The Drill and the Bill: Shale Gas Development

and Property Values

Lucija Muehlenbachs

Elisheba Spiller

Christopher Timmins∗

May 31, 2012

Abstract

Although shale gas development has been a recent economic boon to vari-

ous areas across the United States, there are inherent risks in the process

that could be capitalized into the prices of nearby homes. We conduct a

hedonic study of the impact on property values by permitting and drilling

of shale gas wells both on and near the property. This study allows us to

quantify how property values change due to (1) the expectation of future

drilling in the case of properties with permitted (but not yet drilled) wells;

(2) the impact of having a wellhead situated on or near one's property,

including area impacts such as increased road congestion, noise, and pollu-

tion; and (3) the improvement in economic conditions associated with shale

gas development (such as increased immigration and employment). Using

data on all housing transactions from Washington County, Pennsylvania,

from 2004 to 2009, our preliminary results suggest that houses that rely

on private ground water wells are negatively a�ected by proximity to shale

wells whereas houses with piped water are positively a�ected by proximity.

Jel�Classi�cation: Q53

∗Lucija Muehlenbachs, Research Fellow, Resources for the Future, Washington, DC,muehlenbachs@r�.org ; Elisheba Spiller, Postdoctoral Researcher, Resources for the Future,Washington, DC, spiller@r�.org ; Christopher Timmins, Department of Economics, Duke Uni-versity, christopher.timmins@duke.edu. Acknowledgements: We thank Jessica Chu, CarolynKousky, Alan Krupnick, and Lala Ma. We thank the Bureau of Topographic and Geologic Sur-vey in the PA Department of Conservation and Natural Resources for data on well completions.We gratefully acknowledge support from the Cynthia and George Mitchell Foundation.

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1 Introduction

A recent boom in the extraction of natural gas and oil using unconventional meth-ods has transformed communities and landscapes. The extraction of resourcespreviously too expensive to obtain has resulted in many landowners receiving highresource rents for the hydrocarbons beneath their land. However, an increasingnumber of residential properties signing unconventional hydrocarbon leases are ofgrowing concern to mortgage lenders.1This paper focuses on shale gas extractionin Pennsylvania, which has, with staggering speed, become prevalent in thanks todevelopments in hydraulic fracturing and horizontal drilling. The intensity of theprocesses required to extract natural gas from shale rock suggest that there couldalso be higher risks to air, water, and health than the extraction of conventionalnatural gas. The perception of these risks, as well as increased truck tra�c orthe visual burden of a wellpad, could depress property values. Given a concernthat unconventional extraction will have a negative impact on housing prices,many lenders, such as Wells, First Place Bank, Provident Funding, and Bankof America, do not accept gas and oil leases on mortgaged property. Further-more, there are many limits to property owners signing over the mineral rightsto their lands. For example, Section 18 of the standard Freddie Mac/Fannie MaeMortgage document prohibits a property owner from leasing any portion of theirproperty without consent from the mortgage lender. This implies that a propertyowner who independently signs a lease with a gas company will be technicallyin default under the mortgage terms. Furthermore, standard mortgages do notallow the presence of hazardous materials on mortgaged property. Thus, a shalegas lease on mortgaged land would technically constitute a default under commonmortgages [May, 2011].

The growing concern over leasing mortgaged land is founded on many aspectsof the actual lease terms between the property owner and the gas companies. Onthe one hand, leases typically do not include any form of insurance or indem-ni�cation from damage to the property caused by the gas extraction. Yet thehomeowner has very little control over the terms of the lease. For example, thegas lessee can sell the lease to another party without notifying the homeowner.Additionally, while the original lease is usually contracted for �ve years, this canturn into an inde�nite, extended lease without the signing of a new lease. Infact, if the well is deemed to be in production, the rights to the gas can remainin place for decades.2 This extension of the lease can be problematic in the sec-

1Radow [2011] also illustrated in recent New York Times articles "Rush to Drill for NaturalGas Creates Con�icts With Mortgages", Oct. 19, 2011, and "O�cials Push for Clarity on Oiland Gas Leases", Nov. 25, 2011.

2Furthermore, postponing environmental remediation on wells that are not in production isalso common place; it has been demonstrated that many oil and gas wells that are "temporarilyinactive," and hence not environmentally remediated, will likely never be brought back ontoproduction, nor remediated [Muehlenbachs, 2012].

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ondary mortgage market, especially with the unwillingness of mortgage lenders to�nance land that has been leased for gas exploration. If a home is considered tobe technically in default, then the mortgage lender can require automatic repay-ment of the loan, greatly increasing the probability of loan default and foreclosureRadow [2011].

While mortgage lenders are wary of leasing for hydraulic fracturing, it is un-clear whether the �nancial risk involved with fracking is large enough to warrantan automatic technical default on the loan [May, 2011]. Being able to quantifythe true �nancial risk of allowing for the leasing of shale gas on mortgaged landis essential for mortgage lenders to make appropriate cost and bene�t analysesbefore allowing the sale of mineral rights on mortgaged property. Thus, we usea hedonic property value model to estimate e�ects of shale gas development inorder to quantify this �nancial risk.

2 Application of the Hedonic Model for Non-

Market Valuation

In the hedonic model, formalized by Rosen [1974]), the price of a quality-di�erentiated good is a function of its attributes, and the price increases in thoseattributes valued by individuals. In a market that o�ers a continuous array of at-tributes to choose from, the marginal rate of substitution between the attributelevel and the composite good is equal to the marginal price of the attributelevel. The slope of the hedonic price function with respect to the attribute atany one individuals' optimal choice is equal to the marginal willingness-to-payfor the attribute; thus the hedonic function is the envelope of the bid functionsof all individuals in the market. This implies that we can estimate the overallwillingness-to-pay, or valuation, for certain attributes by looking at how the priceof the good varies with these characteristics.

