uses) that could bias the
result.

Page and Rabinowitz (1993) analyze the impact of groundwater contamination on
residential and non-residential properties. They note that the nature of groundwater flows
complicates the analysis; "Neither the direction nor the rate of movement of plumes of
toxic chemicals in ground water is predictable without a thorough and costly
hydrogeological investigation." (page 473) The analysis of non-residential properties is
based on a few case studies of abandoned industrial properties. The authors find that
groundwater contamination significantly negatively affected the value of these properties
(though it is not clear how they did this). The residential analysis considers properties in
seven rural towns or small cities in Wisconsin that depend on private groundwater wells.
The authors compare units with groundwater contaminated with toxic chemicals to

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similar nearby properties with wells with no identified contamination. They find no
difference in the prices across these two groups of properties.

Dotzour (1997) looks at the impact on sales prices of residential properties in an
area of Wichita Kansas where groundwater contamination had been discovered.
However, few of the properties in the contaminated area used the groundwater as
drinking water. Dotzour compared the change in average sales price of units in the
contaminated area during the year before and after the contamination announcement to
comparable changes in two control areas. The results showed no significant differences
across the three study areas.

2.2 The Impact of LUST/Groundwater Contamination on the Property Values and
Transaction Rates of LUST Sites

Simons, Bowen and Sementelli (1999; henceforth SBS99), Simons and
Sementelli (1997) and Sementelli and Simons (1997) compare property values and
transaction rates of LUST sites versus non-LUST sites. All three studies use data from
the same location and hence cannot be considered to provide independent information.
SBS99 analyze residential and commercial properties in Cuyahoga County, Ohio. Using
the residential properties, SBS conducted a limited hedonic analysis. They found that
residential properties near and/or with actual contamination from a LUST sold for a 14-
16% discount (consistent with SBS97). They also estimated that commercial LUST sites
sold at an annual rate of 2.7% whereas the annual transaction rate for uncontaminated
sites was 4.0%. Hence, the transaction rate for the LUST sites was 33% lower than for
those sites without contamination. These results are suggestive at best since they are

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based on a very small sample of contaminated properties. In the case of the commercial
analysis, it is likely that the difference in transaction rates between contaminated and
uncontaminated properties is not statistically different from zero. Further, this analysis
does not control for the characteristics of the sites, so it is unclear if this difference is
solely driven by differences in contamination levels. Using a similar dataset, Sementelli
and Simons (1997) find that a No Further Action (NFA) letter has no impact on the
transaction rates of LUST sites.

Simons and Sementelli (1997) compare the transaction rates of LUST and
registered nonleaking tank (RUST) commercial sites. They note that in Cuyahoga
County, most of the drinking water comes from Lake Erie and is provided by the City of
Cleveland Water Department. Hence, 98% of the LUST sites use municipal drinking
water and hence the health risks are minimal. But it is expected that LUST sites will be
slower to sell. Results show that the transaction rates for LUST sites over a four year
period was 3.8% versus 10.4% for comparable, uncontaminated (non-RUST or LUST)
sites. Further, the transaction rate for RUST sites was only 4.9%. Relative to sites with
no USTs present, buyers may be reluctant to purchase properties with RUSTs in fear of
future liability, and remediation and removal costs. RUST and LUST sites were also
found to be less likely to obtain secured mortgage financing and loan-to-value ratios were
lower than for other commercial properties.

2.3 The Impact of Contamination on Redevelopment

Many observers suggest that contamination—whether actual or merely
suspected—is likely to impair the redevelopment of properties. Three studies focus on the

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impact of contamination on the redevelopment of such properties. Sigman (2005)
estimates the impact of CERCLA liability laws on the redevelopment rates of industrial
sites in the U.S. The data are annual city-level observations from 1990 to 2000. The data
are from surveys of realtors and are not transaction data. The dependent variable is the
vacancy rate of industrial space. Sigman uses fixed effects to capture unobserved city-
specific factors that can affect vacancy rates. The presence of CERCLA joint and several
liability laws implies a 40% increase in vacancy rates in city centers. There is suggestive
evidence that joint and several liability has a bigger impact in cities with a higher risk of
contamination. Strict liability does not significantly affect vacancy rates. The impact of
joint and several liability on vacancy rates in suburban areas is negative but not
significant. Sigman also finds similar results using a data set of brownfield sites; the
presence of joint and several liability in a city is associated with 67% more brownfield
sites. These results are not as strong as the previous ones since the data are cross-
sectional and hence it is not possible to use fixed effects to capture unobserved city-level
factors that are correlated with liability laws. Also, the definition of a brownfield is not
standardized across cities.

