USING SPATIAL REGRESSION TO ESTIMATE PROPERTY TAX DISCOUNTS
FROM PROXIMITY TO BROWNFIELDS: A TOOL FOR LOCAL POLICY-MAKING1
Oana Mihaescu* and Rainer vom Hofe§
*HUI Research AB, 103 29 Stockholm, Sweden and Dalarna University, 791 88 Falun, Sweden
§
University of Cincinnati, School of Planning, PO Box 210016, Cincinnati OH 45221, USA
This paper assesses the discount in property values due to proximity to brownfields using a
spatial hedonic price model. Using two Bayesian hedonic pricing models, namely the spatial lag
of X (SLX) model and the spatial Durbin error model (SDEM), this study identifies a significant
decrease in property values for properties located within 2,000 feet of a brownfield. The loss in
property value and the subsequent decrease in tax revenue for the City of Cincinnati, Ohio, are
then calculated based on these results. Using logarithmic transformations of the property value
and the distance to the nearest brownfield variables, we calculate that a 1% increase in the
average distance to the closest brownfield leads to a 0.0893% increase in market value. This
translates into a $2,262,569 total annual revenue loss for the City of Cincinnati that could
presumably be recovered following brownfield cleanup. In addition to accounting for the
phenomenon of spatial dependence, this study contributes to the urban planning and
environmental policy literature by providing a method for local policy-makers to identify and
estimate the negative effects of brownfield sites on local tax revenue.
Keywords: spatial hedonic model, brownfield site, property value, tax revenue.
1
Electronic version of an article published as “Using spatial regression to estimate property tax discounts from
proximity to brownfields: A tool for local policy-making” in Journal of Environmental Assessment Policy and
Management 15(1), 2013, 23 pages, DOI 10.1142/S1464333213500087 © Copyright World Scientific Publishing
Company, http://www.worldscientific.com/worldscinet/jeapm.
Introduction
Brownfield sites consist of abandoned, idled, or underused real properties and, depending on
their level of contamination, exhibit very low property values. In addition, real estate properties
surrounding brownfield sites often experience a decrease in value because of their close
proximity to contaminated sites. Using hedonic price models, the relevant literature has paid
much attention to identifying the exact discount magnitude that brownfield sites have on nearby
property values (Bromberg and Spiesman, 2006; Kaufman and Cloutier, 2006; De Sousa et al.,
2009). Based on these estimated hedonic pricing results, a few studies have also determined the
loss in tax revenue for local jurisdictions and the expected gain in property taxes that would
follow the redevelopment of brownfield sites (Simons et al., 1997 and 1999; De Sousa et al.,
2009).
However, with few exceptions (Ihlanfeldt and Taylor, 2002; Svetlik, 2007), these studies do
not account for the phenomenon of spatial dependence that characterizes real property. Spatial
dependence refers to the fact that real property values are determined not only by their structural
(e.g., lot size or condition of the house), neighborhood (e.g., average income or educational
levels by ZIP code or census tract), and locational (e.g., distance to the Central Business District
or to the closest highway) characteristics, but also by the values of the neighboring properties.
Ignoring the phenomena of spatial dependence during the estimation procedure yields estimates
that are not only inconsistent, but also biased.
Our study applies two spatial Bayesian hedonic pricing models – the spatial lag of X (SLX)
model and the spatial Durbin error model (SDEM). Outcomes of both spatial hedonic price
models eliminate spatial bias and provide estimated marginal implicit prices of structural,
neighborhood, and locational residential property characteristics – which also include the negative
impacts that brownfield sites have on nearby residential properties. We use three buffer zones
around the brownfields to determine the “neighborhood effect” (Green Leigh and Coffin, 2005) –
the area around brownfields in which the negative impacts on property values are experienced.
We also find that log-transformations of both variables – the distance to the nearest brownfield
and residential property values – are the preferred functional form to model the influence of
brownfields onto residential property values. The unbiased and consistent coefficient estimate for
distance to the nearest brownfield is the basis for calculating the discount in the value of
residential properties in our study area – the City of Cincinnati, Ohio.
Our study contributes significantly to the urban economics and environmental management
and policy literature through the use of spatial regression techniques to estimate unbiased and
consistent parameter estimates. Using a hedonic pricing framework, we estimate the direct
effects for the subject property as well as the indirect spillover effects from the neighboring
properties. This allows us to calculate the correct discount in property values based on proximity
to brownfields. In the next section, the paper discusses the relevant literature. The following
sections describe the study area and the research design, the model specifications, and the
method for calculating the discount in property values. The paper concludes with a discussion of
the empirical results, both in terms of regression estimates and discounts in property values, and
with a more in-depth discussion of the significance of this revenue loss for the City of
Cincinnati.
Discounts and Premiums in the Brownfield-Related Literature
There is consensus among researchers and practitioners that close proximity to brownfield sites
decreases property values while brownfield cleanup and redevelopment has the opposite effect.
Simons et al. (1997 and 1999) calculated a 15% reduction in sales prices of residential properties
and a 33% reduction in transaction rates of commercial properties that neighbored leaking
underground storage tanks. In addition, they recorded a 30% - 40% reduction in the sales price of
contaminated commercial properties, due to the fact that properties known to be contaminated
were more difficult to sell, experienced reduced marketability, or might have never reached the
market due to stigma and the subsequent tendency of potential tenants to avoid them.
