Home » Posts tagged 'Durham'
Tag Archives: Durham
Following the Great Recession and housing crisis from 2007 to 2009, the United States experienced widespread rates of foreclosure, among other economic turmoil. During this time, new research dedicated to determining the relationship between foreclosure rates and crime rates began to emerge, as researchers and policymakers alike wanted to understand the negative externalities of the falling economy and of the rising foreclosure rates specifically. While there have been a number of studies completed across the country since then, research on the relationship between foreclosure and crime has yet to provide a strong empirical basis for further analysis.
As a result of the relative lack of research, many cities throughout the United States (and abroad), such as Durham, North Carolina, lack a solid foundation on which to model foreclosure and crime relationships. Through this current study, I hope to expand on my previous study and continue analyzing data related to distressed homes in Durham and data related to crime around the sampled homes over the three years from 2012 to 2014. By expanding this study to cover multiple years, I aim to specifically analyze the relationship between crime and a property during the various stages of distress.
The housing crisis that swept the U.S. during the Great Recession contributed to the perceived need to study cities and neighborhoods that have been hit the hardest by poor economic conditions related to foreclosure. Unfortunately, many results of these studies have been inconclusive and mixed. Adding in large part to the confusion is the inconsistency across studies in the units of analysis, definitions and measures of foreclosure and distressed properties, and analytical strategies needed to link these economic events to crime rates (Baumer et al. 2012).
To examine the relationship between foreclosure and crime in a variety of major U.S. cities, Baumer et al. employ foreclosure rates as the primary independent variables and focus on incidents of robbery and burglary as the primary dependent variables. Controlling for neighborhood and prior-crime variables, the researchers found that there was a significant variability across cities. In addition, Baumer et al. find different patterns for their two dependent variables. With respect to robbery, the results suggest that foreclosure is more strongly related to robbery in cities with higher levels of socioeconomic disadvantage and lower overall levels of foreclosure. On the other end, higher rates of foreclosure are more closely associated with higher burglary rates, in addition to other factors such as a shrinking police force and little new housing.
In a similar vein, Katz et al. (2011) focus on the drivers of policy that come from foreclosure-crime research. Namely, the researchers were interested to understand whether and how long foreclosure causes crime rates to rise. Bringing in the context of the most recent U.S. housing crisis, the authors examine whether there is a linear and crisis-specific effect of foreclosure on neighborhood levels of crime and also examine whether there was a time lag in the impact of foreclosure on neighborhood levels of crime. Regarding the first objective, Katz et al. suggest that foreclosed homes may not have long-term negative impacts on crime in a given metropolitan area. Instead, their results show that foreclosure has a short-term impact, typically no more than 3 or 4 months. On the second objective, the authors find that the relationship between the housing crisis and increases in crime are not linear, but rather are characterized by short-term fluctuations.
Jones and Pridemore (2012) contribute to existing research by providing city-level analysis that incorporates nearly 150 major metropolitan areas from across the country. Their approach focuses on data from 2006 forward, so as to capture the trends of foreclosure and crime throughout the period of the Great Recession and in to its aftermath. Jones and Pridemore’s approach differs from previous research in one important aspect: instead of using foreclosure rates as the primary independent variable in their study, the authors instead elect to employ a new indicator, the Housing-Mortgage Stress Index (consisting of three variables: negative equity, loan-to-value ratio, and the monthly mortgage cost-to-income ratio), to capture a more complete and accurate set of information about foreclosure-associated economic stress in a city. Using the Housing-Mortgage Stress Index as the main independent variable, the authors’ results suggest that the housing crisis is not associated with metropolitan rates of serious violent and property crime.
Unlike the previous three teams of researchers, Ellen et al. (2013) employ point-specific and longitudinal crime and foreclosure data to understand the relationship between foreclosure and crime in New York City. Their study uses blockfaces – individual street segments, including properties on both sides of the street – to compare the changes in the crime level of a given blockface before and after homes on the blockface enter foreclosure with the changes in the crime level of a different blockface in the same neighborhood that did not experience a change in foreclosure activity during the same time period. The researchers reason that, as crime trends are likely to be the same along different blockfaces in the same neighborhood, their difference-in-difference model can identify whether foreclosures lead to higher crime rates. They also include estimates that control for future foreclosure notices on a blockface, through which they hope to capture the unobserved trends between blockfaces where foreclosures tend to occur and those where they do not. Their results show that foreclosures on a blockface lead to a small number of additional crimes, with the largest effect on the rate of violent crimes. Furthermore, their results suggest that the effects are largest for foreclosed properties that go all the way through the foreclosure process to an auction. The researchers also note that their methods do not allow for results to determine the net changes in the overall crime in a city, only changes in new crimes around the localized area of a foreclosure.
Similarly, Cui and Walsh (2015) focus on understanding the effects of a given foreclosure on crime levels in the immediate vicinity of properties in Pittsburgh, Pennsylvania. They pay special attention to identify the separate impacts of foreclosure duration and vacancy on the rate of crime observed. Cui and Walsh are unique in defining treated neighborhoods as a 250-foot buffer surrounding each foreclosed house, and they define control neighborhoods as an equal area donut surrounding this buffer, which is the area from 250 feet to 353 feet away from the given property. They elect to use these neighborhood definitions to more specifically lessen concerns regarding the common trends assumption when dealing with larger neighborhood definitions. Their results suggest that the foreclosure process can lead to significant increases in violent crime rates within 250 of the property. In addition, the results suggest that crimes associated with foreclosure do not occur during the early stages of foreclosures, but instead by the vacancies that are associated with the foreclosure process. The impact of vacancy on crime increases when the property stays vacant for longer periods, with effects plateauing between 12 and 18 months of vacancy. Their results also suggest that once a home is reoccupied, the crime impacts of the recent vacancy are reduced.
Criminal Activity in Durham
Despite the housing crisis and Great Recession from 2007 to 2009, the total number of crimes per capita in Durham has been trending steadily downward from 2000 to 2014. Looking only at the trend in Figure 1, it would be very difficult to predict any type of economic turmoil around 2007-2009, given the consistent decline in crime rate. However, from 2012 to 2014, the total number of crimes per capita have leveled off slightly, decreasing about 5.9% from 2012 to 2013, but then rising about 12.0% from 2013 to 2014 (which equates to about a 5.4% increase from 2012 to 2014).
While crime levels appear to be dropping overall since 2000, property crime (defined here as the combination of larceny and burglary) continues to be the type of crime most committed in Durham since 2000, representing at least 80% of the total number of crime incidents in from 2012 to 2014 (see Figures 2-5). Violent crime (defined here as the combination of assault and robbery) is observed in a much smaller proportion, representing from 12-14% of total crime in each of the years from 2012 to 2014.Larceny, which includes any type of theft offense, constitutes over half of the total number of crime incidents from 2012 to 2014. Burglary, which includes any breaking and entering offenses, constitutes over a quarter of the total number of crime incidents during this time period, second only to larceny. The next two most frequent offenses – aggravated assault and robbery – trail significantly, comprising only 8-9% and 5% of total crime incidents, respectively, in the three-year span.
Methodology and Data
Similar to my previous study, I use the Real Property Delinquent Taxpayer list from the Durham County Tax Collector to randomly select 35 delinquent owners and their properties in Durham city. For selection in to the sample, the property must have at least three consecutive years of unpaid (as opposed to partially paid or fully paid) tax bills. The intuition behind this decision is that homes with multiple consecutive years of unpaid bills will be most likely foreclosed by Durham county for failure to pay property taxes; therefore, the homes will be the most distressed.