McGrath (2000) also analyzes the impact of contamination on the likelihood of
redevelopment for 195 industrial properties in Chicago that sold between August 1983
and November 1993; 95 of which were redeveloped. Individual property contamination
levels are not known, so McGrath uses a list of contamination probabilities for 25
industrial and commercial land-uses to generate the probability of contamination
variable, PROBCON. McGrath estimates a probit model where the dependent variable is
whether or not a property that sold is redeveloped. The estimated coefficient for

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PROBCON is negative but not significant. Hence, there is no evidence that
redevelopment of a purchased site is affected by the probability that a site is
contaminated.

Lange and MacNeil (2004) estimate a logit model where the dependent variable is
whether or not the redevelopment of a brownfield site was "successful" or "not-so-
successful" (the authors do not state was it means for redevelopment to be successful).
The data on 26 successful and 26 not-so-successful sites were obtained from surveys sent
to 228 representatives of EPA brownfield assessment pilots (the response rate was 24%).
Four factors were found to significantly affect successful redevelopment: an index of
political support (financial incentives and limitations on developer liability) and the
willingness of the lending institution to cooperate on project financing, adequacy of
infrastructure, the fraction of the site redeveloped as office or commercial use, and the
fraction devoted to greenspace (the latter two are relative to the fraction redeveloped for
residential use).

2.4 Three Studies using Data from Maryland

Thayer, Albers and Rahmatian (1992) estimate the impacts of hazardous and non-
hazardous waste sites on house prices in Baltimore from 1985 -1986. Results show a
strong positive relationship between distance to hazardous waste site and price; prices
increase by approximately 2% per mile further from the site. This positive relationship
seems to level off with increased distance, but remains for at least four miles away from
the site. They also found a significant positive relationship between air quality and price;
a 6% increase in air quality led to an approximate 4% increase in price.

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Howland (2000) focuses on parcels in an industrial area of Baltimore, finding that
contamination reduces the sale price, but does not slow down transactions. Schoenbaum
(2002) examines values, and vacancy and turnover rates for another industrial area in
Baltimore, and reports no evidence of significant differences across brownfields and non-
brownfield properties.

In summary, there have been numerous studies on the effects of hazardous waste
sites on surrounding residential property values. In contrast, based on the literature
review above, there are few studies of the effects on residential property values from
groundwater contamination and specifically from LUSTs. Research on LUSTs and
surrounding residential property values have been confined to just one geographic area
(Cuyahoga, Ohio), and are limited in reliability due to few sales in close proximity of a
LUST site. Further, the analysis of the impact of environmental contamination on non-
residential properties is relatively small and not well developed from a statistical
standpoint.

3. DATA

The hedonic analyses will focus on three counties in Maryland: Baltimore City,
Baltimore County, and Frederick. First we give a description of the UST sites in these
three counties and then provide details of the housing data.

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3.1 UST Sites Description

Data on the 640 "Remediation Cases" in the study area were obtained from the
Maryland Department of Environment's (MDE) Oil Control Program. We focus on the
387 cases where a leak was discovered between 1996 and 2007. This corresponds to the
period of available home sales data. Out of the 387 cases, 180 were in Baltimore County,
123 in Baltimore City County, and 84 in Frederick County. We exclude cases with
invalid coordinates, cases that are simply a residential location with a contaminated
groundwater investigation and not linked to a specific LUST, when the 'leaking' event
was minimal and resulted in nothing that could conceivably affect house prices, and if
contamination was the result of something other than a leaking tank. This leaves 219
cases: 110 in Baltimore County, 66 in Baltimore City County, and 43 in Frederick
County. Figures 1 and 2 display the LUST sites in the three counties.

Table 1 shows the breakdown of case openings and closings by year. A case is
open when an investigation regarding a potential leak is warranted, which may occur for
several reasons, including: odor or water taste complaints from nearby residents, issues
regarding routine onsite groundwater testing or UST system compliance checks,
discrepancies in product inventory records, and if an UST owner reports an issue. Once a
case is opened MDE investigates the situation and determines the best course of action,
which may or may not include active cleanup. Petroleum products naturally degrade over
time, so if there is no public or environmental threat, then ongoing monitoring and natural
attenuation is sometimes deemed the best course of action (US EPA, 2004; Khan et al.,
2004).

