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Environmental hazards : The micro-geography of land-use negative externalities Somerville, Tsur; Wetzel, Jake Sep 26, 2017

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Environmental Hazards: The Micro-Geography ofLand-Use Negative ExternalitiesTsur Somerville* and Jake Wetzel**September 26, 2017AbstractThe decisions on the siting of hazardous facilities and compensation for nearby landown-ers depends on an accurate estimation of the negative externalities these facilities place onproximate land uses, primarily residential properties. In this paper we highlight the sensitivityof these estimates to the treatment of distance from the hazard and to the presence of othernearby land uses identified at a highly granular geographic level. Recent opposition to the ex-pansion of North American pipeline capacity has been intense, mixing concerns about climatechange, environmental damage, and local opposition to the physical presence of pipelines intheir neighbourhoods. This paper studies the disamenity effects associated with the last factor.In doing so we generate results that more broadly address the specification and left out variablebias challenges of measuring the capitalization of negative location-specific environmental ex-ternalities. The key contributions of this paper are first showing that disamenity effects can behighly localized and easily susceptible to errors with parametric specifications. Second, thatthe magnitude of the effect on house prices arising from proximity are sensitive to land usesthat are not the hazard in question, but whose presence may be correlated with the hazard. Andthird, that negative news about a hazard increases the assessment of risk and lowers nearbyhouse values, but that this effect is temporary. We find that the quantitative effects of proxim-ity to oil pipelines are relatively small: prices are lower by 5.7 percent ($39.3k) for propertieswith a pipeline easement, 2.1 percent ($14.4k) lower for those properties adjacent to a propertywith an easement, and 1.4 percent ($9.6k) for those adjacent to the former, one property furtheraway from the pipeline. Though this last result is sensitive to specification choice. The pricesof all residential properties further away from the pipeline in our data are unaffected. Whenexpressed in cardinal distance, only the prices for residential properties within 100 meters ofthe pipeline easement are affected. The findings here suggest that care and flexibility withfunctional forms, the perception of hazards, and attention to land use contexts is necessary foran analysis of the negative externalities for residential property associated with proximity toenvironmental disamenities and that simple parametric treatments are highly likely to result inbiased estimates.JEL Codes: Housing Demand (R21), Valuation of Environmental Effects (Q51), Govern-ment Policy (Q58), Other Spatial and Pricing Analysis (R32)11 IntroductionThe increase in oil production in North America has to led to proposals such as Dakota Accessand Keystone XL in the US, and Energy East, Northern Gateway and Trans Mountain expansionprojects in Canada to move oil from North Dakota and Alberta to ports and refineries elsewhere onthe continent. The greater part of the opposition has targeted the role of pipelines in abetting fossilfuel use and its effects on climate change, but along the proposed pipeline paths there has alsobeen significant local opposition motivated by concern over the environmental risk from pipelinespills. A policy program for evaluating the routing and viability of a pipeline depends on anaccurate assessment of the negative externalities associated with a pipeline. This paper uses avariety of static hedonic and dynamic event study methodologies to estimate the capitalization ofthis latter effect on residential property values.1 Compared to existing work on the capitalization ofenvironmental hazards, our use of a large data set of transactions from a dense suburban area alonga 42 km stretch of pipeline in the Vancouver, BC Canada metropolitan area allows us to applymore precise and detailed treatments of location relative to the pipeline alignment and accountmore completely for the variety of externalities in the land use fabric than is the case in existingwork. We find that the simple parametric treatments of proximity in the existing literature are likelyto suffer from both specification and left-out variable bias. The former occurs because proximityeffects may be highly localized making more naive continuous parametric treatments of distanceinappropriate. The latter results from the correlation in geographic space between environmentalhazards and other land uses that also impose externalities on nearby residential land uses. Theother contribution of this paper is in parsing the effect of new information regarding a hazard,differentiating between a reminder of the risk imposed by the environmental hazard and a reminderof its presence.A consistent, reliable framework for assessing the effects of environmental externalities is neces-1The data for this paper were collected as part of a consulting project for Kinder Morgan examining the ef-fects of oil pipelines on the values of nearby residential property values. This report is available from Canada’sNational Energy Board as filing 2015-08-20 Trans Mountain Pipeline ULC B417-28 - Reply_Evidence-Appendix_9A-Landowner_Compensation - A4S7H52sary for appropriate cost-benefit analysis on facility siting and compensation for spills, leaks, andother hazardous discharges. The existing literature on the effects of proximity to environmentalhazards on residential property values is highly varied and does not offer clear guidelines for as-sessing the magnitude of externalities. This is in part because the nature and awareness of hazardsdiffer dramatically between hazardous waste sites, high-voltage powerlines, landfills, leaking oilstorage tanks, and gas and oil pipelines. In general, work on pipelines tends to find no effect ofproximity on residential property values. However, new information about the reminder of risksas well as pipeline construction does in some studies result in lower values than what would other-wise hold for nearby properties. We add to this literature and help explain possible reasons for thepatchwork of results in existing work. We take advantage of a far richer data set, both in terms ofthe volume transactions, controls for externalities from a variety of other non-residential land uses,and the use of very fine-grained treatments for distance than in previous work.We find that pipeline proximity results in lower property values, but only for the most immediatelyadjacent properties. Properties with a pipeline easement sell on average at a 5.7 percent ($39.3k atthe mean) discount, while those properties adjacent to the easement property sell at a 2.1 percent($14.4k) lower price. Houses on the next furthest lot have a discount of 1.4 percent ($9.6k).2Properties further away are unaffected. However, these results are sensitive to the type of land usethrough which a pipeline passes. The residential property adjacent to the the pipeline easementis 3.5 percent lower when the pipeline is located on a non-residential land use as compared withonly 1.6 percent lower when the land use is residential or open space. In comparison, parametricdistance specifications are quite sensitive to regression form and included variables. In our dataa simple linear distance measure enters positively, but its estimated coefficient is not statisticallydifferent from zero if the specification controls for properties that have an easement on them. Bettertreatment of other highly local non-residential land uses reduces the coefficient point estimate by25 percent. Finally, when we control for distance in a less parametric fashion, we find that distance2Properties within 100 meters and not on the easement have a 1.2 percent lower value. 