Crime, House Prices and Inequality: the Effect of UPPs in Rio

Transcrição

1 Crime, House Prices and Inequality: the Effect of UPPs in Rio Claudio Frischtak (International Growth Centre & Inter.B Consultoria Internacional de Negócios) Benjamin Mandel (Citi Research) April 214

2 Context In 28, the state of Rio de Janeiro enacted a new security policy. Based on the permanent occupation of areas previously monopolized by armed groups in low-income communities. New police units, called Unidades de Polícia Pacificadora ( Pacifying Police Units, or UPP), composed of a new generation of uncorrupted police, are installed in several neighborhoods within Rio de Janeiro. The study examines the effect of the 19 UPPs installed between November 28 and February 212. The locations of the UPPs were determined by factors related to the need to secure critical transportation corridors and tourist areas prior to the upcoming World Cup and Olympic Games.

3 Apartment Prices in Rio ($R per m^2, average for 28-11)

4 Homicide Rates in Rio (per 1, per annum, average for 28-11)

5 Context (cont.) There is a widespread perception that the security policy was effective in reducing crime and improving the well-being of the population. Anecdotal evidence to date suggests that UPPs have contributed to the recovery of property prices already underway in Rio. Overall prices have risen briskly, driven by a combination of falling interest rates, credit expansion and rising income levels.

6 Objectives Systematically measure the relationship between the new security policy, crime rates and residential property prices. Quantify the relationship between crime and the distribution of prices over time. We utilize the estimated impacts of the UPPs on housing prices to show that the policy reduced the dispersion of prices. Using property value as a proxy for household assets, this suggests that the security policy played an indirect role in reshaping the city s wealth distribution.

7 Related Literature Crime rates and house prices are negatively correlated, but the causal relationship is difficult to pin down. Thaler (1978), Hellman & Naroff (1979): Negative elasticity, but crime rates treated as exogenous. Rizzo (1979), Naroff (198), Burnell (1988), Buck et al. (1993), Gibbons (24), Tita et al. (26), Ihlanfeldt & Mayock (21): Negative elasticity using instruments such as demographic information, population density, lags of explanatory var., commercial crime density, distance to a bar [...] Linden & Rockoff (28), Besley & Mueller (211): the threat of crime/violence has an influence on house prices. Our study: The first to use micro-data from a developing country. Uses a crime-related policy as an instrument.

8 Related Literature (cont.) None of the same tools have been brought to bear on the effect of crime on the distribution of house prices Fajnzylber, Lederman & Loayza (22): Across ~4 countries, national crime rates and income inequality are positively correlated within and between countries, reflecting causation from inequality to crime rates. Braithwaite (1979), Stack (1984), Glaeser, Sacerdote & Scheinkman (1996), Bourguignon (2), Imrohoroglu, Merlo & Rupert (2), Morgan (2): examine the effect of inequality on crime outcomes. Our study: Provides micro-foundations for the impact of crime on house price inequality. Examines the crime/inequality relationship in the opposite causal direction (i.e., from crime to inequality).

9 Summary of Results (using neighborhood-level data) We estimate that the UPP policy accounted for: 14% of the reduction in city-wide homicide rates, and 2% of the reduction in robbery rates since 29 15% of the growth in overall house prices since 28 45% of the reduction in the Gini coefficient measuring the level of inequality of house prices across Rio s neighborhoods (although there are significant differences depending on methdology)

10 Summary of Results (Vidigal case study using microdata) We estimate that (at least) one UPP led to large increases in the level and decreases in the variance of prices within close proximity to the UPP. Preliminary result: Conditional on distance from UPP, price dispersion decreased within neighborhoods. This implies that even within groups with relatively homogeneous crime rates, lower priced properties grew faster when crime rates fell.

11 Outline 1. UPP policy evaluation: Effect of UPPs on property prices Effect of UPPs on crime rates 2. Vidigal case study w/ microdata 3. Trends in price skewness w/ microdata

12 House price dataset #1: Average neighborhood prices of residential property

13 Source and Description of the Data: Residential Property Prices Source: The website ZAP.com.br. Unit: Average sale price by type of property, georeferenced by neighborhood. Frequency and duration: Monthly, January 27 through August 211. Total Size: 54,64 observations representing 3.3 million individual property listings.

14 Average Property Prices ($R/m2) Relationship between Homicides and Residential Property Prices 1, 9, 8, 7, 6, 5, 4, 3, 2, 1, Annual Homicide Rate(per 1, people)

15 Analysis of the Effect of the UPPs on Property Prices The coefficient α 3 measures the percent increase in the price of properties near a UPP after the announcemnt of the UPP relative to the price change for properties farther away from the UPP.