A vast body of research has looked at the e�ects of a locally undesirableland use, such as hog operations [Palmquist et al., 1997], underground storagetanks [Guignet, 2012], and power plants [Davis, 2011] to name a few, in orderto estimate the disamenity value of the land use (or willingness-to-pay to avoidthis land use). This paper will use hedonic methods to model the e�ect that thepresence of a nearby shale gas well has on property values in equilibrium.3 Inparticular, we use variation in the market price of housing with respect to changesin the proximity of shale gas operations to measure the implicit value of a shalewell to home owners. As such, it should be able to pick-up the direct e�ect of

3Assuming that the housing supply is �xed in the short-run, any addition of a shale gas wellis assumed to be completely capitalized into price and not in the quantity of housing supplied.Given that the advent of shale gas drilling is relatively recent, we would expect to still bein the "short-run". As more time passes, researchers will be able to study whether shale gasdevelopment has had a discernable impact on new development.

3

disamenities related to drilling operations (e.g., bright lights, odors, truck noise)and environmental risks (e.g. risk of water contamination and consequences ofspills and other accidents), as well as di�erentiate these from the amenity value ofthe improved economic conditions associated with shale gas development. Thisis analogous to the e�ect of a wind turbine [Heintzelman and Tuttle, 2011] wherethe undesirable land use is also accompanied by a payment to the property thatit is located.

The academic literature describing the costs of proximity to oil and gas drillingoperations is small. See, for example, Boxall et al. [2005], which examines theproperty value impacts of exposure to sour gas wells and �aring oil batteriesin Central Alberta, Canada. The authors �nd signi�cant evidence of substan-tial (i.e., 3-4%) reductions in housing price associated with proximity to a well.We contribute to this literature with a focus particularly on shale gas wells inPennsylvania.

We employ various estimation strategies in order to deal with issues that areprevalent when estimating a hedonic model. It could be the case that there areunobservable e�ects associated with the citing of a shale gas well that a�ect theprice but are not caused directly by gas well activity. For example, shale gas wellsmight be located more frequently in areas with existing industrial activities, noise,or unsightliness. We therefore �rst examine the e�ect of shale gas development onthe same properties over time, in order to remove unobserved location attributes.

However, because the hedonic price function is the envelope of individual bidfunctions it therefore depends on the distribution of characteristics in the housingstock. This means that if few of the neighborhoods in our sample are a�ected byincreased tra�c and noise, then there will be a lower premium placed on quietneighborhood location. However, if shale development is widespread and resultsin most of the neighborhoods being a�ected by heavy truck tra�c, then the houseslocated in the relatively few quiet neighborhoods would have a high premium.In the case of a widespread change in the distribution of a particular attributein the housing stock, it is possible that the whole hedonic price function mightshift, so that even the price of properties far from shale wells will be a�ected.Furthermore, the hedonic price function is dependent on the distribution of tastes.If households discover that shale development poses a risk to groundwater, thenthey would place even less value on the attribute of having shale development intheir proximity. If there was a shift across all individuals in their perception ofthe risks to groundwater from shale development then the hedonic price functionwould also shift. This might have been the case after water contamination inDimock Pennsylvania was brought to the public's attention at the beginning of2009, as well as in the release of the documentary �lm Gasland (2010). Bartik[1988] shows that if there is a discrete, non-marginal, change that a�ects a largearea, the hedonic price function shifts, and the total change in property valuesserves as an upper bound estimate for the bene�ts. Thus, to control for thepossibility of shifts to the whole hedonic price function, we also estimate the

4

willingness-to-pay separately by year.There are also studies that tease out the property value e�ect of speci�c en-

vironmental disamenities, rather than an undesirable land use in general, such asthe e�ect of air quality (see Smith and Huang [1995]) for a meta-analysis), waterquality [Leggett and Bockstael, 2000] or health [Davis, 2004]. Our study is framedat estimating the overall impact of a shale gas well, but we test for whether thereis a di�erence for individuals that depend on their own groundwater compared tothose who have access to piped water. This allows us to identify the disamenityof water risk associated with fracking separately from other disamenities as wellas the amenity value of the economic boom.

3 Background on Risks Associated with Shale

Gas

Shale gas extraction is now viable because of advances in hydraulic fracturing(fracking) and horizontal drilling. Fracking is a process in which large quantitiesof fracturing �uids (water, combined with chemical additives including friction re-ducers, surfactants, gelling agents, scale inhibitors, anti-bacterial agents, and claystabilizers) are injected at high pressure so as to fracture the shale rock, allowingfor the �ow of natural gas.4 The multiple risks associated with fracking mighthave an impact on property values and hence mortgage lenders. First, frackingcan cause a contamination of local water supply resulting from improper storage,treatment, and disposal of wastewater. Fracking also generates "�owback �uid",the hydraulic fracturing �uids that return to the surface after fracking, often con-taining salts, metals, radionuclides, oil, grease, and VOC's. These �uids mightbe recycled for repeated use at considerable cost, but may also be disposed of byland application or by treatment at public or private facilities. Mismanagementof �owback �uid can result in contamination of nearby ground and surface wa-ter supplies. Second, air pollution is a concern�escaped gases can include NOxand VOC's (which combine to produce ozone), other hazardous air pollutants(HAP's), methane and other greenhouse gases. Third, spills and other accidentscan occur�unexpected pockets of high pressure gas can lead to blowouts that areaccompanied by large releases of gas or polluted water. Improper well-casingscan allow contaminants to leak into nearby groundwater sources. Fourth, drillcuttings and mud can also be risky. These substances are typically used to lu-bricate drill bits and to carry cuttings to the surface, and often contain diesel,mineral oils or other synthetic alternatives heavy metals (e.g., barium) and acids,these pollutants can also leach into nearby groundwater sources. Other nega-tive externalities such as deterioration of roads due to heavy truck tra�c, minor

4Our description of fracking and its external costs is based on the discussion in Plikunaset al. [2011].

