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Heterogeneous Effects in the Housing Market’s Reactions to Sex Offenders: Evidence from Durham, North Carolina

by Yuan (Ingrid) Zhuang  DP_ZHUANGINGRID

1   Introduction

Since the enactment of the “Jacob Wetterling Crimes Against Children and Sexually Violent Offender Registration Program” in 1994, all states are required to maintain a registry of convicted sexual offenders.  Later in 1996, Megan’s Law amended the Wetterling Act (1994) by requiring law enforcement authorities to disseminate registered sex offenders’ information to the public.  While federal law provides two major information services to the public: sex offender registration and community notification, states are given significant latitude in their implementation of these provisions.  Currently every state has complied with the legislation and most states have websites that provide access to the sex offender registry over the Internet.  Accessible information typically includes offenders’ names, pictures, type of crime(s), incarceration dates, addresses of where offenders currently live, and registration dates.  North Carolina, Florida and Montana are the only states that provide information on offenders’ move-in dates to the public.

Although laws are implemented nationally, 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 and Rockoff, 2006).  Understanding the relationship between sex offense risk and property values is useful for measuring the willingness of individuals to pay to reduce their exposure to crime risk.  Some evidence shows that convicted sex offenders have a substantial probability of reoffending.  Hanson et. al. (2003) found in their study that sexual recidivism rates are approximately 14% after five years, 20% after 10 years, and 30-40% after 20 years.  These results may substantially underestimate the actual rates because some sex offenders re-offend and are not caught.  Therefore, with the increase in transparency level on sex offenders’ information, we expect that the move-in of a sex offender will depress the property values in a neighborhood, particularly for houses in close proximity to a sex offender’s residence.

Literature examining the impact of crime risk on the real estate market is abundant.  As observed by a number of existing papers, individuals’ strong distaste for crime, especially sex offenses, indicates an inverse relationship between housing values and local crime rates.  Earlier studies suffer from potential omitted variable bias.  More recent literature improves on past estimations with their exploitation of data, but still lack detailed analysis of the during/post-recession reactions.  My research explores in particular how sex offenders’ residential locations affect property values in Durham, North Carolina.  It will contribute to the existing literature by firstly strengthening the causal effect of sex offenders’ move-ins on housing prices, and secondly observing whether the recession has changed the inverse relationship (comparing pre-recession reaction to post-recession reaction).  Ideally, we hope to examine whether neighborhoods with various demographics will react differently to the presence of sex offenders.  However due to the limitations on annual neighborhood demographics and income data, this extension is postponed for future research.

The rest of the paper is organized as follows.  In the next section, we provide a brief literature review on related studies concerning the relationship between a sex offender’s move-in and nearby property values.  Section 3 describes the sources of data and explains how we tailored the dataset to our specific use, and additional graphical evidence is presented.  In section 4, we describe the regression model we use to estimate the various parameters.  We present our empirical results in section 5 and analyze how well they fit our hypotheses.  Section 6 finally concludes the paper with our findings, implications of this study and suggestions for future research.


2   Literature Review

A number of papers have found an inverse relationship between property values and local crime rates.  Earlier studies, such as Richard Thaler (1978) and Steve Gibbons (2004) find respectively a 3 percent and a 10 percent decrease in property values for a one-standard-deviation increase in property crime.  However Linden and Rockoff (2006) points out, these results may suffer from potential omitted variable bias in both the cross section and times series, since crime rates are likely to co-move with other unobserved factors and may change as the characteristics of neighborhoods change.  More recent studies Linden and Rockoff (2006) and Jaren C. Pope (2008) improve on past estimates through the use of hedonic estimation methodology to measure the impact of crime risk on property values.  Linden and Rockoff (2006) overcomes 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.

In their paper, 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 Rockoff choose 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.

From the hedonic estimations, Linden and Rockoff conclude 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, which my paper examines, is whether the recession has changed the relationship between a sex offender’s arrival and nearby property values, comparing pre-recession reaction to post-recession reaction.


3   Data     

Our analysis is based upon four sets of data from 2003 to 2012: sex offenders’ data, housing data, income data, and neighborhood demographics data.  North Carolina Department of Justice (NCDOJ) provides information on convicted offenders’ basic characteristics, type of offense, date of offense, current address, and date of registration at current address.  Because of the strict provisions governing timely registration in North Carolina, we could assume the registration date is the best approximation for sex offenders’ move-in dates to their current locations, as Linden and Rockoff (2006) explained in their data source section.

