1# RECORD 3244bfbe46126d82d9734bb6ef3476cad91ccc3f2# TITLE: Impacts of Ground Water Contamination on Property Values: Agricultural Run-off and Private Wells3# YEAR: 2016.0 VENUE: Agricultural and Resource Economics Review DOI: 10.1017/age.2016.164# AUTHORS: D. Guignet; P. Walsh; R. Northcutt5# PAGES: 26 CANDIDATE TABLE PAGES (1-based): [14, 15]6# 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.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1011Impacts of Ground Water12Contamination on Property Values:13Agricultural Run-offand Private Wells14Dennis Guignet, Patrick J. Walsh, and Rachel Northcutt15Few studies have examined the impacts of ground water quality on residential16property values. Using a unique data set of well tests, we link residential real17estate transactions to home-specific contamination and conduct a hedonic18analysis of sales in Lake County, Florida, where pollution concerns relate19primarily to agricultural run-off. We find that recent testing and contamination20of ground water there correspond to a 2–6 percent depreciation in home values,21an effect that diminishes over time. Focusing on nitrogen-based contamination,22we find that prices decline mainly when concentrations exceed the regulatory23health standard, suggesting as much as a 15 percent depreciation at levels twice24the standard.25Key Words: drinking water, ground water, hedonic, nitrate, nitrite, potable well,26property value, water quality27Estimating the value of ground water resources and the services they provide is28critical to formulating policies meant to protect and improve water quality. One29of the most crucial services provided by ground water is that it is an important30source of drinking water. In the United States, ground water is the source for 7731percent of community water systems, and about 15 percent of the population32rely on private wells for water (Environmental Protection Agency (EPA)332012a, 2012b). Private wells are particularly susceptible to potential34contamination because they are not regulated under the Safe Drinking Water35Act and are not required to undergo regular monitoring and treatment to36ensure water quality standards are met. Furthermore, households that rely37Dennis Guignet and Patrick J. Walsh are economists at the National Center for Environmental38Economics of the U.S. Environmental Protection Agency (EPA). Rachel Northcutt is an ORISE39EPA research fellow with the Oak Ridge Institute for Science and Education. Correspondence:40Dennis Guignet ▪National Center for Environmental Economics ▪U.S. Environmental Protection41Agency ▪Mail Code 1809 T ▪1200 Pennsylvania Avenue, NW ▪Washington, DC 20460 ▪Phone4201.202.566.1573 ▪Email guignet.dennis@epa.gov.43We thank Robin Jenkins, Erik Helm, participants at the Northeastern Agricultural and Resource44Economics Association’s 2015 Water Quality Economics Workshop, and two anonymous referees45for helpful comments. We are grateful to Abt Associates for data support and Michael Berry at the46Florida Department of Health for explaining the extensive data set of well-contamination tests. Any47views expressed are solely those of the authors and do not necessarily reflect the views of the U.S.48Environmental Protection Agency or other organizations mentioned.49Agricultural and Resource Economics Review 45/2 (August 2016) 293–31850© The Author(s) 2016. This is an Open Access article, distributed under the terms of the Creative51Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits52unrestricted re-use, distribution, and reproduction in any medium, provided the original work is53properly cited.54https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press555657===== PAGE 14 (score 23, numeric-paren 0, table-words 2, var-words 17) =====5859in the regressions but we report only the estimates of interest.17 The other60coefficient estimates all displayed the expected sign or were insignificant. The61Table 3. Descriptive Statistics for Home and Location Characteristics62Variable63Number of64Observations65Mean66Std.67Dev.68Min.69Max.70Price of home in 201371U.S. dollars721,73073257,20374120,9847520,71376571,35977Age of home in years781,7017912.538015.308108212383Number of bathrooms841,730852.20860.6987188789Interior square footage901,730912,0329278093396946,55895Lot size in acres96956972.10982.40990.1310015.16101Quality of construction102(50–950)1031,730104583.4910579.06106100107710108Air conditioning1091,7301100.97511110.155711201131114Pool1151,7301160.32601170.468911801191120Distance to urban cluster121in