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American Economic Association The Housing Market Impacts of Shale Gas Development Author(s): Lucija Muehlenbachs, Elisheba Spiller and Christopher Timmins Source: The American Economic Review, Vol. 105, No. 12 (DECEMBER 2015), pp. 3633-3659 Published by: American Economic Association Stable URL: https://www.jstor.org/stable/43821388 Accessed: 05-11-2018 02:50 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms American Economic Association is collaborating with JSTOR to digitize, preserve and extend access to The American Economic Review This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms American Economic Review 2015, 105( 12): 3633-3659 http://dx.doi.org/10. 1257/aer.20 140079 The Housing Market Impacts of Shale Gas Development By Lucija Muehlenbachs, Elisheba Spiller, and Christopher Timmins* Using data from Pennsylvania and an array of empirical techniques to control for confounding factors, we recover hedonic estimates of property value impacts from nearby shale gas development that vary with water source, well productivity, and visibility. Results indicate large negative impacts on nearby groundwater-dependent homes, while piped-water-dependent homes exhibit smaller positive impacts, suggesting benefits from lease payments. Results have implications for the debate over regulation of shale gas development. ( JEL L71, Q35, Q53, R31) Technological improvements in the extraction of oil and natural gas from unconventional sources have transformed communities and landscapes and brought debate and controversy in the policy arena. Shale gas plays underlying the populated northeastern United States were thought to be uneconomical less than ten years ago, but now contribute a major share of US gas supply.1 Natural gas has been hailed as a bridge to energy independence and a clean future because of its domestic sourcing and, compared with coal and petroleum derivatives, its smaller carbon footprint and reduced emissions of other pollutants (e.g., particulates, sulfur dioxide, carbon monoxide, and nitrogen oxides). Yet, opposition to unconventional methods of natural gas extraction has emerged, citing the potential for damages from methane leakage, water contamination, and local air pollution (see Mason, Muehlenbachs, and Olmstead 2015 for a review). ♦Muehlenbachs: Department of Economics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4, and Resources for the Future (e-mail: muehlenbachs@rff.org); Spiller: Environmental Defense Fund, 257 Park Avenue South, New York, NY 10010 (e-mail: espiller@edf.org); Timmins: Department of Economics, Duke University, 213 Social Sciences Building, 419 Chapel Drive, Box 90097, Durham, NC 27708 (e-mail: christopher.timmins@duke.edu). We thank Kelly Bishop, Yanyou Chen, Jessica Chu, Elaine Hill, Mark Fleming, Carolyn Kousky, Alan Krupnick, Nicolai Kuminoff, Corey Lang, Lala Ma, Jan Mares, Ralph Mastromonaco, Klaus Moeltner, Jaren Pope, Seth Sanders, Stefan Staubli, Randy Walsh, Zhongmin Wang, and Jackie Willwerth for their support. We thank seminar participants at Carnegie Mellon University, Georgia Institute of Technology, EPA-NCEE, Tinbergen Institute/Free University of Amsterdam, Toulouse School of Economics, University of Massachusetts Amherst, University of Michigan, University of Pittsburgh, West Virginia University, Colorado School of Mines, and University of Stirling for their helpful comments. All remaining errors and omissions are our own. We are grateful to CoreLogic for the data on property transactions. We thank the Bureau of Topographic and Geologic Survey in the Pennsylvania Department of Conservation and Natural Resources for data on well completions. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. +Go to http://dx.doi.org/10.1257/aer.20140079 to visit the article page for additional materials and author disclosure statement(s). 1 In 2000, shale gas accounted for 1 .6 percent of total US natural gas production; this rose to 4. 1 percent in 2005, and by 2010, it had reached 23. 1 percent (Wang and Krupnick 2013). Natural gas from the Marcellus formation currently accounts for the majority of this production (Rahm et al. 2013) and can be attributed to advances in hydraulic fracturing, horizontal drilling, and 3-D seismic imaging. 3633 This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3634 THE AMERICAN ECONOMIC REVIEW DECEMBER 201 5 Economic and environmental impacts may also nomenon, where local areas facing shale develop employment, business activity, and governmen may also suffer from negative social, economic such as increased crime rates, housing rental co Albrecht 1978; Freudenburg 1982). Furthermor a "bust" if benefits from shale gas developmen goods might be expanded during the boom at co underutilized, and sectors with better growth boom, leaving the area worse off in the long ru Properties surrounding shale gas development in value depending on whether the benefits o Moreover, benefits and costs may be heterogene ple, properties that rely on private water may suff confronted with shale gas development if there Access to a safe, reliable source of drinking wat property's value; even a perceived threat to that on housing prices. This is very important, as the to contaminate groundwater has been hotly deb benefits from drilling can vary with a variety of f ing activity, environmental activism, economic urban density (Theodoři 2009; Wynveen 201 1; valid arguments on both sides of the debate sur question of whether the benefits outweigh the c paper uses hedonic theory to better understand analysis exploits the trade-offs between propert neighborhood characteristics and amenities) and the former.4 Measuring the impacts of shale gas a convenient way to quantify its effects (wheth The impact of shale gas development on prope of a growing body of literature. One of the fir McMillan 2005), while not a study of shale ga wells emitting hydrogen sulfide (a lethal gas More recent studies have focused on shale gas (2014) and Muehlenbachs, Spiller, andTimmins ( County, Pennsylvania. Gopalakrishnan and Klaib shale gas wells diminishes property values across Muehlenbachs, Spiller, and Timmins (2013) find only for properties dependent on private-groun source. James and James (2014) find negative im 2 See, for example, Raimi and Newell (2014) and Wynveen (201 1 opment is an active area of research; for papers specific to shale g (2014); and Fetzer (2014). An example from Dimock, Pennsylvania, can be seen in these Contamination in Dimock," Riverkeeper.org, March 22, 2012, ve in Dimock," eidmarcellus.org, August 3, 2012. Under ambiguity of groundwater-dependent properties. 