For each address obtained, I use the Crime Mapper tool from the Durham Police Department (see Figure 6) to show the number of nearby crimes of assault, burglary, larceny, and robbery from January to December in each of the three years from 2012 to 2014. By including data from 2012-2014, I hope to capture the trends in crime over the life of a distressed property. Since most of the sampled properties have been delinquent for exactly three years, the addition of a time characteristic to the data set can provide an understanding about crime rates around a property throughout the distress process.
Figure 6: Example of crime mapping tool, Denfield Street property
Results from the Crime Mapper, similar to the previous study, are limited and recorded first within a 500-foot radius (which I label the “inner zone”) of the distressed property and then within a 1,320-foot (1/4-mile) radius (which I label the “total zone”)of the distressed property. While the Crime Mapper is also capable of providing crime information within a 1/2-mile radius of a given property, I elected to forgo collecting the 1/2-mile data, as results from this distance were unhelpful for showing the relationship between a distressed property and nearby crime under the assumptions of uniform and random distribution. In each of the first few cases collected at this distance, the numbers of total incidents of crime observed were too low to produce meaningful estimates. Given that a circle with a 500-foot radius has an area that is about 3.6% of the area of a circle with a 1/2-mile radius, there must be at least 28 observations of crime in the larger area to yield a random estimate of at least 1 crime in the inner zone. This means that for each type of crime, any 1 incident within the inner zone would be categorized as “above expected” if there were fewer than 28 incidents in the 1/2-mile radius circle, which holds true for the majority of the sampled properties. Thus, I have chosen to continue calculating the number of observed crime incidents within the inner zone with the number of observed crime incidents within a 1/4-mile radius total zone.
Assuming a distressed property is not a hub for increased crime and that crime is uniformly and randomly distributed within the total zone without the influence of a distressed property, I aim to compare the number of observed crimes in the inner zone with the number of observed crimes in the total zone. Given the radii of the inner and total zones and assuming that the areas of focus are perfectly flat and circular, a quick calculation shows that the area of the inner zone is roughly 14.3% of the area of the total zone. Given the number of crimes in the total zone and the relative sizes of the inner and total zones, I then calculate the average number of crimes expected within the inner zone, given uniform and random distribution (i.e., without the concentrating “pull” of a distressed property in the center).
Taking the “expected” number of crime incidents in the inner zone, I then compare the number of expected incidents with the number of observed incidents. If the number of observed crimes is higher than the number of expected crimes for more properties than not, then there might be evidence to support the hypothesis that the presence of a distressed property is associated with more crime. However, if the number of observed crimes is lower than the number of expected crimes for more properties than not, then there might be evidence to reject the same hypothesis.
In discussing the results, I will define a property as “above expected” (AE) if the observed number of crimes within the property’s inner zone is above the estimate given by the property’s total zone. For example, if a property’s total zone estimates an expected 1.87 burglary crimes in 2014 in the property’s inner zone, and the observed number of crimes is 3, then we can categorize this property as an AE property with respect to burglary in 2014. The AE designation of a property is dependent on both the type(s) of crime and the time period specified.In general, if the number of AE properties rises from 2012 to 2014, there might be evidence to suggest that crime rates rise as homes become more distressed or remain equally as delinquent.
Results and Discussion
Among the 35 sampled properties over the three years from 2012 to 2014, a total of 5,672 crime incidents were observed, with total crime falling from 1,824 in 2012 to 1,771 in 2013 and rising back to 2,077 in 2014 (see Table 1). With respect to the distribution of crime between the four types – assault, burglary, larceny, and robbery – used in this study, the observed crimes were directionally representative of the annual distribution of crime in Durham overall.Larceny, consistent with the citywide distribution, was the type of crime most observed in this sample, followed by burglary, assault, and robbery. While assault was observed in a slightly smaller proportion to burglary in this sample, it is noteworthy that the proportion of assault incidents in this sample is much higher than that of Durham overall. Over the three years covered in this study, assault makes up about 23.3% of this sample, but only about 8.5% of Durham crime (as a percentage of total assault, burglary, larceny, and robbery incidents total). This finding, while notable for showing disparities between this sample and Durham overall, is also consistent in this sample for each of the three years observed.
In terms of the number of “above expected” (AE) properties found, more interesting patterns arise when analyzing results over the three years. With respect to all crime incidents reported in a given year, the number of AE properties has been fairly consistent from 2012 to 2014 (see Table 2). This trend is also true of AE properties with respect to reported incidents of assault and larceny in each of the three years.
With respect to burglary and robbery, the numbers of AE properties were much less consistent. The number of AE properties in burglary increased from 15 in 2012 to 19 in 2013 and 2014, while the number of AE properties in robbery decreased dramatically from 12 in 2012 to 4 in 2013 and 6 in 2014 (see Table 3). Recall that, as a result of the selection criteria for this sample, most of these properties have been in delinquency and distress for exactly three consecutive years. The change in the number of burglary and robbery-related AE properties can possibly fit in to the distressed narrative of these properties: as a property becomes more financially distressed, the property can become more neglected, which could provide more opportunities for breaking and entering type crimes. In Durham, breaking and entering is categorized as a burglary, which could explain the increase in the number of burglary-related AE properties from 2012 to 2014.At the same time, as a property becomes more neglected, it may be more likely that the property will be vacated or abandoned. With fewer people in and around the property itself, there may be fewer people to target in a robbery. The presence of fewer people also decreases the likelihood of having witnesses who can report crimes, which would also affect the number of observed robberies (though this works against the increased observation of burglaries).
While these intuitions about the crime behavior around distressed properties can help begin to uncover the underlying mechanism responsible for these changes in observations and the change in the number of AE properties, they do not help explain the relatively smaller change from 2013 to 2014 (when compared to the change from 2012 to 2013). With respect to robbery, the number of AE properties drops dramatically from 12 in 2012 to 4 in 2013, but rises slightly to 6 in 2014. While the change is significant within the sample, it is also important to note that, in 2013, the Durham Police Department reports that the number of robberies were at a 23-year low in 2013. What remains unclear is whether this change is due to changes in prevention tactics, reporting policies, the climate of crime and criminals, or something else entirely.
In considering all of these results, it is also important to note that results are based on crimes that must first be observed by a witness, and then reported and filed by the Durham Police Department. Clearly, it is unreasonable to assume perfect crime observation within any given area, but as mentioned before, the inability to observe and report crime may be increased in areas of high home distress or foreclosure. This is consistent with the idea that distressed and foreclosed homes result in temporary (or sometimes permanent) decreases in the population of the immediate area, particularly within the defined inner zone of 500 feet around a property. Additionally, the crime data used in this study do not completely account for cases in which a crime is catalogued under multiple types. Inclusion of these results affects interpretations about the distribution of the crime types that are observed within a given area.
For improvement on this study in the future, additional research that compares the results of this study to a sample of properties in good financial standing may highlight differences in the level of AE properties found. Further research can also improve upon this study by following the sampled properties throughout the process of foreclosure to monitor the changes in crime levels. Continuing in research similar to the work of Cui and Walsh (2015), this type of research would provide further insight in to the crime behaviors near a foreclosed property through vacancy and re-occupancy. Unfortunately, studies such as these are limited by data related to specific foreclosed properties in Durham (and other cities). In terms of policy implications, understanding the relationship between crime and a property throughout its foreclosure lifecycle can help shape legislated foreclosure programs and processes to mitigate the increasing rates of crime.
To begin understanding the relationship between property distressed and nearby crime in Durham, North Carolina, this study used publicly available data to map incidents of observed crime from 2012 to 2014 on a sample of distressed properties. Analyzing crime over each of the three years, the study took data related to incidents of larceny, burglary, assault, and robbery to determine an expected number of crimes within an inner zone, representing a 500-foot circle around the property, and compared these figures with the number of observed crimes.