In a recent study, De Sousa et al. (2009) used two-stage Ordinary Least Square (OLS)
estimation and stakeholder interviews to measure the impact of publicly assisted brownfield
redevelopments on nearby residential property values in Milwaukee, Wisconsin, and
Minneapolis, Minnesota. They modeled the sale prices of houses in both a pre-redevelopment
stage (1996) and a post-redevelopment stage (2004). The results showed that redevelopment of
brownfields led to a net increase of 11.4% in nearby housing prices in Milwaukee and 2.7% in
Minneapolis. Converting brownfields into parks and residential areas seemed to provide the most
significant benefits to adjacent properties, but there were also significant and positive net
benefits associated with conversion to industrial and commercial uses. Funding and other costrelated variables, such as project size and project cost, had little impact, indicating that both
small- and large-scale projects were worthy of support. The results led to the conclusion that
public investment in brownfield redevelopment helped diminish the negative effects imposed by
deindustrialization, which in turn helped cities raise their property tax base, both on the former
brownfield site and around it. Kaufman and Cloutier (2006) also confirmed that residential
property values increased following brownfield remediation and that the conversion of former
brownfields into greenspaces led to further significant increases in the value of neighboring
residential properties.
Some brownfield focused hedonic price models have attempted to quantify the total loss in
value due to contamination and/or the total benefit from cleanup of the study area. In 1992,
Ketkar used hedonic price modeling to estimate the total impact of hazardous waste sites on
property values as well as the total benefits stemming from the cleanup in New Jersey. He
concluded that the cleanup of one site increased the median price of properties within the studied
municipalities by $1,300 to $2,000. Considering a median property value increase of $1,500 and
assuming that at least one site was cleaned up in each of the municipalities included in the
sample, Ketkar calculated the total appreciation in residential properties to be as much as $1.236
billion for the State of New Jersey, while the estimated remediation costs would come in at $931
million. In a similar study, Ihlanfeldt and Taylor (2002) estimated the total reduction in
commercial and industrial property values based on proximity to hazardous waste sites in Fulton
County, Georgia to be $1 billion for the entire county and argued that such high losses justified
private cost-sharing and Tax Increment Financing (TIF) as cleanup and redevelopment tools. In
Atlanta, this would mean improving the city’s poor financial position in the struggle against
urban sprawl by stimulating economic development within central city neighborhoods. On the
other hand, a study on Superfund sites by Greenstone and Gallagher (2008) compared housing
market outcomes in areas surrounding the first 400 hazardous waste sites chosen for Superfund
cleanups with the areas surrounding non-Superfund contaminated sites and showed that
placement on the National Priorities List produced small and statistically insignificant changes in
property values. At the aggregated level they estimated the economic benefits of Superfund
cleanups to be substantially lower than the $43 million average per site cleanup cost. The high
cost of cleaning up this extremely contaminated Superfund sites may explain the difference in
results.
Our present discussion shows that the hedonic price model is the preferred tool for estimating
the negative impact that brownfield sites have on nearby property values. However, although
property values exhibit spatial dependence, attempts to account for this phenomenon in previous
studies have been scarce. Exceptions include studies by Ihlanfeldt and Taylor (2002) and Svetlik
(2007), the latter using a Spatial Error Model (SEM) to account for the spatial correlation of
property values. In the present study, we calculate and discuss the loss in tax revenue for the City
of Cincinnati, Ohio, and the potential economic benefits in terms of tax revenues resulting from
cleanup. In addition, we focus explicitly on the spatial correlation problem using spatial
regression models.
Study Area and Research Design
Our study area is the City of Cincinnati, which, like many other cities in the Midwest, has been
confronting the problem of contaminated land for a few decades now, partly related to the golden
years of industry and partly because of the subsequent flight of manufacturing firms from the
inner city to its outskirts (Fukuyama, 1999; Lopez, 2004). Our database consists of 87
brownfield sites and contains information from a variety of sources, including the US EPA’s
Comprehensive Environmental Response, Compensation, and Liability Information System
(CERCLIS), the Ohio Brownfield Inventory, the Clean Ohio Revitalization Fund, and the Port of
Greater Cincinnati Development Authority. All brownfields are geographically geocoded in
ArcGIS using the address locator already created by Cincinnati Area Geographic Information
Systems (CAGIS). The Hamilton County Auditor’s Office provided all data regarding property
values and structural housing characteristics. The final selection of properties for our study was
made using buffer zones around the brownfield sites. The use of buffer zones allows us to
determine the area around brownfields in which the negative impacts on property values are
experienced or the “neighborhood effect” (Green Leigh and Coffin, 2005), also known as the
“spatial decay effect” and “brownfields’ geographic scope of influence” (Ding et al., 2000; De
Sousa et al., 2009). This is essential when attempting to estimate the total property loss resulting
from proximity to a brownfield site and the total possible economic gain from cleanup and/or
redevelopment.
The relevant literature suggests the establishment of buffer zones with threshold values from
150 up to 30,000 feet. Using preliminary regressions on properties located within each quarter
mile of the contaminated sites, we found significant variations in the spatial spillover effects
from neighboring properties for different buffer zones around the brownfield sites. Our
preliminary results suggest using three buffer zones around the brownfields: from 0 - 1,000 feet,
1,001 - 1,500 feet, and from 1,501 - 2,000 feet (Figure 1). More specifically, we defined the
buffer zones using the threshold distances as radii to create circular areas around each
brownfield. We are in agreement with Lindsey et al. (2004) that the straight-line approach should
be used whenever accessibility to the site is not of importance. It is also worth mentioning that
for our study area, the influence of brownfields becomes negligent beyond 2,000 feet. Ding et al.
(2000) considered short-distance buffers at 150 feet and at 300 feet in the study of the direction
and intensity of redevelopment impacts. Green Leigh and Coffin (2005) applied buffer zones of
0 - 500 feet, 501 - 1,000 feet, and 1,001 - 1,500 feet and showed that the strongest negative
impact was generally recorded for properties located within 500 feet of a brownfield. However,
the results varied depending on the city and whether or not the brownfields had been cleaned up
and redeveloped. De Sousa et al. (2009) used incremental buffers of 500 feet up to a total
distance of 4,000 feet, while Ihlanfeldt and Taylor (2002) used 1,300 foot increments up to 8,000
feet. The significant difference in distance thresholds notwithstanding, all studies are in
agreement that the most significant impacts occur within the first 1,500 feet of the brownfields.