80 percent of these are theproperties adjacent to or one further away from the easement.3only matters within 100 meters of the pipeline. In contrast continuous measures force the effectto fit all of the properties, potentially underestimating the pipeline effect for nearby properties andoverestimating it for those more distant.Using the same data and specification we also test for the effects of new information about risks oncapitalization. The particular contribution we make is differentiating between news that remindsbuyers of risks as compared with news that reminds them of the presence of a hazard. The pricingof risk should reflect the expected negative effects, the likelihood of an negative event occurring,but both are contingent on an awareness of the presence of the hazard. We conduct two differencein difference event studies, one for a well-publicized localized spill along the pipeline in the studyarea, and the second for the announcement by the pipeline’s owner of a proposal to twin the pipelineand nearly triple the pipeline’s capacity. We treat the former as a reminder of the risk associatedwith the pipeline and the of the presence of the pipeline. We understand the information embeddedthe second news event to be limited to a reminder of the presence of the pipeline. In the six monthsfollowing the spill, transaction prices for properties within 250 meters of the pipeline away fromthe spill site were 5 percent lower than those further away.3 However, this difference disappearsby nine months. In contrast, just the reminder that there is a pipeline is not enough to affect prices:there is no change by location relative to the pipeline alignment in transaction prices following theexpansion announcement.4The contributions of this paper to the literature on the effect of environmental risks on house val-ues in general and the effect of pipelines in particular lie in several areas. First, we present moredetailed and precise measures of proximity at a very localized level than are found in other papers.Second, we address the land use context of the pipeline easement itself and identify its critical in-fluence on the estimated effect of pipeline proximity on home value. The more general implicationis that non-random distribution of land uses near hazards can bias the estimated proximity effects.3The properties that experienced contamination did not sell so we did not estimate the direct effect of contamina-tion.4Pipeline are buried and there presence is not necessarily known to those nearby. It is possible that the announce-ment had no effect because buyers discounted the likelihood the proposed expansion would be allowed.4Finally, using the same data we take advantage of two different kinds of shocks to awareness ofthe pipeline’s presence and the possible risks to see whether increase in both affects prices. Theseare in addition to the narrower value of better identifying the effect of oil pipelines on nearbyresidential property values.The paper follows the standard framework. Immediately below is a brief review of the existingliterature on the capitalization of environmental risks, primarily for oil pipelines, on house prices.This is followed by a description of the oil pipeline in question, the geography of proximity, andthe transaction data we use in this paper. Finally we present the empirical tests of the effects ofpipeline proximity and information shocks, the first a spill on the pipeline and the second expansionannouncement, on house prices.2 Literature ReviewThere is an extant literature that explores the effects of proximity to oil and gas pipelines on houseprices. This research is part of the more general literature on the negative externalities of environ-mental hazards that measures the size of these effects through the relationship between exposureintensity in geographic space and the prices of residential real estate. This literature covers a verybroad range of work on environmental externalities. Surveys of this literature include review pa-pers by Farber (1998), Boyle and Kiel (2001), Jackson (2001), Braden et al. (2011) and Sigmanand Stafford (2011). Their reviews cover papers on the effects on quality of life and risks to personsand property from a wide range of undesirable land uses (e.g. hazardous waste sites, power lines,landfills, incinerators, and pipelines) as measured through a hedonic house price equation with theinclusion of a measure of proximity to the hazard as a right hand side covariate. The methodolo-gies range from simple static hedonic pricing equations to event studies or difference in differencesapproaches. With the the latter, the natural experiment is either some new information about therisk of the hazard or change in its status, such as approval, construction, start of operation, closure,finding of hazard, or remediation. Overall, it is hard to draw specific conclusions about the nature5of proximity due to the varied nature of the externalities associated with each of the different typesof environmental hazard studied in these works and the wide variation in the degree to which pa-pers address surrounding land uses, the distances over which effects are estimated, and the problemof non-random location of hazardous sites. For example, the findings of the hazardous waste siteliterature suggest that house prices fall with proximity to the noxious location, but not always asthe effects are sensitive to other nearby land uses and neighbourhood features.Within this broad group of work, two streams of research more directly inform the research pre-sented here. The first examines the explicit effect of oil pipelines (and relevant effects of gaspipelines), on nearby properties. These papers primarily use a static hedonic analysis methodology,regressing property value against distance to the pipeline and a set of structure and lot characteris-tics. The second studies the economic impact of proximity following some news regarding the riskof the pipeline, either a spill on the pipeline under study or news about spills in general, both ofwhich might be expected to heighten awareness and increase the magnitude or the duration of theassociated price effects. This type of information shock or new information about the presence ofa pipeline is likely to be especially important for pipelines because the presence of pipelines is notnecessarily known to buyers. While environmental disamenities such as high voltage transmissionlines, industrial facilities, and landfills can be seen or have a sensory impact on nearby properties(e.g. air quality, noise, visual), pipelines do not, as they have little visual presence and, unless thereis a leak or spill, entail no ongoing harm to nearby properties. The concern with pipelines is therisk of a catastrophic incident that results in loss of property value or complete loss of use due tocontamination, quality of life or, in the case of gas pipelines, injury or death because of an explo-sion. Since the risks posed by pipelines are limited to the risk of an accident, measuring the effectof proximity to oil pipelines on residential prices helps to parse the mix of impacts associated withdifferent types environmental hazards.The risk to property from a pipeline rupture is non-trivial, with partial losses from nearby oilcontamination on a property exceeding ten percent and for significant oil contamination a complete6loss of use.5 The most consistent work in this area comes from studies that examine the impact ofcontamination arising from leaking from underground oil storage facilities. For example, Simonset al. (1999) find loss of value from contamination of nearby soils to be between 14 and 16 percent.Zabel and Guignet (2012) relate house prices to publicized and unpublicized sites with leakingstorage facilities. They find that only sites where the contamination is well known have proximityeffects in the absence of known contamination, where these negative effects can exceed 10 percent.There is a need to perhaps differentiate between oil and gas pipelines. The spills of the former canlead to contamination and loss of use, while the latter do not, with effects dissipating rapidly. Spillsand ruptures of the former do not represent an immediate risk of injury and death, while the risk ofan explosion is acute with the latter.In a survey on gas pipelines, Wilde et al. (2013) report no evidence of proximity price effects in theacademic and professional appraisal literature, either for proximity in general or in the aftermath ofruptures.6 Even a more extreme form of pipeline risk manifests little pricing risk. In Boxall et al.’s(2005) study of gas wells and pipelines in rural Alberta, the case of sour gas is examined. Sourgas is both more noxious and more dangerous if released than conventional natural gas, gasoline,or crude oil.7 Pipelines appear to be associated with negative values, but this is likely a left-outvariable problem. Contingent on the presence of sour gas wells, the presence of pipelines doesnot result in a further erosion in value. Three general problems with this type of static analysisare: awareness of the presence of the hazard, and then methodological hazards may be located inlower land value locations, and they may also be associated with negative externality land uses forreasons that are distinct from the specific hazard. For example, if for purposes of cost minimization5While property owners typically receive compensation for the loss in value, the value of a property may beimpacted well into the future as a result of ongoing stigma.6For example, Kinnard Jr et al.’s (1994) hedonic study on gas pipelines and Diskin et al.’s (2011) matched-pairappraisal of properties adjacent to gas pipeline right-of-ways in three Arizona suburban subdivisions, both fail to finda negative relationship between pipeline proximity and residential sales prices.7Health and safety risks associated with sour gas facilities represent a special hazard regulations requiring minimumsetback distances between sour gas and oil facilities and residential land uses. In addition to setbacks, emergency planresponse zones (EPZs) are established around sour natural gas facilities, the size of these zones can range up to severalkilometers and the size is related to the maximum potential volumes or rates of release of gas. More more informationsee Boxall et al. (2005)7the original pipeline was laid through lower