4 See online Appendix Section B for a deeper discussion of the h This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL: HOUSING AND SHALE GAS 3635 Delgado, Guilfoos, and Boslett (2014) only find weak evidence o and Bradford Counties, Pennsylvania. At the broader level, Bos Lang (2014) find that groundwater-dependent homes in New Yo the possibility of shale gas development. Weber, Burnett, and X property values in Texas are higher in zip codes with shale, conj by local public finances. A major obstacle to accurately estimating the impact of shale g surrounding homes is the presence of correlated unobservables identification. Shale gas wells are not located randomly, but ma with features that aid in the drilling process, such as near a roa servable property and neighborhood attributes may therefore be proximity to wells and with the property value. Providing evide wells are not randomly assigned (see Figures 4 and 5), we highl of using variation in the price of a property over time to estim new nearby shale gas well. We are able to conduct this estimati long panel of property transactions spanning 1995 to 2012; many mate the impacts of shale gas wells by comparing values across d Facilitated by data from across the Commonwealth of Pennsylv triple-difference (DDD) estimator, combined with a mix of fixe ment boundary techniques to deal with time invariant and tim ables that may be correlated with proximity to shale gas wells source. Moreover, we show that similar results are obtained by ence-in-differences-nearest-neighbor-matching (DDNNM) techn rely on panel data variation for identification. By using a geog dataset of properties, we are able to measure economic impacts local level while controlling for macroeconomic effects (e.g., th outsourcing of manufacturing) at the county level. Finally, our erty transactions creates a solid baseline for our DDD estimator shale gas development. Our results demonstrate that groundwater-dependent homes tively affected by nearby shale gas development, indicating that to groundwater contamination has indeed materialized into a re proximate homes that have access to publicly supplied piped hand, appear to receive small benefits from that development. H only comes from producing wells, suggesting that it reflects roy homeowner from natural gas production. Recently drilled wells the past year) do not contribute to this benefit, providing evide and hydraulic fracturing stages of shale gas development are th The burden of aesthetic disruptions is corroborated by the findi impacts are only driven by wells that are not in view of the pro These results are particularly representative of the economic i development in light of the fact that the Marcellus shale gas play country.5 Given the amount of extraction that may occur in this reg 5 See US Energy Information Administration (201 1) for a ranking of shale gas pla recoverable reserves. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3636 THE AMERICAN ECONOMIC REVIEW DECEMBER 201 5 the effect on property values may have important i the benefits and costs of a large scale shift toward d Our paper proceeds as follows. Section I describ II details our data, and Section III reports our emp results, with a summary of different property value concludes. Finally, we provide an online Appendix th made to our dataset; (ii) hedonic theory, the simplify most of the hedonic literature (including our analysis using panel data when the residential composition sh checks over space and time; (iv) the impact of shale g sociodemographics, the frequency of sales, and new c ical heterogeneity of the results. I. Methodology Our goal is to recover estimates of the nonmarket gas wells by measuring their capitalization into housi tiated by proximity to wells and by water source - e.g that are dependent upon their own private groundwat water versus houses at a similar range in public wat piped water. In this paper we identify the different proximity and drinking water source. A. Impact Categories We categorize the impacts of nearby shale gas expl housing values as follows: Adjacency Effects. - This category refers to all of ated with close proximity to a shale gas well that ar source. Costs in this category may include noise and lution (McKenzie et al. 2012; Litovitz et al. 2013), scape, and visual disamenities associated with drilling The most obvious benefit would be royalties and lease owner for the extraction of the natural gas beneath t 6 Given that property values could be negatively affected by proximit why a homeowner would be willing to lease their mineral rights to the ga important to note that refusing to lease out the mineral rights under one's p drilling on a neighbor's land, which would still expose the holdout-homeo for example, of groundwater contamination). Horizontal drilling requires contiguous area, which implies that a critical mass of homeowners need to occurs. Homeowners may form coalitions to prevent drilling; however, un neighbors, each homeowner may have an incentive to deviate and lease th This may be particularly true if there is the possibility of a large up-fron bor's decision to sign a lease, therefore, leasing one's mineral rights will and still being exposed to the impacts of shale development. We may the choose to lease their rights although it might have been optimal for none 7 In Pennsylvania, upon signing their mineral rights to a gas company, of dollars per acre as an upfront "bonus" payment, and then a 12.5 perce This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL : HOUSING AND SHALE GAS 3637 the mineral rights from the surface (property) rights, leaving fu ability to profit from lease and royalty payments. The extent to have been severed throughout our sample is impossible to know detailed data on leases and deeds, which we do not have. Thus, o find little to no positive impacts for homes located near shale g rights may have been severed, and without knowing which prope their mineral rights, we are unable to capture the positive impac Instead, our adjacency effect estimates an overall net effect: th payments for those households who may be receiving them unable to profit from the lease payments due to severed mineral ative externalities of being located near a drilling site (excludin associated with the property depending on groundwater). Groundwater Contamination Risk ( GWCR ). - This