1 Federal Reserve Bak of New York Staff Reports Crime, House Prices, ad Iequality: The Effect of UPPs i Rio Claudio Frischtak Bejami R. Madel Staff Report o. 542 Jauary 2012 This paper presets prelimiary fidigs ad is eig distriuted to ecoomists ad other iterested readers solely to stimulate discussio ad elicit commets. The views expressed i this paper are those of the authors ad are ot ecessarily reflective of views at the Iteratioal Growth Cetre, the Federal Reserve Bak of New York, or the Federal Reserve System. Ay errors or omissios are the resposiility of the authors.
2 Crime, House Prices, ad Iequality: The Effect of UPPs i Rio Claudio Frischtak ad Bejami R. Madel Federal Reserve Bak of New York Staff Reports, o. 542 Jauary 2012 JEL classificatio: O18, O15, R30, K42 Astract We use a recet policy experimet i Rio de Jaeiro, the istallatio of permaet police statios i low-icome commuities (or favelas), to quatify the relatioship etwee a reductio i crime ad the chage i the prices of eary residetial real estate. Usig a ovel data set of detailed property prices from a olie classifieds wesite, we fid that the ew police statios (called UPPs) had a sustatial effect o the trajectory of property values ad certai crime statistics sice the egiig of the program i late We also fid that the extet of iequality amog residetial prices decreased as a result of the policy. Both of these empirical oservatios are cosistet with a dyamic model of property value i which historical crime rates have persistet effects o the price of real estate. Key words: wealth distriutio, ameity value, real estate Frischtak: Iteratioal Growth Cetre ad IterB Cosultoria Iteracioal de Negócios ( Madel: Federal Reserve Bak of New York ( ejami. This research was coducted with restricted access to real estate listigs data provided y The authors gratefully ackowledge ZAP ad the support of Eduardo Gama Schaeffer ad Caio Biachi for facilitatig access to these data. Victor Chateauriad, Diego Gilsaz, ad Felipe Katz provided super research assistace. The views expressed i this paper are those of the authors ad do ot ecessarily reflect the positio of the Iteratioal Growth Cetre, the Federal Reserve Bak of New York, or the Federal Reserve System.
3 Itroductio Residetial property prices are a importat gauge of ecoomic coditios writ large. They reflect may macroecoomic factors as well as the particular local microecoomy of the property s locatio. Home values also compose a importat part of household wealth, especially i lower icome areas where residetial property is typically a family s primary (or oly) asset. I the Uited States, aout a third of total assets for a give family are accouted for y ower-occupied housig, with that figure closer to two thirds for families elow the media level of et wealth. 1 This statistic might e further skewed i developig coutries, where the capacity of poor families to accumulate fiacial assets is more limited. Take together, these oservatios suggest a powerful mechaism y which ay policy affectig the determiats of house prices ca alter the level ad dispersio of household wealth. I this paper, we ivestigate oe example of this mechaism as it pertais to the coectio etwee crime ad house prices. Our first ojective is to empirically idetify ad documet the relatioship etwee crime ad house prices. As a pulic ad, we fully expect crimes to exert a dowward force o prices; ideed, this is a commo fidig i the related literature o house ameity valuatio ad the ecoomics of coflicts. We quatify the extet to which prices are resposive to crime-related outcomes, as demostrated y a recet policy experimet ad with the use of highly detailed property price data from the olie classified wesite ZAP (www.zap.com.r), ad fid that these effects ca e quite large ad ecoomically meaigful. Our mai iovatio will the e to documet ad explai the distriutioal cosequeces of removig the pulic ad of crime; that is, the removal of crime may have heterogeeous effects o the prices of differet resideces i a maer which alters the degree of overall iequality amog property values. This would happe, for example, if lower valued properties appreciate or depreciate disproportioately to a give chage i the crime rate. We will discuss the circumstaces uder which that would occur i the cotext of a dyamic model of property valuatio. 1 Keickell (2009) presets a road array of statistics o U.S. family icome ad wealth for the years , drawig o the Survey of Cosumer Fiaces (SCF) compiled y the Federal Reserve Board. Family et wealth ad its compoets for various years of the SCF are provided i Tale A2 of that paper. 1