Split into three buffer zones, we ended up with 2,268 properties located within 1,000 feet of the
closest brownfield; 2,421 properties located 1,001 - 1,500 feet away from the closest brownfield;
and 2,174 properties located 1,501 - 2,000 feet away from the closest brownfield.
For each of these buffer zones we calculated the Euclidian distance to the nearest brownfield
as the locational variable for our study. We used the Euclidian distance to the closest highway
exit as a second locational variable (Table 1). Again, we follow the argument of Eiser et al.
(2007) that Euclidean distance measurements are preferred over network distances as the
perception of being in close proximity to a brownfield site is a more decisive factor for
devaluating housing prices than is accessibility to the brownfield site. We calculated both
Euclidean “as the crow flies” distances utilizing ArcGIS. Altogether, we employed three
different categories of explanatory variables: structural characteristics (land and building
characteristics), neighborhood characteristics, and locational characteristics. Guided by the
principle of parsimony and avoiding multicollinearity problems, we excluded some of the
available explanatory variables from the final model version.
Figure 1. Buffers used to select properties for analysis.
Table 1. Variable definitions.
Dependent
Variable
Structural
Characteristics (S)
Neighborhood
Characteristic (N)
Locational
Characteristics (L)
Variable Name
MKTVALU
Description
Market value of land and improvements in 2009
AREALOT
FRONT
ATTIC
BEDROOMS
FULLBATHS
HALFBATHS
AGE
GARAGE
FIREPLACES
AIRCOND
HEATING
CVPOOR
EDUCATION
Land area in square feet
Number of feet of street footage
Attic square footage
Number of bedrooms
Number of full baths
Number of half baths
Age of building in years
Car capacity of the basement garage
Number of fireplace stacks
House with central air conditioning system
House with heating system
House in very poor condition
Percent of population 25 or older with higher education by ZIP code
DISTHW
DISTBR
Euclidean distance to the closest highway exit (in feet)
Euclidean distance to the closest brownfield site (in feet)
With the final set of variables and all distance measures calculated, we then explored the
functional relationship between property values and distance to the nearest brownfield. Despite
the use of linear forms in previous studies (Svetlik, 2007), we find a non-linear form to prove
more accurate in modeling the relationship between DISTBR and MKTVALU. Kaufman and
Cloutier (2006) also argue that non-linear forms of distance are more sensitive in detecting this
relationship between distance to a brownfield and market value of a property. After exploring
different functional forms for our distance measure, we find that the logarithmic transformations
best reveal the positive relationship of the distance variable (DISTBR) and the market value
variable (MKTVALU).
Model Specifications and Method of Analysis
Property values in our study are modeled using the following basic Bayesian hedonic pricing
framework:
,
where
is the market value of each property;
characteristics defined in Table 1 above; and
,
, and
(1)
are matrices of the housing
refers to normally and identically distributed error
terms with a zero mean and a common variance
. As mentioned above, we chose a logarithmic
transformation of the dependent variable, which is supported by the relevant literature and which
allows us to better control for the large variations in the dependent variable (i.e., housing prices,
Svetlik, 2007). This also allows us to directly interpret the estimate for the distance as the
elasticity of the market value with respect to the distance for a property with average
characteristics.
We apply two spatial model specifications of the hedonic price model: the spatial lag of X
model (SLX) and the spatial Durbin error model (SDEM)2. The spatial model variants are
necessary as real estate property values are prone to spatial dependence (Ihlanfeldt and Taylor,
2002; Svetlik, 2007). “Spatial dependence” means that the value of each property is determined
not only by the characteristics of the individual lot, building, and neighborhood, or by mere
2
Here, we would like to thank an anonymous reviewer for her valuable comments on the first draft of our research
paper. Initially, we applied the widely used spatial error model (SEM) and spatial autoregressive (SAR) model.
Based on a small SAR model dependence parameter (ρ) and a relatively large SEM model dependence parameter
(λ), the reviewer pointed out that the spatial dependence problem in our data set is of local rather than of global
nature. We are in complete agreement with the reviewer’s suggestion to give preference to the SLX and SDEM
versions of the spatial hedonic model.
proximity to certain elements such as major roads, city center, or major water streams, but also
by the value of neighboring real estate.
Treating spatial dependence as a consequence of arising externalities from neighboring
properties (LeSage and Pace, 2009), the general form of the SLX model applied in our research
is expressed in matrix form as:
(2)
where
is a
vector of the housing prices,
is the intercept,
denote
spatial weight matrices constructed to reflect the three buffer zones as defined earlier,
vector of parameters reflecting the direct impacts,
are the three
is the
vectors of
parameters containing the local spatial spillovers (i.e., indirect impacts) from neighboring
properties for the corresponding buffer zones, and
For instance,
is the
matrix of explanatory variables.
captures local spatial spillover impacts occurring in the first buffer zone
from 0 - 1,000 feet only. The unobserved random part of the SLX specification has the desired
properties
.
The spatial Durbin error model (SDEM), an extended version of the widely used spatial error
model (SEM), includes spatially structured random effects in the error terms – motivated by
spatial heterogeneity – as well as spatially lagged explanatory variables. The SDEM in matrix
notation is defined as:
, and
(3)
where , the spatial error coefficient, measures the strength of the spatial dependence in the
spatial lag of the error terms and the unobserved random part of the SDEM specification has the
desired properties
. The SDEM specification models local spatial spillover
effects in the same way as the SLX specification by using spatially lagged explanatory variables
for each of the three buffer zones. In addition, the SDEM models global spatial dependence
through the error term structure of the model, which would control for shocks impacting the
Cincinnati real estate market. For both model specifications, the reported t-statistics provide a
legitimate basis for inference regarding the direct and the indirect effects (LeSage and Pace,
2009). The interpretation of all estimated parameters is straightforward and follows standard
OLS convention.