category r tional cost capitalized into adjacent properties that are dependent Our identification strategy assumes that this is the only additio cency associated with reliance on groundwater.8 If royalty rate water source, then this should not impact our estimate of GWCR In addition to these two direct impacts of shale gas activities there are broader Vicinity Effects that can also impact housing p the impacts of shale gas development on houses within a broadl 20 km) surrounding wells and may include increased traffic con damage from trucks delivering fresh water to wells and haulin wastewater disposal (to the extent that is done locally), increase and demand for local goods and services, and impacts on local p allow these vicinity effects to differ by drinking water source a reflect jurisdictional boundaries that determine the extent to wh benefit from, for example, an impact fee.9 Furthermore, there which are not specifically related to shale gas activity and are th be common to areas with and without a publicly provided drink Given the time period that we study, this impact category inclu ble, the subsequent housing bust and national recession, impa and jobs moving overseas, and other regional economic impacts. Figure 1 is useful in describing our identification strategy, and in more detail in Section HIB. Area A represents a buffer drawn defines adjacency. That buffer is located in an area dependent u (GW), i.e., outside the public water service area (PWSA). To choo buffer, we use two pieces of evidence. The first comes from Os who find that drinking water wells within 1 km of shale ga extracted. Natural Gas Forum for Landowners: Natural Gas Lease Offer Tracker, http com/lease_offers_tracker.php?action=resources (accessed September 17, 2015). 8 Data on groundwater contamination resulting from shale gas development in Pennsy available to researchers or homeowners because there was no widespread testing of groun of drilling. What we are measuring is therefore the cost associated with the risk of c homeowners. 9 Impact fees are taxes levied on drilled wells. The total amount of impact fees collected in Pennsylvania through 2014 exceeded $850 million dollars, 60 percent of which is given to local counties and municipalities with wells. See https://stateimpact.npr.org/pennsylvania/tag/impact-fee/ (accessed September 17, 2015). This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3638 THE AMERICAN ECONOMIC REVIEW DECEMBER 2015 Figure 1 . Types of Areas Examined concentrations of methane. Although their findings are not causally identified, th study has received much press attention and to date is one of the most frequently cited studies on the environmental impacts of shale gas development. Second, the distance of the horizontal portion of the well is approximately 1 mile (or 1.6 km).1 This implies that lease payments would be provided to homeowners located within this distance of a well.1 1 We also vary the distance of the buffer to test our localized impact hypothesis, and find that distances less than 2 km are most affected by prox imity, thereby validating our hypothesis. Area B is located outside the adjacency buffer but is within the vicinity of a we and is located above the shale formation. Similarly defined regions of the PWSA area are labeled by C and D, respectively. II. Data We obtained transaction records of all properties sold in 36 count Pennsylvania between January 1995 and April 2012 from CoreLogic, a nati estate data provider. The data contain information on the transaction pri we convert into 2012 dollars), exact street address, parcel boundaries, squ age, year built, lot size, number of rooms, number of bathrooms, and nu stories.12 Figure 2 depicts the location of the Marcellus shale formation from the US Geological Survey) as well as the properties sold. 10 Although electronic records of the location of the horizontal segment of the wellbores are n anecdotal evidence suggests that wellbores are typically between 3,000 feet (0.9 km) and 5,000 feet Energy Information Administration 2013), but could be up to 10,684 feet (3.3 km) which is the longes well in the Marcellus shale (O'Brien 2013). Of course, payments would only be made to those households whose property is located above the while the pipes extend horizontally, they do not necessarily extend radially in all directions and theref of the homes located within 1 .6 km will not be entitled to a payment. Thus, the overall effect of proxi the combined impact on those houses that are eligible for payment and the remaining households eligible. See online Appendix Section A for a description of how we constructed our final samples. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL. 105 NO. 12 MUEHLENBACHS ETAL.: HOUSING AND SHALE GAS 3639 Figure 2. Property Sales Data from CoreLogic Mapped with GIS on Overlay of Marcellus Shale in Pennsylvania To determine the date that wells are drilled, we use the Pennsylvania Departm of Environmental Protection (PADEP) Spud Data as well as the Departme of Conservation and Natural Resources (DCNR) Well Information Sys (the Pennsylvania Internet Record Imaging System/Wells Information Sy [PA*IRIS/WIS]). Combining these two dataseis provides us with the most prehensive dataset on wells drilled in Pennsylvania that is available (for e ple, no other data distributors, such as IHS or Drillinginfo, would provide mo comprehensive data than this).13 The final dataset includes both vertical and zontal wells, both of which produce similar disamenities, including risks of gr water contamination. 14 Because operators are able to drill horizontally underground, they can locate tops of several wellbores close together at the surface, and radiate out the hori tal portion of the wellbore beneath the surface. Therefore, multiple wellbore be drilled within meters of one another on the same "well pad," concentratin surface disruption to a smaller space. Though the data do not group wellbores well pads, we believe this is important to consider when estimating the effec shale gas wells on nearby properties, as the impact from an additional wellbo likely different from the impact of an additional well pad. We therefore assum any wellbore within a short distance of another wellbore is located on the sam (specifically, any wellbore that is closer than 63 m, or the length of an acre, t other wellbore is designated to be in the same well pad).15 We start with 6,260 bores, which we group into 3,167 well pads (with an average of 2 bores per pad 13 We corroborated this by comparing our data with data from Drillinginfo, a credible third source; we 52 more wells than Drillinginfo and, because we have captured completion dates, we are able to use thes the "spud" dates are missing (which was the case for 847 wells). The spud date refers to the first day of dr Drillinginfo does not capture completion dates and thus provides a less complete dataset than that which we Risk of improper well casing or cementing would be present in both vertical and horizontal wells. 