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A case is closed when MDE is satisfied that there is no contamination, or there
may be contamination but no exposure, or, if undertaken, cleanup is well underway or
complete. Overall a case is closed once the LUST is no longer considered an
environmental or health threat. Of these 219 sites, 149 were closed by 2008. A few sites
were open and closed on the same day. It is likely that this may happen when the results
of a relatively small investigation that turned up little to worry about are entered (date
open) at the same time when MDE enters their conclusion (little to worry about; date
closed). Some of these cases are merely investigations in response to a complaint MDE
receives. When the inspector gets to the site they may find nothing and just close the
case right away. This seems to happen often with vapor investigations. Also, surface
spill cases are sometimes minor and cleaned up right away with kitty litter, so these cases
are usually closed right away also.4

Considering the 149 cases that were closed by 2008, the average leak case was
open for 1.53 years, the median is 0.57 years, and the maximum is just under 10.5 years.
Regarding the leak cases that remained open as of 2008, the average case is open for 3.10
years (the median duration is 4.68 years).

There is information on relative risk categories (1-4; 1 is riskiest) but these do not
appear to provide relevant information about the health risks associated with each LUST
site. Instead, we use information on groundwater testing for petroleum concentration.
We use these data because groundwater is the primary exposure path of concern and
testing is done much more often than vapor and soil testing. We focus on concentration
values for BTEX; the summation of benzene, toluene, ethylbenzene, and xylene. This

4 We have information on cleanup dates but they are reported only semi-annually. Therefore, we do not use
this information in this analysis.

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aggregate measure of pollution is commonly reported, though only the individual
components (benzene, toluene, ethylbenzene, and xylene) are regulated. The variable we
use is btexmax; the maximum of the btex summation at any single time and testing
location, including both on and offsite testing associated with a case. Testing is only
carried out at 148 of the 219 LUST sites so we include a testing indicator in the hedonic
model.5 The mean and median values for btex max are 17,818.82 and 280.75,
respectively so the distribution is severely skewed right (concentrations are in
micrograms/liter, which is equivalent to ppb). There are 24 LUSTS where the btex max
concentration is zero.

3.2 Sales Data

The data come from the MDProperty View CAMA (Computer Assisted Mass
Appraisal) Database. This database is created on a yearly basis using data obtained from
the State Department of Assessments and Taxation (SDAT). We have data from the
1996 - 2007 editions of this database. Each year provides information on the most recent
sale for each unit in Frederick County, Baltimore County, and Baltimore City County so
our dataset includes all sales between 1996 and 2007.

Although much of the stock of housing in Baltimore consists of townhomes
(attached and semi-attached homes) and condominium apartments, we will restrict
attention to single-family homes. We do so for the sake of comparability with Baltimore

5 There does not appear to be an explicit testing criterion. Still, testing is more common at sites where there
is a potential exposure path (groundwater being used) and if individual homes are nearby that could
potentially be exposed. Further, the severity of the LUST event is also a factor in determining whether
testing takes place.

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and Frederick Counties, where single family homes are prevalent, and with previous
hedonic studies, which have largely focused on single-family homes.

For each home, we have the exact address, latitude and longitude, and the names
and the address of the owner. The latter information can be used to determine whether a
home is owner-occupied. We also have the size of the lot, the square footage of the
home, the age of the home, the quality of the structure (fair, average, good, very good),
the type of heating and whether air conditioning is present, the number of bedrooms, the
number of baths, the number and type of fireplaces, the presence, type and size of a
porch, the presence and size of a garage, and the type of construction (e.g., brick, stucco).
We have a general description of the dwelling (e.g., "1 story with basement") but we do
not know the style of the home (e.g., Cape Cod, Federal style, etc.).