During the three years overall, results showed that the number of observed crimes is higher than the number of expected crimes with respect to only burglary, while the same is untrue for assault, larceny, and burglary. However, with respect to burglary and robbery, there were more dramatic shifts in the number of AE properties from 2012 to 2013, which could reflect a number of factors including the criteria and opportunity for reporting. Ultimately, this study can serve as the foundation for further research, given additional data related to the foreclosure process and crime behaviors of Durham properties.
Baumer, Eric P., Kevin T. Wolff, and Ashley N. Arnio, 2012, “A multicity neighborhood analysis of foreclosure and crime,”Social Science Quarterly 93(3), 577-601
Cui, Lin, and Randall Walsh, 2015, “Foreclosure, vacancy, and crime,” Journal of Urban Economics 87, 72-84
Durham County NC, Real Property Delinquent Taxpayer List, Accessed Durham 18 March 2015, http://www.ustaxdata.com/nc/durham/durhampolicy.cfm
Durham Police Department, 2014 Annual Report, Accessed Durham 18 March 2015, http://durhamnc.gov/ich/op/DPD/Documents/2014AnnualReportMarch2.pdf
Durham Police Department, 2013 Annual Report, Accessed Durham 15April 2015, http://durhamnc.gov/ich/op/DPD/Documents/2013AnnualReport.pdf
Durham Police Department, 2012 Annual Report, Accessed Durham 15April 2015, http://durhamnc.gov/ich/op/DPD/Documents/2012AnnualRepor0301FINAL.pdf
Durham Police Department, Crime Mapper, Accessed Durham 18 March 2015, http://gisweb.durhamnc.gov/gis_apps/CrimeData/dsp_entryform.cfm
Ellen, Ingrid G., Johanna Lacoe, and Claudia A. Sharygin, 2013, “Do foreclosures cause crime?,” Journal of Urban Economics 74, 59-70
Jones, Roderick W., and William Alex Pridemore, 2012, “The Foreclosure Crisis and Crime: Is Housing‐Mortgage Stress Associated with Violent and Property Crime in US Metropolitan Areas?,” Social Science Quarterly 93(3), 671-691
Katz, Charles M., Danielle Wallace, and E. C. Hedberg, 2011, “A longitudinal assessment of the impact of foreclosure on neighborhood crime,” Journal of Research in Crime and Delinquency 000(00), 1-31
Figures and Tables
Figure 1: Durham crime per 100,000 residents, from 2000-2014
The figure depicts the trend of violent and property crimes since 2000 in Durham, notably showing the overall downward trend (despite the economic shocks of 2007-2009), though property crime continues to occur more frequently than property crime.
Durham Police Department, 2014 Annual Report
Figures 2-5: Durham crime by number of incidents, from 2012-2014
The figures depicts the breakdown of the six most frequent types of crime from 2012-2014, showing that the property crimes of larceny and burglary have consistently total for about 80% of total crime for each of the last three years.
Durham Police Department, 2012 Annual Report, 2013 Annual Report, and 2014 Annual Report
Figure 6: Example of crime mapping tool, Denfield Street property
The figure depicts the results from the Crime Mapper tool available publicly online by the Durham Police Department. For a given address, the tool maps incidents of crime that can by a variety of criteria, including distance, time period (up to 12 months), and type of crime.
Durham Police Department, Crime Mapper
Table 1: Observed crimes in sample by type
This table shows the number of observed crimes by type in each of the three years from 2012 to 2014, along with the percentage of each type observed within a given span of time.
Table 2: Expected number of crimes within 500 ft. of distressed properties, given number of crimes observed within ¼ mile, total totals from 2012-2014
This table shows the number of expected crimes within the inner zone by type of crime. Data points bolded in red text and highlighted pink represent cases in which the number of observed crimes is higher than the number of expected crimes for a given property. Data points in green text represent the opposite. Data points with grey text had zero total observations for the given property.
Table 3: Expected number of crimes within 500 ft. of distressed properties, given number of crimes observed within ¼ mile
This table shows the number of expected crimes within the inner zone by type of crime. Data points bolded in red text and highlighted pink represent cases in which the number of observed crimes is higher than the number of expected crimes for a given property. Data points in green text represent the opposite. Data points with grey text had zero total observations for the given property.
Urban Environmental Hazards: Investigation into Durham’s Brownfields and Nearby Property Values by Haisi Liu
As an urban planning concept, brownfield sites are lands formerly used for industrial or commercial purposes, but the subsequent redevelopment and expansion of these properties may be difficult due to potential contamination by hazardous substances. For instance, gas stations and scrap yards emit high concentrations of subsurface pollutants. If their operations close, the lots that these facilities previously occupied could lie unused for decades as brownfields. Once cleaned up, such zones can accommodate new businesses or serve as green spaces for recreation. Thus, the Environmental Protection Agency (EPA) has sought to empower local governments and community stakeholders to evaluate and remediate brownfields.
Over the past 20 years, shifting market influences have dramatically impaired the City of Durham’s leading manufacturing industries. The collapse and flight to city edges of these industries gave rise to many brownfields—abandoned plots that historically accommodated manufacturing buildings, chemical facilities, railroad property, and automobile repair shops. In 2009 the EPA designated Durham, NC, as a recipient for two brownfields assessment grants: $200,000 for hazardous substances and $200,000 for petroleum. Throughout its EPA-funded reclamation efforts, Durham focused on properties in Northeast Central Durham (NECD), the primary locale for both vacant and currently functioning industrial facilities within the city. Since brownfields can be detrimental for human and ecosystem health, their presence likely has an adverse effect on the property values of neighboring houses.
This paper delves into the predicted impact of brownfields on nearby residential housing prices in Durham. The analysis reveals that, with exception of three brownfield sites, mean sales price tends to be higher for single-family houses located farther away from a brownfield. In the case of eight out of 12 brownfields assessed in this study, average housing prices are 9% to 38% higher in the outlying, surrounding region than within 2,000 feet of the site. The trends in these data seem to correspond with earlier research that establishes brownfields’ negative effects on nearby property values. From a policy perspective, quantifying houses’ lost value due to proximity to brownfields relative to the costs of reclamation could make a strong economic case for granting additional funding toward remediation.
II. Literature Survey
Past studies, employing different hedonic model specifications, have gauged the effect of brownfields on adjacent residential property values in certain regions. For example, Mihaescu and vom Hofe (2012) use ordinary least squares (OLS), spatial autoregressive, and spatial error models to compute the impact of 87 brownfields on the values of nearby single-family homes in Cincinnati, OH, finding that a $100,000 house situated 100 feet from a brownfield loses approximately $9,000 in property value. However, Mihaescu and vom Hofe also estimate that brownfields’ depreciating influence becomes insignificant 2,000 feet past the sites. At this rate, Cincinnati effectively loses more than $2.2 million in annual tax revenue from aggregate decreased property values related to brownfields. Although other studies have previously shown negative impact on housing prices (Bromberg and Spiesman, 2006), Mihaescu and vom Hofe distinguish their approach from existing literature by accounting for trends of spatial dependence among assessed property values.