cost land, the factors that made that land low pricedmay also cause residences built in the area to be lower priced. Thus, proximity to the pipeline iscorrelated with lower house prices, but because of a third factor that attracted the pipeline to thelocation.Using changes in information is common in other research on hazards, where hopefully the dif-ference in differences methodology reduces problems with left-out variable bias from a hazard’snon-random location and the effects of other unmeasured land uses. These studies examine either[1] changes in information regarding the presence of a hazard or [2] changes in the extent of therisk as environmental remediation occurs. For example, Dale et al. (1999) find that at a broad metrolevel, the price of houses near a shutdown lead smelter rose faster than elsewhere after both theclosure and completion of environmental remediation. McCluskey and Rausser (2003) measurelocal house prices following the decommissioning of a hazardous waste incinerator. They find thatthe negative impact on housing prices arising from proximity to the incinerator dissipated after theclosure, but only slowly. The inverse to these natural experiments is the siting and construction ofa new facility. Kiel and McClain (1995) demonstrate that the expected negative proximity priceeffects from a rumoured, then proposed, then constructed, and finally operated garbage incineratorwere only manifested during the construction phase. What is more, this discount partially dimin-ished after ongoing operations commenced. Together, these findings suggests that house pricesdo respond as expected to new information. However, the revelation of new information seems tooccur slowly, and is stronger during negative market conditions (Case et al., 2006). Studies of oilpipelines have used the effect of spills on un-contaminated properties in order to address the sameproblems with identification.The absence of visual clues to their presence may mean that no proximity effects of pipelinesreflects an information failure: buyers do not know they are present so they do not discount housevalues for nearby units. This can be overcome by news about the pipeline, particulary if there isa rupture. Spills convey two types of information. First is the presence of risk, by serving as a8reminder of the pipeline’s existence. Second, a spill conveys information on the magnitude of risk,depending on the severity of the spill event. Researchers have used a spill along a given pipelineas a natural experiment to test for the effect of pipeline proximity. For instance, studies such asSimons (1999) evaluate changes in the relative value of properties located away from a spill sitealong the pipeline easement after a spill. That paper finds that following a spill on the ColonialPipeline in Fairfax County Virginia, the value of properties along the easement elsewhere in thesame county experienced a 4.3-5.5 percent drop. 8 A second similar paper (Simons et al., 2001)where the spill affected local waterways found short run price declines as high as 11 percent in asmall sample of houses on the polluted waterway. Other work on the impact of oil spills over timefinds a rather quick dissipation of the negative effect on prices. The Deepwater Horizon oil spill offthe Gulf Coast resulted in only temporary negative effects on coastal house prices: they returned topre-spill levels within 101 days in Siegel et al. (2013) and no lasting statistically significant effectson sales volume or price levels in Winkler and Gordon (2013).Work by Hansen et al. (2006) finds that in the absence of an information shock, there are nonegative proximity effects from pipelines. Using a highly concave inverse distance specification,they find that prior to the rupture and subsequent explosion on the Olympic Pipeline, there was norelationship between distance to either pipeline and house prices in their data. However, followingthe explosion, which because of the tragic deaths of an adult and two children in a park adjacentto the rupture, was extremely well-known, properties 50 feet from the Olympic Pipeline were anestimated 5.5 percent lower than properties beyond 1,000 feet from the pipeline. Interestingly, therewas no effect on the properties adjacent to a second oil pipeline that did not experience a ruptureand thus lacked apparent "stigma" or remained unknown. The negative price effect reported wastransient in its nature. The discount at which properties within 100 feet of the pipeline tradedfollowing the accident declined by 18 percent between 6 months and a year after the event, and by27 percent after two years.98He does not report whether this effect dissipates with time, and furthermore the number of transactions in theeasement after the spill is quite small, 76 over a four year period.9It is worth noting that very few sales, about 110 occurred within 300 feet (approx. 90m) of the pipeline over the9The Hansen et al. (2006) study highlights the importance of new information but does not explicitlymodel the dynamics of information flow, relying on just a before and after. McCluskey and Rausser(2001) test for the effect of the volume of local media information on the perception of risk andfind an explicit relationship, though they do not tie this to proximity. The same conjecture, thatnegative effects depend on information is explicitly tested by Freybote and Fruits (2015). Theyexamine how proximity to a gas pipeline is affected by perception of risk. To measure risk theyinclude a fixed effect that takes on the value of one if a pipeline explosion with fatalities wasreported in the news in the same month as the transaction. They study a high pressure gas lineover a 14-year period in ex-urban Oregon. Their findings suggest that property values increase by0.8% per foot in distance when there is a media report of a pipeine explosion that resulted in deathelsewhere in the transaction month during the construction period but none during operation. Themagnitude is of concern given the specification as a property 100 feet further away would have aneighty percent higher price whther if it was adjacent to the pipeline or one mile away. An attractivefeature of the data used in the paper is that it covers transactions observed prior to the construction,during the construction, and afterwards. The negative effect only appears during constructionwhen the presence of the pipeline is more perceptible. Similarly, Kask and Maani (1992) find thatnegative price effects from pipelines only occur during the construction phase, when the presenceis apparent, but once the pipeline is buried and operational there is no effect.Three papers of note address issues in specification and left-out variable bias in ways that relatedirectly to our paper. In general the papers on proximity effects look at large areas and modeldistance with a very simple monotonic parametric measure. Even if there is a specification thatallows for non-parametric relationships, for instance using fixed effects for distance bands, theminimum distance are typically at least a quarter mile. An exception is François (2002) whomodels the effect on house prices of proximity to high voltage power lines with a high degree ofgranularity and allows for a highly flexible specification. He finds that price effects are sensitiveto distance, direction, and the extent of visual awareness in ways that are not explicitly linear.multi-year analysis.10For many locations of environmental disamenities there can be other land uses that are consideredundesirable. Failure to account for them will result in a left-out variable problem, that biases thecoefficient on proximity away from zero. Taylor et al. (2016) correctly observe that sites withenvironmental hazards are also typically located near other land uses that may impose negativeexternalities on residential properties. They then account for this by including properties witha similar land use, but without a hazard in their empirical analysis. They find that the mix ofcommercial properties with negative effects on residential properties and sites with environmentalrisk have an additive effect on nearby residential land uses. Furthermore, they find that clean-upand remediation is not fully capitalized, and that a stigma effect remains. Work by Redfearn (2009)very clearly shows the problem with parametric treatments of distance. Using a semi-parametricspecification, distance becomes a highly non-parametric factor that varies by direction as well asdistance. Our treatment is not as general as Redfearn’s in part because our effects are much morelocalized than his study of the value of access to rail transit stations.The existing literature suggests that in general there are no effects of proximity to a pipeline onhouse values. When a spill has occurred, units closer to a pipeline, even if they are not contam-inated, have lower values. There are a number of problems with these studies. First, pipelinework studies a small geographic area or uses relatively few transactions, which in turn may bequite heterogenous. Second, they do not adjust for the nature of the pipeline’s easement, wheredistance from green space can be expected to have