Because we have the coordinates of most homes, we also know which census tract
and block group these homes fall in. There are sufficient sales to allow us to include
block group fixed effects in Baltimore and Frederick Counties and census tract fixed
effects in Baltimore City County. These fixed effects allow us to control for all local
amenities and disamenities that are common to all units in the block group (or census
tract) and are constant over the time period of our analysis; 1996-2007. We believe that
local public goods such as school quality and safety are constant over this time period so
we do not have to include these variables in our model. We also do not include
accessibility (in terms of distances) to the city center, downtown Washington DC and
downtown Baltimore (employment centers), and tunnels (Harbor and Fort McHenry
Tunnels) since these are essentially constant within block groups and census tracts. We
do include distances to local amenities such as lakes, open spaces, commercial districts,

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and major roads. We also have calculated the number of UST facilities (leaking or not)
within a 500 meter radius of each housing unit. We also know whether each house is
within the public water service area, or outside this area and presumably reliant on
private groundwater wells. For Baltimore City County, this is not an issue because all
homes are served by city water.

Units were excluded if lot size is greater than 10 acres (or recorded as zero), if the
house was built prior to 1800, or was larger than 8,000 (enclosed) square feet. Units
were also excluded if there were zero full baths or more than ten full baths and if ten half
baths were recorded. Sales that were not arms length and prices that were less than
twenty thousand dollars or more than five million dollars were dropped. Finally, we
exclude cases with missing geographic coordinates. The final dataset includes 35,552
sales from Frederick County, 76,968 sales from Baltimore County, and 24,296 sales from
Baltimore City County. Summary statistics for these three jurisdictions are given in
Table 2.

4. MODEL DEVELOPMENT

We now develop the framework for using the hedonic method (as applied to
property values) to calculate the benefits from the cleanup of a nearby LUST site. For
this analysis, we focus on measuring the benefits that accrue to residential units, though
this can easily be generalized to include commercial and industrial sites. Assume that the
price for house i in block group g at time t (Pigt) is a log-linear function of house
characteristics (Hlt), neighborhood characteristics (Nigt), and a LUST site (LUST). Given
the prevalence of LUST sites, we allow for the possibility that price can be affected by

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+ p + p	)+v-

j=

p p p

0


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0

0

0	0

0


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=p +p +p +p	+p


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=p +p +p +p	+p	+p

+p	+p	+p

+p	+p

+p	+ +


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= p +p +p +p	+p	+p

+p	+p	+p

+p	+p

+p	+p	+ +

+(- )•


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'( (P "P )- )
•( (P "P )" )
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(p +p -p -p )-)
(p +p -p -p )-)
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(p +p +p -p -p -p )-)


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Policy

Association of American Geographers

Literature

Appraisal Journal

Ecological Economics

Environment and Planning A

Journal of Real Estate Research

Contemporary Economic

Annals of the

Journal of Real Estate

The


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Journal of the American Planning Association

Journal of Environmental Economics and Management

Journal of Real Estate Literature

Journal of Real Estate Research

Journal of Environmental Management

Journal of Urban Economics

The Journal of Real Estate

Finance and Economics

Journal of Urban Planning and Development

Journal of Environmental Planning and Management,


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Journal of Urban Economics

Journal of the American Planning Association,

The Appraisal Journal,

Journal of Political Economy,

Land

Economics

Economic

Development Quarterly

Turning Brownfields into Greenbacks

The Appraisal Journal


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Journal of Real Estate Research

The Appraisal Journal

Estate Research

The Journal of Real


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Table 1



Dates of Opened and Closed LUST sites

Year

Opened

Closed

1996

21

6

1997

12

11

1998

15

8

1999

14

9

2000

12

11

2001

13

10

2002

11

4

2003

22

13

2004

25

19

2005

41

32

2006

25

19

2007

8

16

2008

0

4

Total

219

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Table 2

Summary Statistics for Housing Data



Mean

Std Dev

Minimum

Maximum

Variable

Baltimore City Count;

/

Nominal House Price (in $1,000s)

158.304

147.815

20.06

2520

Real House Price (in $1,000s, base is 2000)

147.037

133.042

17.441

2306.848

Lot Size (Acres)

0.205

0.148

0.003

5.280

Living Area (1000's of sq ft)

