Barry cummings tn sex offender. Registered sex offenders and house prices: An hedonic analysis.



Barry cummings tn sex offender

Barry cummings tn sex offender

This paper uses two methods to evaluate the impact of sex offender registries on house prices in Memphis. The goal of the present study is to use hedonic regression models to evaluate how the location of a registered sex offender in a neighbourhood affects house Corresponding author: We use two measures of the presence of sex offenders in a neighbour- hood: We estimate an hedonic regression model for house prices using OLS, but also estimate two additional models to allow for spatial dependence.

In particular, we esti- mate a mixed regressive-spatial autoregres- sive model and a spatial error model. Information criteria indicate that the spatial error model is preferred. In their excellent survey on hedonic methods Follain and Jiminez conclude that the theoretical basis for hedonic regression mod- els is sound but the econometric applications are weak.

Follain and Jimenez also discuss several estimation techniques. The first method discussed they call the simple hedonic approach where the coefficients of the estimated regression model are interpreted as the marginal will- ingness to pay for a particular characteristic.

Follain and Jimenez state that only under restrictive assumptions can the hedonic regression reveal underlying demand para- meters. One possible set of assumptions is that all households in the sample are alike in terms of income and socioeconomic charac- teristics but supplies of characteristics vary. In this situation the hedonic coefficients rep- resent marginal willingness to pay. There are other criticisms of the hedonic approach.

Citing work by Freeman , Haab and McConnell , Palmquist and Taylor , Nelson details several frequent criticisms of empiri- cal studies using hedonic methods. First, many home buyers are poorly informed about market conditions and, in particular, neighbourhood amenities and cannot make an informed evaluation.

Second, residential properties are heterogeneous and trade infre- quently. Third, market segmentation can lead to differences in hedonic prices by loca- tion. Fourth, some of the neighbourhood characteristics may be provided in relatively fixed bundles making estimation of separate hedonic prices difficult. Despite the concerns with hedonic meth- ods, they have been widely applied in the housing market.

What follows is a sampling of the types of amenities and dis-amenties that have been examined using hedonic methods. Studies including Brasingtion , Irwin , Sunding and Swoboda , Harrison and Rubinfeld and Gibbons and Machin have shown that house prices reflect the surrounding environment.

Many features of the sur- rounding environment have been empirically investigated. For example, Siegel et al. The impact of noise has been exam- ined by Pope b , Clark , Andersson et al. Air pollution has been examined by Huang and overhead transmission lines by Harrison Recent studies examining the impact of crime and illegal activities on property values have been conducted by Buonanno et al. An early study by Thaler finds a strong negative correlation between crime rates and house prices in Rochester, New York.

He interprets the willingness to pay to avoid crimes as implicit in higher house prices. Ihlanfeldt and Mayock find evidence that homebuyers are will- ing to pay premiums for living in neighbour- hoods with less aggravated crimes. In addition, Schwartz et al. In this vein, this paper uses the hedonic pricing model to examine the relationship between house prices and sex offender location and concentration.

There have been few studies of the impact of registered sex offenders on property val- ues for the simple reason that prior to , few convicted sex offenders were required to register with the state. More recently, a few studies have investigated the impact of registered sex offenders on property values. There are at least two possible measures of the influence of sex offender location on house price: Proximity should lead to a reduction in house price.

The first, by Larsen et al. This study is based on data from Montgomery County, Ohio. Linden and Rockoff use data from Mecklenburg County, North Carolina to compare the house prices before and after an offender moves into a neighbourhood. Pope a , using a similar method, concludes that house price falls by 2. The other measurement of offender pres- ence in a neighbourhood is the density or number of sex offenders in the immediate area.

The larger the number of sex offenders nearby, the lower the house price. We mea- sure density as the number of registered sex offenders living within a one-mile radius. Because our hedonic model includes distance to nearest sex offender and density of offen- ders, we are able to separate the two effects and avoid possibly inflating the distance effect due to the presence of other nearby offenders. Furthermore, in contrast to the studies previously cited, our research also tests and controls for the presence of spatial correlation which is typical in hedonic modelling.