a different effect on nearby properties thandistance from an industrial area. Third, the treatment of distance is typically just a parametriccontinuous measure and imposes assumptions about the relationship between proximity and value.Our contribution to this literature comes from having sufficiently rich data and events to addressthe problems identified in the research for a single hazard. First, like François (2002) we test forprice effects that are not parametric in distance, allowing for highly granular effects at extremelyclose distances. Second, we account for a variety of other land uses that can affect property valueswhose locations may be correlated in space with the pipeline, as is the case in Taylor et al. (2016).In addition, we account for land uses through which the pipeline itself passes, trying to separate11the pure pipeline effect from its land use context. Finally, we have two information events, one aspill and one an announcement. Comparing these two allows us to partially differentiate betweenthe effects of presence and the type of risk.3 Data and MethodologyThis study uses data along a segment of the Trans Mountain Pipeline (TMPL) that traverses thecities of Burnaby, Coquitlam, and Surrey in Lower Mainland area of Vancouver, BC. Burnaby isthe pipeline terminus and the three cities reflect the western most and most urban section of thepipeline routing in British Columbia. The cities are all part of the Vancouver, BC Canada CensusMetropolitan Area (CMA), their combined population as of the 2011 census is 842,200, makingup 35 percent of the metro area’s 2.37m population, and 18.6 percent of the area’s land mass.The pipeline was built during 1952-53 and runs 1,155 km from Edmonton, AB to Burnaby, BC.In Burnaby there is a tank farm for storage and a marine terminal for shipments. The pipeline’sinitial capacity was 150,000 barrels per day (bpd). By 1973, the construction of additional pumpstations expanded capacity to it’s current maximum of 410,000 bpd. Peak delivery of 381,871 bpdoccurred in 1972. Shipments have fallen since then as From throughput ranged from 200,000 toalmost 300,000 bpd between 2002 and 2010 Kheraj (2015).3.1 Description of sample and summary statisticsThe primary data for this study are the singe family detached properties between 2000 and 2014within a 1.0 km buffer along either side of the pipeline easement in the area of study. indicatesthe location of the easement of the Trans Mountain Pipeline. The location of the single-family de-tached property transactions in the sample along with the pipeline alignment and buffer are shownin Figure 1. These data were obtained from Landcor Data Corporation, a commercial providerof housing data for the province of British Columbia. The data includes every single family res-idential property transaction in Burnaby, Coquitlam and Surrey between 2000 and 2013 along12with property characteristics such as lot size and floor area, the number of bedrooms, bathrooms,garages, and stories, and year built. For each property, we also obtain information on the dis-tance to the pipeline alignment and also to other land uses including: commercial, industrial; civic(government, institutional, and recreational); major and minor arterial roads; and open or greenspace.Figure 1: Study area with TransactionsOver the period 200 to 2014 house prices in metro Vancouver more than doubled, making pricecomparisons across time hard to interpret. To address this we report an adjusted house price mea-sure, which is the observed price deflated to 2014 dollars using estimated city-specific quarterlyhouse price indexes. The indexes are created using the Case-Shiller version of the more generalBailey-Muth-Nourse repeat sales methodology and paired transactions for single family propertiesin each city that lie outside the pipeline corridor. The final sample we use is this total transactioncounts windsorize to cut the one percent tails in lot size, floor area and adjusted sales price. Thisleaves us with 12,419 transactions of 7,557 units within the 1.0 km band. Descriptive statistics forthese data are presented in Table 1. The first group of variables are price and the standard hedoniclot and structure controls. The second group of variables correspond to the constructed geographic13Table 1: Descriptive statistics for all transactionsmean median sd min maxProperty Characteristics Variables:Log price 12.97 12.97 0.46 11.33 14.54Log of repeat sales index 4.21 4.25 0.29 3.52 4.61Repeat Sales index adjusted price 687,837 652,426 271,018 157,314 2,515,605Lot size (thousands of sq/ft) 8,263 7,649 3,297 3,709 46,174Floor area (thousands of sq/ft) 2,658 2,403 977 812 6,150Number of bedrooms 4.20 4.00 1.25 1.00 8.00Effective age of property 31.67 30.00 14.33 2.00 95.00Number of stories 1.42 1.00 0.49 1.00 2.00Single garage dummy variable 0.22 0.00 0.41 0.00 1.00Multi garage (Ordinal variable) 0.59 1.00 0.51 0.00 3.00Number of full bathrooms 2.03 2.00 1.17 1.00 6.00Number of partial bathrooms 0.88 1.00 0.73 0.00 6.00Dummy, =1 if detached with suite 0.25 0.00 0.44 0.00 1.00Geographic Control Variables:Dummy, =1 if property < 100m from civic land use 1 (park/golf course/open green space) 0.14 0.00 0.34 0.00 1.00Dummy, =1 if property < 100m from civic land use 2 (govt bldg/works yard/cemetary) 0.22 0.00 0.41 0.00 1.00Dummy, =1 if property < 100m from civic land use 3 (institutional land use) 0.26 0.00 0.44 0.00 1.00Dummy, =1 if property < 250m from industrial land use 0.06 0.00 0.24 0.00 1.00Dummy, =1 if property < 250m from commercial land use 0.21 0.00 0.41 0.00 1.00Dummy, =1 if property < 40m from major arterial road 0.07 0.00 0.25 0.00 1.00Dummy, =1 if property within 40m of minor arterial road 0.01 0.00 0.12 0.00 1.00Pipeline Proximity Variables:Distance to pipeline in km 0.47 0.46 0.30 0.00 1.00Observations 12,419control variables. These include dummy variables that take on the value of one if the propertylies within a specified distance of civic, industrial, and commercial land uses and major and minorarterials. We group the civic land uses into three groups corresponding to their likely amenityimpact: civic land use 1 includes land uses that are likely to be have a positive amenity value (e.g.parks, golf courses and open green space), civic land use 2 includes land uses that are likely to bedisamenities (e.g. public works yards, cemeteries, misc government buildings), while use 3 coversinstitutions such as schools and hospitals. While few transactions are near a minor or major arterialroad, seven and one percent within 40 meters of each respective type, approximately one quarterare with 100 meters of an institutional civic use. Most (94%) are more than 250 meters from anindustrial land use, while a fifth are within the same distance of a commercial land use footnoteThechoice of distances from the dummies is the maximum that yields consistent statistically differentthan zero regression coefficients and uses the same distance within land use group types.Properties within 100 meters of the pipeline have similar mean values for these variables. the14exception is mean lot size, which is ten percent larger for properties close to the pipeline than inthe overall sample. This is principally because the mean lot size for properties with a pipelineeasement (so the pipeline transverses the property) is about 12,000 sq ft, which is about 50 percentlarger than the mean lot size in the general sample. Larger lot size is endogenous, as the easementis 18m (59 ft) wide and no structures may be built on the easement. For a residential lot with aneasement to be useful, it must be larger.The large number of observations gives us flexibility to characterize the distance of a property fromthe pipeline using measures that more closely measure the properties which are in close proximityto the pipeline. We measure proximity to the pipeline three separate ways: as a continuous functionof distance (0 to 1.0 km), in discrete bands of distance away from the pipeline, and finally as anordinal measure of adjacency. For the latter we rank each property by distance from the pipelinein the number of properties removed. A property with an easement would have an adjacency ofzero, a property adjacent to the property with an easement would have an adjacency of one, thenext property, the value would be two. Adjacency is calculated along a vector perpendicular tothe pipeline easement. .10 In Table 2 we provide frequency counts on these different ways tocharacterize proximity. The upper panel, Panel (a), shows the frequency counts for transactions asan ordinal ranking of distance. We think that this measure may capture an element of awarenessof a hazard, since even if it is hidden you might be expected to be more aware of it if you abut thehazard’s location. Among our 12,419 transactions, one percent are of parcels with an easementand almost five percent are of a property that abuts the land use with the easement. This wouldbe the immediate neighbour of a property with an easement or along a road where the easementruns down the middle or along side the road. Another four percent are one property further away.Nearly all of the properties identified in Panel (a), are within 100 meters. Panel (b), the lower panelgroups transactions by distance. Using rings will allow us to estimate effects with a less parametric10The latter orders properties 0 to 3, then beyond, based on the number of properties that separate them from theeasement. This latter we think of as distance in information space, as information about the pipeline is highest forthose closest or with neighbours who have an easement. Distance in this case is in the flow of information acrossproperty owners, and measured by the number of properties between a landowner and the easement.15specification. The transactions are distributed fairly smoothly by distance. Nearly twelve percentof the sample is within the nearest 100m ring, and approximately half of the transactions are withinhalf a kilometre of the pipeline.Table 2: Frequency counts on distance to pipeline easement and adjacencyPanel (a) : Pipeline Proximity - Adjacency Measures Count Proportion (%) Cumulative (%)Indicator : Pipeline easement on property 134 1.08 1.08Indicator : Property is 1 parcel from pipeline 587 4.73 5.81Indicator : Property is 2 parcels from pipeline 490 3.95 9.75Indicator : Property is 3 parcels from pipeline 421 3.39 13.14Total 1,498 13.14%Panel (b) : Pipeline Proximity - Distance Bands Count Proportion (%) Cumulative (%)Indicator : Property is 0 - 100m from pipeline 1,474 11.87 11.87Indicator : Property is 100 - 250m from pipeline 2,098 16.89 28.76Indicator : Property is 250 - 500m from pipeline 2,927 23.57 52.33Total 6,499 52.33%There is substantial overlap between the adjacency and distance band measures reported in Table 2.In table 3 we cross-tabulate the distance bands and adjacency measures. There is substantial over-lap between properties no more than one property removed from the easement property (adjacencyvalues of 0, 1, or 2) and being within 100m of the pipeline: eighty percent are within 100 metersand 99% are within 250 meters. Because the variables are so closely related, in the regressions wetest for the fixed effect of each of these variables separately.Table 3: Cross Tabulation of Adjacency and Distance Bands MeasuresProperty is Property is Property is1 Parcel from 2 Parcels from 3 Parcel from (%) TotalPipeline Pipeline Pipeline by RowProperty is 0 - 100m from pipeline 38.18 28.91 13.02 80.11Property is 100 - 250m from pipeline 1.00 3.00 14.95 18.96Property is 250 - 500m from pipeline 0.00 0.80 0.13 0.93(%) of Total by Column 39.19 32.71 28.1 100%Total Observations 1,498163.2 Identification challenges and proposed testsA clean test to measure the impact of pipeline, or any other hazard, proximity on house prices ischallenging because the identification is affected by both left out variable bias and endogeneityin the relationship between pipeline and residential location choices. Ideally a randomly locatedpipeline in a stable unchanging residential area would allow for a straight-forward difference indifferences test. In reality pipelines are not randomly located. The cost of constructing a pipelineis a function of its length, the cost of acquiring the pipeline easement, and the degree of localopposition. Pipeline firms will tradeoff among these factors in determining a pipeline?s alignment.As well, they may invest in local amenities to obtain local approval. This leads to several types ofestimation bias. While we address these factors for a pipeline, these difficulties would pertain tothe estimation of any other hazard.The simple hedonic estimation of house price on proximity to a hazard will be subject to bothleft out variable and endogeneity bias in the estimated coefficient on proximity. Left out variablebias may occur because pipelines may be located on inexpensive land. The unobserved factor thatcauses the low land prices that attracts pipeline development to lower land acquisition costs alsoresults in lower land prices for residential development, so that nearby homes have lower pricestoo. The second type of left out variable bias is the more general hedonic regression problem ofstructure quality when cheaper houses built on less expensive land have lower unobserved quality,so that the effect of proximity is biased away from zero as location is correlated with unobservedunit quality. The third form of bias might result from the endogenous location of amenities andpipelines. This is either because pipeline builders provide amenities as part of the approval processor because following pipeline construction, local governments turn pipeline easements into lineargreen spaces or create amenities on the space that does not involve permanent buildings. Thiswould bias a negative proximity effect towards zero .We are not able to evaluate properties before and after the construction of the pipeline becauseof the pipeline’s age. Also, 92% of the properties in the sample were built after the pipeline17was constructed, and only one detached unit in the data with an easement existed prior to thepipeline construction. Thus, the pipeline presence would be an in-situ reality for all residentialdevelopment. Instead we have two approaches. First, we control for the type of land use throughwhich the pipeline easement passes, so we differentiate between a pipeline on industrial land andon open space, as well as whether properties are proximate to a variety of non-residential landuses including open space and parks. This addresses hopefully helps to address the first and thirdelement of endogenous amenities. The length of the pipeline segment we study and density ofresidential development in the data along with census tract dummies should also help with excludedgeographic factors. The first type of static hedonic regression tests of log price on proximity, lotand structure characteristics, and geographic controls (both dummies for nearby land use types andcensus tract fixed effects) will help with the first and third type of bias, but not the second.We supplement the standard proximity tests with difference in differences tests associated with twoshocks, comparing the difference in response to these shocks by proximity to the pipeline. Theadvantage of these tests is that they should be immune to the identification issues proposed above,but they cannot capture the effect of the pipeline’s presence, only differences in the disamenityvalue. The first is a spill along the pipeline easement and the second is the announced plans tobuild a second pipeline along the existing easement to triple capacity. While not as complete atest as one with a random placement, it does offer some insight on the effects of proximity fromtwo different types of random events that highlight risk (oil spill) and remind residents of thepresence of the pipeline (expansion announcement). In the case of the first we test for the effectin areas away from the spill site. Both tests essentially ask whether houses closer to the pipelineexperienced a change in price post event relative to those further away. Other land uses and housecharacteristics should stay constant over the relatively short analysis window, thus obviating theidentification problems described above.In controlling for the influence of other types of nearby land uses that may also be influencinghouse prices in order address the concerns raised by Taylor et al. (2016). In addition, we take ad-18vantage of the variation in the pipeline easement land use itself is not constant over the alignment.Over our area of study, the pipeline easement occurs on residential, commercial, and industrialproperties; along or under major and minor arterial roads; through open or green space; and acrosscivic (government, institutional, and recreational) land uses. A property adjacent to the pipelineeasement that is green space might be affected differently than one that is adjacent to a pipelineeasement on an arterial road or a non-residential land use because of the amenity value of the greenspace. In our data, we are able to identify the pipeline land use context. We define the pipelineeasement land use context for a given property as the closest along a vector orthogonal to thepipeline. This allows us to test for the effect different pipeline land use contexts by interactingthese pipeline land use contexts with proximity measures.These estimation approaches along with the rich data allow us to do a more effective and completetest of the effect of proximity than in many existing studies. First, the density of the urban areayields a large volume of transactions, all within 1.0 km of the pipeline. Second, in comparisonto rural or ex-urban areas, the characteristics of the single-family houses we study are relativelyhomogeneous and the suburban landscape is rich in transactions.11 Finally, the quality of availablegeographical data is high, which allows us to address identification issues as noted above.In the hedonic regressions we use log house price as the dependent variable. All regressionsinclude census-tract fixed effects and jurisdiction-specific quarter-year dummies to address localneighbourhood effects and city specific temporal variation in house prices. Dependent variables inall regressions include the linear element of lot and structure characteristics along