There are 279 registered sex offenders in Durham, North Carolina as of April 7, 2013.  A total of 20 registered sex offenders are randomly selected from 8 zip codes in Durham.  Each offender is matched with an “arrival date” based on the date him or her registered, and categorized into a “neighborhood” determined by homes within 0.1 miles[1] radius of an offender’s residence.  Some evidence suggests that sex offenders are more likely to commit their offenses locally than other types of criminals.  Larsen et. al. (2003) observe that 85% of sex offenders committed their offense in the same city in which they resided at the time of their arrest, and 65% of sex offenders committed in their own neighborhoods.  Therefore, it is reasonable to assume that houses within 0.1 miles radius of a sex offender parcel are affected the most after the move-in of a registered offender.

The second source of data comes from Zillow, an online real estate database that pin points the exact locations of houses on a map, and provides historical/current estimates and prices of individual properties.  Of the 279 registered offenders in Durham, 153 could not be matched with a parcel on the Zillow database due to the fact that some offenders are currently residing in Durham County Jail, some are homeless, and others simply could not be located on the database, leaving a total of 126 offenders for our analysis.  We merge the matched sex offender data with property prices from January 2003 to December 2012.  Housing prices are normalized to December 2005 dollars using the annual South Urban CPI, and averaged from aggregated annual prices of individual property located within 0.1 miles radius of an offender parcel.

Our third and fourth sources of data are the average income data from 2010 US census[2] and Durham neighborhood demographics (age, race, sex and education level) data from City Data[3].  These two datasets are excluded from our analysis due to the limitations of data availability.  Average annual income data are available only on city level, not neighborhood level, whereas neighborhood demographics data are not recorded yearly.  Therefore, income and neighborhood data could not be matched to the sex offender and the property values data, and thus could not be estimated by the regression model.

3.1   Graphical Evidence

If living close to a registered sex offender induces an increasing fear for crime risk, then close proximity has a negative impact on property values, and we should see prices of homes near the offender’s location fall subsequent to the offender’s move-in.  Figure 3.1 shows an average annual housing price of all neighborhoods in the dataset.  From the graph, we observe a decline in overall housing market around 2006, may be due to the 2007-2008 recession or other factors, which are not the main concerns of this paper.  Figure 3.2 reflects the treatment effect and compares average housing prices before and after sex offenders’ move-in.  There is clearly a decline in housing values after Year 0 (offenders’ move-in year).  This is consistent with our hypothesis that if the decline in property prices coincides with the offender’s arrival and does not reflect preexisting downward trend in prices, then we could support the causal impact of the sex offender’s arrival on the price fall within 0.1 miles radius.


Figure 3.1 Average Property Value by Year


Figure 3.2 Average Property Value Pre- and Post- Offender’s Arrival


4   Model and Estimations

Ideally, if we observe the move-in dates of sex offenders and both time series and cross-sectional data on housing prices, we would be able to estimate a dynamic model of the housing market’s reaction to the presence of sex offenders.  Theoretically, we hope to estimate a static differences-in-differences model, capturing the heterogeneous reactions due to incumbent racial composition, sex ratio, age distribution, and income distribution (Equation 4.1).  However due to data availability on the local level, we could only focus on the impact of an offender’s arrival on nearby housing prices and explore the effect of the recent recession on the relationship (Equation 4.2).  We will apply ordinary least squares with auto-correlated errors to neighborhood level panel data on both level and changes in housing prices, while controlling for neighborhood and year fixed effects and linear time trends.

Hnt=α+β0*SOnt1*Rt2*SOntRt1*racent*SOnt2*sexnt*SOnt3*agent* SOnt+ γ4*incoment*SOnt+X+εnt                                                                             (Equation 4.1)

Hnt=α+β0*SOnt1*Rt2*SOntRt+X+εnt                                               (Equation 4.2)


Equation 4.1 is a theoretical model.  Variables are defined as following: H is the housing price, SO is a dummy for sex offender, R is a dummy for recession, race is a dummy for racial composition of individual neighborhood, sex is a dummy for male and female ratio in a neighborhood, age is a dummy for median age in a neighborhood, income is a dummy for median household income, X represents control variables (neighborhood and year fixed effects, and linear time trends), n stands for neighborhood and t is time.

Equation 4.2 is the actual model we used for our estimations, which extends previous literature by looking at the impact of recent recession.  Because we are not able to find demographics and income data on the neighborhood level, we have to take out the demographics terms, leaving Equation 4.1 for future research. 


