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Leigh Linden Columbia University

There Goes the Neighborhood? Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws. Leigh Linden Columbia University. Jonah Rockoff Columbia Business School. Broad Motivation. Crime is a costly local disamenity

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Leigh Linden Columbia University

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  1. There Goes the Neighborhood?Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws Leigh Linden Columbia University Jonah Rockoff Columbia Business School

  2. Broad Motivation • Crime is a costly local disamenity • Most violent crime occurs less than one mile from victims’ homes • Local governments spend $50 billion a year on police protection • Optimal expenditure on anti-crime policies depends on the demand for crime reduction

  3. Why Focus on Sex Offenders? • “Megan’s Laws” require offenders to register and that their addresses be made public • Laws challenged and upheld by supreme court • Some state and local governments prohibit sex offenders from living in specific areas • Law creates opportunity to measure distaste for increased crime risk at the local level

  4. The Hedonic Method • Estimate demand for neighborhood characteristics through property values • Rosen (1974), Bartik (1987), Epple (1987)… • Technique used to evaluate demand for amenities like school quality, public safety, environmental hazards, etc. • Davis (2004), Chay and Greenstone (2005)…

  5. Crime and Property Values • Houses in high crime areas should, all else equal, sell for lower prices • Identification problem: high crime areas may have other characteristics that are unobservable to the econometrician • Difficult to overcome potential omitted variables bias in cross sectional studies • Larson et al. (2003) on sex offenders

  6. Our Study • Combine housing market data with information from sex offender registrations • Allows us to use variation in the threat of crime within small homogenous groups of homes • The timing of a sex offender’s arrival allows us to control for baseline property values

  7. Megan’s Laws • Federal law (1994) requires registration of sex offenders at the state level • Amended law (1996) requires dissemination • NC law (1996) well suited to our study • Date offender moved into current address • Stringent requirements (e.g., 10 day limit) • High quality data: only 2% fail to register

  8. Types of Crimes Committed (NC)

  9. Data Sources • NC Sex Offender Registry (January 2005) • Locations and move-in dates • Mecklenburg County Tax Data (March, 2005) • GIS data to map offender locations • House characteristics (e.g., sq. feet, # rooms) • Mecklenburg County Sales Data (1994 – 2004) • Only use sales of single family homes

  10. Offender Areas (0.3 mile radius)

  11. Graphical Evidence After Offender Arrival

  12. Graphical Evidence cont’d

  13. Graphical Evidence cont’d

  14. Graphical Evidence cont’d .3

  15. Illustration of Identification Strategy

  16. Estimation of Price Impact • Control for many housing characteristics • Sq. feet, bedrooms, bathrooms, age, # stories, air conditioning, external wall type, building quality • Use all sales in county to estimate b • Control for neighborhood-year fixed effects • Use houses between 0.1 and 0.3 miles as counterfactual difference over time (D-in-D)

  17. Offender Location & Property Value

  18. Price Response and Cost of Crime • Estimates suggest the discount for living near offender is ~$5.5k for median house • If effects are driven by rise in risk of victimization to neighbors, we can use them to estimate welfare costs to victims • Compare estimates with those from DOJ studies that use other data and methods

  19. Victimization Cost Estimates (DOJ)

  20. “Back of Envelope” Methodology • Households can live far from an offender or live close, get a price discount, and face risk • Indifference of marginal household : • Given the distribution of crime risk f(c), we can solve for the cost of crime vc

  21. Measuring Risk to Neighbors • Need an estimate of risk due to living in close proximity to a convicted sex offender • Use data to create a probability distribution with which neighbors are victimized • Data on arrests of prisoners released in 1994 • NCVS estimates of crimes reported to police • FBI UCR clearance rates (arrests per report) • FBI UCR data on victim-criminal relationship • NC data on # households near offender

  22. Cost of Crime Estimates

  23. Conclusions • Proximity to a sex offender causes a significant decline in property value (~4%) • Effects are extremely localized (0.1 mile) • Implies large costs relative to DOJ estimates • A number of potential explanations: • DOJ estimates are too low • Misperception of true crime risk • Utility loss independent of risk increase

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