15 During completion, a multi- well pad, access road, and infrastructure are estimated to encompass 7.4 in size; after completion and partial reclamation, a multi-well pad averages 4.5 acres in size (New York Department of Environmental Conservation 201 1). This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3640 THE AMERICAN ECONOMIC REVIEW DECEMBER 2015 a maximum of 12). Using the geographic infor wells and the properties, we calculate counts o been drilled, within certain distances, at the t also provides information on the GIS location o to count the number of wells that have been pe (only about 60 percent of the wells that have b can also use the date that the well was permitte remained undrilled. We obtain the volume of na from the PADEP's Oil and Gas Reporting websi Pennsylvania has many hilly and mountainous depending on where the property is located, a h see all the wells within the adjacency buffer. F Kousky, and Chu (2013), who examine the p views, we count the number of wells that are To do so we use ArcGIS's Viewshed tool and a Elevation Dataset (at a 30 meter resolution) to p can see from all directions around the property visible wells within different radii at the time of the sale. To identify properties that do not have access to piped drinking water, we utilize data on public water service areas. We obtained the GIS boundaries of the public water suppliers' service areas in Pennsylvania from the PADEP and assume that any property outside these boundaries is groundwater dependent.18 Figure 3 shows the PWSA areas. The unshaded areas are assumed to depend on private groundwater wells for their drinking water source. This figure demonstrates that PWSAs are scattered throughout the state and that there are large areas without access to piped water, further illustrating the importance of estimating the impacts of shale development on groundwater-dependent homes. III. Empirical Strategy and Results In this section, we estimate the impacts of close proximity to shale gas wells on property values. These effects can be positive, such as in the case that the property owner receives royalty or other lease payments from the gas company for the natural gas extracted from their property, or negative, given perceived impacts of groundwater contamination, noise, light, and air pollution, or the alteration of the local landscape. The siting of shale gas wells can be strategic on the part of gas companies and must be agreed to on part of the property owner, so it is also important to account for a wide range of unobservable attributes that may be correlated with both the property and proximity to the shale well. We first provide some evidence that our adjacency buffer correctly identifies localized impacts. We then begin our 16The data are reported as annual quantities until 2009 and then semiannual from 2010 to 2012. 17 Of course, this technology has limitations. It does not tell us whether the homeowner would be able to see the well from the top floor of a home or from the edge of the property; it also does not take into account obstructing vegetation or other houses. Finally, a taller person may better be able to see the well. 18 There is not much financial assistance to households wishing to extend the piped water service area to their location. Doing so is a costly endeavor according to personal communication with the development manager at the Washington County Planning Commission, April 24, 2012. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL.: HOUSING AND SHALE GAS 3641 Figure 3. Public Water Service Areas in Pennsylvania estimation section with a triple-difference technique that also makes use of pr ties on the boundary of the public water supply area. Finally, we show that sim results can be obtained from a difference-in-differences technique combined w nearest-neighbor matching algorithm that does not rely on panel data variation identification. Comparing the effect over time we find it to be similarly sized in ferent time periods, though cutting by subperiod reduces sample size and stati significance. This points to our estimates being robust to the critique described Kuminoff and Pope (2014), though only weakly so due to low statistical power A. Descriptive Evidence of Adjacency Effects and Groundwater Contamination Risk Here we provide some evidence that the prices of groundwater-dependent houses are in fact affected by proximity to shale gas wells. We draw on a strategy similar to that employed by Linden and Rockoff (2008), which determines the point where a localized (dis) amenity no longer has localized impacts. For our application, this method compares the prices of properties sold after the drilling of a well to the prices of properties sold prior to drilling, and identifies the distance beyond which the well no longer has an additional effect. In order to conduct this test, we create a subsample of properties that have been sold more than once and with at least one sale starting after the placement of only one well pad within 10 km. 19 For each water source, we estimate two price functions based on distance to its nearest well pad: one using a sample of property sales that occurred prior to the well pad being drilled and the other using a sample of property 19 For this exercise, we choose to only look at homes that have one well pad within 10 km, as it would be difficult to separate the impact of the nearest well pad before and after the well pad is drilled if the home was already being impacted by another well pad drilled nearby. We chose 10 km because finding properties with only one well pad within farther distances would reduce our sample size, while we think it is a reasonable assumption that vicinity impacts that are felt at more than 10 km will be similar to those at 10 km. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3642 THE AMERICAN ECONOMIC REVIEW DECEMBER 2015 Figure 4. Price Gradient of Distance from Future/Curre Figure 5. Price Gradient of Distance from Future/Curren sales that occurred after the well pad was drill with local polynomial regressions using as depe regression controlling for county-year, quarter Figure 4 depicts the results from the local po on areas with access to piped water. This figu which depicts areas without access to piped w erty values of groundwater-dependent homes however, the prices for groundwater-dependent well remain the same before and after it is drilled. This exercise demonstrates that This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL : HOUSING AND SHALE GAS 3643 adjacency impacts differ by drinking water source within 2 km our usage of buffers less than 2 km in distance. It also demons of controlling for unobserved characteristics that might be cor of a well and the price of the property; in the case of public properties that are the closest to a well are priced lower ev drilled, while the opposite is true in groundwater-dependent ar B. Triple-Difference (DDD) Estimation ofGWCR Considering the impact categories described in Section IA begin by defining the components of the change in a particular time (A P) in each area: APa = A Adjacency + A GWCR + A Vicinity cw + A A Pß = AVicinityGW + AMacro A Pc = AAdjacency + AVicinityPWSA + AMacro A PD = AVicinityPWSA + AMacro, where, for example, A GWCR refers to the change in price a water contamination risk from new wells in area A. We differentiate vicin- ity effects by drinking water source: AVicinityGW refers to the vicinity impa on groundwater-dependent homes, while A Vicinity PWSA refers to the vicinit impact on PWSA homes. Our strategy for identifying adjacency effects us difference-in-differences (DD) estimator: A Adjacency DD = [APC-APD], where the first difference, "A," reflects the change in price of a particular house (e.g. accompanying the addition of a new well pad). The second difference compares change in prices for PWSA properties adjacent to shale gas development to change in prices of PWSA properties not adjacent to development. For the PWS homes, this differences away vicinity and macro effects that are common acro and D. Because vicinity effects may differ by drinking water source, we can on difference these away by looking within water sources; hence, our adjacency reg sions rely only on PWSA homes. Furthermore, note that the corresponding equat for GW homes results in both adjacency and groundwater contamination risk: (A Adjacency + A GWCR)DD = [APa - APB], Therefore, to estimate the effect of perceived groundwater contamination risk must then difference away the effects across PWSA and GW areas by implement the following triple-difference (DDD) estimator: A GWCRddd = [A PA - A PB] - [A Pc - A PD}. Similar to the expression for adjacency, in this expression, A reflects the fir difference, the change in the price of a particular house accompanying the addit This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3644 THE AMERICAN ECONOMIC REVIEW DECEMBER 2015 of a new well pad. The second difference co each adjacency buffer to the change in prices difference differences away relevant vicinit GWCR and adjacency effects. The third (and double-differences, eliminating adjacency effe new well pad. In order to conduct this estimation, we def results of our adjacency test in Section IIIA. In at well pads rather than wellbores to estimate a we focus on well pads because we are capturing When a pad is cleared and drilling begins, it is bore on that pad will have the same impact on Essentially, we assume that the perception of be the same regardless of the number of wellbo In deriving our empirical specification base begin by considering the price of house i at ti (k = 1, 2, ...K), a house fixed effect (/x,), a fixe raphy (i.e., either county or census tract) and y indicating the quarter ( q, ): K (2) In P j, = a0 + Pik wkt + fi¡ + uit + q, + e,„ k= i where k indexes the well and K is the total number of wells in Pennsylvania; wkt = 1 if well pad k has been drilled by time t (in a sensitivity analysis we differentiate between wells that are merely drilled and actually producing); and pik translates the presence of well wkt into an effect on house price based on its proximity. We can decompose equation (2) by dividing the well pads into those that are within 20 km and those outside of 20 km: K In k= K Pit i *=i = 365 days) in K km 0.021 0.023** 0.01 1** (0.018) (9.8e-03) (4.4e-03) New bores (drilled < 365 days) in K km - 4.4e-03 -9.7e-03 - 3.3e-04 (0.029) (0.013) (8.0e-03) Old undrilled permits (> 365 days) in K km 0.055** 0.022 0.01 1 (0.025) (0.014) (0.012) New undrilled permits (< 365 days) in K km 0.04* 7.2e-03 7.2e-03 (0.023) (0.014) (7.9e-03) Pads in 20 km - 6.0e-04* - 6.2e-04* -6.3e-04* (3.3e-04) (3.3e-04) (3.3e-04) Property effects Yes Yes Yes County-year effects Yes Yes Yes Quarter effects Yes Yes Yes Observations 212,207 212,207 212,207 Notes: Dependent variable is log sale price. Each pan per column. Regressors are the count of wells (or annu depending on the column. The sample used includes onl vice areas. Robust standard errors are clustered by cen *** Significant at the 1 percent level. ** Significant at the 5 percent level. ♦Significant at the 10 percent level. 52 wells, or less than 1 percent of the wel abandoned; therefore, examining the marg appropriate than examining the margin of and abandoned. In panel B we show that th by producing wells. This result is intuitiv payments to the homeowner and the closer receive payments.28 Our final specification in panel C explores in particular, we estimate whether newly d 28 In another specification, not shown, the amount of na natural gas production in the year of sale) also increases pr This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3652 THE AMERICAN ECONOMIC REVIEW DECEMBER 201 5 12 months prior to the sale of the home) affect p bores. When examining timing we focus on wellbo tially added to well pads and therefore an old well drilled on it would look similar to a new well pad. impact from proximity only holds for old wellbore newer bores have an insignificant, negative impac disruptions from the drilling and hydraulic fract truck traffic and noise from drilling and hydrauli benefits associated with lease payments. At a very positive effect felt from old drilled wells; howeve permits, implying that expectations for drilling ha erty values in close proximity.29 D. D iff e re nce-irt- D iff e re > i c e s Nearest-Neigh In this section, we find similar GWCR and adjace that do not rely on panel data variation. In the DDD poral variation in price; however, as