# RECORD 3244bfbe46126d82d9734bb6ef3476cad91ccc3f
# TITLE: Impacts of Ground Water Contamination on Property Values: Agricultural Run-off and Private Wells
# YEAR: 2016.0  VENUE: Agricultural and Resource Economics Review  DOI: 10.1017/age.2016.16
# AUTHORS: D. Guignet; P. Walsh; R. Northcutt
# PAGES: 26  CANDIDATE TABLE PAGES (1-based): [14, 15]
# ABSTRACT: Few studies have examined the impacts of ground water quality on residential property values. Using a unique data set of well tests, we link residential real estate transactions to home-specific contamination and conduct a hedonic analysis of sales in Lake County, Florida, where pollution concerns relate primarily to agricultural run-off. We find that recent testing and contamination of ground water there correspond to a 2–6 percent depreciation in home values, an effect that diminishes over time. Focusing on nitrogen-based contamination, we find that prices decline mainly when concentrations exceed the regulatory health standard, suggesting as much as a 15 percent depreciation at levels twice the standard.


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Impacts of Ground Water
Contamination on Property Values:
Agricultural Run-offand Private Wells
Dennis Guignet, Patrick J. Walsh, and Rachel Northcutt
Few studies have examined the impacts of ground water quality on residential
property values. Using a unique data set of well tests, we link residential real
estate transactions to home-specific contamination and conduct a hedonic
analysis of sales in Lake County, Florida, where pollution concerns relate
primarily to agricultural run-off. We find that recent testing and contamination
of ground water there correspond to a 2–6 percent depreciation in home values,
an effect that diminishes over time. Focusing on nitrogen-based contamination,
we find that prices decline mainly when concentrations exceed the regulatory
health standard, suggesting as much as a 15 percent depreciation at levels twice
the standard.
Key Words: drinking water, ground water, hedonic, nitrate, nitrite, potable well,
property value, water quality
Estimating the value of ground water resources and the services they provide is
critical to formulating policies meant to protect and improve water quality. One
of the most crucial services provided by ground water is that it is an important
source of drinking water. In the United States, ground water is the source for 77
percent of community water systems, and about 15 percent of the population
rely on private wells for water (Environmental Protection Agency (EPA)
2012a, 2012b). Private wells are particularly susceptible to potential
contamination because they are not regulated under the Safe Drinking Water
Act and are not required to undergo regular monitoring and treatment to
ensure water quality standards are met. Furthermore, households that rely
Dennis Guignet and Patrick J. Walsh are economists at the National Center for Environmental
Economics of the U.S. Environmental Protection Agency (EPA). Rachel Northcutt is an ORISE
EPA research fellow with the Oak Ridge Institute for Science and Education. Correspondence:
Dennis Guignet ▪National Center for Environmental Economics ▪U.S. Environmental Protection
Agency ▪Mail Code 1809 T ▪1200 Pennsylvania Avenue, NW ▪Washington, DC 20460 ▪Phone
01.202.566.1573 ▪Email guignet.dennis@epa.gov.
We thank Robin Jenkins, Erik Helm, participants at the Northeastern Agricultural and Resource
Economics Association’s 2015 Water Quality Economics Workshop, and two anonymous referees
for helpful comments. We are grateful to Abt Associates for data support and Michael Berry at the
Florida Department of Health for explaining the extensive data set of well-contamination tests. Any
views expressed are solely those of the authors and do not necessarily reflect the views of the U.S.
Environmental Protection Agency or other organizations mentioned.
Agricultural and Resource Economics Review 45/2 (August 2016) 293–318
© The Author(s) 2016. This is an Open Access article, distributed under the terms of the Creative
Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted re-use, distribution, and reproduction in any medium, provided the original work is
properly cited.
https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press