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earthquakes, and clearing of land to drill wells can also a�ect housing prices dueto a lower aesthetic appeal of the region in general.

4 Method

One of the more di�cult problems that arises when implementing the hedonicmethod is the presence of house and neighborhood attributes that are unobservedby the researcher but correlated with the attribute of interest. The speci�cationswe adopt in order to demonstrate and address this problem in the context ofthe hedonic framework include (i) simple cross-section, (ii) �xed e�ects, and(iii) matching. We brie�y review the econometric theory behind each of theseapproaches below.

4.1 Cross-Sectional Estimates

The most naïve speci�cation ignores any panel variation in the data and simplyestimates the e�ect of exposure to a shale gas well by comparing the prices ofhouses in the vicinity of a well to those houses that are not exposed to a well.Considering the set of all houses in the study area, we run the following regressionspeci�cation:

Pi = β0 + β1WELLDISTi +X ′iδ + Y EAR′iγ + εi (1)

where

Pi natural log of transaction price of house iWELLDISTi distance of house i to nearest shale gas well at the time of transactionXi vector of attributes of house iY EARi vector of dummy variables indicating year in which house i is sold

In this speci�cation, the e�ect of exposure to a well is measured by β1.The problem here is that WELLDISTi is likely to be correlated with εi (i.e.,

houses and neighborhoods that are near wells are likely to be di�erent from neigh-borhoods that are not near wells in unobservable ways that may a�ect housingprices). For example, houses located in close proximity to wells may be of lowerquality than those located elsewhere in the county. One way to check for thispossibility will be by comparing observable attributes of houses and neighbor-hoods, both located near and far from shale gas wells. Signi�cant di�erences inobservable attributes suggests a potential for di�erences in unobservables, whichcould lead to bias in the estimation of equation (1).

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4.2 Fixed E�ects

Properties that are near shale wells might di�er systematically in unobservableways from those that are not nearby wells. If properties farther from wells areassociated with better unobserved characteristics, then this would create an el-evated baseline to which the houses near wells would be compared, causing adownward bias in our estimate for the e�ect of a nearby well. As no two housescan occupy the same location, utilizing �xed e�ects allows us to di�erence awaythe unobservable attributes associated with the house's location.

The simplest approach to dealing with unobserved house and neighborhoodattributes that may be correlated with well exposure is to exploit the variationin panel data to control for time-invariant neighborhood attributes. SupposePit measures the natural log of the price of house i which transacts in year t.Xit is a vector of attributes of that house, and WELLDISTit is the distance ofhouse i to the nearest well at the time of the transaction. µi is a time-invariantattribute associated with the house that may or may not be observable by theresearcher, and νit is a time-varying unobservable attribute associated with thehouse. Importantly, µi may be correlated with WELLDISTit. Thus we employa �xed e�ects technique in order to remove µi from the equation:

Pi,t = β0 + β1WELLDISTit +X ′itδ + µi + νit (2)

Taking the within-household means of each variable:

P̄i =1

Ni

∑t

Pit (3a)

WELLDIST i =1

Ni

∑t

WELLDISTit (3b)

X̄i =1

Ni

∑t

Xit (3c)

µ̄i =1

Ni

∑t

µi = µi (3d)

ν̄i =1

Ni

∑t

νit (3e)

Where Ni is the number of transactions of house i. We then generate mean-di�erenced data:

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P̃it = Pit − P̄i (4a)˜WELLDIST = WELLDISTit −WELLDIST i (4b)

X̃it = Xit − X̄i (4c)

ν̃it = νit − ν̄i (4d)

Noting that µi − µ̄i = 0, we re-write equation (2):

P̃it = β1 ˜WELLDIST it + X̃ ′itδ + ν̃it (5)

Estimating this speci�cation controls for any permanent unobservable di�erencesbetween houses that have the shale well treatment and those that do not.

4.3 Matching

While �xed e�ects controls for unobserved household and location attributes, itdoes not take into account the overall time trends that are occurring. This isa concern, as shale gas well drilling is often associated with an economic boomin the area. Thus, areas near shale gas wells may in fact experience an increasein housing values overall given the increase in amenities associated with drilling.These amenities are mostly driven from increased population levels and employ-ment, leading to better neighborhood or city amenities that may be unobservedin the data. Yet this does not imply that, all else equal, being closer to a wellincreases housing values; instead the increasing neighborhood amenities may bedriving that result. This warrants going beyond the �xed e�ects model and con-ducting within-year estimation.

Matching models (such as nearest neighbor and propensity score matching)allow the researcher to isolate the impact of the treatment on the outcome in aquasi-experimental fashion. Since housing prices are only observed if a property iseither treated or not, but not both, matching allows us to create a counterfactualby comparing the outcome of a "treated" property with a similar "untreated"property. This allows us to estimate what would have been the price of a house-hold had it actually been farther away from the well, all else equal.

However, it is di�cult in this setting to identify a treated house vs. an un-treated house. For example, we would expect a house 1km away from a well tobe treated, though it is not clear whether a house seven kilometers away shouldbe treated. Thus, we employ several di�erent methods in order to �nd how thede�nition of treatment impacts the results.

First, we employ a generalized propensity score (GPS) model, as detailed inHirano and Imbens [2004], in order to �nd how the impact of proximity to shalegas wells varies at di�erent distances. GPS allows the treatment to vary continu-ously, while regular matching models assume a binary treatment. Thus, we de�ne

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the treatment as the distance to the nearest well, and estimate the impact onhousing values as this distance is varied. We utilize the Stata doseresponse mod-ule that estimates the GPS and the expected outcome given treatment, specif-ically a di�erent outcome at di�erent levels of treatment. We include as con-trols housing characteristics and census tract attributes. Ideally, we would rundoseresponse on each year separately in order to eliminate the time-varying is-sues that can bias the outcome from the �xed e�ects model. Unfortunately, oursample size is not large enough to run it with each year separately, so we have toestimate the dose response aggregated from 2006-2009. However, to control forthe unobserved attributes correlated with years, we include year dummies.