1.719

0.779

0.104

7.911

Age of House

71.186

20.389

0

206

Number of Full Bathrooms

1.579

0.800

1

10

Number of Half Bathrooms

0.294

0.510

0

5

1 if split foyer 2 levels of living area

0.006

0.080

0

1

1 if split level 3 or more levels of living area

0.007

0.086

0

1

1 if Attic or Attached Garage

0.080

0.271

0

1

1 if dwelling grade is low cost, economy, or fair

0.764

0.424

0

1

Nearest open space in 1,000s meters

0.457

0.291

0

1.454

Nearest surface water body in 1,000s meters

2.263

1.233

0.027

5.592

Nearest major road in 1,000s meters

2.524

1.111

0.017

5.132

Nearest commercial zone in 1,000s meters

0.368

0.250

0

1.281

Number of registered tanks within 500 meters

2.537

2.771

0

21



Baltimore County

Nominal House Price (in $1,000s)

241.483

182.734

22.575

3300

Real House Price (in $1,000s, base is 2000)

226.677

161.197

20.280

2740.689

Lot Size (Acres)

0.512

0.874

0.002

10

Living Area (1000's of sq ft)

1.789

0.852

0

7.976

Age of House

38.217

26.056

0

206

Number of Full Bathrooms

1.711

0.738

1

8

Number of Half Bathrooms

0.550

0.549

0

5

1 if split foyer 2 levels of living area

0.068

0.251

0

1

1 if split level 3 or more levels of living area

0.093

0.290

0

1

1 if Attic or Attached Garage

0.401

0.490

0

1

1 if dwelling grade is low cost, economy, or fair

0.340

0.474

0

1

Nearest open space in 1,000s meters

0.540

0.597

0

7.296

Nearest surface water body in 1,000s meters

2.470

1.669

0

14.656

Nearest major road in 1,000s meters

1.949

1.772

0.001

12.139

Nearest commercial land use in 1,000s meters

0.663

0.676

0

6.775

Number of registered tanks within 500 meters

1.174

2.013

0

18



Frederick County

Nominal House Price (in $1,000s)

270.828

143.943

25

2901.8

Real House Price (in $1,000s, base is 2000)

258.237

120.710

26.411

2901.8

Lot Size (Acres)

0.700

1.129

0.016

10

Living Area (1000's of sq ft)

1.997

0.801

0.348

7.929

Age of House

20.698

27.249

0

207

Number of Full Bathrooms

1.962

0.661

1

7

Number of Half Bathrooms

0.644

0.514

0

5

1 if split foyer 2 levels of living area

0.078

0.269

0

1

1 if split level 3 or more levels of living area

0.053

0.224

0

1

1 if Attic or Attached Garage

0.463

0.499

0

1


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Table 2

Summary Statistics for Housing Data



Mean

Std Dev

Minimum

Maximum

1 if dwelling grade is low cost, economy, or fair

0.098

0.297

0

1

Nearest open space in 1,000s meters

1.700

1.660

0

10.744

Nearest surface water body in 1,000s meters

3.977

2.330

0

12.664

Nearest major road in 1,000s meters

2.545

2.450

0.004

17.760

Nearest commercial zone in 1,000s meters

0.947

0.987

0

9.697

Number of registered tanks within 500 meters

0.644

1.772

0

16


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Table 3
Buffer Counts









public water

non-public water

Buffer

all

tested

cont>0

all

tested

cont>0

all

tested

cont>0



Frederick and Baltimore Counties

PRE 100

155

126

111

138

109

98

17

17

13

OPEN 100

76

72

70

60

56

55

16

16

15

CLOSED 100

77

27

22

74

26

22

3

1

0

PRE 200

720

512

464

634

426

395

86

86

69

OPEN 200

308

262

255

264

218

215

44

44

40

CLOSED 200

421

152

141

402

144

137

19

8

4

PRE 100 200

573

392

359

504

323

303

69

69

56

OPEN 100 200

233

191

186

204

162

160

29

29

26

CLOSED 100 200

344

125

119

328

118

115

16

7

4

PRE 200 500

4190

3372

3038

3724

2926

2681

466

446

357

OPEN 200 500

1696

1380

1359

1549

1233

1224

147

147

135

CLOSED 200 500

2424

1225

1122

2303

1165

1071

121

60

51

Baltimore City County



PRE 100

34

26

26

OPEN 100

11

9

9

CLOSED 100

32

2

2

PRE 200

179

122

122

OPEN 200

76

57

57

CLOSED 200

291

39

39

PRE 100 200

145

96

96

OPEN 100 200

65

48

48

CLOSED 100 200

260

37

37

PRE 200 500

1245

938

935

OPEN 200 500

457

302

302

CLOSED 200 500

2054

538

532


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Table 4
Results for Model 1



Bait/Fred Counties

Bait City



(1)