4 Our empirical work will show that decreasig crime does, i fact, eefit lower valued properties disproportioately, reducig the iequality amog properties. This relatioship is suggested i Figure 1, which plots idices of homicides, roeries ad a Gii coefficiet of house prices for the city of Rio de Jaeiro sice Both homicides ad roeries declied markedly sice mid Though the series for homicides is more volatile, the average decrease for oth types of crime is aout 15 percet y mid The Gii coefficiet measures the level of iequality of house prices across Rio s eighorhoods. It was rather stale at roughly 0.28 through the egiig of 2010 efore fallig to y mid The fact that oth crime ad iequality pivoted ad the fell at aout the same time is suggestive of a relatioship etwee them. Sice there are may differet factors that ca affect house prices simultaeously, we study the housig market aroud the time of a specific policy evet tightly liked to the ojective of crime reductio. This policy is the itroductio of the Uidade Pacificadora da Policia ( Pacifyig Police Uit, or UPP) program i Rio de Jaeiro egiig i late As i may metropolita areas i developig coutries, a sigificat fractio of the populatio of Rio live i very low-icome commuities with a high cocetratio of sustadard, iformal housig; i Rio, home to some of the largest of these commuities i Lati America, they are called favelas. Over the past three decades, the city has ee plagued y coflicts over territory i its favelas with drug gags ad militias, with may favelas effectively eig occupied ad govered y the drug gags. The UPP program, i respose, re-occupies specific favelas y force usig elite police uits, drives out the drug gags ad roots out caches of weapos ad drugs, ad the istalls permaet police statios staffed y highly traied, well-paid ad ewly-recruited officers; eightee such statios have ee istalled sice The asic ojective of re-occupatio is the reewed assertio of the rule of law ad the aatemet of drug gag-related crimes. The program, to the extet it is effective, is resposile for may positive exteralities associated with the accomplishmet of these ojectives. Usig detailed mothly data o residetial property prices i Rio s formal housig market, as well as o homicide ad 2 Data sources ad the details of idex costructio for the series i Figure 1 are provided elow i Sectios III ad V. 2
5 roery rates i each of Rio s eighorhoods, we formally test the hypotheses that eighorhoods closer to a UPP statio experieced larger tha average decreases i crime ad larger tha average icreases i house prices after the UPP was put ito place. I additio to the variatio across eighorhoods ad time, we exploit the staggered timig of the policy across the 18 UPPs y joitly estimatig the idividual effect of each oe o house prices ad crime. We fid that, coditioal o a UPP eig istalled eary, house ad apartmet sales prices icreased y a average of 5-10 percet, homicides decreased y a average of percet, ad roeries decreased y a average of roughly percet. To gai perspective o the ecoomic sigificace of the decrease i crime due to the UPPs, we use our regressio results to costruct couterfactual price ad crime rates ad, with those, city-wide statistics. I the asece of the UPPs, the overall house price idex i Rio would have grow aout 15 percet slower sice 2008, ad homicide ad roery rates would have falle y aout 14 ad 20 percet less tha they did, respectively. We ote that sice we do ot oserve house prices iside the favelas themselves, our estimated price effects are quite likely to e uderestimates of the true city-wide effects. The empirical results, otwithstadig some heterogeeity i the effectiveess of idividual UPP statios, cofirm widely reported aecdotes of aated violece ad of skyrocketig residetial property prices i the formal housig markets surroudig the favelas. Our fidigs complemet ad exted previous work o the effectiveess of the UPPs. Based o household survey data, Neri (2011a) foud that retal prices withi all favelas i Rio rose y aout 7 percet etwee 2007 ad However, those results are ot specific to each commuity protected y a UPP ad do ot cotrol for secular treds i the Rio housig market. The positive exteralities of UPPs are also explored i Cuha ad Mello (2011), which focuses o the formalizatio of services provisio i a favela followig the istallatio of a UPP. I additio to the direct valuatio of disameities due to crime that we emphasize elow, formalizatio ad ura regularizatio are other importat chaels through which crime reductios affect property prices, ad which are captured i our estimates of the effect of the UPPs. Havig estalished that the UPPs iflueced crime ad house prices i opposite directios (that is, that the UPPs seem to e a reasoale istrumet for the effect of crime o house 3