We used both SLX and SDEM specification results to calculate revenue loss for the City of
Cincinnati for the 6,863 properties located within our three buffer zones. In the first step, we
determined the non-discounted market value for each property – the value of each residential
property in the absence of any influence from proximity to brownfield sites. The non-discounted
market values were calculated as:
(4)
where the discount in the value of each property (discount) was derived from the coefficient
estimate for the distance from each property to the closest brownfield. Given that a 1% increase
in distance to the closest brownfield leads to a ̂
calculate the discount per foot for each property as
% increase in the property value, we could
̂
.
We then used the discount per foot to calculate the total discount for each property in out sample
as
.
Next, calculating the tax associated with the discounted value and the tax associated with the
non-discounted value allowed us to estimate the total revenue loss for the city given the presence
of brownfields in the neighborhood of single-family residential properties:
3
(5)
4
(6)
5
6
–
(7)
(8)
(9)
Using this method, the total aggregated annual revenue loss for the City of Cincinnati
associated with the presence of brownfield sites is calculated.
2
The assessed value is, in Ohio, equal to 35% of the total market value of each property (Ohio Revised Code 2010).
Idem.
5
The current millage rate established by the City of Cincinnati is 10.23% (Hamilton County Auditor’s Office 2011).
6
Idem.
4
Discussion of Empirical Results
Based on the Variance Inflation Factor (VIF), the Condition Number (CN), and bivariate
correlation coefficients, we removed some of the explanatory variables to avoid the problem of
ambiguous regression results due to multicollinearity. The coefficient estimates for the direct
effects in the two presented models have the expected sign, a further indication that
multicollinearity is not present. The univariate global Moran's I test statistic of 0.4919 is
statistically significant at the 99% level (p-value = 0.001), indicating that spatial dependence in
housing prices is present. Though the Moran's I test is a global measure of spatial dependence, it
has some explanatory power with respect to local spatial dependencies, as it is the average of the
Local Indicators of Spatial Association (LISA). This finding is further supported by the spatial
parameter
= 0.4188 in the SDEM specification, which is statistically significant at the 99%
level (Table 2).
Our regression results for the two spatial model specifications are listed below in Table 2. It is
important to note that the R2 for both models is quite different, with an R2 of 0.49 and 0.60 for
the SLX model and the SDEM respectively. Both model specifications are conceptually
identical, using the same functional forms and the same variables. This suggests that the
difference in the R2 values is explained by the fact that the SDEM addresses the problem of
heterogeneity in the disturbance terms, in addition to including lagged explanatory variables as in
the SLX model. Based on the difference in the R2 values, the SDEM provides the better fit to the
data.
The non-linear relationship in the SLX and SDEM models requires the use of marginal effects
when interpreting the estimated regression coefficients. The results of the SDEM model show
that the presence of a nearby brownfield site does devalue residential properties. The estimated
coefficient for the distance to brownfield (DISTBR) is highly statistically significant at the one
percent level with a value of is 0.0893. For a one percent increase in DISTBR, housing prices
will increase by 0.0893 percent. The average property in our study area has a market value of
$103,108 and is located 1,205 feet away from the closest brownfield. This translates into a
$92.09 (0.0008931 x $103,108) increase in the value of the average property for every 1 percent
increase in distance, or for every 12 ft (0.01 x 1,205 ft). In other words, every 1 foot increase in
distance to a brownfield appreciates the market value of the average house in our sample by
$7.64. All of the other fourteen explanatory variables explaining the direct effects are statistically
significant, except for the property’s distance to the closest highway exit. For instance, adding
one bedroom to the average house increases its value by 6.19%. A full bath increases its value by
14.33%, while a half bath still adds 9.50% to the average property. Land around brownfield sites
is discounted largely as shown by the AREALOT variable. Adding 1,000 ft2 in lot size adds a
mere 0.50% to a property’s value. Contrarily, for every year increase in age, a property loses
0.28% in value. Somewhat unexpectedly, air-conditioning systems and heating represent the
most valuable additions to a house with 21.65% and 19.60%, respectively. We explain this
finding by the fact that our sample properties lie in rather distressed parts of town and many
houses do not have proper heating and air-conditioning systems. Education is a factor as well, as
is the garage’s car capacity. Every percent point in the population aged 25 or older with higher
education (i.e., bachelor’s degree or higher) in the ZIP code adds 0.62% and a car space adds
4.80% in value to a property.
Table 2. Regression estimates for SLX, and SDEM specifications.