(2)

(3)

Variable/Impact

100 Meter Buffer

PRE

-0.072*





OPEN

-0.026





CLOSED

-0.037





p-value for joint sig

0.067







200 Meter Buffer

PRE



-0.029**

0.048

OPEN



-0.015

0.024

CLOSED



-0.024

-0.056

p-value for joint sig



0.025

0.239



100-200 Meter Buffer

PRE

-0.019





OPEN

-0.012





CLOSED

-0.021





p-value for joint sig

0.159







200-500 Meter Buffer

PRE

0.001

0.001

0.028

OPEN

0.016

0.016

0.016

CLOSED

-0.008

-0.008

-0.004

p-value for joint sig

0.243

0.239

0.518



Percent Impacts for 100 meter buffer

OPEN PRE

4.713





CLOSED PRE

3.613





CLOSED OPEN

-1.051







Percent Impacts for 200 meter buffer

OPEN PRE



1.396

-2.378

CLOSED PRE



0.514

-9.836**

CLOSED OPEN



-0.870

-7.639



Percent Impacts for 100-200 meter buffer

OPEN PRE

0.704





CLOSED PRE

-0.250





CLOSED OPEN

-0.947







Percent Impacts for 200-500 meter buffer

OPEN PRE

1.523*

1.523*

-1.126

CLOSED PRE

-0.838

-0.858

-3.112

CLOSED OPEN

-2.326

-2.346*

-2.009

Observations

112502

112502

24296

Number of bg/tract

602

602

128

Adj R-squared

0.788

0.787

0.442

SER

0.205

0.205

0.410

** p<0.01, * p<0.05


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Table 5

Regression Results for Model 2



Baltimore/Fred Counties

Baltimore City County



200 200-500

200 200-500

Variable/Impact

Without Testing

PRE

-0.011 -0.005

-0.010 0.051*

OPEN

-0.026 0.054

-0.049 -0.063

CLOSED

-0.019 0.005

-0.065* 0.004

p-values for joint sig

0.706 0.119

0.284 0.198



With Testinc

, Contamination = 0

PRE

-0.046*** -0.002

0.133*** 0.008

OPEN

-0.004 0.013

0.006 0.044

CLOSED

-0.034 -0.016

0.125 -0.046

p-values for joint sig

0.032 0.208

0.000 0.426



With Testing, Contamination = 10,000 (around 90th pctile)

PRE

-0.043*** -0.001

0.036 0.040

OPEN

-0.007 0.012

0.110 0.088***

CLOSED

-0.029 -0.018

0.020 -0.024

p-values for joint sig

0.033 0.197

0.398 0.006



Percent Impact, Without Testing

OPEN - PRE

-1.446 6.050**

-3.913 -10.777*

CLOSED - PRE

-0.784 0.951

-5.386 -4.675*

CLOSED-OPEN

0.672 -4.808

-1.534 6.839



Percent Impact, With Testing, Contamination = 0

OPEN - PRE

4.252* 1.586

-11.896*** 3.721

CLOSED - PRE

1.191 -1.376

-0.800 -5.231

CLOSED-OPEN

-2.936 -2.915

12.594 -8.631



Pet Impact, With Testing, Contam = 10,000 (around 90th pctile)