with quadraticterms for floor area and lot size. Through different specifications we test the various measures ofadjacency along with identifying the effects of better controls for nearby land uses and the pipelineland use context.11The pipeline transverses one other municipality in the metropolitan area, Langley. We exclude this stretch becausemuch of the area is agricultural with a quite heterogeneous mix of residential and agricultural land uses.194 ResultsTable 4 presents the baseline regressions for the relationship between distance to the pipeline andhouse prices. Specification (1) is the the regression of log house price on characteristics withoutany pipeline controls. In specification (2) we add distance to the pipeline. In specifications (3) - (5)we include the dummy variables for nearby land uses. In specifications (4) - (5) we add a dummyfor whether the property has a pipeline easement and interact the easement dummy with propertysize in specification (5).In the simplest treatment in regression (1), lot and structure coefficients have all of the expectedsigns. The inclusion of census tract fixed effects and city specific quarterly time series dummiesyields a fairly high R-squared of 0.867. In regression (2) we add distance to the pipeline in asimple linear form to the baseline regression (1). Unlike other work, here the results would suggestthat proximity matters: properties one km from the pipeline are 1.6 percent more valuable (about$10,800 at the mean). On a per 100m basis the effect of distance of $1,080 this would appear to beof marginal importance.In regression (3) we add dummies for proximity to major roads and a variety of civic, commercial,and industrial land uses.12 These dummy variables are all statically different from zero, off theexpected sign and have a greater effect on price than does the pipeline proximity measure. Thelargest effect of 10 percent is for properties within 40 meters of a major arterial, which includethe TransCanada, Barnett, or Lougheed Highways, and the second largest negative effect of 6percent is for properties within 250 meters of an industrial land use. Including these controls lowersthe pipeline proximity effect by nearly 30 percent, though the point estimate of 0.011 remainsstatistically different from zero. As per Taylor et al. (2016), this effect highlights the problems forany hedonic study on proximity to a negative amenity that does not carefully address other landuses.12Within 100 meters of a civic land use, 250 meters of a commercial or industrial land use, and 40 meters froman arterial, with major and minor arterials treated separately. Civic land uses are type 1 - parks and schools, type 2 -dumps and corporation yards, type 3 - municipal buildings and facilities.20Table 4: Baseline regression specifications with simple distance measuresDependent variable = ln(price) (1) (2) (3) (4) (5)Property CharacteristicsLot size (thousands of sq/ft) 0.0164*** 0.0167*** 0.0171*** 0.0175*** 0.0175***(0.0015) (0.0015) (0.0015) (0.0015) (0.0015)Lot size squared –0.0001*** –0.0001*** –0.0001*** –0.0001*** –0.0001***(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)Floor area (thousands of sq/ft) 0.1566*** 0.1555*** 0.1507*** 0.1503*** 0.1503***(0.0100) (0.0101) (0.0099) (0.0099) (0.0099)Floor area squared –0.0073*** –0.0072*** –0.0069*** –0.0069*** –0.0069***(0.0015) (0.0015) (0.0015) (0.0015) (0.0015)Number of bedrooms –0.0053*** –0.0055*** –0.0057*** –0.0057*** –0.0057***(0.0018) (0.0018) (0.0018) (0.0018) (0.0018)Effective age of property –0.0024*** –0.0024*** –0.0024*** –0.0025*** –0.0025***(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)Number of stories 0.0352*** 0.0350*** 0.0343*** 0.0346*** 0.0346***(0.0049) (0.0049) (0.0049) (0.0049) (0.0049)Single garage dummy variable 0.0071 0.0071 0.0061 0.0060 0.0060(0.0044) (0.0044) (0.0043) (0.0043) (0.0043)Multi garage (Ordinal variable) 0.0523*** 0.0526*** 0.0516*** 0.0509*** 0.0509***(0.0045) (0.0045) (0.0044) (0.0044) (0.0044)Number of full bathrooms 0.0028 0.0029 0.0042 0.0041 0.0041(0.0028) (0.0028) (0.0028) (0.0028) (0.0028)Number of partial bathrooms 0.0130*** 0.0133*** 0.0107*** 0.0105*** 0.0105***(0.0029) (0.0029) (0.0028) (0.0028) (0.0028)Dummy, =1 if detached with suite –0.0002 0.0001 0.0035 0.0035 0.0036(0.0041) (0.0041) (0.0040) (0.0040) (0.0040)Geographic Control VariablesDummy, =1 if property < 100m from civic land use 1 (park/golf course/open green space) 0.0110** 0.0110** 0.0110**(0.0054) (0.0054) (0.0054)Dummy, =1 if property < 100m from civic land use 2 (govt bldg/works yard/cemetary) –0.0261*** –0.0258*** –0.0258***(0.0051) (0.0051) (0.0051)Dummy, =1 if property < 100m from civic land use 3 (institutional land use) –0.0079** –0.0078** –0.0078**(0.0038) (0.0038) (0.0038)Dummy, =1 if property < 250m from industrial land use –0.0596*** –0.0600*** –0.0601***(0.0075) (0.0075) (0.0075)Dummy, =1 if property < 250m from commercial land use –0.0094** –0.0095** –0.0095**(0.0040) (0.0040) (0.0040)Dummy, =1 if property < 40m from major arterial road –0.1056*** –0.1062*** –0.1063***(0.0064) (0.0064) (0.0064)Dummy, =1 if property within 40m of minor arterial road –0.0400*** –0.0401*** –0.0401***(0.0125) (0.0125) (0.0125)Pipeline Proximity VariablesDistance to pipeline in km 0.0158** 0.0111* 0.0077 0.0077(0.0064) (0.0066) (0.0067) (0.0067)Pipeline Easement on Property –0.0515*** –0.0424(0.0151) (0.0259)Interaction : Easement Dummy = 1 x Lot Size –0.0007(0.0016)Census Tract Dummies Y Y Y Y YJurisdiction Quarter Dummies Y Y Y Y YAdj. R-square 0.867 0.867 0.871 0.871 0.871Number of Cases 12,419 12,419 12,419 12,419 12,419* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.For regression (4) we include a dummy if a property has a pipeline easement, so the pipelinetransverses the property. Controlling for these properties eliminates the more general proximity21effect. Properties with an easement transact at a five percent discount ($35,400 at the mean). Whileperhaps unique to this case, this result does suggest that distance effects observed in the literaturemay be highly localized and including a parametric continuous distance measure to a hazard forproperties that are a considerable distance away is a source of specification bias. We pursue thisfurther below with more granular treatments of distance. Finally, in regression (5) we introduce aninteraction between this easement dummy test and lot size. Although neither the coefficients forthe easement or the interaction are statistically different from zero, the magnitude and sign of thecoefficients suggest a two part effect in which the per square foot discount for properties that havean easement is declining in lot size. The sensitivity of the distance coefficient in the regressionsin Table 4 to the specification suggests that simple parametric relationships for proximity may beproblematic. The effect of accounting for the closest properties, those with an easement, raisesthe possibility that proximity effects are driven entirely by lots extremely close to the hazard inquestion.In Table 5 we apply a very general treatment of distance to the pipeline and transaction price withdummy variables for distance bands of 1-100m, 100-250m, 250-500m, and the excluded 500m-1km, where the effect is fixed within a band to shed light on these issues. Regression (1) highlightsthe sensitivity of measurement to extreme proximity, as the proximity effect is only for thoseproperties within 100m of the pipeline alignment and the properties with an easement, for whichthe point estimate is unchanged from table 4. The effect for the close non-easement propertiesremains small, a 1.25% discount. What cannot be determined, though, is whether these resultsreflects differences in risk assessment, spills may be perceived to be very localized, or awareness,as a buried pipeline may not register for a homebuyer 250m away.1313Wikipedia reports a standard Manhattan block as 274m in length.22Table 5: Distance in Discrete BandsDependent variable = ln(price) (1) (2)Pipeline Easement on Property –0.0547*** –0.0576***(0.0151) (0.0151)Dummy, =1 if property 0 - 100m from pipeline –0.0125**(0.0055)Dummy, =1 if property 100 - 250m from pipeline 0.0048(0.0049)Dummy, =1 if property 250 - 500m from pipeline 0.0037(0.0043)0 - 100m from pipeline x pipeline context = Civic/Comm/Ind/Utility –0.0416***(0.0115)0 - 100m from pipeline x pipeline context = Open/Residential/Res.Road –0.0104(0.0063)100 - 250m from pipeline x pipeline context = Civic/Comm/Ind/Utility –0.0054(0.0091)100 - 250m from pipeline x pipeline context = Open/Residential/Res.Road 0.0021(0.0057)250 - 500m from pipeline x pipeline context =Civic/Comm/Ind/Utility –0.0014(0.0073)250 - 500m from pipeline x pipeline context = Open/Residential/Res.Road 0.0018(0.0050)Property Control Variables Y YGeographic Control Variables Y YCensus Dummies Y YJurisdiction Quarter Dummies Y YAdj. R-square 0.871 0.871Number of Cases 12,419 12,419* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.In regression (2) of Table 5 we further address left out variable bias from nearby land uses, inthis case those through which the pipeline passes. We interact each of the distance bands withthe pipeline’s land use