Haninger, Ma, and Timmins (2014) determine brownfields’ impact from a different angle, by measuring the value of brownfield redevelopment as encapsulated in nearby housing prices. A simple comparison of regions with untouched brownfields and remediated brownfields can cause problems because the Brownfields Program gives cleanup grants based on a competitive procedure; therefore, communities that obtain funding may differ systematically from those that do not receive it. Haninger et al. overcome this issue by means of several fixed effects and ‘difference-in-differences’ (DID) specifications, which all generate a consistent solution—homes can undergo large observed rises in property values associated with brownfield remediation, ranging from 4.9% to 24.8%. Yet, this analysis has limitations in that its models cannot capture cleanup-related health benefits that local residents are unaware of and thereby are not reflected in house buying choices and prices. These benefits are best quantified in more environmentally-focused studies that demonstrate how remediating a brownfield helps to decrease harmful effects of the area’s soil, air, and groundwater pollution on human health as well as ecological systems (Alberini et al., 2005). Meanwhile, other analyses by Barnett (2006) and Amekudzi et al. (2003) show remediation’s positive economic impacts in form of increased local employment and greater tax revenues, in addition to higher property values.
To study the extent of association between brownfields’ presence and housing prices, I use publically available data provided by Concurrent Technologies Corporation (CTC), a company that, among other services, offers brownfields redevelopment consulting. The CTC listings give locations (either street addresses or approximate intersections for larger land parcels), acreage, property descriptions, and histories of the brownfields. Specifically, I identify 12 properties in NECD and the Pettigrew Street Corridor currently under assessment for possible participation in the EPA grant system. These properties range from small vacant lots to multistory buildings. As an example, one of the brownfield sites, located on 200 East Umstead Street, formerly served as the building for J.A. Whitted Junior High School. Although an abandoned school is a disamenity for reasons unrelated to environmental hazards, J.A. Whitted is nevertheless included in the CTC listings due to its high difficulty of redevelopment. The three-story, 73,500 square foot facility has been sitting vacant since 2004; a glance on Google Maps reveals fading school letters and boarded windows.
For each of these 12 potential brownfields, I collect sale price information on a total of 204 neighboring houses from Zillow, an online real estate databank. The analysis includes prices of homes currently for sale, along with potential sale prices of properties that may be emerging on the market shortly but do not appear yet on multiple listings service.
Using satellite imagery, I begin by pinpointing the center of a Durham brownfield site, which by itself can span almost the entirety of one block. After finding the midpoint, I mark a circular zone with a 2,000 feet radius on the Zillow map, making sure to scale accurately. Properties within this first zone lie within 0.4 miles of the brownfield. I also trace a larger circle with a radius of 3,000 feet; all properties in the second zone sit within 0.6 miles from the site. I then use these demarcations to compare property values of (1) houses within 2,000 feet of the brownfield and (2) houses situated 2,000 to 3,000 feet away. I calculate the mean sales price of all properties within each zone and subsequently use the two averages to compute the percentage change obtained by moving from the inner circle to the outer one. Both intuition and prior studies suggest that properties in the closer zone will be more affected by the brownfield; my rationale behind selecting 2,000 feet as the comparison boundary stems from the aforementioned study by Mihaescu and vom Hofe, which estimates that brownfields’ impacts become negligible beyond 2,000 feet.
When observing differences in residential property values, it is important to consider potential biases due to spatial dependence among housing prices. While a hedonic pricing approach can address this issue through certain econometric techniques (e.g., spatial autoregressive models), I attempt to minimize spatial externalities via the grouping design. Since most properties in the analysis lie within a 0.6 mile radius of each other and are thus situated in the same part of town, they largely encompass similar economic conditions and housing quality levels. By assuming spatial characteristics remain fairly constant within a small area, this analysis offers groundwork for more complex future research.
Data findings indicate that, with the exception of three Durham brownfield assessment sites, average sales price tends to be consistently higher for residential properties situated 2,000 to 3,000 feet away from a brownfield. For eight of the 12 brownfields evaluated in this study, mean housing prices are 9% to 38% higher in the outer zone than within 2,000 feet of the site. The patterns in these data seem to be in line with prior studies demonstrating brownfields’ negative impacts on nearby property values. However, this type of study cannot isolate brownfields’ effects from other potential causes of the difference in average housing prices. For example, if the local government prefers to invest in land redevelopment in areas that seem to have high promise of new business development, the fact that these brownfield sites have not already been remediated could indicate lower neighborhood quality.
Table 1: Proximity-Based Discrepancies in Average Housing Values
Moreover, a closer examination of observations that deviate from expected outcomes can be insightful as well. For instance, houses farther from the ‘Eliot Square Apartments’ brownfield surprisingly cost 23% less on average than those adjacent to the site. However, a satellite image of the site reveals that it simply looks like a large, overgrown grass field—pleasant overall, although strangely vacant compared to all the nearby land occupied with buildings. Previous research has demonstrated that property values can only capture brownfields’ negative externalities to the extent that local inhabitants are aware of them and can thus incorporate these externalities into their willingness to sell or buy a property at a given price (Haninger, Ma, and Timmins, 2014); in this light, the trend reversal at Eliot Square Apartments makes sense, especially given that the First Baptist Church and Durham County Library are located one block away from the seemingly innocuous cleared grassy lot. On a similar note, the Ridgeway Avenue Property (percentage change: -2.5%) appears from Google Maps street view to be a small, paved vacant area next to a convenience store. Since this brownfield does not look like an environmental disamenity to passersby, its presence hardly factors into nearby residential property values.
On the other extreme, houses on sale near former J.A. Whitted Junior High School are on average almost 40% less expensive than homes farther from the site. As mentioned, the abandoned multistory building is conspicuous and visibly rundown; its appearance is consistent with the earlier explanation that, when residents are cognizant of the brownfield, property values are more affected. Nevertheless, a discrepancy of 40% still seems very high, resembling an outlier. Further scrutiny shines light on a potential influence: a newly renovated neighborhood three blocks away on Chestnut Street (i.e., 2,000 to 3,000 feet from the site) currently has pre-construction sales on four well-built houses, each posted at a price from $180,000 to $299,000.
This study reveals a negative association between the presence of brownfields and adjacent residential property prices in Durham, hence corroborating earlier research on this significant urban planning issue (Schwarz et al., 2013; Watkins, 2010; Attoh-Okine and Gibbons, 2001). However, rather than establishing a cause and effect relationship, the analysis is intended to serve more as a broad introductory survey of patterns in housing values surrounding Durham’s brownfields.
Though most sample zones in the study share consistent socioeconomic conditions and housing qualities within the group, a main limitation involves the lingering possibility of spatial correlation. Real estate data commonly contain spatially dependent property values, meaning that high-priced homes tend to group together, as do low-priced homes. Conventional real estate wisdom discloses that expectations on price often develop based on neighboring housing values (Wang and Ready, 2005); even a single neighborhood block could experience spatially correlated prices among its row of houses. Furthermore, since this study is a descriptive survey on brownfields and housing prices in Durham, any underlying factors that affect property value cannot be fully captured by comparison of averages. Therefore, future research could reduce bias and increase predictive power by employing spatial hedonic pricing models—which can be suitably applied to variations in housing prices that reflect the value of a local environmental amenity, or in this case, disamenity. Later studies can also improve upon the analysis to distinguish between different stages of brownfield remediation at various Durham sites.
Investigations of this nature can have immensely beneficial policy implications. The ability to effectively demonstrate brownfields’ negative impact on surrounding communities could raise governmental and private sector incentives to invest in revitalizing ecologically and financially troubled areas.
“Brownfields 2009 Assessment Grant Fact Sheet Durham, NC.” Brownfields and Land Revitalization. Environmental Protection Agency, <http://cfpub.epa.gov/bf_factsheets/gfs/index.cfm?xpg_id=6952>.
“Brownfields and Land Revitalization.” EPA Brownfields Program Benefits. Environmental Protection Agency, <http://www.epa.gov/brownfields/>.