Constant
AREALOT
FRONT
ATTIC
BEDROOMS
FULLBATHS
HALFBATHS
AGE
GARAGE
FIREPLACES
AIRCOND
HEATING
CVPOOR
EDUCATION
DISTHW
DISTBR
0-1,000 feet
indirect effects
AREALOT
FRONT
ATTIC
BEDROOMS
FULLBATHS
HALFBATHS
AGE
SLX
Coefficient
(p-value)
9.6482**
(0.0000)
5.0000E-06**
(0.0000)
5.0000E-06**
(0.0000)
0.0001**
(0.0000)
0.0647**
(0.0000)
0.1423**
(0.0000)
0.0950**
(0.0000)
-0.0028**
(0.0000)
0.0467**
(0.0000)
0.1620**
(0.0000)
0.2121**
(0.0000)
0.2452**
(0.0053)
-0.9625**
(0.0000)
0.0081**
(0.0000)
-6.0000E-06*
(0.0427)
0.1301**
(0.0000)
SDEM
Coefficient
(p-value)
10.0073**
(0.0000)
5.0000E-06**
(0.0000)
6.0000E-06**
(0.0000)
0.0001**
(0.0000)
0.0619**
(0.0000)
0.1433**
(0.0000)
0.0950**
(0.0000)
-0.0028**
(0.0000)
0.0480**
(0.0000)
0.1519**
(0.0000)
0.2165**
(0.0000)
0.1960*
(0.0409)
-0.9055**
(0.0000)
0.0062**
(0.0000)
-4.0000E-06
(0.1944)
0.0893**
(0.0096)
1.0000E-06
(0.4320)
-6.0000E-06*
(0.0118)
-0.0001*
(0.0276)
0.0591**
(0.0020)
-0.0441
(0.0993)
0.1371**
(0.0004)
-0.0003
(0.3253)
-4.0000E-06
(0.1307)
-6.0000E-06
(0.0718)
-0.0001
(0.2096)
0.0720**
(0.0040)
-0.0052
(0.4509)
0.0962*
(0.0322)
-0.0017*
(0.0473)
GARAGE
FIREPLACES
AIRCOND
HEATING
CVPOOR
EDUCATION
DISTHW
DISTBR
1,001-1,500 feet
indirect effects
AREALOT
FRONT
ATTIC
BEDROOMS
FULLBATHS
HALFBATHS
AGE
GARAGE
FIREPLACES
AIRCOND
HEATING
CVPOOR
EDUCATION
DISTHW
DISTBR
1,501-2,000 feet
indirect effects
AREALOT
FRONT
ATTIC
0.1149**
(0.0000)
0.2901**
(0.0000)
0.3398**
(0.0000)
0.4344*
(0.0193)
0.0251
(0.4480)
0.0030*
(0.0349)
-3.0000E-06
(0.2513)
-0.1146**
(0.0000)
0.0982**
(0.0073)
0.2601**
(0.0000)
0.2634**
(0.0000)
0.3226
(0.1211)
-0.1760
(0.2644)
0.0049*
(0.0111)
-5.0000E-06
(0.1976)
-0.0877*
(0.0198)
2.2000E-05**
(0.0000)
-1.1000E-05**
(0.0000)
-0.0001*
(0.0262)
-0.0140
(0.2473)
0.0715*
(0.0178)
-0.0434*
(0.0996)
0.0030**
(0.0000)
0.1121**
(0.0000)
0.2402**
(0.0000)
0.3559**
(0.0000)
0.1413
(0.3482)
-0.5915**
(0.0011)
0.0053**
(0.0024)
-4.0000E-06
(0.2096)
-0.1201*
(0.0153)
1.2000E-05**
(0.0016)
-7.0000E-06
(0.0540)
-0.0001
(0.1573)
-0.0092
(0.3649)
0.0706
(0.0582)
-0.0166
(0.3482)
0.0006
(0.2544)
0.0934*
(0.0118)
0.2502**
(0.0000)
0.2626**
(0.0000)
0.6646
(0.0938)
-0.1018
(0.3649)
0.0048*
(0.0144)
-5.0000E-06
(0.2371)
-0.1511*
(0.0209)
8.0000E-06**
(0.0013)
-1.0000E-05**
(0.0009)
0.0001
(0.1160)
9.0000E-06**
(0.0042)
-9.0000E-06*
(0.0198)
3.0000E-05
(0.3938)
BEDROOMS
FULLBATHS
HALFBATHS
AGE
GARAGE
FIREPLACES
AIRCOND
HEATING
CVPOOR
EDUCATION
DISTHW
DISTBR
0.0053
(0.3891)
0.0727*
(0.0127)
0.0507
(0.0722)
0.0023**
(0.0004)
0.0239
(0.1842)
0.3544**
(0.0000)
0.4478**
(0.0000)
0.6903*
(0.0333)
-0.7921**
(0.0002)
0.0009
(0.3004)
2.0000E-06
(0.2920)
-0.1879**
(0.0002)
0.4902
-0.0017
(0.4831)
0.0840*
(0.0293)
0.0512
(0.1376)
0.0004
(0.3524)
0.0518
(0.0924)
0.2951**
(0.0000)
0.3350**
(0.0000)
0.7566
(0.0651)
-0.6021*
(0.0309)
0.0026
(0.1229)
0.0000
(0.4818)
-0.1668*
(0.0100)
0.4188**
(0.0000)
0.6003
** significant at the one percent level; * significant at the five percent level.
The estimated W1, W2, and W3 coefficients represent the marginal impact of changes in the
characteristics of neighboring properties on the market value of a property that is located in
either the inner (0 - 1,000 feet), the middle (1,001 - 1,500 feet), or the outer (1,501 - 2,000 feet)
buffer zone respectively. Given that a property is located in one of the three buffer zones, only
one of the three modeled indirect effects would be considered. For a property located within the
first buffer zone, for instance, the estimated indirect effects show that increasing the number of
bedrooms, half-baths, or garage spaces, adding a fireplace or an air conditioning, or improving
the education system would lead to positive spillover effects from neighboring properties onto
the market value. Analogously, the front size and the age of the house have negative indirect
spillover effects on the market value. The remaining estimated parameters for the variables lot
size, attic, full bath, heating, very poor condition, and distance to the closest highway exit are not
statistically significant at the 10% level. Of interest for our research is that all three estimated
parameters for the distance variables – W1DISTBR, W2DISTBR, and W3DISTBR – are
statistically significant. However, and as indicated by the negative sign, the indirect spillover
effects originating from all three buffer zones depreciate market values, while the direct effect
has the opposite impact. The likely explanation is that increasing the distance from the
neighboring residential properties to the nearest brownfield is analogous to moving the subject
property closer to the brownfield, that is, the relative distance between the subject property and
its neighbors impacts the market value, rather than the absolute distance of the subject property
towards the brownfield7. For the inner buffer zone, increasing the distance of neighboring
properties to the brownfield indirectly depreciates the market value by 0.0877 percent for every
one percent increase in distance. The magnitude of this indirect effect – which is comparable to
the own direct effect (i.e., 0.0893) – is expected when considering that the relative distance
between the subject property and its neighbors is decisive for the magnitude of this indirect
effect. In other words, the effect on the market value is very similar in magnitude, when moving
the subject property or when moving its neighbors. Interesting is also that indirect effects for
W2DISTBR and W3DISTBR increase in size which we explain by the fact that residential
properties further away from the brownfield have higher market values to begin with.