Our second approach utilizes propensity score matching (PSM), but takes intoaccount the fact that we have several treatment levels. In a binary treatment case,PSM predicts the outcome of "being treated" given house characteristics in orderto capture the fact that treatment is not exogenously assigned. This is calculatedthrough a probit regression, and the probabilities are then used to match treatedhouses with non-treated houses. This allows two similar houses, one that wastreated and one that was not, to be matched on observable characteristics, andprovides our counterfactual outcome.

Imbens (2000), Lechner (2001), and Lechner (2002) provide di�erent ways toestimate PSM under varying treatment outcomes. One of the proposed methodsis based on a series of binomial models, where each treatment level is compared toa di�erent treatment. This is the approach we entail. We choose the treatmentlevels as bu�ers around wells: each bu�er is a donut one mile wide, and wedo side-by-side comparisons. For example, we compare individuals living withina mile to a well with those living within one to two miles to a well. This isdemonstrated graphically in Figure 1. Here, house A is within one mile of thewell, and house B is within one to two miles of the well. This implies that houseA is in the treatment group and house B is part of house A's control group. Weemploy only side-by-side comparisons, that is, we do not compare, for example,a house that is �ve miles away with a house one mile away, as we would expectthe hedonic price gradient to be very di�erent at these di�erent treatment levels,thereby invalidating the assumption of marginal changes in amenities.

Unfortunately, we do not observe whether a household has leased out its landto a gas company. The size of the lease could possibly be capitalized into thevalue of the home, causing an increase in housing transaction value, which ifnot controlled for, would appear that willingness to pay for proximity to a wellis positive. Since we do not know which home pertains to a lease owner northe quantity of that lease, we control for those most likely to be lease owners.Horizontal wells in Pennsylvania tend to extend less than a mile in diameter5, so

5CONSOL reports that its average lateral well length is 3,400 feet.http://shale.typepad.com/marcellusshale/2011/02/consol-energy-reports-on-marcellus-shale-reserves-for-2010.html

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Figure 1: Bu�er Zones Around Wells for PSM

we control for houses within one mile from a horizontal permitted well as well asone mile of a horizontal producing well.

As a robustness check, we repeat the PSM method but utilize nearest neighbormatching, or covariate matching (CM). CM �nds the houses that are closest interms of attribute space by utilizing a Mahalanobis distance, which is calculatedas:

(Xs −Xr)′diag

−1∑X

(Xs −Xr)

Here, Xs and Xr are vectors of attributes, such as square feet, number of bed-rooms, lot size, and other house attributes. We also include census tract at-tributes so that we can match across similar areas in Washington County.

∑−1X is

the covariance of the matrix of housing and census tract attributes. By minimiz-ing the distance, we �nd the house that is closest in attribute space and utilizeit as a control. We also test this method by choosing the four closest homes andaveraging across their outcomes. However, Abadie and Imbens (2005) demon-strate that bootstrapping nearest neighbor matching techniques does not yieldvalid results, so these estimates should be read with caution.

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4.4 Water Wells vs. Piped Water

Much of the concern surrounding shale gas development is associated with waterwell owners. Houses that utilize water wells may be a�ected if the surface casingcracks and methane or other contaminants leak into the groundwater. Housesthat receive drinking water from piped wells, on the other hand, do not facethis risk. We hypothesize that this risk may be capitalized into the value of thehome: households using water wells may be more adversely a�ected by proxim-ity to shale gas wells than households using piped county water, and thereforewould face a lower transaction value. In order to capture this di�erence acrosshouses, we adjust our methods by splitting up the sample into houses relyingon groundwater and piped water. We use GIS data on the location of publicwater service areas and map the houses into their respective groups. When weconduct matching, we only match within water-use groups in order to capturethis unobserved attribute and to estimate the di�erential e�ect from proximityto shale gas wells in groundwater areas versus piped water areas.

5 Data

Our main dataset is provided by Dataquick Information Services, a national realestate data company. These data include information on all properties sold inWashington County, Pennsylvania from 2004 to 2009. The buyers' and sellers'names are provided, along with the transaction price, exact street address, squarefootage, year built, lot size, number of rooms, number of bathrooms, number ofunits in building, and many other characteristics. We begin with 41,266 ob-servations Washington County, PA, and remove transactions that did not list atransaction price or a latitude/longitude coordinate, had "major improvements",are described as only a land sale (a transaction without a house), or claim to bea zero square footage house. The �nal cleaned dataset has 18,160 observations.

In order to control for neighborhood amenities, we match the house locationwith census tract information, including demographics and other characteristics.The census tract data come from the American Community Survey, which is arolling average between the years 2005 and 2009.

We geocode the housing transactions data in order to match it to our secondmain data source: the location of wells in Washington County. We obtaineddata on the permitted Marcellus wells from the Pennsylvania Department ofEnvironmental Protection. To determine whether the permit has been drilled werelied on two di�erent datasets. A well is classi�ed as drilled if there was a spuddate (date that drilling commenced) listed in the Pennsylvania Department ofEnvironmental Protection Spud Data or if there was a completion date listed inthe Department of Conservation and Natural Resources Well Information System(PA*IRIS/WIS). As there were many wells not listed in both datasets, using both

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Figure 2: Well Pad with Multiple Wells

datasets allows us to have a more complete picture of drilling in Pennsylvania.Many of these wells are in very close proximity to one another, yet the data

do not identify whether these wells are on the same well pad. Well pads are areaswhere multiple wells are placed close to each other, allowing the gas companiesto expand greatly the area of coverage while minimizing surface disturbance. Asfracking involves horizontal drilling, a well pad can include many wells in closeproximity while maximizing access to shale gas below the surface. This can beseen in Figure 2, which demonstrates how six horizontal wells can be placed on asmall well pad, minimizing the footprint relative to vertical drilling� which wouldrequire 24 wells evenly spaced apart (as outlined in blue).

