Environmental Science & Policy 7 (2004) 499–508
www.elsevier.com/locate/envsci
Hedonic analysis of hazardous waste sites in the presence
of other urban disamenities
B. James Deatona,*, John P. Hoehnb
a
Department of Agricultural Economics and Business, University of Guelph, Guelph, ON NIG 2W1, Canada
b
Environmental and Natural Resources, Department of Agricultural Economics,
Michigan State University, East Lansing, MI 48824, USA
Abstract
Hedonic research indicates that residential property values are reduced by increased proximity to hazardous waste sites, a measure of
diminished environmental quality. Standard hedonic procedures measure proximity by the linear distance between a property and a waste site
of interest. A sample of properties is then used to estimate a distance-to-site coefficient that measures the effect of the hazardous waste site on
surrounding property values. These estimates can help policy makers assess the external effects of hazardous waste sites and may also be
useful in both prioritizing and assessing the benefits of cleanup. In an urban situation, hazardous waste sites are often located near other
industrial disamenities such as railroads, storage tanks, industrial noises, and air pollution. This spatial grouping of hazards may be due to
economic forces or due to policy instruments such as zoning. As a result, distance to hazardous waste site may be correlated with distances to
other industrial disamenities. Standard hedonic procedures that use a distance-to-site variable may suffer from omitted variable bias when the
bundled industrial disamenities are present but ignored. Our empirical analysis examines this bias by assessing hedonic regressions with and
without a measure that accounts for industrial activity.
# 2004 Elsevier Ltd. All rights reserved.
Keywords: Industrial disamenities; Hedonic analysis; Spatial correlates; Superfund; Omitted variable bias
1. Introduction
Contamination of land with toxic materials is a worldwide environmental problem. In the major industrial nations,
the OECD has identified 475,000 potentially contaminated
sites and estimates that US$ 330 billion may be required to
contain contamination at these sites (OECD, 2001). Since
1980, the Superfund program has made major efforts to
clean up and contain hazardous waste sites in the United
States (Probst et al., 2001). Hedonic analysis provides one
method of assessing the economic damage of hazardous
waste sites. Hedonic analysis measures the adverse impact
of a site’s hazards on property values in the areas
surrounding the site (Farber, 1998; Boyle and Kiel, 2001;
Jackson, 2001). This paper examines the performance of the
hedonic method in an urban setting where multiple
* Corresponding author. Tel.: +1 519 824 4120x52765.
E-mail address: bdeaton@uoguelph.ca (B.J. Deaton)hoehn@msu.edu
(J.P. Hoehn).
1462-9011/$ – see front matter # 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envsci.2004.08.003
perceived hazards may be grouped together in industrial
zones.
The hedonic hypothesis is that goods are valued for their
utility-bearing attributes (Rosen, 1974). Statistical methods
such as regression analysis are used to measure the value of a
particular attribute, taking into account additional attributes
associated with the particular good under study. Many
hedonic studies examine the value of attributes that
contribute to overall housing value. In these studies housing
prices are often used as the dependent variable and
explanatory variables generally include structural characteristics of the house, neighborhood, and measures of
environmental quality. A measure of the distance between
each home or neighborhood to the nearest hazardous waste
site is used as one measure of environmental quality
(Kohlhase, 1991; Kiel and McClain, 1995; Hite et al., 2001;
Kiel and Zabel, 2001; Ihlanfeldt and Taylor, 2004). A
hazardous waste site is generally viewed as disamenity, a
negative influence on perceptions of environmental quality.
Reduced proximity to hazardous waste sites becomes a
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B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
measure of environmental quality, a valued attribute of
property.
In urban areas, hedonic analysis confronts a problem
insofar as industrial disamenities are bundled together by
economics and by policies such as zoning. It is not
surprising to find high tension power lines, railroads, and
storage tanks located in industrial zones in order to serve
industrial demands for power and transportation. Metal
and plating shops are likely to locate near manufacturers
that require their services. Waste facilities locate near
waste producers in order to reduce handling and
transportation costs. Each of these industrial services
and activities has a demonstrated impact on property
values. Hazardous waste sites have a negative impact on
property values (Ketkar, 1992; Kiel, 1995; Kohlhase,
1991; Thayer et al., 1992) and so do other types of sites
associated with industrial activity, such as overhead power
lines (Colwell, 1990), pipelines (Maani and Kask, 1991;
Simons, 1999), incinerators (Kiel and McClain, 1995),
storage tanks (Simons et al., 1997), and railroad tracks
(Strand and Vagnes, 1990).
The bundling of hazards in industrial zones has
significant implications for hedonic analysis and its
interpretation. First, a single distance-to-site coefficient is
likely to measure the marginal impact of a particular type of
site, as well as impacts of other zonal hazards. Second, the
particular site of interest is only one of a portfolio of
disamenities. Failure to differentiate one disamenity from
the portfolio of disamenities may explain anomalies in
previous hedonic research. For example, Colwell (1990)
finds the property value affect of power lines decreases over
time. Other studies indicate that property prices may not
rebound when a site is cleaned up (Kiel, 1995) or may
dissipate when a site is closed, but not cleaned up
(Guntermann, 1995).