described by estimates would be biased if the hedonic gradient that argument is that methods based on using pan time-invariant unobserved property or neighborh rately describe the slope of the hedonic price function residential composition changes over time, causing move. Their argument is summarized in our discuss in the online Appendix. In this subsection, we descr relies on cross-sectional data but uses the logic of d junction with matching techniques to achieve ident be within the same year; although the estimate is a only relies on within-year variation. We focus on re within-year estimates over time as our sample s year-by-year estimates. However, dividing the sam and late) provides weak evidence of a stable gradie The fundamental problem of causal inference is th observation in its untreated state and vice-versa; i observe the price of a house located in close proxim house instead located farther away ("same," in this c and neighborhood attributes, both time-invariant a are frequently used to control for time-invariant u may be correlated with the (dis) amenity of interest 29This provides some evidence that homeowners expect future drilli be some attenuation bias given future expectations. However, formally outside the scope of this research, both in terms of data and computa for a description of the method and data needed to conduct such an est transparent) static hedonic framework in this paper, but note that it is lik muted to the extent that buyers of houses unexposed to wells consider This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL: HOUSING AND SHALE GAS 3653 Matching estimators impute counterfactual observations by with similar houses from a control group.30 The effect of trea averaging across the price differences for matched pairs. Mo niques involved in matching estimators can be found in Abad Abadie and Imbens (2006); Abadie and Imbens (2011); and (201 1); our main specification uses the nearest-neighbor mat The key to the success of this type of matching estimator is to so that unobservable house and neighborhood attributes are no ment status. We do so here by limiting the control sample in by requiring exact matches in other dimensions.31 In particula matching algorithm allows us to require exact matches in th sion (i.e., census tract) to control for neighborhood unobserv poral dimension (i.e., transaction year) to control for time-v Performing nearest-neighbor matching on house attributes, w to be exact in these dimensions to help control for various f that might otherwise bias our results. Moreover, we limit the houses that we expect to be in a relatively homogeneous neig census tract. Thus, we (i) limit our analysis to only houses th a well pad (defining the treatment buffer to be 1, 1.5, or 2 k small adjacency buffer found in Section IIIA); (ii) require exa tract; (iii) require exact matches by year of sale; and (iv) perf rately for groundwater and PWSA houses. The idea behind th houses within 6 km of a well pad in the same census tract that source will be located in similar neighborhoods, thereby r that may be correlated with the location of the property. Req by year of sale will further eliminate differences in unobser year to year at this level of the neighborhood. The nearest neighbor matching algorithm is used to recove average treatment effect on the treated (ATT), or the impact a nonadjacent house inside the adjacency buffer. In Figure 1, a move from B to A for groundwater houses, and from D to We now show that, by differencing these ATT estimates, we estimate of GWCR. We begin by defining the price of properties in each of the four areas in Figure 1 in a cross-sectional analogue of equation (1). Rather than using the change in price of a particular property over time (i.e., A), we focus on cross-sectional differences in prices. Our nearest neighbor matching algorithm applied to ground- water houses yields an estimate of the GWCR combined with the adjacency 30 For more background on the advantages of matching compared to parametric hedonic methods, see Cochran and Rubin (1973); Rubin (1974); Rosenbaum and Rubin (1983); Rubin and Thomas (1992); and Heckman, Ichimura, and Todd (1998). It is important to note that there may exist residual impacts of shale gas development for homes that are not immediately adjacent to a shale gas well. For example, homes that depend on piped water may face some level of drinking water contamination if the public water source is contaminated; while rivers and streams have been found to be affected by shale gas development (see Olmstead et al. 2013) there have yet to be any studies of the impacts on tap water. Key to our identification is that outside of a clearly defined adjacency buffer, the homes are not only less likely to be affected by shale gas development but also that these homes will be equally affected by development regardless of location (i.e., the contamination of publicly sourced piped drinking water is not correlated with adjacency). This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3654 THE AMERICAN ECONOMIC REVIEW DECEMBER 2015 Table 4 - log Sale Price on Groundwater Contamination Risk of Well Pads from a Matching Estimator Sample 1 Panel All A. km 1 .5 km 2 km years PWSA (n = 9,278) 0.002 0.024 -0.013 (-0.08,0.08) (-0.03,0.08) (-0.05,0.03) GW(n = 1,869) -0.070 -0.092 -0.030 (-0.18,0.04) (-0.18,-0.01) (-0.11,0.05) GWCRDD -0.072 -0.116 -0.016 (-0.21,0.06) (-0.22,-0.02) (-0.10,0.07) Panel B. Before 2010 PWSA (n = 3,541) 0.113 0.032 0.052 (-0.04,0.26) (-0.08,0.14) (-0.02,0.13) GW(n = 807) 0.046 -0.083 -0.040 ( -0. 1 2, 0.2 1 ) ( -0.2 1 , 0.05) ( GWCRDD -0.067 -0.115 -0.092 (-0.29,0.16) (-0.28,0.05) (-0.22,0.04) Panel C. 2010 and later PWSA {n = 5,737) -0.059 0.004 -0.046 (-0.15,0.03) (-0.06,0.06) (-0.09,0.00) GW(n = 1,062) -0.104 -0.082 -0.032 ( -0.24, 0.04) ( -0.20, 0.03) ( -0. 1 3, 0.07) GWCRdd -0.044 -0.087 0.014 (-0.21,0.12) (-0.21,0.04) (-0.10,0.13) Notes: Samples comprise all houses within 6 km of a well pad (panel A), within 6 km and sold before 2010 (panel B), and within 6 km and sold in 2010 or later (panel C). Each house in the treatment buffer is matched with four houses in the control sample. Exact match required on year of sale and census tract. Matching also based on house attributes (lot size, square footage, number of bedrooms, number of bathrooms, and year built). Treatment buffer size varies between 1 and 2 km. Bias adjustment equation contains all house attributes. 