Without being able to identify well pads, we could be overestimating thenumber of wells that a house is treated by: for example, a house near the well padin Figure 2 would be identi�ed as being treated by six wells, though presumablythe impact on that house's transaction value would be more similar to a housebeing treated by only one well6. Thus, we create well pads using the distancebetween the wells, and treat these well pads as one entity that is a�ecting thehouse. In order to create well pads, we choose all wellbores that are within oneacre of each other and assign them to the same well pad.7

There are many wells that are permitted and drilled but have remained inac-tive. To determine whether a well has started production we obtained productiondata from the Pennsylvania Department of Environmental Protection. Pennsyl-vania only required �rms to report production on an annual basis until 2009(from 2010 to present biannual reporting is being required). We use the �rst dateof production from any wellbore in the wellpad to indicate that the wellpad isproducing.

6In future work we will examine this claim.7Pads vary from half an acre to �ve acres in size, however the major-

ity of wellbores in a pad are within one acre of each other. http://un-naturalgas.org/INFODROP%20What%20you%20need%20to%20know%20CBA%20v2.pdf

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We match the house transactions to wells located within 20 km of the house,including permitted but not drilled wells, drilled wells, and pre-permitted wells(wells which are permitted and drilled after the time of the housing transaction).Once these wells are matched, we create variables which measure each house'sEuclidian distance to the closest well that is either permitted or drilled at the timeof the transaction. These are our main variables of interest, as they identify the"treatment"�we want to measure how proximity to wells a�ects housing values.We also calculate the inverse of the distance (Inv. Dist. to Well) and use thisvariable as the treatment in the cross sectional and �xed e�ects speci�cations,allowing for an easier interpretation of the results in that an increase in inversedistance implies a closer distance to a well.

In order to capture the water risks that home owners may face from shale gasextraction, we utilize data on groundwater service areas in Washington Countyin order to identify houses that do not have access to public piped drinking wa-ter. We obtained the GIS boundaries of the Public Water Supplier's ServiceArea from the Pennsylvania Department of Environmental Protection. Houseslocated outside of a public water service area (PWSA) most likely utilize privatewater wells, since the county does not provide much �nancial assistance to in-dividuals who wish to extend the piped water area to their location (personalcommunication with the Development Manager at the Washington Coutny Plan-ning Commission). Given the high costs associated with extending the PWSA,we hypothesize that the majority of the individuals outside of PWSAs are waterwell owners. This allows us to separate the analysis by water service area intoPWSA and "groundwater" areas, and we use this distinction to identify the watercontamination risk that may be capitalized into the transaction value.

Summary statistics of the property transactions including information onhouse and census tract attributes and proximity to wells is presented in Table 1.The summary statistics show that the majority of houses are within �ve miles of awell, and 23% are within one mile of a well, though less than 1% are within a mileof a horizontal producing well. Individuals within a mile of a horizontal produc-ing well are most likely those who are receiving signi�cant lease payments fromthe gas company, and are thus controlled for in most of our analysis. Since wealso divide the sample into groundwater and PWSA areas, as well as propertiesnot in a city, we include summary statistics in Table 2 of three di�erent groups:groundwater, PWSA, and PWSA excluding the two largest cities in WashingtonCounty (Washington and Canonsburg). The groundwater group is quite di�erentfrom the PWSA group in some dimensions: the houses are smaller, though thelot sizes are much larger. On the other hand, the PWSA group excluding thecity is comparable to the PWSA group including the city.

Figure 3 shows the map of Washington County, Pennsylvania, and shows thepermitted and drilled (spudded) wells, property transactions, and the water ser-vice area. This map is a snapshot of all wells and transactions during the timeof the sample, so some of these houses were not close to a well at the time of

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Table 1: Summary Statistics

Mean Std. Dev. Min. Max.Housing Characteristics:

Transfer (Dollars) 126113 134570 150 3000000Ground Water .0896 .286 0 1Age 55 39.8 0 308Total Living Area (1000 sqft) 1.8 .875 .108 13.7No. Bathrooms 1.68 1.01 0 9No. Bedrooms 2.72 1.12 0 8Sold in Year Built .116 .32 0 1Lot Size (1000 sqft) 24.3 76.2 .004 4643Census Tract Characteristics:

Mean Income 65655 23778 28150 120886% Under 19 23.9 4.19 15.1 37% Black 3.78 5.87 0 31.7% Hispanic .426 .72 0 3.7% 25 w/High School 39.2 10.5 18.5 57.3% 25 w Bachelors 16.7 7.51 2.6 34% Same House 1 Year 88.6 6.75 52.6 97.3% Unemployed 6.19 2.84 .8 14.3% Poverty 7.63 6.93 0 29.3% Public Assistance 2.21 2.13 0 9.6% Over 65 17.7 4.92 7.1 36% HH Female 10 5.6 0 28.4Shale Well Proximity:

Distance to Closest Well (m) 10110 4313 146 19999Distance to Closest Permit (not Drilled) (m) 10230 4674 146 199981 mile from Horizontal Producing Well .00285 .0693 0 41 mile from a Well .233 .423 0 11 to 2 miles from a Well .322 .468 0 12 to 3 miles from a Well .451 .498 0 14 to 5 miles from a Well .409 .492 0 1Observations 19310

Notes: All transactions in Washington County, 2004-2009.

the transaction date. The large clustering of transactions in the center part ofthe county corresponds to the two major cities in the county: Washington andCanonsburg. These cities fall along the major highway that cuts through thecounty (the 79, which connects with the 70 in Washington City). We hypoth-esize that houses within these major cities face important amenities due to theeconomic boom associated with shale gas development. Thus, we exclude thesecities in certain speci�cations in order to identify the disamenity value associ-ated with proximity to a well separately from the amenity value associated withthe economic boom. We also look at these cities separately from the rest of thecounty in order to identify the positive amenity value of economic developmentcapitalized into property transactions.