16 Measures of Proximity to a UPP (Dist) Urca Leme UPP at Chapeu- Mangueira Copacabana Botafogo Proximity to Chapeu-Mangueira: 1. The neighborhood in which the favela with UPP is located [Leme] 2. Neighborhood with a border <2km from UPP [Leme, Urca, Botafogo, Copacabana] 3. Neighborhoods whose central point <2km from the UPP [Leme, Urca, Botafogo]

17 16 UPPs and Prices: An Illustrative Example Occupation of Chapéu- Mangueira in Leme Occupation of Pavão-Pavãozinho in Copacabana/Ipanema Leme Casa Copacabana Casa

18 UPPs and Prices: An Illustrative Example Occupation of Chapéu- Mangueira in Leme Occupation of Pavão-Pavãozinho in Copacabana/Ipanema Leme Apartamento Copacabana Apartamento Botafogo Apartamento Leblon Apartamento

19 Results: α 3 Date UPP Bairro Bairro Nov-8 Santa Marta Botafogo, Humaitá 1.9% ** Nov-8 Cidade de Deus Jacarepaguá -5.2% * Dec-8 Batam Realengo -5.9% ** May-9 Babilônia / Chapéu-Mangueira Leme 21.2% ** Nov-9 Pavão-Pavãozinho / Cantagalo Ipanema, Copacabana 5.2% ** Dec-9 Tabajaras / Cabritos Copacabana, Botafogo 1.7% ** Mar-1 Providência Gamboa, Santo Cristo, Saúde -5.1% Apr-Jul 1 Borel / Formiga / Salgueiro^ Tijuca 8.% ** Jun-1 Andaraí Grajaú, Andaraí 17.% ** Aug-1 Turano Tijuca, Rio Comprido 5.5% ** Oct-1 Macacos Vila Isabel 1.8% ** Jan-11 São João / Matriz / Quieto Engenho Novo 8.9% ** Feb-11 Escondidinho / Prazeres Santa Teresa 1.1% ** Feb-11 São Carlos / Coroa / Fallet / Fogueteiro^ Estácio, Rio Comprido 19.5% ** Jun-11 Mangueira / Morro do Tuiuti Mangueira, São Cristóvão -7.%

20 Analysis of the Effect of UPPs on Crime Rates A similar equation is used to measure the relationship between crime rates and the introduction of the UPPs

21 Results: α 3 Date UPP Homicidio Roubo Nov-8 Santa Marta -26.9% * 4.5% Nov-8 Cidade de Deus -5.3% 3.% Dec-8 Batam -6.9% 5.9% May-9 Babilônia / Chapéu-Mangueira 9.1% 9.1% Nov-9 Pavão-Pavãozinho / Cantagalo 11.7% -1.3% Dec-9 Tabajaras / Cabritos -8.7% -11.% Mar-1 Providência -66.2% ** -39.7% ** Apr-Jul 1 Borel / Formiga / Salgueiro^ -6.8% ** -19.7% * Jun-1 Andaraí -55.3% ** -52.9% ** Aug-1 Turano 3.6% -11.3% Oct-1 Macacos -68.8% ** -52.2% ** Jan-11 São João / Matriz / Quieto -67.4% ** -7.% Feb-11 Escondidinho / Prazeres -73.8% * -79.7% ** Feb-11 São Carlos / Coroa / Fallet / Fogueteiro^ -4.9% -58.9% ** Jun-11 Mangueira / Morro do Tuiuti -7.2% -32.3%

22 Effect of the UPPs on Property Prices Homicides and Property Prices Price = *Crime Effect of the UPPs on Homicide Rate -.5

23 Counterfactual Estimations What would property prices and crime rates be in the absence of UPPs? We attempt to answer this question by using the regression estimates to construct counterfactual prices Counterfacutal estimates were constructed using the difference between observed values and the values projected by the coefficients of each UPP The results reflect the contribution of the UPP to rising real estate prices and declining crime rates for the city as a whole

24 Average price per m^2 Observed Prices and Counterfactuals 6, 5,7 5,4 ~15% of the gains since 28 5,1 4,8 4,5 4,2 3,9 3,6 3,3 3, Actual Bairro (OLS) Bairro (WLS) Bairro (Heckman) Border Dist<2km Cent. Dist.<2km

25 Annual Crime Rate (per 1 mil per.) Observed Homicides and Counterfactuals ~14% of the reduction after May Actual Bairro (OLS) Border Dist<2km Cent. Dist.<2km

26 Robbery Rate (per 1, A.R.) Observed Robberies and Counterfactuals ~2% of the decline since May Actual Bairro (OLS) Border Dist<2km Cent. Dist.<2km

27 Gini Coefficient for Apartment Prices 28m1 28m3 28m5 28m7 28m9 28m11 29m1 29m3 29m5 29m7 29m9 29m11 21m1 21m3 21m5 21m7 21m9 21m11 211m1 211m3 211m5 The Effect of the UPPs on Inequality ~45% of the reduction since Actual Bairro (OLS) Bairro (WLS) Bairro (Heckman) Border Dist<2km Cent. Dist.<2km

28 House price dataset #2: Individual residential properties (by Latitude/Longitude, 211/12)

29 Source and Description of the Data: Residential Property Prices Microdata Source: The website ZAP.com.br. Unit: Price for an individual property listing, georeferenced by latt/longitudinal coordinates. Frequency and duration: Monthly, January 211 through February 212. Total Size: 275k individual property listings.