This analysis uses hedonic methods to estimate the
marginal effect of reduced proximity to Superfund
hazardous waste sites in an urban area of the United
States—Lansing, Michigan. Superfund sites are hazardous
waste sites that have been evaluated by the US Environmental Protection Agency (EPA) as posing the most serious
health and environmental threats, relative to other hazardous
waste sites and are on the EPA’s National Priorities List
(2000) for remedial action. These sites can have cleanup
paid from the Superfund, the national hazardous waste trust
fund.
Two hedonic regressions are examined; one regression
omits a measure of industrial activity and the other
regression includes a measure of industrial activity. The
distance-to-hazard coefficient in the former regression is
larger than the same coefficient in the latter equation, a
finding consistent with our hypothesis. These empirical
results support our primary argument that many urban
spaces are best conceptualized as ‘bundles’ or ‘portfolios’
of disamenities and environmental risks. Empirical
approaches that focus on one element of the portfolio—
e.g., Superfund sites—may, as in our case example,
exaggerate the impact that the one element has on
surrounding property values. The potential implications
for environmental science and management are straightforward. In situations where hazardous waste sites are
spatially correlated with industrial activity there may be
multiple investment strategies for mitigating the damages
caused by environmental disamenities. Emphasis on one
source of environmental hazards, when there are many,
may not achieve the greatest benefits for surrounding
residents.
2. Examining omitted variable bias
Our general hypothesis is that the hazard effect, the
property value impact of hazardous waste sites, is biased
upwards by the omission of a variable that accounts for the
industrial effect—the property value impact of industrial
disamenities. The hedonic price function is specified such
that the residential sales price of a home is a function of
neighborhood and environmental characteristics, structural
characteristics of the home, and the year that the home was
sold. Economic theory provides little guidance regarding the
choice of functional form (Freeman, 1993) and hence,
regressions are often examined under alternative functional
specifications (Irwin, 2002). For the purposes of our
discussion on omitted variable bias, the hedonic price
model is specified as follows:
I
Pi ¼ b 0 þ b 1 D H
i þ b2 Di þ QZi þ FYi þ ui
(1)
where the price of a house, Pi, is a function of: (1)
proximity to hazardous waste sites, DH
i ; (2) proximity to
areas of high industrial activity, DIi ; (3) a vector of attributes that describes the house and the character of the
neighborhood in which the house resides, Zi; and (4) and a
set of dummy variables to account for the year in which the
house was sold, Yi. The error term, ui, is assumed to have a
conditional mean of zero and a constant variance. Table 1
provides the full set of variables we use to estimate the
hedonic price function.
The hazard variable, DH
i , measures distance from each
house, i, to the nearest Superfund site. The standard
hypothesis is that reduced proximity to the hazardous
waste site results in reduced perceptions of exposure and
subsequently results in a marginal increase in property
value. Thus, b1 is hypothesized to be positive. However, in
situations where a hazardous waste site is spatially
correlated with zones of industrial activity, residents’
perceptions of exposure to the disamenities of any one site
may be confounded with the portfolio of disamenities that
characterize industrial activity. In these situations the
hazard variable is spatially correlated with zones of high
industrial activity, a bundle of disamenities. In these
situations a correctly specified model includes a measure
of residential exposure to industrial activity. One measure,
B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
501
Table 1
Variables used in estimating the hedonic equation
Variable
Description
Dependent variable
Price
Hazard and industrial variables
Hazard
Industrial
Nominal housing sale price for sales from 1992 through 2000
Distance in meters from a house to the nearest Superfund site
Distance in meters from a house to the nearest perimeter of an area zoned
as highly industrial by the city of Lansing
Housing structure variables
Bath
Floor
Age
Acres
sty 11/4a
sty 11/2
sty 13/4
sty2
dstyle
Number of bathrooms in each house sold
Residential floor area in square feet
Years elapsed from the date when a house was built to the recorded sales date
Total acreage of the land parcel sold with the house
= 1, if 1.25 story home; 0 otherwise
= 1, if 1.5 story home; 0 otherwise
= 1, if 1.75 story home; 0 otherwise
= 1, if 2 story home; 0 otherwise
= 1, if raised ranch, tri-level, or 21/2 story home; 0 otherwise
Neighborhood variables
Crime
Income
Edu
Black
Hisp
Rent
Commute
Number of malicious destruction of property violations by block group for 1996
Median household income, by block group
Percentage of persons with a college degree, by block group
Percentage of the population that is black, non-Hispanic, by block group
Percentage of population that is Hispanic, by block group
Percentage of the households that rent, by block group
Percentage of those whose commute to work is less than 20 minutes; by block group
Year variables
d93b
d94
d95
d96
d97
d98
d99
d2000
=1,
=1,
=1,
=1,
=1,
=1,
=1,
=1,
a
b
if
if
if
if
if
if
if
if
year = 1993;
year = 1994;
year = 1995;
year = 1996;
year = 1997;
year = 1998;
year = 1999;
year = 2000;
0
0
0
0
0
0
0
0
otherwise
otherwise
otherwise
otherwise
otherwise
otherwise
otherwise
otherwise
1 story house is the omitted variable (sty1).
