The Value of Public Safety: Crime and Property Market Capitalization

Karl Russo Business and Public Policy The Wharton School June 4, 2007

Outline of Presentation

      Motivation Literature Review Model Specification Data Results Conclusion 2

Left: Median Sales Price Right: Assault Rate

3

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?

4

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 5

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 6

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 7

Specification

  ln value + G jt β 4 ijt = β 0 + S ijt β 1 + ε j + ε t + ε ijt + L 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 ij β 2 + ln crime jt β 3 8

Crime Model

    Return from honest work: r = w + φ Return from crime: v(m i ,n i ) = (1 – p(m i ,n i )) z(n i ) – p(m i ,n i )*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 9

Crime Model

income v* r 1 v 0 r 0 n 2 n 1 n 0 thieves 10

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 11

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 12

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 13

Crime Statistics

City New York Los Angeles Houston Philadelphia Phoenix San Diego Las Vegas Population 8,101,321 3,864,018 2,043,446 1,484,224 1,428,973 1,281,366 1,239,805 Violent Crime 55,688 42,786 23,427 20,902 9,465 6,774 9,783 Property Crime 171,188 125,200 123,425 60,931 94,406 45,443 59,654 Total Crime Violent Crime Rate 226,876 0.0069 167,986 0.0111 146,852 0.0115 81,833 0.0141 103,871 0.0066 52,217 0.0053 69,437 0.0079 San Antonio Dallas 1,235,128 1,228,613 7,846 16,165 81,255 94,066 89,101 0.0064 110,231 0.0132 Detroit 914,353 15,913 57,415 73,328 0.0174 United States 293,655,404 1,367,009 10,328,255 11,695,264 0.0047 Property Crime Rate Total Crime Rate 0.0211 0.0280 0.0324 0.0435 0.0604 0.0719 0.0411 0.0551 0.0661 0.0727 0.0355 0.0408 0.0481 0.0560 0.0658 0.0721 0.0766 0.0897 0.0628 0.0802 0.0352 0.0398 14

Crime Statistics

Crime 1998 1999 2000 2001 2002 2003 2004 Murder Rape Robbery Assault Burglary Theft Auto Theft Total 338 752 11,435 8,701 15,437 49,892 19,523 292 934 319 1,021 11,104 10,425 309 1,014 9,604 10,701 11,047 10,477 288 1,035 8,869 9,865 348 1,004 9,617 9,651 330 1,001 9,757 9,814 14,042 12,089 11,629 11,244 10,656 10,536 49,874 46,952 45,318 38,789 37,864 37,808 17,711 16,147 15,527 13,302 13,934 12,587 106,078 104,658 98,000 93,878 83,392 83,074 81,833 15

Summary Statistics

Variable price2000 avgtaxrate entzone murder crime violent property ncrime nviolent nproperty lagcrime lagviolent lagproperty education parkbord parkqtrm Mean 74119.22 0.0160 0.0691 0.0002 0.0578 0.0105 0.0473 0.0647 0.0114 0.0533 0.0602 0.0104 0.0498 2342.16 0.0401 0.7628 Std Dev 82605.09 0.0084 0.25355 0.0002 0.0638 0.0121 0.0552 0.0389 0.0083 0.0332 0.0647 0.0117 0.0568 166.1474 0.1963 0.4253 16

Income Strata Statistics

Census Statistics

Median Income Range From: To: Mean Median Household Income Number of Block Groups

Sample Means

Median Household Income House Price in Sample Average Tax Rate Murder Rate Crime Rate Violent Crime Rate Property Crime Rate Education Score Park within Quarter-mile Number of Observations Low $0 $15,373 $9,632 271 Middle $24,597 $36,895 $30,584 630 High $46,119 $200,001 $60,759 225 $12,112 $27,928 0.0188