The SLX results may be interpreted in an analogous manner to the above discussion of the
SDEM results. In the SLX specification, all fifteen explanatory variables are statistically
significant at the five percent level. Increasing the distance measure DISTBR by one percent
leads to an increase of housing prices by 0.1301 percent, equivalent to an increase of $11.13 per
7
We realize that this brief discussion of the indirect effect of W 1DISTBR, is of rather hypothetical nature and that
the distance between a property and its neighbors is not subject to change. The interpretation of the estimated partial
coefficient for W1DISTBR, however, implies to hold all other explanatory variables – including DISTBR –
constant, which in return would lead to an increase of the relative distance between a property and its neighboring
properties.
foot. Adding bedrooms, full baths, or half baths add 6.47%, 14.23%, or 9.50% to the averagely
priced house, respectively. Further, the presence of fireplaces, air conditioning, and heating
contribute as much as 16.20%, 21.21%, and 24.52% to the market value, respectively. Finally,
the total impacts for AGE, GARAGE, EDUCATION, and DISTHW are -0.28% (year), 4.67%,
0.81%, and 0.60% (1000 ft), respectively.
The important takeaway from our spatial regression analysis is that brownfield sites do
marginally depreciate residential properties located within close proximity, which confirms the
findings of previous studies. This finding allows us to calculate the potential property tax
revenue loss for the City of Cincinnati as it relates to the presence of brownfield sites in the
following section.
Calculating the Property Loss from Proximity to Brownfields
In this section we calculate the depreciation in property value using the parameter estimates from
the spatial Durbin error model (SDEM) and the spatial lag of X (SLX) model. We illustrate the
calculations using the SDEM results and then present total revenue losses for both models. As
already calculated above, a 1% increase in the average distance to the closest brownfield leads to
a 0.0893% increase in market value. Again, for the average property in our sample this means an
increase in price of $92.09 for an increase in distance to the closest brownfield of 12 feet,
equivalent to an increase of $7.64 in market value per foot increase in distance.
The relationship between residential property values and the properties’ distances to the
closest brownfield sites is then the basis for estimating the total revenue loss for the City of
Cincinnati. More specifically, we calculate the loss in tax revenue for the City of Cincinnati for
each property lying within 2,000 feet to a brownfield – in one of the three buffer zones, i.e., 0 1,000 feet; 1,001 - 1,500 feet; and 1,501 - 2,000 feet away from the nearest brownfield – as
outlined in the equations (4) to (9). For a property with a market value of $100,000 lying 100 feet
away from the closest brownfield, for instance, the discount per foot is: (0.0893% x 100,000) /
(1% x 100)= $89.30. This leads to a total discount for this particular property of $89.30 x 100 =
$8,930. In return, we can calculate the non-discounted value of the property as: $100,000 +
$8,930 = $108,930. Given that Ohio uses assessed property values – defined as 35% of true or
market value, determined by actual sales of comparable properties – we calculate the nondiscounted assessed value as: $108,930 x 35% = $38,126 and the discounted assessed value as:
$100,000 x 35% = $35,000. Using these two assessed values, we calculate the tax on the nondiscounted value as: 0.1023 x $36,337 = $3,900 and the tax on the discounted value as: 0.1023 x
$35,000 = $3,581. The difference between these two property tax revenues, i.e., $3,900 - $3,581
= $319, represents the estimated annual revenue loss for a $100,000 residential property that is
100 feet away from a brownfield. In equal manner, we calculated the total property tax revenue
loss using the estimated spatial lag of X coefficient of 0.1301 (see Table 2).
Altogether, we estimate the aggregated tax revenue loss for all 6,863 residential properties in
our sample. Based on the spatial Durbin error model results, we estimate the total annual revenue
loss for the City of Cincinnati from the location of properties in the vicinity of brownfields at
$2,262,569. For comparison and based on the spatial lag of X coefficient, we estimate the total
annual revenue loss for the City of Cincinnati from the location of properties in the vicinity of
brownfields to be $3,296,307.
Further Discussion of the Significance of Revenue Loss for the City of Cincinnati
We find the City’s estimated forgone tax revenue income to be significant, when using either, the
SDEM result of $2,262,569, or when using the SLX model result of $3,296,307. For the
following discussion, we will refer exclusively to the tax revenue loss from the SDEM model
estimation, since this provides the better fit to the data according to the R2 values.
The estimated $2,262,569 in foregone tax revenues for 2009 for all the 6,863 single-family
residential properties within the established 2,000 feet buffer zone, represents as much as 6.78%
of total property tax presently collected by the City of Cincinnati (Prendergast, 2010). In other
words, the potential increase of the city’s tax revenue income from removing contaminationrelated negative externalities would be $2,262,569 annually, assuming 2009 tax and assessment
rates. From the City of Cincinnati’s perspective, however, the question arises whether the
additional tax revenue income of $2,262,569 per year is large enough to consider brownfield
redevelopment as a strategy for increasing property tax revenues in the long run. Nevertheless,
this potential increase in tax revenue may be precisely the motivation needed to justify the
support for brownfield redevelopment for city officials, keeping in mind that other issues must
be taken into consideration as well. Most important are, of course, the cleanup/redevelopment
costs. Unfortunately, direct comparisons of foregone tax revenues with cleanup/redevelopment
costs are very difficult. As Ihlanfeldt and Taylor (2002) state, any information about how much
these processes cost at specific sites is scarce and hard to find, due to significant differences in
the type and level of contamination, the technology used for cleanup, and the types of
redevelopment carried on at the respective sites. They indicated in their Georgia study that
cleanup (remediation) costs range anywhere from $650,000 to $10,000,000 per site, as indicated
by Georgia Environmental Protection Division (Georgia EPD). In the case of the 87 identified
brownfields within the City of Cincinnati, the Port of Greater Cincinnati Development Authority
estimates the minimum costs for environmental assessment alone to be approximately $150,000
per site. In addition, cleanup costs range anywhere from $750,000 to almost $3,000,000 per site
(Port of Greater Cincinnati, 2010). If one would consider these 87 brownfields to be assessed and
cleaned up, and if one would further consider favorable cleanup costs of $900,000 per site8, the
city would still need $78,300,000 to convert these sites back to more productive residential
and/or commercial uses. If this amount were to be covered through Tax Increment Financing
(TIF) and the estimated increase in annual tax revenue of $2,262,569 would be the basis for
paying back the TIF bonds, the City would need additional funding to entirely pay off the bonds,
since the tax revenue alone would be balanced out by a current interest rate of 3.0%.
