ECON 345 Urban Economics Yuan (Ingrid) Zhuang

ECON 345 Urban Economics Yuan (Ingrid) Zhuang

Technical Presentation Mar. 1st, 2013

Linden, Leigh, and Joanh E. Rockoff. 2008. “Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws.” American Economic Review, 98:3, 1103-1127

Crime rate, victimization, and the fear of crime risk are studied predominantly as local issues. In response to crime risk, residents either vote for anti-crime policies, or they choose to relocate. Therefore, local response to crime is particularly discernible in the housing market, since individuals can reduce their exposure to crime without moving great distances (Linden andRockoff, 2008). Individuals’ strong distaste for crime, especially sex offenses,indicates an inverse relationship between housing values and proximity to registered sex offenders, as observed by existing studies. Understanding the relationship between property values and local crime risk is important in determining optimal policy decisions, such as proper level of policing and expenditures for programs that reduce crime.

Technique:

Linden and Rockoffindicate in their paper that previous literature had potential omitted variable bias in both the cross-sectional and the time series models, and crime rates are likely to co-move with other unobserved characteristicsina neighborhood. Linden and Rockoff (2008) improves on past estimates through the use of hedonic estimation methodology to measure the impact of crime risk on property values. Theyovercome the bias problem by using cross-sectional and time series data on the timing and the exact locations of sex offenders’ arrival based on the implicit assumption that the small neighborhood around a sex offender is relatively homogeneous. The timing of a sex offender’s move-in allows Linden and Rockoff to confirm that the change in property values is not caused by other preexisting shocks.

Three sets of data are analyzed. The first is a January 2005 data on registered sex offenders in Mecklenburg County, North Carolina, provided by the North Carolina Department of Justice (NCDOJ). It contains information on sex offenders’ names, basic characteristics, types of crimes, incarceration dates, addresses of where offenders currently live, and registration dates. The second set of data is collected from the Mecklenburg County division of Property Assessment and Land Record Management, providing information on all real estate parcels in the county and comprehensive physical characteristics for each parcel. The third set is a total of 169,577 property sales of a ten-year period (from January 1994 to December 2004) in the Mecklenburg County. Linden and Rockoffchoose to limit the time period to a four-year window: two years prior and two years after the offenders’ arrivals. They match the first dataset with the second dataset to pin down the exact location of registered sex offenders, and merge with the property sales data to exploit the exogenous variation from the move-in to estimate the property value changes.

Assuming living in close proximity to a sex offender has a negative impact on nearby property values, one should expect a fall in prices of homes near the offender’s location subsequent to the offender’s arrival, with the largest impact on homes closest to the offender. The graphical evidence confirms the hypothesis. Comparing to pre-arrival price gradient of distance, figures2B and 3Bexhibit a clear decline for sales during the year within 0.1 miles of the offender. Homes slightly farther away are less affected.

Model and Estimation:

Linden and Rockoff (2008) uses a cross-sectional difference estimator and a difference-in-differences estimator to test the graphical evidence.

Cross-sectional difference estimator: , (1)

where the log of the deflated sale price (sale price/CPI) of the house is a function of a measure of distance from the sex offender, a year specific effect (αt), and a random error term (εijt). D1/10 ijtis an indicator variable:when a property sale occurs within 0.1 miles of a sex offender’s location it equals to one.

Difference-in difference specification: , (2)

adds an indicator variable (D3/10ijt) for homes within 0.3 miles of an offender’s address and an interaction of this indicator with an indicator to test whether the sale took place after the offender’s arrival (Postit). αjt is a neighborhood-year fixed effect, Xiobservable property characteristics, and the term π1 gives the estimated impact of close proximity to a sex offender on property values.

Cross-sectional difference estimator is used to confirm the absence of preexisting differences in the characteristics of homes located within 0.1 miles of an offender. Table 2 shows two regressions, where Panel A estimates all houses within 0.1 miles of the sex offender’s location that are sold BEFORE his arrival. Panel B has the same estimation but included all houses regardless of whether sold or not, thus they could not estimate the first two columns of Table 2. The insignificance of the results demonstrates a high degree of homogeneity in the data.

Table 3 presents statistical estimates of the impact of a sex offender’s arrival on the nearby housing values. Column 1 shows estimates of equation (1), including sales of all homes in the dataset and sale-year fixed effects. The estimate of the impact (π1, which in this case is simply a measure of the average price difference between houses within 0.1 miles of an offender’s future location and other houses sold within the same year) is approximately 34 percent. Significant at 5 percent level, the estimate confirms that homes closer to offenders’ locations are relatively cheap compare to other parts of the county. Column 2 builds onto column 1 by adding neighborhood-year fixed effects and house characteristics to the regression. The results are not statistically different from zero at any confidence level, implying that control variables in the regression capture almost all of the differences between areas in which offenders move and the rest of the county. In a simple pre-post comparison, column 3 shows estimates of equation (2) without the indicator variables for houses selling between 0.1 and 0.3 miles from the offenders. Linden and Rockoff find that a sex offender’s arrival caused on average 4 percent decline in housing prices within 0.1 miles of an offender’s location, 0.7 percent pre-existing difference in prices. The 3.3% difference is significant at the 10 percent level. Linden and Rockoff’s difference-in-differences specification results are recorded in column 4. The estimates show a slightly higher impact of a sex offender’s arrival, with -4.1 percent at the 4 percent significant level. The impact of offender’s arrival for homes located between 0.1 and 0.3 miles of an offender’s location is curious but statistically insignificant. The result indicates that homes slightly farther away experienced little to no decrease in property values on average. Column 5 re-estimates equation (2),using only property sales from areas with sex offenders’ presence. Instead of controlling for neighborhood-year fixed effects, Linden and Rockoff controloffender area-year fixed effects and estimate standard errors clustering at the offender area level. This approach provides better identification by focusing on houses within 0.1 miles of offenders. Since the results are consistent with those from columns 3 and 4, they conclude that additional data from sales outside of offender areas did not bias their estimates. Adding an interaction of the dummy variable indicating a sale within 0.1 miles of an offender after the offender has moved in, with distance from the offender, column 6 shows no changes in results. Robustness and falsification tests are conducted further to attest their conclusion. Due to the focus of this technical presentation, detailed explanations on these results are omitted.

Conclusion:

From the hedonic estimations, Linden and Rockoffconclude that houses within a one-tenth mile area around the home of a sex offender fall by 4% on average (about $5,500). The result suggests that residents have a significant distaste for living in close proximity to a known sex offender, and they would be willing to pay a high cost for policies that remove sexual offenders from their neighborhoods. As Linden and Rockoff mentioned in their paper, one possible extension for this study could be adding data on buyer or seller characteristics in order to avoid overestimating or underestimating the average willingness to pay due to the fact that only prices for houses that sell were analyzed. Another contribution would be to explore whether the recession has changed the relationship (comparing pre-recession reaction to post-recession reaction) or examine whether neighborhoods ofvarious demographics will react differently to the presence of sex offenders.

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