context, the land use. on which the easement rests. For simplicity wepool these into negative and positive land uses based on the coefficients for geographic featuresin Table 4. The regressions include the general geographic control dummies so this interactionin regression (2) reflects the specific pipeline effect on that land use. The results suggest that theeffect of pipeline proximity is a function of a pipeline’s land use and not the pipeline itself: apipeline under a benign land use such as residential, residential road, or open space and park doesnot have a statistically different than zero effect on properties within 100m, but when the pipelineeasement is on land used for industrial, commercial, or civic purposes properties within 100m havea 4.2%. These results further reinforce the importance of controlling for all highly-local land useswhen evaluating the effects of hazards on residential prices.23As an alternative to cardinal distance we also measure distance ordinally in terms of the number ofproperties a particular house is distant from the pipeline alignment. We use this distance measurein Table 6, where 0 is a property with an easement, 1 is a property adjacent to the property with theeasement, 2 is one further away, 3 one again, and 4 plus the excluded default. As shown in Table3, these measures are highly correlated with linear distance: 97 percent of properties adjacentto the easement property (one property distant) are within 100m of the pipeline alignment, 88percent of those two properties distant, and 46 percent of those three distant. In regression (1)the importance of extreme proximity manifests as the discount for adjacent properties is 2.1% andfor those properties one property distant 1.4%, and after that no effect, even though nearly halfof transacting houses three properties distant are within 100m of the alignment. In the secondspecification, regression (2), we interact these adjacency measures with the pipeline’s land usecontext. Unlike in Table 5, here we find that immediate proximity, even when the pipeline’s landuse is the more positive residential or open space uses, lowers property values slightly (1.6%). Thenegative effect for the unfavourable land use types remains higher at -3.5%. The single atypicalresult is that the negative effect when the pipeline runs under negative land uses increases forproperties two distant from the alignment, though it is not statically significant when the land useis residential or open space. These results do reinforce the highly localized nature of negativeeffects: transacting residences one property from the easement property are a mean distance of22m from the alignment, for those two properties distant the mean distance is 67m, and even thosethree distant the mean distance from the pipeline is only 98m. Proximity matters, but only at thatextreme margin of closeness, where one would expect the buyers and sellers to be most likely tobe aware of the pipeline’s presence and to see a spill as most likely to effect their property.24Table 6: Distance in Adjacency MeasuresDependent variable = ln(price) (1) (2)Pipeline Easement on Property –0.0568***–0.0567***(0.0149) (0.0149)Dummy, =1 if property 1 parcel from pipeline –0.0211***(0.0074)Dummy, =1 if property 2 parcels from pipeline –0.0143*(0.0079)Dummy, =1 if property 3 parcels from pipeline 0.0072(0.0085)1 parcel from pipeline x Pipeline context = Civic/Comm/Ind/Utility –0.0352**(0.0172)1 parcel from pipeline x Pipeline context = Open/Residential/Res.Road –0.0164*(0.0089)2 parcels from pipeline x Pipeline context = Civic/Comm/Ind/Utility –0.0673***(0.0159)2 parcels from pipeline x Pipeline context = Open/Residential/Res.Road 0.0000(0.0099)3 parcels from pipeline x Pipeline context = Civic/Comm/Ind/Utility –0.0127(0.0250)3 parcels from pipeline x Pipeline context = Open/Residential/Res.Road 0.0112(0.0101)Property Control Variables Y YGeographic Control Variables Y YCensus Dummies Y YJurisdiction Quarter Dummies Y YAdj. R-square 0.871 0.871Number of Cases 12,419 12,419* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.5 Event Studies : Difference in Difference RegressionsThe static hedonic analysis in the previous section uses detailed measures of local land uses andthe land use context of the pipeline alignment to address the bias issues in estimating the effect ofproximity to the pipeline hazard and house prices. While we believe this treatment to be amongthe most thorough in the environmental hazard literature, the concern regarding bias ion coefficientestimates cannot be said to have been eliminated. In this section we use a difference in differencesapproach to avoid the bias issue by observing the variation in price response by distance from thepipeline following two events: the first a spill on the pipeline and the second the announcement of25plans to triple the pipeline capacity. These two events should have different price effects becausethey convey different information. The spill both reminds sellers and prospective buyers of thehazard posed by pipelines and serves as a reminder of the pipeline’s existence. The expansion an-nouncement suggests an increase in the possible size of a spill and also a reminder of the pipeline’spresence. We are interested both in the magnitude of any effect and its persistence.The first event, the "Westridge Oil Spill occurred on July 24, 2007 when a backhoe penetratedthe Trans Mountain Pipeline spur in Burnaby, BC. This stretch of pipeline runs less than twokilometers from the Trans Mountain terminus on Burnaby Mountain to Port Metro Vancouver’sWestridge Marine Terminal. The spill was small, it released 1,500 barrels of heavy crude oil andonly one home was severly contaminated but there was considerable media attention.14 The secondevent we use is the May 2012 announcement in by Kinder Morgan of their plans to nearly triplethe pipeline’s capacity by twinning the pipeline along its existing alignment. We find no evidenceof a public discussion of this plan prior to the announcement.For these tests we conduct simple difference in difference tests around each of the event dates.The after event dummy is intersected with a distance band dummy, where the coefficient on theinteraction term identifes the change in the effect of proximity as a result of the event. The narrowtime window of analysis limits the number of transactions in the data so we are not able to usemeasures for houses one property away from the alignment or even within 100 meters. insteadwe compare the change in the effect of being within 250 meters of the pipeline as a result of theshock.5.1 Effect of SpillTable 7 below shows the difference in price appreciation after the spill for properties within 250meters of the alignment compared to those that are 250 to 1,000 meters. We use windows forthe before and after element of the diff in diff methodology of 0-3 months, 0-6 months, and 0-9,14According to the operator eight homes were "heavily oiled" and 36 less so. They report that "one residencereceived extensive renovations to both its interior and exterior." https://www.transmountain.com/westridge-2007-spill.26and then 0-12 months for the difference comparisons. In the three months following the spill -regression (1) - units within 250 meters of the alignment sold at a 5.4% discount compared withthose further away relative to values for both in the period previous to the spill.15 This is a largeeffect when compared with other findings here. It is similar in size to the discount for a propertywith an easement and almost four ties as large as the discount for a property adjacent to an easementproperty that is residential or open space. The magnitude is similar to what Simons (1999) finds,but in our case limited to properties much closer to the pipeline. Regressions (3)- 4) show thatthe negative proximity effect associated with the information shock dissipates quickly with time.After six months there is no statically different than zero effect on the relative change in prices fornearby properties, and the point estimate is even coser to zero after one year. This dissipation isconsistent with Hansen et al. (2006),though we find that it evaporates completely. Our finding ofan effect differs from Freybote and Fruits (2015) who report no effect of news from other pipelineevents on property prices and proximity to a buried pipeline, just to a pipeline under construction.There is a substantial literature on how people perceive risks ( see Tversky and Khaneman (1974)and Pachur et al. (2012) ) and these findings may be understood within the assessment of risk. Thistest cannot distinguish between the event highlighting the presence of a risk, which is then pricedaccurately, but quickly forgotten, or an overreaction to new news on risks that then returns to anappropriate assessment.15With a 500m band the point estimates are lower and not statistically different from zero.27Table 7: Difference in Difference Regressions: Effect of Oil Spill (250m Bands)Dependent variable = ln(price) (1) (2) (3) (4)Dummy, =1 if property < 250m from pipeline 0.0250 0.0264 0.0183 0.0102(0.0227) (0.0178) (0.0149) (0.0125)Dummy, =1 if Sale 3 months post spill 0.0658**(0.0270)Sale < 