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Figure 3: Transactions in Washington County 2004-2009. Includes PermittedWells, Drilled Wells, and Water Service Areas

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Table 2: Summary Statistics by Water Service Type

Ground Piped Piped, Not City

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.Housing Characteristics:

Transfer (Dollars) 103203 97080 128445 137605 123228 153178Age 53.02 38.7 55.23 39.88 60.3 38.91Total Living Area (1000 sqft) 1.645 .8487 1.813 .8759 1.825 .9456No. Bathrooms 1.496 .8586 1.701 1.018 1.648 1.008No. Bedrooms 2.653 .9701 2.729 1.129 2.736 1.113Sold in Year Built .06138 .2401 .1217 .327 .09882 .2984Lot Size (1000 sqft) 78.12 219.2 18.78 34.43 20.89 38.14Census Tract Characteristics:

Mean Income 61343 7856 66096 24795 67382 27670% Under 19 23.46 2.635 23.98 4.312 23.64 4.164% Black .6862 1.143 4.094 6.068 2.737 4.158% Hispanic .348 .3727 .4338 .7457 .4834 .8215% 25 w/High School 46.7 5.307 38.44 10.59 38.79 11.66% 25 w Bachelors 12.93 4.007 17.14 7.672 16.87 7.992% Same House 1 Year 92.39 2.329 88.19 6.927 89.96 5.78% Unemployed 6.491 2.467 6.163 2.873 5.924 2.566% Poverty 6.202 2.483 7.776 7.217 7.432 7.089% Public Assistance 1.77 1.151 2.256 2.206 2.248 1.767% Over 65 15.71 2.835 17.94 5.045 18.5 4.463% HH Female 8.116 3.286 10.23 5.75 9.319 5.228Shale Well Proximity:

Distance to Closest Well (m) 9520 5584 10161 4180 10908 4685Distance to Closest Permit (not Drilled) (m) 9563 5535 10288 4587 11042 50171 mile from Horizontal Producing Well .01965 .1973 .001195 .03769 .0011 .03581 mile from a Well .3595 .4814 .183 .3872 .2188 .41411 to 2 miles from a Well .5537 .4985 .2853 .4518 .3521 .4782 to 3 miles from a Well .4051 .4922 .4566 .4983 .4988 .50034 to 5 miles from a Well .5088 .501 .3992 .4898 .3499 .4771Observations 1730 17580 10914

Notes: (1)Groundwater (2) Piped Water (3) Piped Water & Not in Two Largest Cities

6 Results

Our �rst estimation technique is OLS, where we regress logged transaction priceson regression controls for house and census tract attributes and several treatmentvariables. These treatment variables include both inverse distance to the nearestdrilled well and this variable interacted with a dummy for ground water (equalsone if the house is located outside a PWSA). This allows us to separately identifythe impact of proximity to a well for households living in groundwater areas. Weexpect this coe�cient to be negative, as being closer to a well causes a greater risk(and thus larger disamenity values) to households living in groundwater areas.We also include inverse distance to the nearest permitted well in order to identifywhether there is a di�erent impact from permitted wells relative to drilled wells.This variable is also interacted with a groundwater dummy. We run the regressionfor the full sample as well as the subsample excluding the cities.

The results for the OLS regression are inconclusive. The coe�cient on inversedistance to nearest drilled wells is positive and signi�cant, while the interaction

16

with groundwater is negative and insigni�cant. Inverse distance to permitted wellinteracted with groundwater is positive but insigni�cant. The positive sign on thecoe�cient may be picking up the fact that proximity to a permitted well impliesa lease payment. In fact, these lease payments increase with the amount of landlease, and lot size in ground water areas is much larger than in the PWSA areas.Thus, the groundwater area houses may positively capitalize on the permittingof the well before the negative amenities associated with drilling occur.

However, the magnitude of these coe�cients is very large: the interpretationof the coe�cient is that a one unit increase in the variable causes a 100 ∗ β%increase in the housing price. Yet a one unit increase in inverse distance is huge.For example, if a house is 5km away from a well, a one unit increase implies thatthe house moves 5km closer to the well. Thus, for the average household thatlives approximately 10km away from a drilled well, this implies moving 10km tobe directly next to the well, which is arguably not a marginal change in amenities.A 0.01 increase in inverse distance is equivalent to being 1m closer for the averagehouse, so we can calculate this marginal change as β ∗ 0.01, though the resultingcoe�cient is still very large: 6.26, which implies a 626% increase in housingvalues.

This implies that there are certain unobserved attributes that are not beingpicked up by the regression and that may be correlated with geography or time,biasing the OLS coe�cients. Thus, we employ a �xed e�ects approach in order toremove these unobservable attributes. While this speci�cation removes all housesthat are not sold more

The results from the �xed e�ect and OLS techniques are presented in Table3. The sign of the coe�cients are intuitive, though the magnitudes are still verylarge. For the full sample (including cities), we �nd a positive impact of drilledshale gas well proximity on housing values, though it is negative (and larger)for those households living in groundwater areas. This implies that shale devel-opment causes an increase in housing values given the increase in neighborhoodamenities, though houses that do not have access to piped water have a largernegative impact due to fracking risks. When we exclude the cities, this e�ect iseven more pronounced: the size of the coe�cient on proximity to drilled wells de-creases, demonstrating that the amenity value of development is concentrated inthe cities. However, the magnitude of these impacts is still much larger than wewould expect. For example, for houses in groundwater areas, the overall impactof (inverse) distance to a well is 1079.430− 1353.804 = −274.37, which implies a274% decrease in housing values given a 1m decrease in proximity. This impliesthat perhaps there are unobservables that are biasing our result. Thus, we turnto matching techniques in order to account for some of these issues.