“Durham Brownfields: Properties.” Durham Brownfields: Properties. Concurrent Technologies Corporation, <http://www.ctcbrownfields.com/durham/properties.php>.
Haninger, Kevin, Lala Ma, and Christopher Timmins. “Estimating the Impacts of Brownfield Remediation on Housing Property Values.” Duke Environmental Economics Working Paper Series (2012): 1-66.
Linn, Joshua. “The Effect of Voluntary Brownfields Programs on Nearby Property Values: Evidence from Illinois.” Journal of Urban Economics 78 (2013): 1-18.
Mihaescu, Oana, and Rainer Vom Hofe. “The Impact of Brownfields on Residential Property Values in Cincinnati, Ohio: A Spatial Hedonic Approach.” The Journal of Regional Analysis and Policy (2012): 223-36.
Schwarz, Peter M., Gwen Gill, Alex Hanning, and Caleb A. Cox. “Estimating the Effects of Brownfields and Brownfield Remediation on Property Values in a New South City.” Verifying the Social, Environmental, and Economic Promise of Brownfield Programs (2013): 1-40.
Wang, Li, and Richard C. Ready. “Spatial Econometric Approaches to Estimating Hedonic Property Value Models.” American Agricultural Economics (2005): 1-49.
Watkins, Stefan. “The Impact of Brownfield Reclamation on Surrounding Land Values and Crime.” (2010): 1-18.
“Zillow: Real Estate, Apartments, Mortgage & Home Values in the US.” Zillow. <http://www.zillow.com/>.
This paper investigates the association between apartment rental prices in Durham and their linear distance to Duke University. Considering the substantial role Duke plays in the economic activity and housing demand, in particular that of apartments, in the city of Durham, one would expect a positive relationship between proximity to campus and the rental price of apartments. This paper aims to quantitatively examine whether or not such a relationship exists.
While not impossible, a comprehensive quality-adjusted approach to apartment prices for Durham apartments involves the matching of several datasets and is not utilized in this paper. An alternative is found in online ratings of apartments provided by review sites. Ideally, these reviews are proxies for the relative quality of accommodations and services of each given apartment. Once matched for distributions of ratings on different websites, average scores can be seen as directly comparable between apartments. Potential correlation caused by a bias towards higher quality closer to Duke’s campus because of the higher endowed wealth of Duke students, or lower quality caused by their bad behavior, can be accounted for using an interaction term.
An initial model is established using only apartment distance to Duke University as the dependent variable. Apartment listing prices are collected from Apartmentguide.com and separated by room class: one, two and three-bedroom offerings are counted separately, with maximum, minimum and average listing price for each room type provided. A mean price for each room type is calculated by taking the average of the maximum and minimum prices – note that this may not be an accurate estimate, since the composition of apartments by price for each room type is unknown. Distance values are estimated using a linear point-to-point approach. Apartment locations are obtained using the Bing Map geocoding API, and the East/West campus bus stops are used as point proxies for the two campuses, respectively. A third estimate uses the shorter of the two distances to the campuses. This allows for the possibility that because of the extensive public transit options between East and West, individuals may not have a particular preference for one campus but simply desire to live closer to one of the C1 bus stops.
For the regression model, natural log transformations are applied both to listing prices and distance estimates. A fairly strong association has been found between the prices of most of the room types and all three distance estimates. The strongest association occurs between the price of double apartments and their distance to West Campus, with a doubling of distance translating into a price decrease of 5.1%. All three regressions using the price of double apartments reports significance over 95% when regressed against distance estimates. The association between price of single apartments and distance is significant at the 90% level for both the distance to West Campus and the minimum distance estimate to both East and West Campus. One could speculate that single rooms, being not only more expensive but also less conductive to a social lifestyle, are not as preferable to Duke students as double rooms, leading to the weaker price association.
Coefficients for single-variable regressions between distance estimates and average price
Interestingly, the link between price of triple apartments and distance to campus is much weaker than that of double and single apartments. No distance estimate seems to be even slightly correlated with the price of triple apartments. Part of this could be explained by the relatively few number of apartment buildings that offer triple rooms. However, it is also possible that since triple room represent an inferior good compared to single or double rooms, Duke students with high average spending power would typically not choose to live in them. Some students might move off campus to escape a triple or double dormitory and have little interest in similar living conditions. It could also be possible that local families unaffiliated with Duke University are more likely to occupy the large units, in particular three-bedroom apartments.
A possible way to indirectly check the validity of this explanation is to see if triple room-offering apartments are, on the average, further away from Duke than single or double apartments. If students don’t care for triple-room apartment, they ought to be distributed more randomly in Durham and be, on average, further away from the Campuses. However, this does not seem to be the case for the apartment sample in the dataset. The average distance to West Campus for all apartments offering triple rooms is 9.01 kilometers, closer than the figures for apartments offering single and double rooms (9.07 and 9.35 kilometers, respectively). The average distance to East Campus displays a similar trend, at 8.33 kilometers on average for apartments that offer triple rooms, 8.58 kilometers for apartments that offer single rooms, and 8.87 kilometers for apartments that offer double rooms.
It is also possible that the price response of distance is not only non-linear but also somewhat binary in nature. Intuitively, apartments beyond a reasonable walking range will only be marginally affected by extra distance: the extra mile should matter very little if one has to drive to school anyways. To test this, dummy variables indicator certain levels of proximity to West Campus, starting from closer than two kilometers, were regressed against prices of double apartments. Indicators at the 2,3 and 4-kilometer level report significance at greater than 95% levels, while all coefficients of indicators until 14 kilometers show significance at greater than 90% levels. Double apartments that are closer than 2 kilometers to campus show a substantial, 28.9% price premium over those that are further than 2 kilometers away from campus. Double apartments that are closer than 3 kilometers to campus have a 16.3% premium. However, there is also a premium of 4-7% (6.2% on average) of apartments closer to campus than a range of distances from 5km to 15km.
Price difference between apartments within and outside radii 2-16 kilometers, 1 = 100%
A potential explanation for this behavior is that apartments very far away from campus are so grossly inferior in quality terms that everything else offered seems better in comparison. This does not seem likely, but a positive association between quality and distance might exist simply because the presence of Duke raises surrounding land prices and makes low-quality, low-margin apartments less profitable. Being far from Duke may also mean being far away from downtown Durham, which in itself implies a number of other negative influences on price. A third explanation is that even if an apartment is beyond walking distance to Duke’s campus, there can still be indirect benefits from being somewhat close to Duke. Better policing, the coverage of Duke transit systems, short driving distances to the Duke Hospital might all be factors that play a role in prices of apartments beyond the walkable range.
One issue to consider is that positive price effects of larger radii are much weaker with regard to single apartments and virtually nonexistent for triple apartments. Single apartments closer than 2km and 3km to West Campus enjoy a 22% and 14% respective price premium, which is comparable in absolute terms with those of double apartments. However, when the indicator search radius is expanded the positive price effect quickly disappears. Using search radii of 11 and 12 kilometers, double apartments within the radius enjoy a 7% and 5.1% price premium compared to those that are outside, both significant at the 90% level. Single apartments within the same radii, on the other hand, only enjoy a 3.3% and 0.8% price premium. Neither of these relationships are statistically significant. No regression model with triple apartment prices reports statistical significance at the 90% level, although the 2-kilometer indicator represents a 10.4% price premium (P=0.47) for triple apartments.
Concluding the results thus far, all three apartment types show some level of response to difference distance gradients. Prices of double apartments are the most responsive, while evidence for price responses of triple apartments is somewhat weak. If we take the average price premium level for double apartments between 5-16 kilometers as reasonable expectations for price of a resident not living within walking distance to campus, then the model suggests that compared to not being able to walk to school at all, living closer than two kilometers to west campus comes with a price premium of 22.7%. Living closer than three kilometers has a relative premium of 10.1%. The same respective rates for single apartments are 18.5% and 10.5%, and for triples 9.5% and 6.2%. However, the triple apartment figures are not statistically significant.