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in the regressions but we report only the estimates of interest.17 The other
coefficient estimates all displayed the expected sign or were insignificant. The
Table 3. Descriptive Statistics for Home and Location Characteristics
Variable
Number of
Observations
Mean
Std.
Dev.
Min.
Max.
Price of home in 2013
U.S. dollars
1,730
257,203
120,984
20,713
571,359
Age of home in years
1,701
12.53
15.30
0
123
Number of bathrooms
1,730
2.20
0.69
1
7
Interior square footage
1,730
2,032
780
396
6,558
Lot size in acres
956
2.10
2.40
0.13
15.16
Quality of construction
(50–950)
1,730
583.49
79.06
100
710
Air conditioning
1,730
0.9751
0.1557
0
1
Pool
1,730
0.3260
0.4689
0
1
Distance to urban cluster
in kilometers
1,730
17.3645
5.9100
0.2083
27.8515
In 100-year flood plain
1,730
0.0688
0.2532
0
1
Number of gas stations
within 500 meters
1,730
0.0491
0.2241
0
2
Waterfront home
1,730
0.1156
0.3198
0
1
Distance in meters to:
Nearest protected area
1,730
1,874
1,504
12.7204
6,553
Nearest primary road
1,730
1,1177
6,415
150
24,140
Nearest lake or pond
1,730
340
271
0
2,132
Nearest citrus grove
1,730
365
385
0
5,816
Nearest row/field crop
1,730
250
241
0
1,735
Nearest golf course
1,730
2,645
2,786
19
17,722
In public water system
service area
1,730
0.1844
0.3879
0
1
N ¼ 1,730 sales
Notes: All characteristics are dummy variables unless otherwise noted. Construction quality is based on
county assessor gradings in which 50 is poorest quality and 950 is best quality.
17 The only exception is that the distance to the nearest major road was excluded due to
concerns about multicollinearity; the estimates of interest are robust to that exclusion. Lot size
and interior square footage entered in log form, and we used the inverse distance to the
nearest citrus grove, row crop, and golf course instead of linear distance. Companion dummy
variables were included to account for missing values for lot size, number of bathrooms, and
age of the home.
Agricultural and Resource Economics Review
306 August 2016
https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press


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Table 4. Base Hedonic Regression Results: Tested Zero to Three Years Prior to Sale
Variable
Base Model
Base Model plus Additional Interaction Term
OLS
FE
SEM
SEM þ FE
OLS
FE
SEM
SEM þ FE
(A)
(B)
(C)
(D)
(A1)
(B1)
(C1)
(D1)
Tested
0.0239
0.0021
0.0220
0.0022
0.0239
0.0020
0.0220
0.0020
(0.0189)
(0.0188)
(0.0172)
(0.0170)
(0.0189)
(0.0189)
(0.0172)
(0.0170)
Tested × Above DL
0.0146
0.0276
0.0150
0.0277
0.0145
0.0288
0.0150
0.0289
(0.0181)
(0.0173)
(0.0182)
(0.0179)
(0.0181)
(0.0177)
(0.0185)
(0.0181)
Tested × Above MCL
—
—
—
—
0.0007
0.0153
0.0004
0.0155
—
—
—
—
(0.0400)
(0.0379)
(0.0367)
(0.0354)
Lambda (λ)
—
—
0.1040***
0.0490***
—
—
0.1040***
0.0490***
—
—
(0.0145)
(0.0119)
—
—
(0.0145)
(0.0119)
%Δptest
2.36
0.21
2.17
0.21
2.36
0.20
2.17
0.20
(1.84)
(1.89)
(1.68)
(1.70)
(1.84)
(1.89)
(1.68)
(1.70)
%Δptest→DL
1.45
2.73
1.49
2.73
1.44
2.84*
1.49
2.85
(1.79)
(1.68)
(1.79)
(1.74)
(1.78)
(1.72)
(1.82)
(1.76)
%ΔpDL
3.77***
2.52**
3.63***
2.52*
3.77***
2.65**
3.63***
2.65**
(1.32)
(1.23)
(1.31)
(1.33)
(1.35)
(1.33)
(1.35)
(1.36)
%ΔpMCL
—
—
—
—
0.07
1.55
3.66
1.13
—
—
—
—
(4.00)
(3.85)
(3.47)
(3.46)
Observations
1,730
1,730
1,730
1,730
1,730
1,730
1,730
1,730
Block-group fixed effects
No
Yes
No
Yes
No
Yes
No
Yes
Continued
Guignet, Walsh and Northcutt
Impacts of Ground Water Contamination on Property Values 307
https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press


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Table 4. Continued
Variable
Base Model
Base Model plus Additional Interaction Term
OLS
FE
SEM
SEM þ FE
OLS
FE
SEM
SEM þ FE
(A)
(B)
(C)
(D)
(A1)
(B1)
(C1)
(D1)
No. of fixed effects
—
65
—
65
—
65
—
65
R-squared
0.7981
0.7701
0.8001
0.8224
0.7981
0.7702
0.8001
0.8224
Note: The dependent variable is the natural log of the real transaction price (2013 U.S. dollars). Only coefficients of interest are shown, including Tested and
interaction terms capturing the incremental impact of contamination levels above the detectable limit (Tested × Above DL) and above the MCL (Tested × Above
MCL). *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. Robust standard errors are provided in parentheses. In the block-group FE models,
standard errors are clustered at the block-group level. Models C, D, C1, and D1 are spatial error models (SEMs) in which the error terms are allowed to be
spatially correlated based on inverse-distance SWMs with a distance radius of 3,200 meters and a time constraint of twelve months (see the empirical
model for details).
Agricultural and Resource Economics Review
308 August 2016
https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press