Consequently, policy-makers may not find the needed support for justifying the use of TIF
money by arguing that brownfield redevelopment will inevitably lead to increases in property tax
revenues.
One reason for the rather low expected property tax increase from the remediation of
brownfield sites in Cincinnati is the relatively low value of properties lying in our study area.
Out of the 6,863 residential properties included in our study, 4,144 are valued at below $100,000,
while only 69 have market values of over $400,000. The cheapest property is priced at $1,750;
the most expensive one is priced at $3,070,940. The mean property market value is $103,108 and
the median value is $88,920. Because of the presence of some very expensive properties, our
property sample is skewed positively. Thus, it is worthwhile mentioning that the first quartile is
$60,575 and the third quartile is $125,020. The average discount in property value is $9,208,
8
We chose a lower value of $900,000 due to the fact that the City of Cincinnati regularly applies for State and
Federal funding when cleaning up brownfield sites.
which represents 8.20% of the average non-discounted property value of $112,316. This value is
somewhat larger than what Simons and Saginor (2006) found in a meta-analysis of studies about
the effect of environmental contamination on residential real estate values. They report that
previous regression-based studies present an average loss in property values due to the proximity
to contaminated sites of 4% ($6,443).
Another important factor that influences the magnitude of the impact of brownfields on
property values is the limited information that is made available publicly on the location, type,
and amount of contamination at the brownfield sites. Jenkins-Smith et al. (2002), Green-Leigh
and Coffin (2005), and Longo and Alberini (2006) all argue that the true impact of brownfields
on property values cannot be observed until information about the respective sites had been
released to the public. For our brownfield study in Cincinnati it is therefore reasonable to assume
that the estimated devaluation of nearby properties is underestimated. In addition, while our
study only includes residential properties, the relevant literature indicates that nonresidential
properties are affected by nearby brownfields as well, and consequently their value may also
increase following cleanup and redevelopment of the contaminated sites. Ihlanfeldt and Taylor
(2002) calculated the loss in property value for 13,230 properties located within 1.5 mile
(approximately 8,000 feet) of a site listed on CERCLIS (EPA’s Comprehensive Environmental
Response, Compensation, and Liability Information System) or HIS (Georgia’s Environmental
Protection Division Hazardous Site Inventory) in Fulton County, Georgia. The recorded losses in
property value were $377 million for residential properties; $54 million for industrial properties;
$347 million for office space; $64 million for retail space; and $231 million for vacant
properties, totaling approximately $1 billion9. Of this amount, only 37.7% represented losses in
value from residential properties10.
Bringing it all together, our estimated amount of foregone property tax revenues for the City
of Cincinnati must be considered as the minimum amount of revenue that could be regained by
the City through cleanup. Previous studies have shown that the increase in property values from
redevelopment may actually be much larger than the increase in property values from the
cleanup. For instance, Kaufman and Cloutier (2006) studied 890 properties in the Lincoln
neighborhood in Kenosha, Wisconsin. Applying two different forms of distance measures for
their analysis – inverse Euclidean distances and natural logarithmic transformed distances – the
authors find an increase in property values following the cleanup of contaminated sites. The
model using inverse Euclidean distances indicates an average increase in property values from
$78,540 to $79,881 leading to a total increase in property value of $1.19 million. A second
increase in property values to $81,234 occurred once the former brownfields were converted into
green spaces, leading to a total increase in property values of $2.40 million. Using natural
logarithm transformed distance showed an increase from $78,540 to $83,382 on average from
the cleanup (totaling to $4.31 million increase) and a further increase to $86,413 with
redevelopment (totaling to a $7.01 million increase).
A Port of Greater Cincinnati Development Authority project shows that cleaning up
contaminated sites combined with mixed-use redevelopment holds the potential to collect
additional property, sales, and income taxes. The Red Bank Village project, a former 35-acre
9
This represents 10% of the total market value (as estimated by Fulton County assessors) of all properties in Fulton
County located within 1.5 mile of the 44 sites listed on CERCLIS or HIS.
10
It can be assumed that the loss in property values in our sample would be accompanied by a loss in the value of
properties with other land uses (counting for the remaining 62.3% ($3,738,940) of a total $6,001,509 property value
loss (based on the percentage results from Ihlanfeldt and Taylor (2002)). Also knowing that tax millage is higher for
properties zoned as commercial and industrial, it can then be assumed that the potential tax increase following the
removal of the source of contamination may be much higher than initially calculated.
Ford Transmission Plant and now home to over 164,000-square-foot of retail space and over
75,000 square feet of office space (with an additional 11,900 square feet of retail and 38,500
square feet of office space planned) is expected to increase tax revenues for the city from
$35,000 to $2.6 million annually (Port of Greater Cincinnati, 2010). An often-referred to
example is that of Atlantic Station, Atlanta, Georgia. The redevelopment transformed the
decaying property of the Atlantic Steel mill into a live-work-play community aimed at becoming
home to 10,000 residents and more than 30,000 workers and operating out of 7.5 million square
feet of office space, retail shops, hotels, and entertainment venues. The redevelopment is said to
yield $30 million in tax revenue annually, as compared to the $300,000 that the Atlantic Steel
mill used to generate (Murdock, 2003).
Last, but certainly not least, the positive impacts of brownfield redevelopment may not only
be monetary, but environmental, social, and aesthetic as well. The latter three categories of
benefits may be translated into monetary terms, e.g., fewer diseases produced by pollution may
mean less health-related costs for the city. However, research focused on these benefits of the
brownfield redevelopment process is very scarce, mainly due to the lack of data and the
difficulty of quantifying the non-monetary benefits of redevelopment.