Many of the traditional criticisms detailed above on hedonic methods are less conse- quential in the present study. First, our housing sample is based around the location of the registered sex offenders.

In this way we expect the households to be similar in terms of income and socioeconomic Caudill et al. However, there should be some variation in housing characteristics. The second is a legal obligation of real estate agents to inform potential buyers about the location of registered sex offenders.

This is one characteristic about which buyers should not be poorly informed. The issue of market segmentation is likely to be less rele- vant because, once again, we use a fairly homogeneous group of houses. Although the provision of some neighbourhood char- acteristics in fixed bundles makes the estima- tion of hedonic prices difficult, this is not a problem in the present study.

We use dummy variables for neighbourhoods, which is exactly the right solution to the problem. In addition, we incorporate spatial effects into our hedonic regression models. The present study adds to the rather scant literature on the impact of sex offenders on property values in several respects.

Ours is the only study based on data for Shelby County, Tennessee and we are the only study to include a density measure for sex offenders.

We are also one of the few studies to incorporate spatial elements in our estimation. Measuring the impact of sex offenders on property values is important for at least two reasons. First, the location of a sex offender into a neighbourhood will cause a loss in property value for all nearby owners. Second, if one is contemplating moving into an area in the vicinity of a sex offender, the value of the sex offender disamenities should be capitalised into the house price.

Measuring these costs is an important task. Data and model There were registered sex offenders liv- ing in Memphis as of 2 November The ratio of residents to sex offenders in Memphis is to 1 and Shelby County has a ratio to 1, which is the highest in the state of Tennessee. Our data come from two sources: We use an automated script to scrape data from the Shelby County Assessor of Properties, which is an official local website and provides probably the most complete information on property transactions in Shelby County, and the Tennessee Bureau of Investigation, which is a governmental data base of regis- tered sex offenders available online.

After removing duplicates and missing data, we have unique house transactions within 1 mile of sex offenders from to

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Barry cummings tn sex offender

This paper uses two methods to evaluate the impact of sex offender registries on house prices in Memphis. The goal of the present study is to use hedonic regression models to evaluate how the location of a registered sex offender in a neighbourhood affects house Corresponding author: We use two measures of the presence of sex offenders in a neighbour- hood: We estimate an hedonic regression model for house prices using OLS, but also estimate two additional models to allow for spatial dependence.

In particular, we esti- mate a mixed regressive-spatial autoregres- sive model and a spatial error model. Information criteria indicate that the spatial error model is preferred. In their excellent survey on hedonic methods Follain and Jiminez conclude that the theoretical basis for hedonic regression mod- els is sound but the econometric applications are weak.

Follain and Jimenez also discuss several estimation techniques. The first method discussed they call the simple hedonic approach where the coefficients of the estimated regression model are interpreted as the marginal will- ingness to pay for a particular characteristic.

Follain and Jimenez state that only under restrictive assumptions can the hedonic regression reveal underlying demand para- meters. One possible set of assumptions is that all households in the sample are alike in terms of income and socioeconomic charac- teristics but supplies of characteristics vary. In this situation the hedonic coefficients rep- resent marginal willingness to pay.

There are other criticisms of the hedonic approach. Citing work by Freeman , Haab and McConnell , Palmquist and Taylor , Nelson details several frequent criticisms of empiri- cal studies using hedonic methods. First, many home buyers are poorly informed about market conditions and, in particular, neighbourhood amenities and cannot make an informed evaluation.

Second, residential properties are heterogeneous and trade infre- quently. Third, market segmentation can lead to differences in hedonic prices by loca- tion. Fourth, some of the neighbourhood characteristics may be provided in relatively fixed bundles making estimation of separate hedonic prices difficult. Despite the concerns with hedonic meth- ods, they have been widely applied in the housing market.