3 months post spill x property < 250m from pipeline –0.0543*(0.0308)Dummy, =1 if Sale 6 months post spill 0.0671**(0.0269)Sale < 6 months post spill x property < 250m from pipeline –0.0517**(0.0234)Dummy, =1 if Sale 9 months post spill 0.0586**(0.0271)Sale < 9 months post spill x property < 250m from pipeline –0.0300(0.0201)Dummy, =1 if Sale 12 months post spill 0.0545**(0.0268)Sale < 12 months post spill x property < 250m from pipeline –0.0197(0.0167)Property Control Variables Y Y Y YGeographic Control Variables Y Y Y YCensus Tract Dummies Y Y Y YJurisdiction Quarter Dummies Y Y Y YAdj. R-square 0.767 0.743 0.736 0.745Number of Cases 570 922 1,286 1,735* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.As a simple robustness check to the results in Table 7 we re-test the spill effects assuming that thespill occurs two years earlier, with a placebo spill in July 2005. What we hope to capture is anytrend effect in the data associated with properties close to the pipeline. The results in Table 8 belowshow no evidence of some trend in the data that pre-dates the spill. All difference in differenceinteraction coefficients show no statistically different from zero relative difference before and afterthe placebo spill between units close to the pipeline and those further away. A similar test for aplacebo spill July 2009, two years after the actual spill also yields no results.28Table 8: Robustness Check : Westridge Oil Spill Falsifications RegressionsDependent variable = ln(price) (1) (2) (3) (4)Dummy, =1 if property < 250m from pipeline 0.0002 –0.0058 0.0044 0.0058(0.0207) (0.0180) (0.0168) (0.0147)Dummy, =1 if Sale 3 months post spill –0.0069(0.0229)Sale < 3 months post spill x property < 250m from pipeline 0.0237(0.0277)Dummy, =1 if Sale 6 months post spill –0.0050(0.0222)Sale < 6 months post spill x property < 250m from pipeline 0.0202(0.0258)Dummy, =1 if Sale 9 months post spill 0.0162(0.0226)Sale < 9 months post spill x property < 250m from pipeline –0.0090(0.0227)Dummy, =1 if Sale 12 months post spill 0.0107(0.0216)Sale < 12 months post spill x property< 250m from pipeline 0.0040(0.0191)Property Control Variables Y Y Y YGeographic Control Variables Y Y Y YCensus Tract Dummies Y Y Y YJurisdiction Quarter Dummies Y Y Y YAdj. R-square 0.756 0.730 0.725 0.738Number of Cases 544 804 1,066 1,359* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.5.2 Effect of Expansion AnnouncementOur second natural experiment uses the pipeline expansion announcement from May 2012. Thereis no coverage in the local media prior to the announcement of a possible expansion, so we treatit as a surprise event. We understand the information content of the annlouncement event to be areminder of the presence of the pipeline, and while there is nothing to suggest the scope of the risk,it is possible that people perceived any risk to be elevated because a larger capacity would meanworse spills. We apply the standard difference in difference methodology around the announce-ment date. The results in Table 9 show no differential price response following the expansionannouncement between properties within 250 meters of the pipeline easement and properties 250-291,000m from the easement. This is consistent with Freybote and Fruits (2015), where they onlyobserve pipeline proximity effects during construction. Our interpretation of the results in Table 9combined with our earlier findings, is that except in the case of an event like a spill that heightenssubjective assessments of risk, only those properties closest to a pipeline experience a proximitydiscount. This is not due to a lack of market awareness, but for the reason that properties not withinone to two properties of the alignment, the presence is not treated as a risk.Table 9: Pipeline Expansion Announcement Event Study RegressionsDependent variable = ln(price) (1) (2) (3) (4)Dummy, =1 if property < 250m from pipeline –0.0283 –0.0274 –0.0333** –0.0228**(0.0241) (0.0186) (0.0147) (0.0111)Dummy, =1 if Sale < 3 months post announcement 0.0095(0.0197)Sale < 3 months post announcement x property < 250m from pipeline –0.0104(0.0368)Dummy, =1 if Sale < 6 months post announcement 0.0132(0.0181)Sale < 6 months post announcement x property < 250m from pipeline –0.0158(0.0257)Dummy, =1 if Sale < 9 months post announcement 0.0117(0.0176)Sale < 9 months post announcement x property < 250m from pipeline –0.0094(0.0211)Dummy, =1 if Sale < 12 months post announcement 0.0096(0.0171)Sale < 12 months post announcement x property < 250m from pipeline –0.0068(0.0166)Property Control Variables Y Y Y YGeographic Control Variables Y Y Y YCensus Tract Dummies Y Y Y YJurisdiction Quarter Dummies Y Y Y YAdj. R-square 0.815 0.826 0.844 0.869Number of Cases 401 603 808 1,174* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.6 Summary and ConclusionsThis paper uses the example of oil pipelines to explore the sensitivity of estimates of the cost ofproximity to hazards to both the specification of distance and the types of highly localized existenceland uses. There are important implications for both the siting of hazards and the structure ofcompensation to existing landowners from a more accurate proximity cost methodology. We find,unlike previous papers, that houses near to pipelines do trade at a discount. In our data it is30only the property with an easement and then the closest one or two properties that experiencenegative effects on value from nearby pipelines. Even at this level the effects are sensitive to thecorrect treatment of other land uses and, in the case of a pipeline, to the land use under whichthe pipeline passes, a result based on a more precise and granular treatment of land uses and butentirely consistent with the more general finding in Taylor et al. (2016). For example, when thepipeline transverses industrial and commercial land uses, the negative proximity effect is at leasttwice as large as when the pipeline is passing through a residential area.The magnitude of the effects we find are relatively small. A residential property with an easementhas up to a 5.7% lower value on average, though we cannot separate the risk effect from the lossof use effect because of restrictions associated with the easement. A property adjacent to such aneasement property has a price that is 1.6-3.5% lower than a more distant unit depending on theland use on which the easement lies. Beyond the second property distant there is no effect in ourdata. Given that these narrow effects are only for the closest properties, it is not surprising that theliterature using parametric specifications has not previously found a relationship between negativevalues and closeness to an oil pipeline except following a spill. Interacting these proximity by thetype of land use further strengthens our contention of the importance in modelling proximity andland use effects with fine granularity.Our difference in difference analysis supports the findings elsewhere that risk assessments areaffected by recent pertinent information. However, the results also show that this effect is quitetransient and not true for all types of information shocks. We only see changes in risk pricingfollowing a local spill and not following news of a planned increase in the pipeline. In the cvaseof the spill the effect on the pricing discount is large, over five percent, but these effects disappearafter six months following a spill elsewhere on the pipeline. As with our static price effects, theseonly apply to closer units, but they extend on average throughout a 250-meter band. The tests onpipeline expansion announcement yield no difference in difference results. In combination thesefindings suggest that it is heightened perception or a reminder of pipeline risk and not a renewed31reminder of the presence of the pipeline that causes lower transaction prices.This paper sheds light on some of the factors that contribute to large variations in results of studiesthat examine the effect of proximity to environmental hazards on residential prices. The richnessof our data permits us to model distance from the hazard more finely and account for a broad rangeof other land uses, proximity to which can be expected to affect residential property values. Wefind that both have notable effects on the relationship between proximity and house prices. Themore precise tests here show that proximity may only matter at very short distances, and that thefailure to account for other land uses, and even the land use type of the hazard, will bias estimatesaway from zero.32ReferencesPeter C Boxall, Wing H Chan, and Melville L McMillan. The impact of oil and natural gas facilitieson rural residential property values: a spatial hedonic analysis. Resource and energy economics,27(3):248–269, 2005.Melissa Boyle and Katherine Kiel. A survey of house price hedonic studies of the impact ofenvironmental externalities. Journal of Real Estate Literature, 9(2):117–144, 2001.John B Braden, Xia Feng, and DooHwan Won. Waste sites and property values: a meta-analysis.Environmental and Resource Economics, 50(2):175–201, 2011.Bradford Case, Peter F Colwell, Chris Leishman, and Craig Watkins. 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