Our next approach utilizes a generalized propensity score technique in order tomeasure the impact of proximity to shale gas wells at di�erent levels of treatment.We include in the estimation controls for housing and census tract attributes andyear dummies. Furthermore, in order to measure how the treatment e�ect varies

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Table 3: Cross Section and Fixed E�ects

(1) (2) (3) (4)OLS OLS FE FE

Inv. Dist. to Well 239.430** 218.115* 1079.430** 666.970**(115.111) (121.583) (464.225) (316.742)

Inv. Dist. to Well*Ground Water -282.562 -394.995 -1353.804*** -1078.964**(206.421) (267.485) (413.192) (445.818)

Inv. Dist. to Permitted (not Drilled) Well 197.390 -66.716 601.489 2194.478**(132.798) (216.609) (564.923) (957.259)

Inv. Dist. to Permitted* Ground Water 349.608 626.896 334.152 269.448(250.607) (481.129) (831.945) (1821.669)

Ground Water -.164** -.163*(.068) (.088)

1 mile from Horiz. Producing Well -.050 -.023 -.530 -1.044*(.154) (.179) (.477) (.528)

1 mile from Horiz. Permitted (not Drilled) Well -.391* -.106 -1.241 -3.009**(.204) (.256) (1.244) (1.373)

Age -.014*** -.013***(.000) (.001)

Total Living Area (1000 sqft) .274*** .280***(.019) (.025)

No. Bathrooms .073*** .061**(.021) (.030)

No. Bedrooms -.016 -.026(.018) (.024)

Sold in Year Built -.197*** -.358***(.040) (.067)

Lot Size (1000 sqft) .003*** .003***(.001) (.001)

Lot Size Squared (1000 sqft) -.000** -.000**(.000) (.000)

% 25 w/High School -.010*** -.008**(.002) (.004)

% Black -.005 -.032***(.003) (.007)

% Hispanic -.112*** -.086***(.019) (.027)

% Unemployed -.033*** -.035***(.006) (.009)

Mean Income .000*** .000***(.000) (.000)

2006 -.069* -.102 .336 .320(.039) (.063) (.208) (.358)

2007 -.102** -.057 .700*** .673**(.040) (.063) (.196) (.333)

2008 -.258*** -.244*** .845*** .851**(.042) (.065) (.205) (.336)

2009 -.512*** -.510*** 1.386*** 1.493***(.059) (.083) (.260) (.357)

n 10,834 5,848 11,087 6,070Mean of Dep. Var. 11.09101 10.94333 11.06106 10.89657

Notes: Robust standard errors clustered at the census tract (102 census tracts). Columns (3) and (4) in-clude property �xed e�ects. Columns (2) and (4) do not include the two largest cities in Washington County(Washington and Canonsburg). *** Statistically signi�cant at the 1% level; ** 5% level; * 10% level.

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10.8

11

11.2

11.4

11.6

11.8

E[lo

g p

rice

]

0 2000 4000 6000 8000 10000Distance (m)

Dose Response 95% Confidence

Water Service Area (Full Sample)

10.4

10.6

10.8

11

11.2

E[lo

g p

rice

]

0 2000 4000 6000 8000 10000Distance (m)

Dose Response 95% Confidence

Groundwater Area (Full Sample)

Figure 4: Impact on Housing Values from Proximity to the Nearest Shale GasWell

across di�erent water service areas, we separate the estimation into two groups:groundwater and PWSA houses. Figure 4 demonstrates the impact of proximityto shale gas wells for the entire sample (including cities), and it appears that thetreatment e�ect of proximity varies substantially with water service. For housesin a PWSA, being closer to a well actually increases housing values. This impliesthat the local amenities associated with shale development can boost the housingmarket substantially, but only if the house is protected in some way from theenvironmental impacts. For houses without piped water, being closer to a shalegas well signi�cantly decreases housing values. Thus, we see strong evidence fora contrasting impact across di�erent water service areas. Figure 4 also showsthat the impact of proximity to shale wells tapers o� after approximately 6km,demonstrating that the impacts of shale development are signi�cantly localized.

While the results until now indicate that there exist both positive and negativeimpacts of shale gas development on housing values, the positive and increasingcoe�cients on the year dummies in the �xed e�ects speci�cation demonstratethat there are unobserved attributes that vary across the years, which could bebiasing our results. There was a lot of change in the area over the time period. Forinstance, the number of wells that were drilled in Washington County increaseddramatically between 2005 and 2009. Table 4 shows the increase in wells overtime, and demonstrates that the average distance to a well decreased by almost50%. Thus, it is important to restrict the analysis to within-year so that thechanges over time do not bias the results or distort the actual disamenity valueof well proximity. We are not able to run the dose response model on each year

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Table 4: Shale Gas Activity Over Time in Washington County, PA

Year No. Wells No. Permitted Dist. To Nearest Wells Distance to Nearest Permit2005 5 9 11952.9 11952.872006 25 32 11879.44 11883.632007 80 116 9370.836 7806.5282008 188 221 7336.622 7329.3012009 188 268 6326.31 6323.57

Notes: Wells and permits refer to the wellpad (there may be multiple wellbores on each well-pad).

separately given small sample sizes, so we instead utilize a di�erent matchingtechnique.

We employ propensity score matching in order to �nd the impact of movingcloser to a well on housing values. In order to allow for varying treatment e�ects,we divide the sample into groups of houses that are located at di�erent distancesto a well. The �rst group of individuals is located nearest to a well: within1 mile of a well. As we cannot identify lease holders or payments but thosewithin 1 mile may be receiving payments, we group these individuals together.The second group of individuals is located between 1 and 2 miles of a well; thethird is between 2 and 3 miles; and so on. We do side-by-side propensity scoreestimations to see what happens, for example, when someone in the second groupmoves into the �rst group, or someone in the third group moves into the second.This allows us to test the impact on housing values of moving one mile closer toa well. Since there are not many houses that are located in each group, we needto use a mile bu�er in order to allow for enough matches across groups.