30 The Effect of the Vidigal UPP on Property Prices Given the shorter panel of microdata, this set of regressions will focus on one UPP: Vidigal. In November 211, police and military forces occupied Rocinha/Vidigal, which is Brazil s largest favela complex. The Vidigal UPP was installed in January 212.

31 The Effect of the Vidigal UPP on Property Prices Dist is now computed based on the precise distance between a property and the UPP in Vidigal α 3 measures the percent increase in the price of properties near Vidigal after the announcemnt of the UPP relative to more distant properties.

32

33 House price dataset #3: Individual residential properties (by CEP, 212/13)

34 Source and Description of the Data: Residential Property Prices Microdata Source: The website ZAP.com.br. Unit: Price for an individual property listing, georeferenced by 9-digit postal code. Frequency and duration: Monthly, March 212 through April 213. Total Size: 2k individual property listings.

35 5.e-7 Density 1.e-6 1.5e-6 The Skeness of Prices is Declining Rio de Janeiro, ALL

36 2.e-7 Density 4.e-7 6.e-7 2.e-74.e-76.e-78.e-7 Density 5.e-71.e-61.5e-62.e-6 Density 2.e-7 4.e-7 Density 6.e-7 8.e-7 1.e-6 The Skewness of Prices is Declining Zona Sul Rio de Janeiro, BOTAFOGO Rio de Janeiro, CATETE Rio de Janeiro, COPACABANA Rio de Janeiro, FLAMENGO Rio de Janeiro, GAVEA Rio de Janeiro, HUMAITA

37 1.e-72.e-73.e-74.e-7 Density 5.e-7 Density 1.e-6 1.e-7 2.e-7 Density 3.e-7 4.e-7 5.e-7 Density The Skewness of Prices is Declining Zona Sul Rio de Janeiro, IPANEMA Rio de Janeiro, LAGOA 5.e-7 1.e-6 1.5e-6 Rio de Janeiro, LARANJEIRAS Rio de Janeiro, LEBLON Rio de Janeiro, LEME Rio de Janeiro, URCA

38 1.e-62.e-63.e-64.e-6 Density 5.e-7 1.e-6 Density 1.5e-6 2.e-6 2.5e-6 5.e-71.e-61.5e-62.e-6 Density 2.e-6 Density 4.e-6 6.e-6 The Skewness of Prices is Declining Zona Norte Rio de Janeiro, ENGENHO DE DENTRO/NOVO Rio de Janeiro, GRAJAU Rio de Janeiro, MADUREIRA Rio de Janeiro, MARACANA Rio de Janeiro, MEIER Rio de Janeiro, TIJUCA

39 5.e-7 Density 1.e-6 1.5e-6 5.e-71.e-61.5e-62.e-6 Density 2.e-74.e-76.e-78.e-7 Density 1.e-62.e-63.e-64.e-6 Density The Skewness of Prices is Declining Zona Oeste Rio de Janeiro, ANIL Rio de Janeiro, BARRA DA TIJUCA Rio de Janeiro, CAMPO GRANDE Rio de Janeiro, JACAREPAGUA Rio de Janeiro, RECREIO DOS BANDEIRANTES Rio de Janeiro, VARGEM GRANDE/PEQUENA

40 Towards a model of house prices & inequality At least two types of price response heterogeneity might decrease price variance when crime abates: 1. Conditional on amenities and crime, properties with a higher initial crime rate have greater price appreciation. (var(p) ) 2. Conditional on initial crime rate and crime, properties with lower non-crime amenity values have greater price appreciation. (var(p) )

41 Conclusions The evidence suggests that the UPP policy is quite effective in reducing crime in the city of Rio de Janeiro. An important corollary is that urban violence has a very significant effect on the destruction of property values. This also likely applies to other cities affected by high levels of violence.

42 Conclusions (cont.) The UPP policy also affects the price distribution. In particular, the lower the level of security, the greater the gains in terms of the price distribution. Is this a channel by which policymakers could indirectly target the wealth distribution? The results suggest that crime rates should be utilized as criterion for locations of future UPPs.