The Value of Public Safety: Crime and Property Market Capitalization

1. The Value of Public Safety: Crime and Property Market Capitalization Karl Russo Business and Public Policy The Wharton School June 4, 2007

2. Outline of Presentation • Motivation • Literature Review • Model Specification • Data • Results • Conclusion

3. Left: Median Sales PriceRight: Assault Rate

4. Motivation • Need marginal benefits to allocate resources efficiently • Property market captures welfare benefits if wages fixed Rosen/Roback • To what extent does crime affect house prices? • Does crime next door matter? • Do effects vary by type of crime?

5. Preview of Results • Value of statistical life $7.8M - $8.6M • Eliminating one crime raises property values city wide by $51,350 or $2575 annually • Violent crimes have and estimated impact 3.5 times that of property crimes ($5922 vs. $1636 per year) • Neighboring crime rates have an estimated elasticity four-five times that of own crime rates • Effects vary by income

6. Literature Review • Rosen 1979 and Roback 1982 compensating differentials • Thaler 1978 elasticity -0.07, cross-sectional, transaction prices (n=398), rejected using neighboring crime rates • Hellman and Naroff 1979 elasticity of -0.63, self-reported census value, limited controls, extend to property tax effect • Lynch and Rasmussen 2001 transaction prices, n=2880, weight by seriousness of crime to obtain “cost of crime”, little overall effect, high crime areas highly discounted • Schwartz et al. 2003 transaction prices, n=246,743, precinct level crime data, violent crime elasticity -0.13, property -0.01 • Linden and Rockoff 2004 Megan’s Law 4% drop for homes in 0.1 mile radius of sex offender • Gibbons 2004 1/10 standard deviation increase in criminal damage decreases property values just under 1% L110/household/year

7. Housing Model • Rosen/Roback • max U(x, h; G) subject to w = x + h*p(S, L, N) • Long-run equlibrium equalizes utility across locations • Variation in house values V = v(S, L, N, w, G) compensates for variation in amenities across locations

8. Specification • ln valueijt = β0 + Sijtβ1 + Lijβ2 + ln crimejtβ3 + Gjtβ4 + εj + εt + εijt • Public Goods, G • Murder rate • Crime rate • Neighboring crime rate • Temporally lagged crime rate • Educational Quality • Average Tax Rate • Enterprise Zone • Proximity to public parks

9. Crime Model • Return from honest work: r = w + φ • Return from crime: v(mi,ni) = (1 – p(mi,ni)) z(ni) – p(mi,ni)*F • Crime is decreasing in w, φ, m, and F and increasing in z • Impact of n is ambiguous criminals • positive externalities via arrest probability • negative externalities via competition

10. income v* r1 v0 r0 n2 n1 n0 thieves Crime Model

11. Data Sources • Cartographic Modeling Lab/GIS Lab • Philadelphia Police Department • Pennsylvania Department of Education • Board of Revision of Taxes • Penn Real Estate Department • Census Bureau

12. Data: CML/GIS/PPD/PDE • violent crimes: robbery, aggravated assault • property crimes: burglary, theft, auto theft • Annually 1998-2003 • Population data • Create crime rates for each block group • Construct neighboring crime rates using GIS • Precinct level murder rate-averaged over previous three years • Fifth grade PSSA score

13. Data: BRT/RED • All arm-length single-family sales 1/2000-6/2004 • Transaction price, parcel identifier • Property tax rate- averaged over block-group-year • Structural: lot sqft, lot sqft sq, bldg sqft, bldg sqft sq, FAR(tract), # stories, type of structure, exterior material, exterior condition, fireplaces, multifamily, garage, central air, amenities (e.g. pool), improvements • Locational: distance to CBD, dist sq, mixed use, irregular, above street, view, corner, adjacent to vacant lot, borders park, ¼ mile from park, enterprise zone • Neighborhood dummies: block group

14. Crime Statistics

15. Crime Statistics

16. Summary Statistics

17. Income Strata Statistics

18. Results: Crime

19. Results: Crime

20. Results: Crime • Value of statistical life $7.8M - $8.6M (EPA $6.94 Million) • Eliminating one crime raises property values city wide by $51,350 or $2575 annually • Violent crimes have and estimated impact 3.5 times that of property crimes ($5922 vs. $1636 per year)

21. Results: Public Goods • Low income neighborhoods do not appear to value crime reduction- possible lack of alternatives • Middle class neighborhoods have the highest valuations on safety $8450 for violent and $1900 for property crimes • High income areas have strong aversion to own violent crime, neighborhood effects are insignificant- insulating effect

22. Results: Public Goods • 64% property tax capitalization • Education elasticity 0.38 • Proximity to public parks valued only in low income areas, at approximately $240 per year or 2% of income • NTI impact on low income and property tax abatement impact on high income consistent with pattern observed here

23. Conclusion • Deterring crime has a significant positive economic impact on the city’s housing values and property tax base • Failure to account for geographic spillovers would lead to a substantial underestimate of the total effect • Violent crimes have a greater impact on housing values than property crimes • Effects vary by neighborhood income

24. Conclusion • Police department should spend resources up to the point where annual marginal cost of crime reduction is less than $5922 per violent crime or $1636 per property crime • Aggregate calculations suggest • Direct impact of crime $227 Million over 5 years • Impact neighboring crime $1 Billion • Marginal Justice Spending ~$900M