1992 is the omitted variable (dum92).
each home’s straight-line distance to the perimeter of the
nearest area zoned as highly industrial, DIi , is included and
therefore, b2 is also hypothesized to be positive.
The inclusion of an industrial measure allows us to
develop hypotheses concerning the bias that results from a
failure to include a measure of industrial activity. The
expected value for b1, Eðb̃1 Þ when the industrial variable is
omitted from empirical estimation is:
Eðb̃1 Þ ¼ b1 þ b2
I
CovðDH
i ; Di Þ
H
VarðDi Þ
(2)
where Eðb̃1 Þ depends on unbiased estimates of b1 and b2, the
covariance between the hazard and industrial variables, and
the variance of the hazard variable (Wooldridge, 1999, p.
92). We use Eq. (2) to develop directional hypotheses rather
than exact measures of bias. Under the hypothesized relaI
tionships—b1 > 0, b2 > 0, Cov(DH
i , Di ) > 0—the omission
of the industrial variable is expected to place an upward bias
on estimates of the hazard coefficient ðEðb̃1 Þ > b1 Þ.
3. Study area and data collection
The city of Lansing, Michigan encompasses an area of
approximately 33.8 square miles with a total population of
119,128 persons (US Census Bureau, DP-1, MI, 2000).
Lansing is also the state’s capital. The property value and
household income levels in Lansing are lower when
compared to the rest of the state. For example, the 1990
median value of housing in Lansing was $ 48,400 as
compared to the median value of housing in the state which
was $ 60,600 (US Census Bureau, DP-1, Lansing City, 1990;
US Census Bureau, DP-1, State of Michigan, 1990).
Moreover, the 1990 median household income for the city
of Lansing was $ 26,398 while the median household
income for the state of Michigan was $ 31,020 (US Census
Bureau, DP-4, Lansing City, 1990; US Census Bureau, DP4, State of Michigan, 1990).
We examine housing proximity to two Superfund sites,
Motor Wheel and Barrels, Incorporated (Inc.). Both sites are
historically and presently linked to industrial activity. The 24
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B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
Fig. 1. Location of sites and zones of high industrial area.
acre Motor Wheel site served as a waste area for the Motor
Wheel corporation from 1938 to 1978 (US Environmental
Protection Agency, 2001). Barrels, Inc. (1.8 acres) recycled
industrial metal barrels from 1964 to1981 (US Environmental Protection Agency, 2001). Both of these sites are
located in the northern section of the city and in close
proximity to areas that continue to be areas of high industrial
activity. Fig. 1 provides a map identifying Motor Wheel and
Barrels Inc., as well as areas zoned for high industrial
activity.
The EPA’s Superfund process involves three general
steps, (1) site discovery, (2) site evaluation, and (3) cleanup.
(Hamilton and Viscusi’s (1999) book, Calculating Risks?,
provides a detailed description of Superfund processes.) In
1981 the EPA discovered Motor Wheel and, after further
investigation, Motor Wheel was placed on the National
Priorities List (NPL) in 1986. Once listed on the NPL sites
can receive federal funding from the national hazardous
waste trust fund. Areas within Motor Wheel were
determined to pose a direct health risk to humans. The
groundwater, for example was contaminated with volatile
organic compounds. Moreover, it was determined that Motor
Wheel might pose a broader area threat if site hazards
eventually contaminated the underlying water aquifer.
During Barrels Inc.’s active years the company recycled
metal barrels. As an initial step in the cleanup process,
contents of the Barrels were often dumped on site. Since
these barrels came from a variety of industries, the residual
in the barrels contained a variety of wastes that present
themselves as hazards. In 1982 the EPA discovered the
Barrels Inc. site and it was added to the NPL in 1989 (US
EPA, 2001a, 2001b). The soil was found to be contaminated
with heavy metals, volatile hydrocarbons and PCB’s.
A number of activities, related to Superfund, have taken
place since the EPA listed these sites. A record of decision
(ROD), which outlines the general procedure for cleanup,
was submitted for Motor Wheel in 1991 and Barrels in 1996.
However, actual clean up does not begin until the submission
of a remedial design (RD), which provides engineering
details. The RD for Motor Wheel, though initiated in 1992,
was not actually completed until 1997, and the RD for
Barrels, Inc., is still under development. Currently, Motor
Wheel remains in the remedial action phase of cleanup and
the EPA has not closed-out either site.
The Lansing Assessor’s office provided data on
residential housing sales and associated structural characteristics for the years 1992–2000. The universe of sales
available to the Assessor’s office includes all housing sales
registered by the counties in which the city of Lansing lies.