Conclusion
Hedonic price modeling is the preferred and most widely used tool for estimating the negative
impact of brownfield sites on the value of nearby residential properties. However, the
phenomenon of spatial dependence among property values and its impacts on the parameter
estimates is often disregarded. Consequently, the resulting parameter estimates from these
studies are often biased and inconsistent, together with all subsequent estimations of possible
discounts from proximity to brownfields and possible revenue gains from cleanup and
redevelopment.
In the present study we use two spatial Bayesian hedonic model specifications – spatial lag of
X (SLX) model and spatial Durbin error model (SDEM) – to estimate how proximity to
brownfields and other locational, neighborhood, and structural characteristics impact the value of
residential properties in Cincinnati, Ohio. Based on the unbiased and consistent coefficient
estimate for distance to the nearest brownfield, we then calculate the discount in property values
and, consequently, the loss in tax revenue to the City of Cincinnati.
According to our SDEM model results, a one percent increase in distance to the nearest
brownfield (i.e., moving further away from the brownfield) leads to an increase of 0.0893
percent in the housing price. Consequently, the City of Cincinnati’s total revenue loss is as high
as $2,262,569 per year. The total forgone revenue represents 6.78% of the total property tax
annually collected by the City of Cincinnati.
Further research is needed to determine whether this amount could be significant enough for
city officials to consider brownfields redevelopment as a viable means to increase the City’s
income. Our analysis shows that this amount should be regarded only as the minimum revenue
that could be regained by the City through cleanup. Findings of previous research suggest that
this amount is underestimated (Ihlanfeldt and Taylor, 2002; Longo and Aberini, 2006; Kaufman
and Cloutier, 2006). Factors that could help reflect the real and total discount in property values
are the release of information about the respective sites to the public, inclusion of other types of
properties in the analysis (e.g., industrial or commercial), and inclusion of non-monetary benefits
from redevelopment (e.g., environmental, social, or aesthetical).
Acknowledgements
The authors would like to thank the anonymous reviewer for her exceptional comments that
helped improve the manuscript. Her suggestion of using the SLX and SDEM models benefited
the outcome of our study to a great extent. In addition, we would like to thank Olivier Parent and
Andrea Yang for their help and advice throughout the project.
References
Bromberg, LM, and T Spiesman (2006). Turning an economic liability into an asset: the anatomy
of a redevelopment project. New Jersey Law Journal, 184(13), 1-4.
De Sousa, CA, C Wu, and LM Westphall (2009). Assessing the effect of publicly assisted
brownfield redevelopment on surrounding property values. Economic Development
Quarterly, 23(2), 95-110.
Ding, C, R Simons, and E Baku (2000). The effect of residential investment on nearby property
values: evidence from Cleveland, Ohio. The Journal of Real Estate Research, 19(1/2), 23-48.
Eiser, RJ, T Stafford, J Henneberry, and P Catney (2007). Risk perception and trust in the
context of urban brownfields. Environmental Hazards, 7, 150-156.
Fukuyama, F (1999). The great disruption. Centre for the Study of Democracy Bulletin, 6(2), 3-5.
Green Leigh, N, and SL Coffin (2005). Modeling the relationship among brownfields, property
values, and community revitalization. Housing Policy Debate, 16(2), 257-280.
Hamilton County Auditor’s Office (2011). “Rates of taxation for taxes paid in 2011”. Accessed
June 10. http://hamiltoncountyauditor.org.
Ihlanfeldt, KR, and LO Taylor (2002). Assessing the impacts of environmental contamination on
commercial and industrial properties. Paper presented at the World Congress of
Environmental and Resource Economists, Monterey, California (June/July).
Jenkins-Smith, HC, CL Silva, RP Berrens, and A Bohara (2002). Information disclosure
requirements and the effect of soil contamination on property values. Journal of
Environmental Planning and Management, 45(3), 323-339.
Kaufman, DA, and NR Cloutier (2006). The impact of small brownfields and greenspaces on
residential property values. Journal of Real Estate Finance and Economics, 33, 19-30.
Ketkar, K (1992). Hazardous waste sites and property values in the State of New Jersey. Applied
Economics, 24, 647-659.
LeSage, J, and RK Pace. 2009. Introduction to Spatial Econometrics. Boca Raton, Florida: CRC
Press.
Longo, A, and A Alberini (2006). What are the effects of contamination risks on commercial and
industrial properties? Evidence from Baltimore, Maryland. Paper presented at the European
Regional Science Association Conference, Volos, Greece (August/September).
Lopez, SH (2004). Reorganizing the Rust Belt: An Inside Study of the American Labor
Movement. Berkeley, California: University of California Press.
Murdock, J (2003). “Next stop: Atlantic Station”. Accessed June 1. www.allbusiness.com.
Ohio Revised Code (2010). “Taxation”. Accessed February 1. http://codes.ohio.gov.
Port of Greater Cincinnati Development Authority. 2010. “Brownfield redevelopment”.
Accessed July 15. www.cincinnatiport.org.
Prendergast, J (2010). “City council sets 2011 property tax millage”. Accessed July 15.
www.nky.cincinnati.com
Simons, RA, and JD Saginor (2006). A meta-analysis of the effect of environmental
contamination and positive amenities on residential real estate values. American Real Estate
Society, 28(1), 71-104.
Simons, RA, WM Bowen, and AJ Sementelli (1997). The effect of underground storage tanks on
residential property values in Cuyahoga County, Ohio. Journal of Real Estate Research,
14(1/2), 29-42.
Simons, RA, WM Bowen, and AJ Sementelli (1999). The price and liquidity effects of UST
leaks from gas stations on adjacent contaminated property. The Appraisal Journal, 67(2), 186194.
Svetlik, JB (2007). Externality effects of local brownfields on residential property values. Paper
presented at the Business of Brownfields Conference, Pittsburg, Pennsylvania (April).