The matching process is restricted to two types of exact matches: withinyears and water service areas. By matching within these groups, we capture boththe separate e�ect on groundwater houses as well as remove the time-varyingunobservables that can a�ect housing prices. We de�ne the comparisons in thefollowing way: inner-circle (group 2 moves into group 1), donut 1 (group 3 movesinto group 2), donut 2 (group 4 moves into group 3), and so on. Within theexact match, we match on the propensity score to be in a group, depending onobservable housing and census tract characteristics. We also control for whetherany households might have signed a lease by also matching on the number ofpermitted (not yet drilled) horizontal wells within one mile of the house.

Our results demonstrate a variety of impacts on housing values from proximityto well. First of all, we �nd no impact on houses within PWSAs�the coe�cientsare insigni�cant. This provides some evidence that houses with piped water areable to bene�t from the amenity value of growth more than they are negativelya�ected by the environmental impact of shale gas development. Given that wedo not have information on the lease payments to the home owners, it is notsurprising that we do not �nd an impact at the nearest area to the well (within

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Table 5: E�ect on Log Housing Price when Moving Closer to A WellPiped Water Groundwater

Year <1 mi. 1-2 mi. 2-3 mi. 3-4 mi. <1 mi. 1-2 mi. 2-3 mi. 3-4 mi.2006 -1.550 1.066 0.040 -0.296 -1.128 -0.212 -0.247 0.057

(.) (.) (0.504) (0.396) (.) (0.334) (0.833) (0.757)2007 3.696 -0.771 -0.121 -0.087 -0.918 0.258 -0.111 -0.016

(2.049) (0.582) (0.231) (0.183) (.) (1.179) (0.812) (0.999)2008 0.349 -0.097 0.039 0.272 0.176 1.061* -0.267 -0.819*

(0.876) (0.365) (0.172) (0.174) (1.724) (0.643) (0.983) (0.424)2009 1.170 -0.159 0.078 -0.044 0.148 -1.277* -0.737 -1.435

(1.072) (0.322) (0.247) (0.241) (0.731) (0.714) (1.291) (3.297)

Notes: Each column represents the di�erence between matches within the closest area to the well and the nextclosest area to the well. For example, Inner-circle refers to the di�erence in the treatment e�ect on houses within1 mile of a well and the treatment e�ect on houses between 1 and 2 miles from a well. Donut1 is the di�erencein the treatment e�ect on houses between 1 and 2 miles and the treatment e�ect on houses between 2 and 3miles (standard errors in parenthesis). * 10% Signi�cance level.

one mile). It may be that these houses within a mile are capitalizing on the leasepayments, which may overshadow the (presumably large) negative impact frombeing located very close to a well. Thus, it is di�cult to draw conclusions at thatproximity without information on lease payments.

However, farther away from the well (outside of one mile proximity), we do �ndsome evidence that houses that have groundwater appear to be more negativelya�ected by proximity to wells. For example, moving from 2 miles to 1 mileaway from a well (donut 1) raises the housing value in 2008, though lowers itin 2009. This may be due to the increase in public knowledge about the waterrisks associated with fracking during 2009, along with a signi�cant increase innumber of overall wells in the area. An increasing intensity of exposure to wellsmay have caused the housing values to react more negatively in 2009, while thehouses in 2008 were capitalizing on the positive amenity value associated withshale development impact on the local economy. However, we also �nd a negativeand signi�cant impact of moving from 4 miles to 3 miles (donut 3) in 2008. Thiscould be due to positive amenities being quite a bit more localized and at 4 milesthese bene�ts are not felt as strongly as they are at 1 mile of proximity. On theother hand, this may also be picking up on the fact that we have small samplesizes. In any case, the coe�cients are larger and more negative in 2009, lendingsome evidence that the disamenity of shale gas development increased in thatyear.

The results indicate that proximity to shale gas well decreased the log ofhousing prices in 2009 by 1.277 for houses in groundwater areas and 1 to 2 milesaway from a well compared to houses 2 to 3 miles away from a well. The averagehousing price in 2009 for this group of houses was $80,140, implying an overalldecrease of 28% in housing values, which is a more reasonable number than the�xed e�ects results.

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7 Conclusion

We �nd that shale gas development has some positive and negative impacts onhousing values. On the one hand, shale gas activity is an economic boom andincreases the amenity values of households living near wells. This is especiallytrue for houses in the cities and in PWSAs, as they are able to capitalize on theincreased amenities, possibly due to increased immigration and employment. Onthe other hand, the environmental disamenities, including water risks, decreasethe value of nearby homes, especially for those who live in groundwater areas.Thus, being able to mitigate the risk of fracking (through utilization of pipedwater) allows houses to bene�t from the increased amenities in their neighborhoodwithout having to bear the cost of the environmental risks. This implies that therecould be important welfare bene�ts from increasing access to piped water in areaswith a lot of shale gas development.

Though we �nd some evidence that the value of shale development is positivefor those in PWSAs, this does not imply a positive valuation of fracking. In-stead, the positive impact of development on housing values may overshadow thenegative environmental impacts of fracking. Being able to tease out these twopreferences for individuals with piped water in order to evaluate their willingness-to-pay is a matter of ongoing research.

Our current data source is quite limited, given both the geographic range anddates of transactions. This implies that our analysis (especially for matching)relies on few observations in the treatment group, which can cause our resultsto be skewed. Future steps in research involve acquiring newer data (includingsome data from 2012) on the entire state of Pennsylvania, and information onproperty boundaries in order to identify those houses that have a shale gas wellsituated on their property. Furthermore, we will incorporate information on roadconstruction and water withdrawals in Pennsylvania to account for increasedtruck tra�c due to shale gas development, which may cause an impact on housingvalues.

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