We examined sales considered to reflect arm’s length
transactions that involve independent buyers and sellers in
competitive bargaining situations. For example, quick claim
deeds were not considered because they often reflect
transactions between buyers and sellers who are familiar
(e.g., related) to each other.
Geographic information systems (GIS) were used to
determine the straight-line distance between housing
observations and the perimeter boundary of the nearest
Superfund site (either Barrels, Inc. or Motor Wheel). The
perimeters of the Superfund sites were mapped using a
global positioning system. The coordinates were applied to
the base map files using 1990 Tiger Base File maps and
Michigan framework data. GIS was also used to measure the
straight-line distance from each housing sale to the area
zoned as ‘high industrial’. The assessor’s office provided a
boundary map of areas zoned as high industrial. The
boundary areas for high industrial remained stable over the
time period we analyze.
The police department provided the data enabling the
specification of a crime variable. The number of malicious
B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
destruction of property violations—damaging someone’s
property with unlawful motives—that occurred in each
block group for 1996 defines the crime variable. The
neighborhood characteristics of income, education, race,
ethnicity, rent, and commute (defined in Table 1 in the next
section) come from the 1990 US census1 summary tape file #
3. We used GIS to link the location of each housing sale with
the census data.
4. The econometric model
The empirical analysis uses the Lansing data to estimate a
hedonic price function analogous to Eq. (1). Table 1
describes the dependent and the independent variables used
to estimate the hedonic price function. Section 2 provided
testable hypotheses regarding the housing price effect of the
two distance variables, hazard and industrial. In addition,
omitting the industrial measure was hypothesized to bias
estimates of the hazard effect upwards. This section provides
hypotheses for the relationship between residential housing
prices and the remaining independent variables.
All else constant, an increase in housing prices is
hypothesized to result from increases in the number of
bathrooms, square footage, floor area, and acreage of the
home. Increases in the age of the home are hypothesized to
cause a decline in housing values, all else constant. The price
effect associated with the style of the home (i.e. one-story
versus two-story) is uncertain given the fact that the model
controls for floor area. However, it may be that construction
costs or preferences differ by housing style. Thus,
categorical variables are included to account for different
housing styles.
Quality of neighborhood measures such as crime,
income, education, and percentage of renters were also
included as variables in the hedonic price function. The
occurrences of crime in a neighborhood may adversely
affect housing values. The number of reported cases of
malicious destruction of property is used as a proxy for the
total occurrence of crime in the area and is included in the
hedonic price function. Higher levels of neighborhood
income and education are presumed to be valuable
neighborhood attributes. Therefore, higher levels of income
and education in a neighborhood are expected to raise
housing prices. The percentage of renters in a neighborhood
may also affect the price of housing in a neighborhood.
Renters may have less incentive to invest in property or
neighborhood maintenance than residential homeowners.
Thus, higher percentages of renters in a neighborhood are
hypothesized to adversely influence surrounding housing
prices, all else constant.
1
The United States Census Bureau takes a census of US population and
housing every 10 years. File # 3 reports this information at the block level.
When the data for this study were collected, the 1990 Census data were the
most current data available.
503
Three additional variables that this study uses are the
race, ethnicity, and a commute variable. The race and
ethnicity of a neighborhood have been shown to influence
housing prices, thus these variables are included in the
analysis (Cutler et al., 1999; Massey and Denton, 1988).
Greater proximity of households to areas of employment
should reduce the costs of commuting and this savings may
be capitalized into property values and potentially result in
higher housing prices. The commute variable measures the
percentage of those whose commute to work is less than 20
minutes in a specified neighborhood. Higher levels of the
commute variable are expected to result in higher housing
prices, all else constant.
Because the hedonic price function measures the locus of
supply and demand, there is little information to guide an
initial choice of functional form (Freeman, 1993). Our initial
model presumes a log–log relationship between price of the
house and proximity to the hazardous waste site and
proximity to areas of high industrial activity. Thus, the
marginal effect of exposure to a hazard, as measured by
distance-to-site, is expected to decrease at a decreasing rate
as distance between the site and the residence increases. The
remaining relationships between housing price and housing
attributes are specified as a log-level function, with the
exception of floor area, age of the house, and income, which
also appear in logarithmic form. However, we examine the
sensitivity of our empirical results to alternative specifications including both quadratic and log-level functional
forms.
Palmquist (1992) and Kiel and Zabel (2001) argue that
the externalities associated with hazardous waste sites are
likely to be ‘localized’; that is, the externality affects those in
relative close proximity to the site. For this reason, and
because of the difficulties inherent in correctly specifying a
hedonic price function for an entire urban area, Palmquist
estimates the hedonic price function over a smaller, more
homogenous area than the entire urban market. Following
Palmquist’s reasoning this study focuses on a smaller area.