6 prices), we use our estimates to aalyze the associatio etwee crime ad the dispersio of house prices. We preset a model of property valuatio i which there are dimiishig returs to crime reductio; this implies that properties with either high iitial crime rates or low ameity values have disproportioately large icreases i price for a give declie i crime which, i tur, lowers iequality amog properties. The mechaism i the model that gives rise to dimiishig returs is the iclusio of historical crime rates as a determiat of curret property values. This treatmet of the dyamic trasmissio of crime rates ito house prices is quite similar i spirit to the way Besley ad Mueller (2011) model the umer of killigs due to coflict as a fuctio of the latet state of the peace process i Norther Irelad. I that model, the persistece of crime i a particular area has a earig o what sigal agets take from a chage i the umer of killigs aout the proaility of eterig a state of peace, ad hece o the trasmissio of the rate of killigs ito house prices. I our model, we have a simpler treatmet of agets' expectatios ut the trasmissio of a chage i the crime rate ito prices depeds similarly o the history of crime, which is a additioal state variale. Thus, curret ad future cosumptio flows from housig deped o oth the level ad duratio of crime rates i the past; lower iitial crime rates with low historical duratio gives rise to the iggest icreases i price whe the crime rate declies. We documet that the disparity i house prices i Rio did i fact declie followig the implemetatio of the UPP policy. A Gii coefficiet costructed with the actual ad couterfactual house prices descried aove shows that the disparity i house prices across eighorhoods has ee fallig faster after istallatio of the UPPs tha for the couterfactual Gii. Moreover, i several eighorhoods with a UPP eary, we fid evidece that the dispersio i property prices withi those eighorhoods arrowed, suggestig that eve withi more homogeeous sets of properties the lowest valued oes are most sesitive to a chage i the crime rate. This paper cotriutes to several areas of active research, ragig from studies of the ecoomics of coflict to the aalysis of the wealth distriutio. Most closely related are the works idetifyig the impact of crime ad violece o property prices, with the paper y Besley ad Mueller (2011) as a closest atecedet; as elow, Besley ad Mueller (2011) 4
7 exploit oth spatial ad temporal variatio i crime data to idetify the effect o house prices, ad they provide a model i which the respose of property prices depeds o the level ad persistece of historical crime rates. The preset study uses more disaggregate price data, at the level of eighorhoods i Rio, ad has a differet modelig approach that focuses more o the implicatios of crime for the dispersio of house prices. To our kowledge, ours is the first study to draw a coectio etwee crime reductio ad wealth iequality. Our empirical measuremet of the crime elasticity of house prices is coected to a sequece of papers estimatig this (largely egative) elasticity. Early examples iclude Thaler (1978), i which a oe stadard deviatio icrease i per capita property crime decreased sigle-family home prices y 3 percet, ad Hellma ad Naroff (1979), i which the elasticity was A kow drawack of these estimates is that they each treated the crime rate as exogeous, which may have iased the elasticity estimates if, for example, crime occurs disproportioately i poorer eighorhoods with low property values or, coversely, if crimials target areas with higher-priced homes. I a recet survey, Ihlafeldt ad Mayock (2009) foud 12 istaces i of a set of 18 empirical studies relatig house prices ad crime that treat crime as exogeous as such, those studies do ot accout for this reverse causality or other sources of edogeeity. Of the recet studies that do istrumet for crime, Gios (2004) ad Tita, Petras ad Greeaum (2006) agai fid a egative sigificat relatioship, a effect that is particularly proouced for violet crimes. We attempt to get aroud issues of cross-sectioal edogeeity y exploitig the time variatio aroud a exogeous policy experimet, the UPPs i Rio. A widely ackowledged ojective of the UPP policy is to icrease the safety aroud key veues for the 2014 soccer World Cup ad 2016 Summer Olympics, the locatios of which are ot systematically related to historical crime rates or the levels of property prices. As such, we will argue that the UPPs are a reasoale istrumet for the effect of crime o house prices. We proceed y estimatig a differece i differeces estimator of property values i eighorhoods with a eary UPP, straddlig the pulic aoucemet that a UPP would e istalled i those eighorhoods. This method for estimatig the (dis)ameities of housig is used aalogously i Lide ad 5