Frequency Plot of Ratings of Sample Apartments
To further develop the model, online ratings of apartments are added in as a control variable. Ratings are collected from several websites and matched by average value and standard deviation. Ratings from different websites are aligned not by absolute score but by standard deviations from the mean, capped at 0 and 100. Apartments with fewer than five ratings for all websites aggregated are not considered in the dataset to avoid misrepresentation. Apartments with ratings from multiple websites have a composite score derived by first matching scores by mean and SD and then averaging over all scores. Unfortunately, not all apartment groups in the dataset have at least five ratings. Out of the 238 total observations with price data and 119 total observations with at least five reviews, only 89 observations have both price data and the minimum number of rating scores. Scores are tallied at a maximum of 100 and minimum of 0, with an adjusted average score of 55.7 out of 100 for the 89 usable observations. The 25th percentile score is 35 and the 75th percentile score 84.3.
With the inclusion of ratings as a control, no distance gradient terms in any of the regression models report statistical significance above 90%. This is the case for all three distance estimations, three apartment types and radii 2 – 6 kilometers. Distances terms remain below the 90% level of significance after adding in an interaction term between distance and ratings. However, the coefficient of the ratings term is highly significant for all regression models. Regressing only the ratings term against price of single, double and triple rooms results in strongly positive connections. For 10 extra points in the rating score, there is a price premium of 1.82%, 1.81% and 1.13% for the three room types, respectively. Note that these effects are weaker than those associated with distance to campus.
Influence of distance estimates/indicators on apartment ratings and respective significance
The reduction of statistical significance in distance gradient coefficients after introducing ratings as a control suggests a relationship between distance to campus and ratings. This can be demonstrated by a variety of metrics. Using the full, 119-observation dataset of apartments with ratings, the correlation coefficient between average review score and log-distance to West campus and East campus is -0.206 and -0.207, respectively. Modelling distance preferences using a natural log transformation, a doubling of distance to West Campus results in an average expected rating decrease of about 8.2 points. However, it must be noted that this association is rather weak considering the large spread of apartment ratings, which reports a standard deviation of 30.6 points.
The association between distance gradients and ratings can be explained in several ways. The obvious rationale is that raters are taking distance into account when giving scores, boosting the scores of apartments closer to campus. It is also possible that there is an inherent bias in apartment quality, with higher quality apartments generally being built closer to campus. If Duke students do actually have higher spending power than the average apartment resident in Durham, developers could be selectively offering high-quality, expensive apartments at locations closer to school in response greater demand. Even if apartment quality is not inherently associated with distance, students could be on average less responsive to quality differences. This could be the case either because most students know that they will be moving out in the near future (upon graduation) and have little concern for quality factors, or because their social lives are centered on Duke’s campus regardless of how far away they live. If apartments closer to Duke have larger student populations that only view the apartment as a place to shower and sleep, scores of bad apartments close to Duke could be buoyed even if such groups do not care about distance.
It is not difficult, at least in principle, to test for these explanations. However, currently available data do not offer a straightforward way to introduce controls beyond rating scores. Several websites do offer extra information about size and amenities, but the total number of observations in the dataset that have such information is small. Future research on such issues could focus on the obtaining of apartment quality data and geospatial quality variables such as crime rates and distances to public utilities. The distance estimate itself could also be improved, for example using actual walking/driving distance estimates instead of linear distance to campus. It might also be useful to model Duke Campuses as shapes with different points of priority (bus stops, Bryan Center, etc.) instead of a single point.
In conclusion, this paper has provided evidence of distance gradient effects on apartment prices in Durham. On average, a 100% increase in linear distance to west campus results in a 3.6% price decrease for single apartments and a 5.1% price decrease for double apartments. Using distance indicators, being within 2 kilometers of the west campus bus stop translates to a substantial, 22.7% price premium for double apartments and an 18.5% price premium for single apartments. The same figures derived with a 3-kilometer radius estimate are 10.1% and 10.5%. There is also evidence of a negative association between rating scores of apartments and their distance to campus. For a doubling of distance to West Campus, ratings scores decrease by approximately 8.2 out of 100 or 0.27 standard deviations.
 * = P<0.1, ** = P<0.05
 Only 142 apartments out of the 238 listings in the dataset offered triple rooms. In contrast, 228 apartments offered double rooms and 212 apartments offered single rooms.
 The 2km-radius indicator is not used here because only 2 apartments out of the 16 observation within the 2km radius have at least five reviews.
by Chris, Whittaker DP_WHITTAKERCHRISTOPHER
Urban planners and economists often debate the merits of cul-de-sacs, or circular, dead end streets that serve adjacent dwellings. Proponents claim that cul-de-sacs reduce urban congestion, improve community relations and reduce crime. However, economists occasionally challenge such claims, asserting that cul-de-sacs do not provide additional safety benefits or that such benefits are negligible. This research explores the relationship between crime and the existence of cul-de-sacs in the city of Durham, North Carolina. By utilizing Geographical Information Systems (GIS) and publically available crime data, I compare the spatial differences in crime rates between communities built on cul-de-sacs and two-way streets. Though crime in cul-de-sacs appears to be markedly lower than crime on nearby two-way streets, further economic analysis is necessary to separate the spatial effects of cul-de-sacs from other socioeconomic factors.
CURRENT CUL-DE-SAC RESARCH
The cul-de-sac is a hallmark of suburban sprawl. The term itself comes from a French expression that means “the bottom of the bag” (Lonngren). It is a dead end street with only one entrance for vehicle traffic. Further, cul-de-sacs tend to differ from typical dead end streets such that they end in a circular turn-around space that permits vehicles to exit by making a wide U-turn. An aerial view of a typical cul-de-sac is provided in the appendix (figure 1).
Cul-de-sacs have a number of important benefits. For suburban developers, cul-de-sacs allow them to place more homes in oddly shaped tracts of land (Nielsen and Lonngren). For residents, cul-de-sacs provide privacy and limit the noise from traffic while still remaining a part of the larger suburban community. Their one entrance and exit naturally help to constrain the speed of traffic, as well as the frequency of unknown vehicles passing through. Because of lower traffic, they encourage walking, bicycle use and outdoor activity by children. Unsurprisingly, homebuyers perceive houses surrounding cul-de-sacs to be safer than those located on two-way streets, and are willing to pay a premium for them (Neilsen).
However, others criticize cul-de-sacs. By definition, these communities are not well connected to other streets and they are often far from the central business district (CBD) and other areas of economic activity and community participation (Jagannath). On one hand, they encourage automobile use, as public transportation services are unable to accommodate their select residents. This, in turn, produces urban congestion in other parts of the community as well as a host of environmental problems associated with increased vehicle use. On the other hand, they create a number of inefficiencies with respect to the provision of public goods and services. It is more difficult to sweep streets or plow snow in cul-de-sacs. Further, it is more difficult to patrol cul-de-sacs and emergency vehicle access can sometimes be limited (Lonngren).