In the case of Lansing, the city is divided into a northern and
southern segment by a major east-west expressway. Since
the Superfund sites under examination are in the northern
portion of that northern segment, we examine housing sales
in the area north of the expressway. Also, we do not include
housing sales from housing locations that were relatively
closer to a Superfund site located north-west of Lansing,
outside the city’s incorporated area. As in the case of
functional form, we examine the sensitivity of our results to
a number of alternative spatial specifications.
5. Empirical estimates and regression results
Table 2 lists the means and standard deviations of the
dependent and explanatory variables. The data means are
fairly close to Census data discussed above in Section 3. For
example, the average value of a house in the data set was
504
B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
Table 2
Descriptive statistics (4280 observations)
Table 3
Hedonic equation estimates, Huber–White standard errors
Variables name
Mean
S.D.
Variable
Continuous variables
Price
Hazard
Industrial
Bath
Floor
Age
Acres
Income
Educ
Black
Hisp
Rent
Crime
Commute
49610
1672
901
1.312
1168
71.61
0.149
24604
16.8
12.28
10.686
43.74
22.14
74.243
28768
722
647
0.528
467
23.617
0.103
9769
12.3
10.183
8.604
19.733
12.843
8.907
Categorical variables
sty1
sty 11/4
sty 11/2
sty13/4
sty2
style
d92
d93
d94
d95
d96
d97
d98
d99
d2000
0.380
0.092
0.070
0.110
0.330
0.010
0.093
0.088
0.101
0.101
0.116
0.116
0.127
0.134
0.120
0.485
0.290
0.256
0.312
0.470
0.103
0.290
0.284
0.301
0.302
0.320
0.320
0.333
0.340
0.325
In (hazard)
ln (industrial)
Bath
ln (floor)
ln (age)
Acres
sty11/4
sty11/2
sty13/4
sty2
dstyle
Crime
ln (income)
Educ
Black
Hisp
Rent
Commute
d93
d94
d95
d96
d97
d98
d99
d2000
Constant
Coefficient estimatesa
Model 1
Sources of data: Lansing Assessor’s Office, Lansing police department,
1990 United States Census Bureau Summary tape file #3.
approximately $ 49,000. The median housing value for
Lansing, as provided by the 1990 census, was approximately
$ 48,000.
Ordinary least squares is used to estimate the hedonic
price function. Table 3 lists the estimated coefficient for two
versions of the hedonic equation, Model 1 and Model 2.
Model 1 is a hedonic price function that includes a hazard
variable, but omits the industrial variable. Model 2 includes
both the hazard and industrial measures. A Breusch–Pagan
test rejected the null hypothesis of homoscedastic errors for
both models (Wooldridge, 1999, p. 257). Consistent
estimates of the standard errors were obtained for both
models using White’s method (Wooldridge, 1999).
The results from Model 1 are consistent with standard
expectations—increased proximity to hazardous waste sites
reduces the environmental quality of an area and thereby,
reduces housing prices. The coefficient for the hazard effect
is positive so that increased distance to a site increases
housing prices. The coefficient is also statistically different
from zero at the 95% confidence level. The coefficient
indicates that a 10% increase in distance from a Superfund
site increases house prices by 0.32%.
Equation statistics
0.032 (0.012)b
Model 2
0.005 (0.016)
0.690 (0.039)b
0.175 (0.036)b
0.149 (0.273)
0.014 (0.018)
0.054 (0.024)b
0.073 (0.022)b
0.097 (0.021)b
0.094 (0.055)
0.0002 (0.0005)
0.090 (0.038)b
0.008 (0.0007)b
0.005 (0.0007)b
0.008 (0.001)b
0.004 (0.0005)b
0.0008 (0.0008)
0.016 (0.029)
0.123 (0.025)b
0.155 (0.025)b
0.192 (0.026)b
0.262 (0.025)b
0.334 (0.025)b
0.381 (0.024)b
0.479 (0.026)b
5.347 (0.548)b
0.012 (0.013)
0.029 (0.010)b
0.004 (0.016)
0.686 (0.040)b
0.179 (0.037)b
0.171 (0.286)
0.011 (0.018)
0.053 (0.024)b
0.069 (0.022)b
0.095 (0.022)b
0.099 (0.056)
0.0005 (0.0005)
0.113 (0.037)b
0.007 (0.0008)b
0.005 (0.0007)b
0.008 (0.001)b
0.003 (0.0006)b
0.001 (0.0008)
0.015 (0.029)
0.123 (0.025)b
0.154 (0.025)b
0.192 (0.025)b
0.261 (0.025)b
0.334 (0.025)b
0.381 (0.024)b
0.478 (0.025)b
5.08 (0.531)b
Number of obs = 4280
F(25, 4254) = 350.63
R-squared = 0.6224
Number of obs = 4280
F(26, 4253) = 336.96
R-squared = 0.6240
a
Dependent variable is house price, which is logged. The standard errors
of coefficients are given in parentheses.
b
Indicates that the coefficient is statistically different from zero at the
95% confidence level.