kilometers1221,73012317.36451245.91001250.208312627.8515127In 100-year flood plain1281,7301290.06881300.253213101321133Number of gas stations134within 500 meters1351,7301360.04911370.224113801392140Waterfront home1411,7301420.11561430.319814401451146Distance in meters to:147Nearest protected area1481,7301491,8741501,50415112.72041526,553153Nearest primary road1541,7301551,11771566,41515715015824,140159Nearest lake or pond1601,73016134016227116301642,132165Nearest citrus grove1661,73016736516838516901705,816171Nearest row/field crop1721,73017325017424117501761,735177Nearest golf course1781,7301792,6451802,7861811918217,722183In public water system184service area1851,7301860.18441870.387918801891190N ¼ 1,730 sales191Notes: All characteristics are dummy variables unless otherwise noted. Construction quality is based on192county assessor gradings in which 50 is poorest quality and 950 is best quality.19317 The only exception is that the distance to the nearest major road was excluded due to194concerns about multicollinearity; the estimates of interest are robust to that exclusion. Lot size195and interior square footage entered in log form, and we used the inverse distance to the196nearest citrus grove, row crop, and golf course instead of linear distance. Companion dummy197variables were included to account for missing values for lot size, number of bathrooms, and198age of the home.199Agricultural and Resource Economics Review200306 August 2016201https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press202203204===== PAGE 15 (score 81, numeric-paren 65, table-words 2, var-words 2) =====205206Table 4. Base Hedonic Regression Results: Tested Zero to Three Years Prior to Sale207Variable208Base Model209Base Model plus Additional Interaction Term210OLS211FE212SEM213SEM þ FE214OLS215FE216SEM217SEM þ FE218(A)219(B)220(C)221(D)222(A1)223(B1)224(C1)225(D1)226Tested2270.02392280.00212290.02202300.00222310.02392320.00202330.02202340.0020235(0.0189)236(0.0188)237(0.0172)238(0.0170)239(0.0189)240(0.0189)241(0.0172)242(0.0170)243Tested × Above DL2440.01462450.02762460.01502470.02772480.01452490.02882500.01502510.0289252(0.0181)253(0.0173)254(0.0182)255(0.0179)256(0.0181)257(0.0177)258(0.0185)259(0.0181)260Tested × Above MCL261—262—263—264—2650.00072660.01532670.00042680.0155269—270—271—272—273(0.0400)274(0.0379)275(0.0367)276(0.0354)277Lambda (λ)278—279—2800.1040***2810.0490***282—283—2840.1040***2850.0490***286—287—288(0.0145)289(0.0119)290—291—292(0.0145)293(0.0119)294%Δptest2952.362960.212972.172980.212992.363000.203012.173020.20303(1.84)304(1.89)305(1.68)306(1.70)307(1.84)308(1.89)309(1.68)310(1.70)311%Δptest→DL3121.453132.733141.493152.733161.443172.84*3181.493192.85320(1.79)321(1.68)322(1.79)323(1.74)324(1.78)325(1.72)326(1.82)327(1.76)328%ΔpDL3293.77***3302.52**3313.63***3322.52*3333.77***3342.65**3353.63***3362.65**337(1.32)338(1.23)339(1.31)340(1.33)341(1.35)342(1.33)343(1.35)344(1.36)345%ΔpMCL346—347—348—349—3500.073511.553523.663531.13354—355—356—357—358(4.00)359(3.85)360(3.47)361(3.46)362Observations3631,7303641,7303651,7303661,7303671,7303681,7303691,7303701,730371Block-group fixed effects372No373Yes374No375Yes376No377Yes378No379Yes380Continued381Guignet, Walsh and Northcutt382Impacts of Ground Water Contamination on Property Values 307383https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press384385386===== PAGE 16 (score 13, numeric-paren 0, table-words 3, var-words 6) =====387388Table 4. Continued389Variable390Base Model391Base Model plus Additional Interaction Term392OLS393FE394SEM395SEM þ FE396OLS397FE398SEM399SEM þ FE400(A)401(B)402(C)403(D)404(A1)405(B1)406(C1)407(D1)408No. of fixed effects409—41065411—41265413—41465415—41665417R-squared4180.79814190.77014200.80014210.82244220.79814230.77024240.80014250.8224426Note: The dependent variable is the natural log of the real transaction price (2013 U.S. dollars). Only coefficients of interest are shown, including Tested and427interaction terms capturing the incremental impact of contamination levels above the detectable limit (Tested × Above DL) and above the MCL (Tested × Above428MCL). *** 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,429standard 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 be430spatially correlated based on inverse-distance SWMs with a distance radius of 3,200 meters and a time constraint of twelve months (see the empirical431model for details).432Agricultural and Resource Economics Review433308 August 2016434https://doi.org/10.1017/age.2016.16 Published online by Cambridge University Press435