OPEN - PRE

3.652* 1.262

-7.454 4.910

CLOSED - PRE

1.366 -1.735

-1.553 -6.288*

CLOSED-OPEN

-2.206 -2.960**

6.376 -10.674**

*** p<0.01, ** p<0.05, * p<0.10


-------




Table 6







Regression Results for Model 6
Baltimore and Frederick Counties





Public Water Source

Non-Public Water Source



200

200-500

200

200-500

Variable/Impact



Wthout

Testing



PRE

-0.010

-0.002



OPEN

-0.025

0.055*



CLOSED

-0.018

0.004

-0.082

0.019

p-values for joint sig

0.752

0.176





With Testing, Contamination = 0

PRE

-0.042**

-0.009

-0.046*

0.040**

OPEN

0.004

0.017

-0.018

0.002

CLOSED

-0.036

-0.016

-0.065

-0.044**

p-values for joint sig

0.132

0.101

0.340

0.003



With Testing, Contamination

= 10,000 (around 90th

pctile)+

PRE

-0.044**

-0.010

-0.046*

0.040**

OPEN

-0.002

0.013

-0.018

0.002

CLOSED

-0.033

-0.019

-0.060

0.294**

p-values for joint sig

0.085

0.077

0.000

0.000





Percent Impact,

Wthout Testing



OPEN - PRE

-1.504

5.866**



CLOSED - PRE

-0.830

0.651



CLOSED-OPEN

0.684

-4.926





Percent Impact, With Testing, Contamination

= 0

OPEN - PRE

4.771*

-3.067

2.910

-3.692

CLOSED - PRE

0.578

-1.340

-1.811

8.015***

CLOSED-OPEN

-4.002

1.782

-4.588

-4.488



Pet Impact, With Testing, Contam = 10,000 (around 90th

pctile)+

OPEN - PRE

4.271*

-0.322

2.906

-3.692

CLOSED - PRE

1.084

-0.230

-1.407

7.704***

CLOSED-OPEN

-3.057

0.092

-4.191

-4.166

*** p<0.01, ** p<0.05, * p<0.10

+ The contamination level at which the LUST impact is calculated is 1,000 for the

private water source since this is the highest contamination level recorded for

these cases










-------




Table 7





Regression Results Dependent Variable: Number of Transaction Rate



Baltimore/Fred Counties

Baltimore City County



200

200-500

200

200-500

Variable/Impact

Without Testing

PRE

0.253***

0.245***

0.259*

0.366***

OPEN

0.116

0.513***

-0.133

0.536***

CLOSED

0.279**

0.463***

0.362***

0.648***

p-values for joint sig

0.001

0.000

0.000

0.000



With Testinc

, Contamination = 0

PRE

0.430***

0.384***

0.422***

0.120***

OPEN

0.389***

0.434***

0.267

0.135***

CLOSED

0.827***

0.498***

0.321

0.267**

p-values for joint sig

0.000

0.000

0.000

0.000



With Testing, Contamination = 10,000 (around 90th pctile)

PRE

0.402***

0.371***

0.571***

0.496***

OPEN

0.371***

0.424***

0.396**

0.221

CLOSED

0.796***

0.465***

0.319*

0.552***

p-values for joint sig

0.000

0.000

0.000

0.000



Partial Elasticity, Without Testing

OPEN - PRE

-6.825

13.329**

-15.436

6.705

CLOSED - PRE

1.264

10.850**

4.074

11.119**

CLOSED-OPEN

8.089

-2.479

19.510*

4.415



Partial Elasticity, With Testing, Contamination

= 0

OPEN - PRE

-2.011

2.464

-6.087

5.592

CLOSED - PRE

19.746*

5.675*

-3.986

8.880

CLOSED-OPEN

21.757

3.211

2.102

10.952



Partial Elasticity With Testing, Contam = 10,000 (90th pctile)