However, the results from Model 2 that includes an
industrial variable, presents a decidedly different coefficient
estimate for the hazard effect. In Model 2, the coefficient for
the hazard effect, 0.012, is nearly two-thirds the size of the
same coefficient in Model 1. And, the coefficient estimate is
no longer statistically different from zero, at any conventional confidence level. In contrast, the industrial effect
variable is positive and similar in size to the hazard effect
coefficient in Model 1. The industrial effect coefficient is
also statistically different from zero at the 95% confidence
level. The industrial effect coefficient shows that 10%
increase in distance from an industrial area increases
housing prices by 0.29%.
The above results are consistent with the hypothesis that
omitting the industrial variable (i.e., Model 1) leads to an
upwards bias on the coefficient estimates for the hazard
variable, b̃1 . Eq. (2) provides the framework for that
hypothesis. The furthest right hand term, in equation two,
suggests an upwards bias if both the coefficient estimates for
the industrial variable b2, and the covariance between the
hazard and industrial variables, are positive. As discussed
B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
above, the coefficient estimate for the industrial variable is
positive. Also, the covariance between the hazard and
industrial variable is positive. Therefore, the furthest right
hand term in equation two is positive and b̃1 is expected to be
greater than the coefficient estimate of the hazard variable,
b1, generated from the correctly specified model.
While Eq. (2) is useful for predicting the direction of
omitted variable bias, it is unlikely to provide an exact
calculation of the bias because it assumes zero correlation
between the distance variables and all other independent
variables. Hence, differences between the hazard effect in
Model 1 and Model 2 cannot be exclusively attributed to
the inclusion of the industrial variable. Still, it may be
instructive to predict, b̃1 , using equation two and compare
the estimate with Model 1’s coefficient estimate for b1.
Model 2’s coefficient estimates for the hazard and
industrial variables, b1 and b2 are provided above. The
covariance between the hazard and industrial variable is
approximately 0.333, and, the variance of Model 2’s hazard
variable is 0.313. Putting this information into equation
two predicts b̃1 to be approximately 0.04, slightly higher
than Model 1’s b1 coefficient, .032. Both coefficients, b̃1
and Model 1’s b1, are greater in magnitude then Model 2’s
b1 coefficient, and this is consistent with the hypothesis
that the hazard coefficient is biased upwards by the
omission of the industrial variable.
The estimated coefficients of the other variables in the
hedonic price function were generally consistent with a
priori expectations. The floor area of the home and the age of
the house were found to be important factors explaining
variation in housing values. For example, both Model 1 and
Model 2 coefficient estimates of floor area suggest that a
10% increase in floor space raises the housing price by
approximately 7%. Moreover, the floor area coefficient is
statistically different from zero at the 95% level. Both
models also suggested that increases in the age of the house
affected a decline in the house’s value. A 10% increase in the
age of the house decreases housing prices by 2%. Neither
acreage coefficient nor the number of bathrooms coefficient
were statistically different from zero.
The estimated coefficients for income, education, and
commute variables suggest that homes located in neighborhoods characterized by higher incomes, higher levels of
education, and in greater proximity to areas of work are
associated with relatively higher housing prices, all else
constant. With the exception of the commute variable, the
coefficient estimates are statistically different from zero at
conventional significance levels. Increases in the percentages of minorities and renters in a neighborhood are
associated with relatively lower housing prices. These
estimates are also statistically different from zero. Higher
levels of crime, as measured by malicious destruction of
property, were also hypothesized to be associated with lower
property values, all else constant. However, the coefficient
for the crime variable is not statistically different from zero
at conventional levels of significance.
505
6. Sensitivity analysis
This section examines the sensitivity of the empirical
results to alternative empirical specifications. The paper’s
primary result supports the hypothesis that omitting the
industrial variable places an upwards bias on coefficient
estimates of the hazard effect. Hence, Model 1’s estimate of
the hazard coefficient is expected to be greater than Model
2’s coefficient estimate. Specifically, we address the
following issues: (1) whether the primary result is sensitive
to semi-log or quadratic functional forms; (2) whether the
primary result changes over time; and, (3) whether the
primary results are sensitive to different spatial considerations.2
Two functional forms were examined as alternatives to
the double-log form of the main results. The first alternative
used the logarithm of the dependent variable regressed on
the unlogged levels of the distance variables. In this log-level
form, the sign of the hazard coefficient was unstable. It was
positive and statistically different from zero in Model 1
where industrial activity was omitted from the equation. It
was negative and statistically different from zero in Model 2,
the model that included an industrial variable. These
findings are qualitatively identical to those of the double-log
model.
The second alterative was a quadratic function. The
logarithm of the dependent variable was regressed on the
distance variables that appeared in both a level and squared
term. The hazard effect did not appear to decline in model 2.
This result is qualitatively different from the log–log and
log-level specifications discussed above. However, there is a
great deal of additional correlation introduced by the
squared distance terms. The squared terms were highly
correlated with each other and, in addition, each squared
term was highly correlated with the hazard and industrial
variable. These correlations make it even more difficult to
interpret the coefficients of interest separately.