8 Rockoff (2008) i their study of the effect of the proximity of registered sex-offeders o house prices. 3 Fially, this paper is related to a large literature o the determiats of wealth iequality. Wolff (1992) illustrates that wealth cocetratio ad iequality i the Uited States varied a lot over much of the 20 th cetury (oth icreasig ad decreasig) ad moved fairly closely with chages i the icome distriutio. Brazil, i particular, has made great strides recetly to reduce its level of iequality. 4 Our work demostrates a ovel ad potetially importat chael y which policy ca cotriute to chages i the distriutio of wealth. 5 The paper proceeds as follows. The ext sectio provides some ackgroud o the favelas i Rio ad the official madate of the UPP program. Sectio II descries the empirical model, followed y the details of the property price data ad crime data i Sectio III, ad the empirical results i Sectio IV. The valuatio model ad its predictios for the dispersio of house prices, as well as some empirical measures of house price iequality i Rio, ca e foud i Sectio V. Sectio VI cocludes. I. Backgroud o the UPP program Most of Rio de Jaeiro s favelas are situated o the hillsides of the city ad may are located i close proximity to affluet eighorhoods. Both of these factors have made them a favored have for drug gags. By commadig the high groud ad through a mix of cooptatio ad explicit threats, heavily-armed gags have gaied effective cotrol over the residet populatios of certai favelas ad have used these locatios as ases to process, stockpile ad distriute drugs. The profitaility of such traffickig operatios has led to 3 There are may other studies that estimate property (dis)ameities more roadly defied to iclude factors such as eviromet. Boyle ad Kiel (2001) provide a thorough, if dated, survey of that literature. 4 Sice 2001 the Gii coefficiet for Brazil s icome distriutio has decreased mootoically from i 2001 to a estimated 0.53 i 2010 still relatively high compared to 0.36 for Idia or 0.42 for the U.S. For a recet discussio, see Neri (2011). 5 Though our results suggest that crime reductios have eefited the owers of low-priced housig disproportioately, low-wealth households are less likely to ow a home tha wealthier households. I a recet study of U.S. households durig the fiacial crisis, Bricker et al. (2011) show that household wealth declied most severely i the upper percetiles. This reflects oth the dramatic fall i house (ad other asset) prices ad the fact that wealthier families are more likely to ow those assets. I the ottom quartile of wealth, oly 15 percet of families had ay home equity i 2007, compared to 96.8 percet i the top quartile. 6
9 itesifyig territorial dispute, growig levels of violece ad, through ries to protect the operatio from other drug gags ad other illicit activities, the icreasig complicity of the police. Over the past three decades, a complex system has crystallized i which drug-related ad other crimial activities have fed o police corruptio, ad spilled over ito the political area, with drug moey used to fiace politicias i the muicipal ad state legislatures, ad reportedly reachig the highest levels of the state govermet. A ew, reformist state govermet assumed power i 2007, settig improvig security ad reducig the levels of violece as priorities. The selectio of Brazil as the host of the 2014 World Cup ad Rio as the seat of the 2016 Olympics added impetus to these ojectives. The ew govermet recogized that achievig these goals etailed dealig simultaeously with the territorial power of drug lords i the favelas as well as police corruptio. There was also a realizatio that a ew security policy for the favelas would have to e more permaet i ature. Previous attempts to comat drug traffic ivolved occasioal icursios ito the favelas for specific operatios, ofte resultig i the deaths of iocet ystaders caught i the crossfire. The core of the ew policy, which took early two years to desig ad deploy, was uilt roud the cocept of territorial occupatio y state forces, ad the istallatio of a large, permaet presece of a ewly traied police force of youg officers utaited y corruptio. 