90 percent confidence intervals reported in parentheses. effect: PA - PB = GWCR + Adjacency (hence, PA is the price of a house A, etc.). Applied to PWSA houses, it yields an estimate of the adjacency alone: Pc - Pd = Adjacency. Differencing these two estimates leaves us estimate of the GWCR: GWCRddnnm = (PA - PB) - (Pc - PD)The results of the nearest neighbor matching procedure are reported in Table 4. The first two rows report the point estimates and 90 percent confidence intervals for PWSA houses using 1, 1.5, and 2 km treatment buffers. The next two rows report comparable figures for groundwater houses. In all cases, the difference-in-differences estimate of the GWCR effect based on these estimates is negative. In the case of the 1.5 km treatment buffer, the DD estimate is large (-11.6 percent) and significant at the 10 percent level. The Kuminoff and Pope critique emphasizes that the temporal average gradient may not always provide a policy-relevant measure of welfare. However, dividing the sample by properties sold before 2010 (panel B) and properties sold in 2010 or after (panel C), the coefficients are similar across time periods though statistically insignificant (potentially due to smaller sample sizes of treated wells in each distinct time period). Therefore, our results weakly address the This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL. : HOUSING AND SHALE GAS 3655 Kuminoff and Pope critique. Importantly, we also show that, rely variation yields an average effect over time that is similar to the get using intertemporal variation.32'33 IV. Summary of Impacts Using a variety of empirical methodologies, we demonstrat groundwater contamination negatively affects house values in th Although data are not available to measure the impact of actual tamination, the perception of these risks is large, causing importa on groundwater-dependent properties near wells. While it is clear that the perceived risk of groundwater contam impacts property values, homes that rely on piped water may in being adjacent to drilled and producing wells. These results appea royalty payments (or expectations of royalties) from productive w evident from how the results change when we use different sized that the positive impacts from being in close proximity to a we distance becomes very small. The overall positive impacts are net near a well; i.e., net of any negative environmental externality ( noise pollution from drilling) that is common to all properties r ing water source. Thus, even homes with piped water are better farther from a well, as long as they are able (i.e., not too far) to payments. Consistent with the increase in property values being and lease payments, we find that the property value increase is d wells. We also find that this positive finding is explained by wells over a year prior to the sale, most likely because disruptions suc the drilling rig, and hydraulic fracturing equipment are present in well's life. Coinciding with the visual disamenity of a shale gas w these positive effects for wells that are not visible from the prope Similarly, for groundwater-dependent homes, the negative im are large when the property is very close (1.5 km or closer) t and become more negative the closer a home gets to a shale gas w the costs of groundwater contamination risk are large and si from -9.9 percent to -16.5 percent), suggesting that there c to the housing market from regulations that reduce the risk. U net impact from adjacency and GWCR and data on the house recent year (April 2011 to April 2012), we calculate the avera groundwater-dependent homes within 1.5 km of a well to be $30, 32 While the DDNNM point estimate is larger than the DDD estimate, it is important to confidence intervals overlap the DDD estimate. Furthermore, it is unlikely that we would the same results, given that the DDD estimator utilizes property fixed effects and the bo DDNNM estimate does not. In further supporting evidence provided in the online Appendix, we show that neighborhood characteristics are not found to have changed in an economically significant manner with the introduction of shale gas. 34This value is calculated using all groundwater-dependent properties that are within 1.5 km of a well and sold between April 201 1 and April 2012. For these properties, the number of well pads in 1 km and between 1 and 1.5 km are combined with the adjacency and GWCR coefficients from our boundary sample (columns 2 and 4, in the first panel of Table 2). This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms 3656 THE AMERICAN ECONOMIC REVIEW DECEMBER 2015 annual loss for GW properties is larger than the properties within 1.5 km of a shale gas well ($4, to keep in mind that our estimates do not full with groundwater contamination risk. Owners may purchase expensive water filters to clean t a shale gas well nearby; whole home filters can extent that our estimates do not capture adapt lower bound to the actual costs incurred by wells, implying that contamination risk reduc nearby homes. The use of the properties in the band surroun to using the full sample of homes) demonstrat servable attributes that vary with location can negative impacts on groundwater-dependent ho water-dependent neighborhoods may be differ ways when compared with more urban PWSA n might vary over time. Using a sample containi specifically limited to be within the PWSA bou for these unobserved neighborhood differences ting comparison based on water source. V. Conclusion Development of shale deposits has become increasingly widespread due to advances in technology that allow for the inexpensive enhanced extraction of natural gas. This rapid expansion in development has generated ample debate about whether the benefits from a cleaner domestic fuel and the accompanying economic development outweigh the local negative impacts associated with the extraction technology. This paper addresses many of these questions by measuring the net capitalization of benefits and costs of shale gas development at various levels of proximity and water source exposure. The ability of shale gas development to impact nearby groundwater sources has been a major point of discussion. We estimate the local impacts on groundwaterdependent homes to be large and negative, which is not surprising given the attention the media has been placing on this potential risk. As groundwater contamination can cause severe economic hardship on homes without access to piped water, the perception that a nearby shale gas well will cause irreversible harm to an aquifer can have significant effects on nearby property values. These forces are beginning to show up in the way housing markets located on shale plays operate - e.g., recent evidence 35This is calculated using properties that have access to piped water, are within 1.5 km of a well, and are sold in the most recent year of our data. If we also include properties within 2 km of a well and include coefficients from column 6 for properties within 1.5 km and 2 km of a well, the groundwater losses are smaller on average while the piped-water properties have similar gains (i.e., the average loss for G W homes within 2 km of a well is $16,059 compared to gains for PWSA homes on average of $5,070). 