In order to examine the sensitivity of our results over
time, the hedonic price function (as specified in Eq. (1)) was
estimated for three epochs: 1992–1994, 1995–1997, and
1998–2000. The hazard coefficient was larger in Model 1
and appears to be inflated, in each epoch, by the omission of
the industrial variable. Moreover, in the epochs 1992–1994
and 1998–2000 the sign and statistical significance,
associated with the hazard and industrial variables, are
consistent with the findings presented in the preceding
section. Surprisingly, the 1995–1997 coefficient estimates of
hazard and industrial were not statistically different from
zero at even the 90% confidence level.
The Superfund sites in our study are located within a mile
(0.92 mile) of each other. Thus, it is possible that some
residents, in some locations, perceive exposure to both sites.
In these situations our measure of environmental quality,
2
Econometric results pertaining to the sensitivity analysis will be made
available upon request to the authors.
506
B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
distance to the nearest site, may not capture the
agglomeration effect. This may not, however, pose a major
problem. In a recent hedonic study, Ihlanfeldt and Taylor
(2004)) find that the ‘‘. . . primary spillover effect of
hazardous waste sites occurs through proximity to the
closest site and not through the density of sites (p. 129).’’ We
recognize that distance to nearest Superfund site is not a
perfect measure of the environmental quality influence of
hazardous waste sites. Nevertheless, given the agglomerative effects already explicit in the analysis (i.e. hazardous
waste sites and zones of industrial activity) and the
precedent in previous literature, distance to the nearest site
remains a reasonable, albeit imperfect, measure.
We do, however, examine the sensitivity of our results to
a number of alternative spatial assumptions. The first
alternative reduced the area under examination to housing
sales within a mile of the hazardous waste sites. The
regression estimates support our primary finding—the
omission of the industrial variable appears to place an
upwards bias on the estimate of the hazard coefficient
estimate. In the second alternative, the housing data was
divided into two subsets. The Motor Wheel data contained
only the data for the homes nearer the Motor Wheel site. The
Barrels data contained only data for the homes closest to the
Barrels, Inc., site. Additional regressions restricted data to
examine housing sales within a mile of one of the site. In the
regression for Motor Wheel, Model 1 coefficient estimates
for hazard were higher than Model 2 coefficient estimates.
This was also true for regressions that restricted the area of
inquiry to housing sales within a mile of the site.
Limiting the data to sales that were relatively closer to
Barrels Inc., generated coefficient estimates of the hazard
variable that were consistent with expectations for bias but
were not statistically significant. However, restricting the
regression to examine housing sales within a mile of the
Barrels site generated coefficient estimates of the hazard
effect, in Model 1 and Model 2 that were statistically
significant. Moreover, in the restricted regression, omitting
the industrial measure inflates coefficient estimates of the
hazard effect as expected.
We are not able to test the sensitivity of our results using
spatial econometrics, an econometric approach that includes
measures to account for the spatial dependence between
observations (Brasington and Hite (forthcoming), Ihlanfeldt
and Taylor (2004), Leggett and Bockstael (2000)).
Brasington and Hite argue that the use of ‘‘spatial statistics’’
in hedonic analysis can capture the influence of all omitted
variables that vary across space (p. 7). However, they do not
discuss how the parameter estimates of the hedonic price
function are influenced by spatial measures of dependence.
Ihlandfeldt and Taylor (2004) and Leggett and Bockstael
(2000), both of whom explicitly examine issue, fail to find
compelling qualitative differences between hedonic models
with and without measures of spatial autocorrelation.
Leggett and Bockstael, for example, conclude that ‘‘Spatial
autocorrelation appears to introduce no consistent direction
of bias in the standard errors of the estimated coefficient. . .,
and the estimated coefficients remain significant in all
specifications (p. 140).’’
Whether or not measures of spatial dependence fully
address issues of omitted variable bias remains an important
area of inquiry. The primary purpose of our empirical
analysis was to examine how an omitted variable for
industrial activity confounds and potentially biases estimates of the hazard effect in hedonic analysis. Our
econometric technique is similar to much of the previous
literature (cited in the introductory section) and enables us to
explicitly examine and discuss the effect of omitting a
particular spatial measure—proximity to zones of high
industrial activity.
7. Implications for benefit estimation
The omission of an industrial variable leads to practical
differences for benefit estimates. One common method for
estimating benefit is to interpret the derivative of the hedonic
price function, with respect to the pollution variable, as the
marginal benefit of reduced exposure (Small, 1975). For this
type of benefit estimate the implications of omitting the
industrial variable are evident. For example, when the
industrial effect was omitted in Model 1 (of Section 4) the
estimate of the hazard coefficient was positive and
statistically significantly at the 95% level. Hence, the
marginal benefits of reduced exposure appear to be positive.