6 This presece would maifest itself i a large police statio, a UPP, ad would e preceded y a carefully plaed ad swiftly executed process of expulsio of the drug gags y crack police uits ad special forces. The program, iitiated i Decemer 2008, is cosidered to e a ew paradigm of police actio agaist the ecroachmet of drug gags i the favela commuities. 7 Of the may favelas affected y drug gags, the selectio of which favelas were to receive a UPP was largely a political outcome. This is importat for our empirical idetificatio of the effect of UPPs o crime ad house prices, sice it mitigates the extet of reverse causality 6 Breakig with traditioal repressio techiques, ewly traied officers are taught to e commuity policeme or proximity police y itegratig themselves withi the occupied commuity. Ackowledgig the skepticism ad mistrust that local populatios have historically had with police activity, all UPP staff are ewly admitted ad traied for this specific purpose. This hirig ad traiig practice is cosistet with the idea that UPPs are meat to e the gateway for may other services eyod the suppressio of crimial activity. 7 I a op-ed i the ewspaper Gloo, State Pulic Safety Secretary José Mariao Beltrame compared the UPP program (which he maages) to the Plao Real i 1994, which drastically reduced iflatio ad stailized the ecoomy (http://ogloo.gloo.com/opiiao/apeas-primeiro-passo ). 7
10 etwee our policy variale (the UPP) ad each outcome. I other words, UPPs were ot simply placed i the eighorhoods with the highest crime rates or lowest house prices. Rather, the policy has ee implemeted y prioritizig importat locatios for the World Cup ad Olympic Games, givig geographic factors a domiat role i determiig the locatio of UPPs. This ca e see i the top pael of Figure 2, i which the exact locatios of the 18 existig UPPs are mapped with the gradiet of average apartmet sales prices for each eighorhood i Rio. It is evidet that some UPPs were placed i high-priced eighorhoods while others were placed i low-priced eighorhoods. Similarly for the rate of homicides, show i the ottom pael of Figure 2, UPPs appear i some low homicide eighorhoods i the south zoe, high homicide eighorhoods i the orth zoe, ad eighorhoods with itermediate homicide rates i the west. Media commetary o the UPPs has suggested that while some eighorhoods received a UPP due to their high icidece of crime, UPPs were istalled to garer political support for the UPP program ad protect key World Cup locatios i the high- ad middle-icome South Zoe (or Zoa Sul). 8 Implemetatio of a UPP i a give favela occurs i a four-stage process. A similar protocol has ee oserved for most favelas, though it is ot a official stadard. First, the commuity or set of commuities to e occupied is aouced y the police up to 6 moths i advace, though o specific date is give. Secod, a series of aoucemets idicatig the immiece of the occupatio occur, icludig a aoucemet that it will happe i the ext 1-2 weeks. Betwee 4-7 days prior to the occupatio, the specific date is made pulic ad police egi ecirclig the favela(s). Third, heavily-armed Civil ad Military Police, led y elite forces, ivade a favela i the early twilight hours ad expel the drug traffickers i the eighorhood. Over the ext few days of week, they systematically sweep the area to clear ay remaiig crimials or cotraad ad set up a temporary statio. Fially, the permaet physical statio is istalled ad cotrol is haded over to a ew UPP attalio. I the majority of cases, ad as a iteded cosequece of the pre-aoucemets, this process has led to very little violet cofrotatio as crimials have already left the area. 8 Several sources idicate that geography rather tha crime rate is the domiat factor i determiig the locatio of UPPs. I a iterview i Jauary 2012, the director of commuicatio for the state police force, Frederico Caldas, liked expadig the umer of police at UPPs with the goal of esurig security ahead of the World Cup ad Olympic Games. A prime example of this goal is the ivasio of Rociha, Vidigal ad Chacara do Céu i the affluet (relatively low-crime) South Zoe of Rio, a operatio which is widely cited for protectig tourist ifrastructure. 8