Much of the common knowledge regarding the popular appeal of cul-de-sacs is based on the idea of safety. As the logic goes, criminals try to avoid areas that lack easy entrance and exit. Further, if a crime is committed in a cul-de-sac, criminals will have a more difficult time escaping. Since residents of cul-de-sacs tend to be more familiar with each other, they are more likely to report and deter suspicious activity on behalf of their neighbors. Further, since fewer people frequently pass through cul-de-sacs, potential criminals would be unaware of opportunities in those communities. However, as noted by Lonngren, it is more difficult to police cul-de-sacs; these homes may actually provide better targets for criminals. Neilsen further notes that cul-de-sac statistics reveal some of the highest rates of traffic accidents involving young children. According to William Lucy, a professor of environmental studies at the University of Virginia, “the actual research about injuries and deaths to small children under five is that the main cause of death is being backed over, not being driven over forward” (Neilsen).
Little academic research exists regarding criminal activity with relation to the spatial layout of cul-de-sacs, and the majority that does remains inconclusive. As Hillier and others admit, it is difficult to untangle additional socioeconomic variables from the spatial layout of such communities (Hillier). However, it remains a useful exercise to conduct natural experiments that try to minimize such socioeconomic concerns in order to better understand our communities. Though it may not be possible to understand the true magnitude of the additional safety benefit provided by cul-de-sacs, it is certainly feasible to look at direction the answers is pointing. As such, it is possible to evaluate the following question: is living on a cul-de-sac in Durham safer than on a two-way street? To better answer this question, it is worth taking a brief look at recent criminal activity in Durham.
CRIMINAL ACTIVITY IN DURHAM
According to FBI statistics, the year 2012 marked an all-time low in violent crime (murder, rape, robbery and aggravated assault) and property crime (burglary, larceny and motor vehicle theft) committed in Durham (Durham Police Department). Crime continued its downward trend from the year 2000; this is shown in the appendix, figure 2. In particular, property crime reached its lowest level since 1988, due to a decrease in larceny and burglaries. Perhaps most striking, property crime is down 44% since the year 2000, with burglaries falling by 15% and larceny by 7% since 2011 (Durham Police Department). Still, larceny and burglary are the two largest contributors to Durham crime, constituting 80% of all criminal activity. These statistics are demonstrated graphically in the appendix, figure 3.
Though criminal activity is at a 23-year low, there were still over 15,000 crimes committed in Durham last year, approximately 80% of which are related to property. Consequently, analyzing the differences in property crime between housing communities on cul-de-sacs and two-way streets may prove useful. Thus far, no specific research exists regarding Durham spatial differences and crime rates. I attempt a first pass at demystifying and exploring these differences below.
I begin by identifying communities in Durham that are built around cul-de-sacs using satellite imaging tools available through Google Maps. I selected 36 suitable cul-de-sacs that are relatively similar with respect to spatial layout. I then pair each of these cul-de-sacs with a nearby two-way street comprised of similarly priced houses. While pairings differ in terms of relative pricing, each pair itself reflects a comparatively equivalent level of housing quality. I then compare each pairing with approximately 15,000 pieces of data regarding criminal activity in the city from the year 2012; such data is publically available from the Durham Police Department and their Geographical Information Systems (GIS) software. I then determine the total number of crimes committed in cul-de-sacs and on adjacent two-way streets. Further, I narrow criminal activity to single-family dwellings to remove possible biases from high traffic commercial properties and high-density apartment complexes. I conclude by separating crimes into three categories: crimes related to larceny, crimes related to assault, and crimes related to burglary/breaking and entering. This provides a more detailed assessment of the common types of crime that occur in cul-de-sacs and on two-way streets.
While the approach is simplistic, it has several advantages. First, pairing cul-de-sacs with nearby (often adjacent) two-way streets removes certain spatial biases: geographically close streets create a sensible natural experiment such that we can assume many spatial variables are held constant. Second, it mitigates socioeconomic biases by evaluating housing communities of similar economic qualities. Third, the approach applies uncomplicated economic principles to thinking about the relationship between criminal activity in cul-de-sacs and two-way streets. It ultimately provides a foundation for more detailed future research.
FINDINGS AND ANALYSIS
The data is summarized in the table below (table 1):
Table 1: Summary statistics of crime rates between cul-de-sacs and two-way streets
In total, there were 89 crimes committed within the selected communities, with 74 occurring on properties located on two-way streets and 15 within communities located on cul-de-sacs. This yields a 1-to-4.93 cul-de-sac to two-way street crime ratio. Stated differently, for every crime committed in a community based around a cul-de-sac, there are nearly 5 committed in a related community along a two-way street.
Compared to total Durham crime, this sample reflects less than 1% (approximately .60%) of total crime committed in Durham. However, this appears reasonable. Given a number of generous assumptions such that there are 20-30 homes per selected pairing, and approximately 107,000 total households in Durham County according to the U.S. Census Bureau, assuming that crime is evenly distributed among only households we would expect this to approach a 1% crime rate (30*36/107,000 = 1.01%). Knowing that crime is not evenly distributed and that much criminal activity occurs in highly frequented public spaces, dense residential communities and commercial areas, we can generally infer that this sample appears appropriate to provide a picture of residential criminal activity.
It is important to interpret these results in the context of total Durham crime in 2012 (see appendix, figure 3). While larceny was the largest category of criminal activity, constituting 53% of total Durham crime in 2012, it only comprises 25.83% of crime in the 36 selected communities. There are several notable considerations that may explain the difference. According to the Durham Police Department Annual Report, shoplifting constituted 25% of all larcenies (4). Further, the single largest percentage of larcenies at 40% consisted of the theft of motor vehicles and motor vehicle parts (4). Since this survey only compared single-family homes, and not criminal activity regarding places of commerce or high-density vehicle parks, these results appear more reasonable.
Burglary constitutes the majority of criminal activity in the 36 paired communities at 58%. This again appears intuitively reasonable as the sample reflects only single-family homes that, by their very nature, are more spatial isolated. This isolation and lower population density makes these homes better targets for potential criminals. The final 15.7% of crimes come in the form of assault. This is a bit higher than the Durham average for violent crime. However, approximately 80% of these assault offenses are deemed simple assault, a misdemeanor, and are not captured in the aggravated assault numbers that figure 3 highlights. Thus, aggravated assault rates are actually found to be lower in the 36 selected communities than Durham on the whole.
Though it would appear that cul-de-sacs in Durham are less prone to criminal activity than adjacent two-way streets, taking this result at face value would be misleading and overshadows several notable concerns. First, the relative breakdown of types of crime between two-way streets and cul-de-sacs is not markedly different; larceny, assault and burglary are all proportionally similar. As it looks, there is simply more crime by volume on two-way streets, perhaps pointing to housing volume concern.
I assume that the selected pairings of cul-de-sacs and two-way streets have a relatively similar number of housing units. While a handy approximation, this is most likely not the case. Cul-de-sacs tend to have fewer housing options than comparable two-way streets, given the nature of their short circular design. Additionally, housing on two-way streets may be more densely concentrated with smaller lot sizes. Adjusting for lot size is necessary to smooth out these crime rates. This is a systematic limitation of relying on GIS data; it is not specific enough to determine and adjust for property sizes. However, in order to claim that crime differences are completely negligible between cul-de-sacs and two-way streets purely on the grounds of total housing units, it would be necessary to assume nearly five houses on each two-way street for every one in each cul-de-sac. This seems unlikely, as plot sizes appear comparable and such housing communities were carefully selected. Thus, cul-de-sacs do likely maintain some spatial advantage with respect to criminal activity holding housing volume constant and lot size constant.
Johnson and Bowers raise another relevant point in their study of cul-de-sac safety: it is possible that the type of people who live on cul-de-sacs differ from those who live on two-way streets in ways that might increase their risk of victimization (107). Such people may be of a different socioeconomic status, age, marital status or ethnicity. Perhaps such households predominantly have young children. Given these considerations, it is possible that risk profiles differ among residents and may contribute to a difference in criminal activity exposure that is not linked to the spatial design of such communities.