0.0003

0.0968

0.0256

0.0711

2219 0.8191

3041 $30,957 $62,128 0.0160

0.0002

0.0606

0.0106

0.0500

2323 0.7778

26734 $55,629 $146,737 0.0140

0.0001

0.0429

0.0045

0.0384

2447 0.7058

9371 17

Results: Crime

Income Group lnmurder lnviolent lnproperty nlnviolent nlnproperty lnlagviolent lnlagproperty Block Group Effects Year Fixed Effects Structure Covariates Adjusted R-squared (1) All -0.0714

-4.81

-0.0127

-2.56

-0.0212

-1.89

(2) All -0.0642

-4.16

-0.0121

-2.30

-0.0210

-1.77

-0.0099

-1.84

-0.0424

-3.54

(3) All -0.0625

-4.08

-0.0080

-1.55

-0.0108

-0.89

-0.0537

-4.51

-0.0489

-2.04

-0.0069

-1.29

-0.0383

-3.19

(4) Low 0.0618

0.69

-0.0334

-0.62

-0.0070

-0.09

-0.0866

-0.84

-0.0210

-0.15

-0.0089

-0.18

-0.1319

-1.86

(5) Middle -0.0674

-2.45

-0.0053

-0.58

0.0034

0.16

-0.0998

-4.44

-0.0865

-2.18

-0.0116

-1.34

-0.0073

-0.31

(6) High -0.0903

-4.00

-0.0229

-2.83

-0.0306

-1.56

-0.0116

-0.71

-0.1159

-2.24

-0.0137

-1.46

-0.0306

-1.73

Yes Yes Yes 0.6878

Yes Yes Yes 0.6838

Yes Yes Yes 0.6839

Yes Yes Yes 0.5655

Yes Yes Yes 0.5415

Yes Yes Yes 0.7298

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Results: Crime

(1) Income Group Murder (2) (3) (4) (5) Violent t-1 Property NViolent t-1 t-1 NProperty t-1 Change in Value (1) All 7,815,111 (1,915,468) Annualized Change (2) All 390,756 (95,773) 15,356 (9,907) 5,918 (6,649) 103,080 (22,856) 768 (495) 296 (332) 5,154 (1,143) Change in Value (3) Low Annualized Change (4) Low Change in Value (5) Middle Annualized Change (6) Middle Change in Value (7) High Annualized Change (8) High (2,911,681) (145,584) 7,064,208 353,210 22,353,629 1,117,681 (4,219,828) (210,991) (2,883,350) (144,168) (5,588,407) (279,420) 24,157 (38,963) 1,445 (16,058) 62,635 (74,565) 1,208 (1,948) 72 (803) 3,132 (3,728) 8,528 (14,703) (1,562) (9,760) 160,575 (36,165) 426 (735) (78) (488) 8,029 (1,808) 87,024 (30,751) 33,195 (21,279) 44,082 (62,087) 4,351 (1,538) 1,660 (1,064) 2,204 (3,104) 26,795 (13,135) 1,340 (657) 4,336 (28,905) 217 (1,445) 39,729 (18,224) 1,986 (911) 125,727 (56,128) 6,286 (2,806) (6) (7) Violent t-2 Property t-2 13,245 (10,267) 20,986 (6,579) (2) + (4) Total Violent t-1 (3) + (5) Total Property t-1 118,436 (24,911) 32,713 (14,722) 662 (513) 1,049 (329) 5,922 (1,246) 1,636 (736) 6,437 (35,762) 27,232 (14,641) 86,792 (84,131) 5,781 (33,066) 322 (1,788) 1,362 (732) 4,340 (4,207) 289 (1,653) 18,664 (13,928) 3,353 (10,816) 169,102 (39,040) 38,167 (20,673) 933 (696) 168 (541) 8,455 (1,952) 1,908 (1,034) 52,062 (35,659) 33,195 (19,188) 131,106 (69,285) 158,922 (60,026) 2,603 (1,783) 1,660 (959) 6,555 (3,464) 7,946 (3,001) 19

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) 20

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 21

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 22

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 23

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 24