11 As show i Tale 1, 20 favelas have ee occupied ad 18 UPP uits istalled etwee late-2008 ad the ed of 2011, as part of a pla to reach 40 UPPs y The rollout of the program has ee fairly steady over time, with a ew occupatio takig place o average every few moths. 9 The territorial footprit of the curret 18 UPPs ecompasses over 50 commuities, cotaiig more tha half of a millio ihaitats. Over 3 thousad police officers are curretly deployed i the program, which is expected to reach 12 thousad y Tale 1 also shows the ames of the eighorhood (or airro) i which the favelas are located. I some istaces, due to their sprawlig layout over hillsides, they are located i more tha oe eighorhood; for example, the favela Sata Marta is located i Botafogo ad Humaitá. Due to the large umer ad proximity of the eighorhoods (there are 153 official airros i the muicipality of Rio), we also list those which have a order withi 2km of the address of each UPP; this sigificatly wides the umer of eighorhood ad the size of the populatio classified as close to a UPP. Fially, cogizat of the fact that oe eighorhood might share a order with aother where a UPP is located ut oly have a small fractio of its populatio livig close to the order, we use ArcGIS mappig software to compute the distace etwee each UPP ad the cetral poit (called cetroid ) i each airro. The set of eighorhoods with a cetroid withi 2km of a UPP statio is a suset of those with a order withi 2km, ad is used elow as aother measure of UPP proximity. II. Estimatig the effect of UPPs o property prices ad crime rates Our aselie measure of the effect of the UPPs is a differece i differeces estimator for all of the 18 UPPs istalled etwee Novemer 2008 ad Novemer Our elemetal 9 Whe availale i media reports, Tale 1 icludes the date that the UPP was aouced, though i may istaces these dates, if reported, were oly a few days prior to occupatio. Sice the aoucemet data are ot pulished officially, we rely o media reports to ascertai their timig; ewspaper coverage teds to pick up the story of a UPP oly oce ivasio is immiet (i.e., shortly efore the third stage descried aove). 10 I additio, as of the date of this mauscript, the cluster of favelas kow as Complexo do Alemão, cotaiig approximately 125,000 ihaitats, was occupied y 3,000 highly traied army troops, while aother immese tract of commuities i Rociha, Vidigal ad Chácara do Céu, occupied i Novemer 2011, had a ukow umer of crack uits still searchig for drugs ad weapos. 11 Two large clusters of commuities, Complexo do Alemão ad Rociha/Vidigal, are excluded as o UPP was istalled yet y the ed of 2011 (as of Jauary 2012, two ew UPPs were estalished i Vidigal ad Chácara do Céu, to e followed y Rociha). Give sigificat differeces i the size ad scope of those UPPs, it is difficult to apply our fidigs for the first 18 UPPs to them (or, for that matter, to susequet UPPs). However, uder the 9
12 uit of measure is the mothly average listig price of a property with certai characteristics, such as dwellig type (i.e., apartmet or house) ad umer of edrooms, i a give eighorhood. For istace, oe price oservatio would e the average price of a 3-edroom apartmet i Botafogo i Jauary The localized ature of the policy i specific favelas as well as its sequetial rollout leads to variatio i property prices across eighorhoods, time ad property characteristics. The elasticity of house prices to the istallatio of a UPP is estimated usig the followig specificatio: (1) l i i i Pt 0 1, UPP 2, Dist 3 UPP * Dist tz t, t where P is the average sale price of property type i i airro i moth t, UPP is a dummy takig the value 1 for periods after the occupatio date (or, if availale, the aoucemet date) of the th UPP, Dist is a dummy deotig proximity of airro to UPP, i Z is a vector of cotrol variales for property characteristics, icludig the type of property (i.e., i apartmet or house) ad the umer of rooms, ad t is a mea zero error. A full set of airro ( ) ad time ( t ) fixed effects are icluded to asor commo eighorhood factors ad aggregate moth-to-moth variatio i the housig market, respectively. Sice the average prices of properties i the same airro ut with differet characteristics might have correlated errors, the stadard errors of the estimates are clustered y airro ad period. Our treatmet group cosists of properties that are i eighorhoods withi close proximity of a UPP. Ideally, oe would use the exact distace etwee a property s address ad the UPP as the measure of proximity. However, give that we oserve property prices oly withi the geographic uit of eighorhood, we operate uder a rage of assumptios aout how close adjacet eighorhoods are to a UPP. A set of three specificatios of the variale Dist is used: (i) the eighorhood i which the UPP is located (the peultimate colum of Tale 1), (ii) eighorhoods with a order withi 2km of the UPP address (the fial colum of Tale 1), ad (iii) eighorhoods whose cetroid is withi 2km of the UPP address. assumptio that susequet UPPs are o average as effective as previous oes, our estimates of the overall effect of UPPs o house prices ad crime uderstate the effects of the program as a whole. 10