OPEN - PRE

-1.529

2.621

-6.916

-10.838*

CLOSED - PRE

19.576**

4.662

-9.913

2.211

CLOSED-OPEN

21.106*

2.041

-2.997

13.050

***p<0.01,** p<0.05

* p<0.10








-------
Table 8

Publicized Cases in Baltimore and Frederick Counties

Case no

Spill Location

City

Date
Opened

# of sales
before/after
opening

96-2047FR

GRESHAM STORE/FLINTHILL GROCY

ADAMSTOWN

30-Sep-96



97-0257FR

HAHN TRANSPORT

NEW MARKET

12-Aug-96

47/321*

97-0646FR

BARNES STORE

FREDERICK

8-Oct-96

3/38

00-0575FR

CARL CLINGAN

LIBERTYTOWN

27-Sep-99

20/37

00-1125FR

SHELL

MT. AIRY

28-Dec-99

16/18

00-1183FR

FARMERS & MECHANICS BANK

UNION BRIDGE

11-Jan-00

54/42*

00-1301FR

GREEN VALLEY GARAGE

MONROVIA

9-Feb-00

100/102*

00-1332FR

7-ELEVEN STORE 28961

LIBERTYTOWN

15-Feb-00

21/36

03-1335BA2

FORMER STEBBINS BURNHAM

OWINGS MILLS

10-Mar-03

59/36*

03-1758FR

SHEETZ STORE #176

KNOXVILLE

7-May-03

43/17

04-2121BA4

CROWN MD-81

JOPPA

23-Jun-04

4/2

05-0326 BA2

AMOCO STATION #3033

PHOENIX

9-Sep-04

127/38*

05-0522BA3

CHEVRON/EXXON STATION

HEREFORD

25-Oct-04

21/5

05-0834FR

GREEN VALLEY CITGO

MONROVIA

19-Jan-05

214/47*

05-0856BA2

JACKSONVILLE CITGO

JACKSONVILLE

24-Jan-05

129/33*

06-0239FR

MT. PLEASANT CITGO

FREDERICK

21-Sep-05

49/13

06-0245FR

EXXON #26463

FREDERICK

22-Sep-05

116/9

06-0303BA2

EXXON SERVICE STA 2-8077

PHOENIX

6-Oct-05

141/23*

06-0317FR

CIFCO #1 6/10 GAS MART

CLARKSBURG

13-Oct-05

40/9

06-0675FR

JEFFERSON BP

JEFFERSON

9-Feb-06

239/14

06-0825BA2

FORK CITGO #23

KINGSVILLE

31-Mar-06

46/11

06-0826BA2

MARYLAND LINE GARAGE

MD LINE

31-Mar-06

23/4

07-0593FR

GAS MART OF FREDERICK

FREDERICK

16-Feb-07

2/0

Note: * - chosen for individual analysis


-------
Table 9

Results for Publicized LUST Sites;
Baltimore and Frederick Counties



500 Meter Buffer

Variable/Impact







PRE

0.020





OPEN

0.000





p-values for joint sig

0.425





OPEN - PRE

-1.935







500-1000 Meter Buffer

PRE

0.013





OPEN

-0.041**





p-values for joint sig

0.067





OPEN - PRE

-5.257**







1000 Meter Buffer

PRE



0.015

0.015

OPEN 1



0.035**



OPEN 2



-0.005



OPEN 3G



-0.025*



OPEN 13





0.058**

OPEN 46





0.026

OPEN G6





-0.070**

p-values for joint sig



0.000

0.000



Percent Impacts

OPEN 1 - PRE



2.037



OPEN 2-PRE



-1.900



OPEN 3-PRE



-3.872**



OPEN 2-OPEN 1



-3.858***



OPEN 3G - OPEN 1



-5.791***



OPEN 3G - OPEN 2



-2.010



OPEN 13-PRE





4.467**

OPEN 46-PRE





1.100

OPEN G6 - PRE





-8.151***

OPEN 46-OPEN 13





-3.224*

OPEN G6 - OPEN 13





-12.079***

OPEN G6 - OPEN 46





-9.150***

Observations

112502

112502

112502

Number of bg/tract

602

602

602

Adj R-squared

0.787

0.788

0.788

SER

0.205

0.205

0.205

*** p<0.01, ** p<0.05, * p<0.10


-------




Table 10







Results for Individual Publicized LUST Sites; Baltimore and Frederick Counties



1000 Meter Buffer

1000-2000 Meter Buffer

LUST SITES

PRE

OPEN

IMPACT

PRE

OPEN

IMPACT



(1)

(2)

(3)

(4)

(5)

(6)

Jacksonville Exxon

0.087

-0.045

-12.358**

0.003

-0.033

-3.537



(0.076)

(0.038)



(0.022)

(0.023)



Green Valley

-0.023

0.036**

6.015**







Garage/CITGO















(0.018)

(0.011)









Hahn Transport

-0.173**

-0.164**

0.903









(0.027)

(0.026)









Farmers & Mechanics

-0.046**

-0.102**

-5.438**







Bank















(0.010)

(0.015)









Former Stebbins

-0.062

0.070

14.180**







Burnham















(0.167)

(0.154)









Observations

112502











Adj R-squared

0.788











SER

0.205











Robust standard errors in parentheses

** p<0.01, * p<0.05


-------
Baltimore City & County

area

0 3,7507,500 15,000 22,500 30,000

I Yards

Major Roads

Public water service
Subset of LUSTs
for Hedonics

Original 640
LUST Cases


-------
Frederick County

0 4,100 8,200

A
~

16,400 24,600

Major Roads
Public water service area

Subset of LUSTs for Hedonics

Original 640 LUST Cases

32,800
l Yards


-------