36These water filters can cost about $l,480/year for a family of four (http://www.ezclearwater.com/ wordpress/tag/whole-house-water-filtration-system/, accessed September 17, 2015). Given the cost to adjacent groundwater-dependent homes is near $30,000, this implies a yearly cost of approximately $1,500 under a 20 year mortgage, which aligns with the price of installing a filter to clean the drinking water. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL: HOUSING AND SHALE GAS 3657 that major national mortgage lenders are refusing to make loan close proximity to shale gas wells, and that insurance providers policies on those houses.37 However, shale gas development can also bring positive impac through increased employment opportunities, economic exp tantly, lease payments to the holders of mineral rights. Our es there are localized benefits to homes that are adjacent to produ drilling stage is complete. We find that the negative impacts o during the active portion of drilling activities; minimizing con aspects of drilling (such as truck traffic and land clearing) may the benefits of shale gas development. Therefore, while we find small benefits from being in c shale gas wells, we find strong evidence of localized costs bo groundwater-dependent homes. As these negative impacts are ba groundwater contamination risk rather than actual risk or contam understanding the probability of groundwater contamination wo REFERENCES Abadie, Alberto, and Guido W. Imbens. 2002. "Simple and Bias-Corrected Matching Estim Average Treatment Effects." National Bureau of Economic Research Working Paper 283 Abadie, Alberto, and Guido W. Imbens. 2006. "Large Sample Properties of Matching Estim Average Treatment Effects." Econometrica 74 (1): 235-67. Abadie, Alberto, and Guido W. Imbens. 2011. "Bias-Corrected Matching Estimators fo Treatment Effects." Journal of Business & Economic Statistics 29 (1): 1-11. Abbott, Joshua K., and H.Allen Klaiber. 201 1 . "The Value Of Water As an Urban Club Good: ing Approach to HOA-Provided Lakes." Paper presented at the Agricultural and Applied Association Annual Meeting, Pittsburgh, PA. http://econpapers.repec.org/paper/agsaaeal 1/ htm (accessed September 17, 2015). Albrecht, Stan L. 1978. 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Proceedings of the National Academy of Sciences 108 (20): 8172-76. Rahm, Brian G., Josephine T. Bates, Lara R. Bertoia, Amy E. Galford, David A. Yoxtheimer, and Susan J. Riha. 2013. "Wastewater Management and Marcellus Shale Gas Development: Trends, Drivers, and Planning Implications." Journal of Environmental Management 120: 105-13. Raimi, Daniel, and Richard G. Newell. 2014. "Shale Public Finance: Local Government Revenues and Costs Associated with Oil and Gas Development." Duke University Energy Initiative Report. Rosenbaum, Paul R., and Donald B. Rubin. 1983. "The Central Role of the Propensity Score in Observational Studies for Causal Effects." Biometrika 70 (1): 41-55. Rubin, D.B. 1974. "Estimating Causal Effects of Treatments in Randomized and Nonrandomized studies." Journal of Educational Psychology 66 (5): 688-701. Rubin, Donald B., and Neal Thomas. 1992. "Characterizing the Effect of Matching Using Linear Propensity Score Methods with Normal Distributions." Biometrika 79 (4): 797-809. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms VOL 105 NO. 12 MUEHLENBACHS ETAL : HOUSING AND SHALE GAS 3659 Theodoři, G. L. 2009. "Paradoxical Perceptions of Problems Associated wit Gas Development." Southern Rural Sociology 24 (3): 97-1 17. Throupe, Ron, Robert A. Simons, and Xue Mao. 2013. "A Review of Hydro tial Effects on Real Estate." Journal of Real Estate Literature 21 (2): 205 US Energy Information Administration. 2011. Review of Emerging Res Shale Oil Plays. Washington, DC: EIA. http://www.eia.gov/analysis/stu September 17, 2015). US Energy Information Administration. 2013. Technically Recoverabl Resources: An Assessment of 137 Shale Formations in 41 Countries O Washington, DC: EIA. http://www.eia.gov/analysis/studies/worldshaleg 17, 2015). Walls, Margaret, Carolyn Kousky, and Ziyan Chu. 2013. "Is What You See What You Get? The Value of Natural Landscape Views." Resources for the Future Discussion Paper 13-25. Wang, Zhongmin, and Alan Krupnick. 2013. "A Retrospective Review of Shale Gas Development in the United States." Resources for the Future Discussion Paper 13-12. Weber, Jeremy G. 2012. "The Effects of a Natural Gas Boom on Employment and Income in Colorado, Texas, and Wyoming." Energy Economics 34 (5): 1580-88. Weber, Jeremy G., J. Burnett, and Irene M. Xiarchos. 2014. "Shale Gas Development and Housing Values over a Decade: Evidence from the Barnett Shale." United States Association for Energy Economics Research Paper Series 14-165. Wynveen, Brooklynn J. 2011. "A Thematic Analysis of Local Respondents' Perceptions of Barnett Shale Energy Development." Journal of Rural Social Sciences 26 (1): 8-31. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:50:44 UTC All use subject to https://about.jstor.org/terms

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Running head: HOUSING MARKET IMPACT ON SHALE GAS PRODUCTION

Housing Market Impact on Shale Gas Production
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HOUSING MARKET IMPACT ON SHALE GAS PRODUCTION

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Shale Gas Production
Shale gas originates from shale "plays," which are shale developments containing a
considerable amount of flammable gas and which share comparable geologic and geographic
properties. Shales are fine-grained sedimentary rocks with generally low porousness that can be
rich wellsprings of oil and petroleum gas. Natural gas production from shales is among the rapid
growth trends in the United States (Muehlenbachs, Spiller & Timmins,...

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