13 The ifluece of each UPP o house prices is give y the coefficiets iteractio etwee Dist ad UPP. 3,, the 3, ca e iterpreted as the percet icrease i average property prices i eighorhoods proximate to a UPP after its istallatio relative to the chage i price for properties that are ot located close to a UPP. We idetify the separate cotriutio of 15 out of 18 UPPs to house prices. I three istaces, the overlap of the timig or the eighorhood of multiple UPPs is such that the effect of each UPP caot e separately idetified. For example, the occupatio of the favela i São Carlos (i the eighorhoods Estácio ad Rio Comprido) was aouced i Feruary 2011, the same moth as favelas i Coroa, Fallet ad Fogueteiro (i the eighorhood Rio Comprido). The resultig coliearity led us to merge these two UPPs ito a sigle estimate of 3,. We did likewise for Borel, Formiga ad Salgueiro, three UPPs i the Tijuca eighorhood itroduced withi moths of oe aother i A importat cocer aout the use of average price chages (of a sample of listigs) to measure treds i the housig market is that the compositio or quality of the sample could e chagig over time. Chagig property compositio, such as the etry of a high-quality, high-priced property ito the listigs, would affect the average price used i the estimatio of (1) eve if the true quality-adjusted price of housig had ot chaged. This is a relevat issue if compositio is chagig systematically i a way that is correlated with the timig of the UPP policy. It is therefore importat to check whether housig compositio might e chagig i respose to the itroductio of UPPs ad to correct for this selectio ias i equatio (1). To do so, we use the two-stage correctio procedure y Heckma (1976, 1979) ad estimate the proaility that a property with certai characteristics (i.e., eighorhood, type, # rooms) is listed. The first stage proit regressio is of the form: (2) z i t 0 4, UPP 1, UPP * Dist 2, Dist * StateListigs t UPP * Dist 3, t i t Z t i where i i 1 if Pt 0 z t. 0 otherwise 11
14 I (2), the treatmet group is additioally iteracted with the total umer of property listigs o ZAP.com.r i the state of Rio de Jaeiro. We thus allow listigs to e a fuctio of all of the explaatory variales i (1), icludig the UPP policy, as well as ay factors that affect the aggregate listigs o ZAP.com.r. Stated differetly, the first stage allows for the possiility that crime has a outsized ifluece o listigs whe other factors affectig listigs are high. We implemet (2) as a stadard Heckma correctio factor y addig the iverse Mills ratio of this regressio as a additioal explaatory variale i (1). We ote that the sample selectio correctio, while useful as a gauge for the determiats of listigs for groups of properties (e.g., 2- versus 3-edroom apartmets i Botafogo), still does ot accout for compositioal effects withi those groups (e.g., high- versus low-quality 2-edroom apartmets i Botafogo). These compositioal effects might e taitig our results elow, though it is ot ovious whether oe would expect a decrease i crime iduced y the UPPs to cause higher- or lower-quality properties withi each group to e listed. Fially, estimates of the UPP effect o house prices from equatios (1) ad (2) are used to costruct a series of couterfactual average property prices; these are the prices that the regressios suggest would have ee oserved i the asece of the UPP policy. The growth rate of the couterfactual price, i P ~ t, i a give period is costructed as follows: ~ i Pt (3) ~ i exp l ˆ i P Pt t1 i where P ~ P i t i t1 ˆ UPP * Dist 3, ~ i Pt 1 if if UPP * Dist UPP * Dist l Pˆt is the predicted value of average prices from the regressios (1) or (2), ˆ 3, is 0 0 the estimated elasticity of property prices due to the UPPs ad ~ for every property i i P0 P0 type ad eighorhood. Couterfactual prices are equal to oserved prices prior to the arrival of a UPP or i eighorhoods that do ot have a UPP eary (i.e., UPP * Dist 0 ) ut sutract out the estimated effect of the UPP otherwise. Therefore, 0 implies that couterfactual prices are lower tha oserved prices. 12 ˆ 3,