Under this interpretation one might expect a rebound effect,
removing the hazard by cleaning-up the site is expected to
increase housing values, all else constant. However, when
the industrial variable was included in the analysis, the
hazard coefficient was not statistically different from zero at
standard confidence levels. The latter result provides no
statistically significant evidence of a marginal value for
reduced exposure to the hazardous waste sites. This leads to
a qualitatively different conclusion than before. In this case,
one may not expect housing values to rebound from
marginal improvements in hazardous waste sites when a
bundle of other hazards, represented by an industrial zone,
are present.
Welfare estimates of non-marginal benefits of reduced
exposure to hazardous waste sites (e.g., complete clean-up
of the hazardous waste sites) can also be biased by the
omission of a variable that accounts for industrial activity.
One approach for estimating non-marginal welfare gains
from clean-up involves estimating a marginal willingnessto-pay function (Rosen, 1974). Another approach uses
the hedonic price function itself (Freeman, 1993). In
both cases, the welfare estimates are derived from the
hedonic price function’s coefficient estimate of the hazard
variable.
The econometric difficulties of estimating the marginal
willingness-to-pay function are well known (Freeman, 1993;
Bartik, 1987) and a full discussion of these issues is beyond
B.J. Deaton, J.P. Hoehn / Environmental Science & Policy 7 (2004) 499–508
the scope of this study. However, for the purposes of this
paper, it is sufficient to recognize that the dependent
variables, used in regression estimates of the marginal
willingness-to-pay function, are marginal benefits. These
marginal benefits are not observed directly, rather, they are
derived using the estimated hedonic price function’s hazard
variable coefficient. In ‘‘special’’ circumstances, where the
hazardous waste sites influence a small number of houses
relative to the total urban area, the estimated hedonic price
function is used to provide a direct measure of the welfare
gains expected from hazardous waste clean-up (Kiel and
Zabel, 2001). Applying either approach to this paper’s
estimates of the hedonic price function leads to qualitatively
similar results; Omission of the industrial variable, as in
Model 1, may generate welfare estimates of hazardous waste
clean-up that exceed those derived from Model 2, which
includes a measure of industrial activity.
8. Summary
In the US, as in many places throughout the world, past
waste disposal activities frequently took place in close
proximity to industrial activity: sometimes on the same
sites. As a result, contemporary hazardous waste deposits
are often located in areas that remain engaged in industrial
activity. Industrial activities may generate a host of
perceived disamenities such as noise, traffic, congestion,
and odors as well as perceived risks from electrical lines,
pipelines, pressure tanks, chemicals, rail lines, and heavy
equipment. In situations where hazardous waste sites
and industrial activity are spatially correlated, urban
residents are likely to be confronted with a portfolio of
disamenities.
In this paper we demonstrate that failure to include
measures of industrial activity in hedonic analyses may, in
some cases, overstate the effect that urban hazardous waste
sites have on surrounding property values. The omitted
variable bias we examine can confound hedonic analysis
of any hazardous waste site that is spatially fixed near
other disamenities. Future researchers and policy makers
addressing environmental quality issues in urban areas
may benefit from an analytical approach that emphasizes
the relationship between the hazardous waste site and
surrounding industrial activity. This may generate additional hypotheses as well as new approaches for improving
environmental quality in urban areas. For example, future
studies might hypothesize that the relative importance of
the hazardous waste site, within a portfolio of disamenities, influences the extent to which hazardous waste
cleanup improves perceptions of environmental quality
and corollary increases in property values. Policy makers
might, for example, consider the possibility that the netbenefits of a hazardous waste clean-up may increase
through additional efforts to address the disamenities
associated with surrounding industrial activity.
507
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B. James Deaton, Assistant Professor, Department of Business and Agricultural Economics, Guelph CA; PhD, Michigan State University. MS
Virginia Polytechnic Institute, BA University of Missouri, Columbia. My
research examines environmental and natural resource issues. I am particularly interested in the manner in which laws, rules, and standards
influence environmental quality, natural resource use, and economic development. Additional research examines: the relationship between different
forms of private property and economic development; public support for
various criteria used to preserve farmland; and, the social construction of
production externalities in the agricultural sector. Prior to my PhD training, I
worked on economic development projects in Lesotho (Southern Africa)
and the Appalachian region of eastern Kentucky. I can be reached at the
Department of Agricultural Economics and Business, Room no. 321 J.D.
Maclachlan Building, University of Guelph, Guelph, Ont., NIG 2W1,
Canada.
John P. Hoehn, Professor, Environmental Economics Program, Department of Agricultural Economics, Michigan State University. PhD, University of Kentucky; BA, University of California, Berkeley. Past Board
member, Association of Environmental and Resource Economists; Associate Editor, Journal of Environmental Economics and Management; American Journal of Agricultural Economics. Publications include entries in
Resource and Energy Economics, Journal of Water Resources Planning and
Management, Ecological Economics, Journal of Environmental Economics
and Management, American Journal of Agricultural Economics, and American Economic Review. My research includes the benefit and cost analysis
of environmental and infrastructure investments and methods for valuing
non-market goods, especially contingent valuation and stated choice analysis. Other research interests include the analysis of investments in
resource-based recreation and issues of environmental management in
developing countries.