What follows is a sampling of the types of amenities and dis-amenties that have been examined using hedonic methods. Studies including Brasingtion , Irwin , Sunding and Swoboda , Harrison and Rubinfeld and Gibbons and Machin have shown that house prices reflect the surrounding environment. Many features of the sur- rounding environment have been empirically investigated.

For example, Siegel et al. The impact of noise has been exam- ined by Pope b , Clark , Andersson et al. Air pollution has been examined by Huang and overhead transmission lines by Harrison Recent studies examining the impact of crime and illegal activities on property values have been conducted by Buonanno et al.

An early study by Thaler finds a strong negative correlation between crime rates and house prices in Rochester, New York. He interprets the willingness to pay to avoid crimes as implicit in higher house prices. Ihlanfeldt and Mayock find evidence that homebuyers are will- ing to pay premiums for living in neighbour- hoods with less aggravated crimes. In addition, Schwartz et al. In this vein, this paper uses the hedonic pricing model to examine the relationship between house prices and sex offender location and concentration.

There have been few studies of the impact of registered sex offenders on property val- ues for the simple reason that prior to , few convicted sex offenders were required to register with the state. More recently, a few studies have investigated the impact of registered sex offenders on property values.

There are at least two possible measures of the influence of sex offender location on house price: Proximity should lead to a reduction in house price.

The first, by Larsen et al. This study is based on data from Montgomery County, Ohio. Linden and Rockoff use data from Mecklenburg County, North Carolina to compare the house prices before and after an offender moves into a neighbourhood. Pope a , using a similar method, concludes that house price falls by 2.

The other measurement of offender pres- ence in a neighbourhood is the density or number of sex offenders in the immediate area. The larger the number of sex offenders nearby, the lower the house price. We mea- sure density as the number of registered sex offenders living within a one-mile radius. Because our hedonic model includes distance to nearest sex offender and density of offen- ders, we are able to separate the two effects and avoid possibly inflating the distance effect due to the presence of other nearby offenders.

Furthermore, in contrast to the studies previously cited, our research also tests and controls for the presence of spatial correlation which is typical in hedonic modelling. Many of the traditional criticisms detailed above on hedonic methods are less conse- quential in the present study. First, our housing sample is based around the location of the registered sex offenders.

In this way we expect the households to be similar in terms of income and socioeconomic Caudill et al. However, there should be some variation in housing characteristics. The second is a legal obligation of real estate agents to inform potential buyers about the location of registered sex offenders. This is one characteristic about which buyers should not be poorly informed. The issue of market segmentation is likely to be less rele- vant because, once again, we use a fairly homogeneous group of houses.

Although the provision of some neighbourhood char- acteristics in fixed bundles makes the estima- tion of hedonic prices difficult, this is not a problem in the present study. We use dummy variables for neighbourhoods, which is exactly the right solution to the problem. In addition, we incorporate spatial effects into our hedonic regression models.

The present study adds to the rather scant literature on the impact of sex offenders on property values in several respects.

Ours is the only study based on data for Shelby County, Tennessee and we are the only study to include a density measure for sex offenders. We are also one of the few studies to incorporate spatial elements in our estimation. Measuring the impact of sex offenders on property values is important for at least two reasons. First, the location of a sex offender into a neighbourhood will cause a loss in property value for all nearby owners.

Second, if one is contemplating moving into an area in the vicinity of a sex offender, the value of the sex offender disamenities should be capitalised into the house price. Measuring these costs is an important task. Data and model There were registered sex offenders liv- ing in Memphis as of 2 November The ratio of residents to sex offenders in Memphis is to 1 and Shelby County has a ratio to 1, which is the highest in the state of Tennessee.

Our data come from two sources: We use an automated script to scrape data from the Shelby County Assessor of Properties, which is an official local website and provides probably the most complete information on property transactions in Shelby County, and the Tennessee Bureau of Investigation, which is a governmental data base of regis- tered sex offenders available online. After removing duplicates and missing data, we have unique house transactions within 1 mile of sex offenders from to

Barry cummings tn sex offender

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