5   Results

Table 5.1 Impact of Sex Offenders’ Arrival on Housing Value and Recession’s Effect



Model 1

Model 2











Notes: year and community fixed effects and time trends are controlled but not reported. P-values are reported in parentheses.
* statistically significant at 10% level, ** statistically significant at 5% level
# of observations = 200, Prob>F = 0.0000, Adjusted R2 = 0.9758





β0 represents the sex offender term, and β2 is the interaction term (=offender dummy * recession dummy).

Since it is not absolutely clear if we are still in a recession, we tried running regressions on all different combinations of recession years.  As shown in Table 5.1, Model 1 considers all years after 2007 as the recession period; Model 2 considers only the years 2007 and 2008 as the recession period.  Model 2 is Model 1’s robustness test, and our model is robust to the definition of recession.

Before running our regressions, we predict that the negative impact of sex offender’s arrival on houses in close proximity would be lessened by the effect of recession.  We suspect that during recession time, people have other worries and would be willing to tradeoff dis-amenities such as living close a sex offender for more important amenities and characteristics of a neighborhood.  Therefore, we do not expect to see such a dramatic depression of housing prices affected by sex offenders’ arrival during/post-recession compared to pre-recession years.

In Model 1, we observe a 5.7% decline in housing price following a sex offender’s arrival.  Although the results of the offender and the interaction terms are significant at 5% and 10% level respectively, but the negative effect is almost completely offset by the presence of recession (the slightly higher absolute value of the coefficients on the interaction term is puzzling, and the total effects of these two terms are statistically insignificant from zero, so we cannot conclude anything from this).  Similar results are observed in Model 2.  Note that the adjusted R square (0.9758) is very close to one, meaning that the fixed-effect panel data model has explained most of the variation.  We hoped but could not include relevant demographic control variables due to limitation of data on the very local level, but neighborhood fixed effects should be able to capture much of these characteristics.


6   Conclusion

In our analysis, we employ the hedonic pricing method to estimate the impact of local crime risk on housing values.  Exploiting detailed data on the current locations and move-in dates of registered sex offenders (information that is publicly available on the North Carolina Sex Offender Registry), we find that the arrival of a sex offender in a neighborhood depresses the values of houses within 0.1 miles radius by roughly 5.7%.  Although we could not explain the magnitude of the coefficients of the interaction term, the results observed imply a lessened effect for during/post-recession since overall income goes down and homebuyers are less critical about the dis-amenities of a neighborhood.  Also the adjusted R square being nearly one should be especially noted.  Since our estimations are consistent with existing literature, we believe that our recession results improve on and add a new dimension to the research question regarding the causal relationship between sex offenders’ presence and property values.

Unfortunately, we are unable to test the heterogeneous effects caused by differences in neighborhood demographics.  Theoretically, it is reasonable to predict that neighborhoods with older residents tend to care less about sex offenders’ presence whereas, neighborhoods where the majority of the residents are young families, they would strongly prefer residing in a sex offender free zone.  Our hypothesis could not be statistically tested in this paper.  However when the income and demographics data become available on the local level, we could proceed to test Equation 4.1 in our future research.  And thus understanding the heterogeneous reactions to local crime risk (due to neighborhood demographics effects) becomes important in determining optimal policy decisions, such as proper level of policing and expenditures for programs that reduce crime.




Gibbons, Steve. 2004. “The Costs of Urban Property Crime.” Economic Journal, 114(499): F441-63.

Hanson, Karl R., Kelly E. Morton, and Andrew J. R. Harris. 2003. “Sexual Offender Recidivism Risk.” Annals of the New York Academy of Sciences, 989: 154-166.

Larsen, James E., Kenneth J. Lowery, and Joseph W. Coleman. 2003. “The Effect of Proximity to a Registered Sex Offender’s Residence on Single-Family House Selling Price.” The Appraisal Journal, 71(3): 253-65.

Linden, Leigh, and Jonah E. Rockoff. 2006. “There Goes the Neighborhood? Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws.” American Economic Review, 98(3): 1103-1127.

Pope, Jaren C. 2008. “Fear of Crime and Housing Prices: Household Reactions to Sex Offender Registries.” Journal of Urban Economics, 64(3): 601-614.

Thaler, Richard. 1978. “A Note on the Value of Crime Control: Evidence from the Property Market.” Journal of Urban Economics, 5(1): 137-45.


[1] The use of 0.1 miles radius is based upon the reasoning Linden and Rockoff (2006) gave in their paper.

[2] Data obtained from http://www.census.gov/2010census/data/

[3] Data obtained from http://www.city-data.com/nbmaps/neigh-Durham-North-Carolina.html