Spatial permeability is another concern. Johnson and Bowers reference a study by Armitage (2007) that showed crime rates were typically lower in communities built on cul-de-sacs, except when such communities were connected to other streets or public areas by footpaths or trails (107). This is a spatial concern that is directly linked to crime rates and is notably absent from the findings above. Future research would need to account for such connectivity differences in cul-de-sacs; available GIS data is again limiting.
Though cul-de-sacs appear moderately safer than two-way streets, ultimately, correlation does not prove causality. While there appears a strong correlation between lower crime rates and spatial housing layout, one does not dictate the other. Further research is necessary to better evaluate such claims.
EXTENSIONS AND APPLICATIONS
It is difficult if not impossible to accurately assess the magnitude that spatial differences play in determining criminal activity. A variety of socioeconomic variables, as well as serendipity, are often present and problematic to untangle from pertinent spatial differences. Others biases may yet be at play. For example, residents of cul-de-sacs tend not to be random; they are self-selecting and seek the benefits and style of living that cul-de-sacs provide. Further, they may also be more affluent. Given two ideal communities that exist ceteris paribus, the one located on a cul-de-sac will command a higher economic premium than the one on a two-way street. Therefore, a comprehensive analysis of spatial differences in crime rates remains illusive. Moreover, even if such an analysis were possible, it would be questionable to extend the findings to communities outside of a given region; local political and socioeconomic differences appear too nuanced to realistically do so.
However, that is not to say that such research is fruitless. The careful public policy strategist or city planner may continue to utilize GIS data to better understand local spatial differences in order to craft germane policy. Further, such spatial criminal research may be used to identify high-crime communities and lead to better policing of public spaces. A good example comes from the East Weaver Street public housing community. Though East and West Weaver Street are over one mile in length, 6 out of every 7 crimes committed in the area occur in a small public housing community that constitutes a tenth of mile. Insights like this may be useful to help local authorities increase policing and to direct city planners’ time to addressing the causes of criminal activity in the area.
Figure 1: A typical cul-de-sac as shown from Google Maps.
Figure 2: Index Crime Rate per 100,000 Residents by Year for Durham, North Carolina (Durham Police Department).
Figure 3: Crime Breakdown for 2012 in Durham, North Carolina (Durham Police Department).
Figure 4: Durham Crime Mapper Software screenshot. Provides a graphical layout of crime locations as well as police and sheriff tables that detail the exact location of criminal offenses. Red dots indicate police responses while yellow stars indicate sheriff responses.
Durham Police Department. Annual Report: 2012 Durham Police Department. Durham, 2012. http://durhamnc.gov/ich/op/DPD/Documents/2012AnnualRepor0301FINAL.pdf.
Durham Police Department. Crime Mapper Online Software. Durham, 2012. http://gisweb.durhamnc.gov/gis_apps/crimedata/dsp_entryform.cfm
Hillier, Bill. “Can Streets Be Made Safer?” Palgrave Macmillan 9.1 (2004): n. pag. ProQuest. Apr. 2004. Web. http://proxy.lib.duke.edu/login?url=http://search.proquest.com.proxy.lib.duke.edu/advanc ed?url=http://search.proquest.com.proxy.lib.duke.edu/docview/194522636?accountid=105 98.
Jagannath, Thejas. “Do We Need Cul-de-sacs?” Urban Times RSS. N.p., 25 Jan. 2013. Web. 01 Apr. 2013. <http://urbantimes.co/2013/01/do-we-need-cul-de-sacs/>.
Johnson, Shane D., and Kate J. Bowers. “Permeability and Burglary Risk: Are Cul-de-Sacs Safer?” Journal of Quantitative Criminology 26.1 (2010): 89-111. Print.
Lonngren, Betty. “Cul-de-sacs Unproven As Deterrent To Crime.” Chicago Tribune. N.p., 25 Apr. 1993. Web. <http://articles.chicagotribune.com/1993-04-25/business/9304250091_1_sacs- cul-chicago-neighborhoods>.
Nielsen, John. “Cul-de-Sacs: Suburban Dream or Dead End?” NPR. NPR, 07 June 2006. Web. 07 Apr. 2013. <http://www.npr.org/templates/story/story.php?storyId=5455743>.
by Bernadette Lowell DP_LowellBernadette
Historic designation and the process of historic preservation have saved homes and commercial properties across the country from being torn down for newer construction. As space becomes sparse in larger cities, occasionally it is necessary to tear down these properties, however this can cause unpleasant construction and “mismatched” neighborhoods with homes from many different eras. Through national and local historic designation, owners can receive tax breaks and other incentives to keep their home in its original, historic form.
Durham is a prime example of a city that would need historic designation. Although it is not lacking for open land, there is a rich history in many downtown buildings and homes that needs to be preserved. Tobacco factories line downtown streets along with many homes dating back to the 1920s and 30s.
Durham has sought to preserve its local historic resources by “inventorying historically significant structures in the City and County, designating local historic districts and landmarks, establishing and supporting the Historic Preservation Commission (HPC), and nominating properties and districts for listing on the National Register of Historic Places.”(City of Durham) The HPC meets throughout the year to approve changes for any historic home and possibly designate new neighborhoods. Homes in any of the designated neighborhoods are taxed at 50% of the properties’ value. (City of Durham) However, with this local designation come certain restrictions. In order for any change to be made the exterior of the building, the owner must receive approval in the form of a Certificate of Appropriateness after a meeting with the HPC. (City of Durham)
A few homes and neighborhoods in Durham are also nationally designated. According to the National Register of Historic Places website, Durham county has 77 listed historic places and districts. Although this can mean some federal tax breaks, there are little to no restrictions on any changes to the homes.
Historic neighborhoods in Durham
This study primarily looks at homes in two of the city’s seven historic districts. The Lakewood Park Historic District (Fig. 1), in southwest Durham, was listed as a national historic district in 2003. (Lakewood Form) It includes the blocks 2002-2112 Chapel Hill Road; 1601-1907 West Lakewood Avenue; 1406-1602 James Street; and 1809-1819 Bivins Street. (Lakewood Form) According to the application for historic designation, the buildings were built in 3 “generations” from 1902-1920, during the 1920’s, and in the mid 1930’s. These houses progressed from one-story homes with “modestly stylish Queen Anne features” to bungalows and into the “Minimal Traditional style.” (Lakewood Form) Many of these homes retained a high level of integrity throughout the years, making them prime candidates for historic designation and preservation. Each contributing home was constructed before 1952 and maintains enough of the original design and workmanship to be considered historic.
The Holloway Street District (Figure 2) was nationally designated in 1985 and is also considered to be a local historic district. The neighborhood, located much closer to the downtown area, dates back to the 1860’s, though it was reported in the application that many of the homes only date back to the 1880s through 1920’s. At the time of designation, many homes were “intact but deteriorated,” with some left vacant and vandalized. (Holloway Form)
For the following regressions, I primarily used data from Zillow.com, which listed the number of bedrooms and bathrooms, square footage, lot size, year built, date and price of last sale, and their own “Zestimate.” This number is Zillow’s own estimate of the home’s market value.
The Lakewood Park Historic District has 83 designated properties. Of these, 13 were commercial properties, vacant lots, or did not have enough information on Zillow, and 8 others were considered “multifamily” and were not included in the regression. These 62 homes in the district had an average Zestimate of $163,167 with a median of $154,112 and were on average built in 1931 with a median of 1924. (Figure 3)
The historic neighborhood was compared with the surrounding area, which is not historically designated on the national or local level. Using similar constraints—no multifamily or commercial properties— and removing any home without information on Zillow, 48 properties were chosen. These homes had an average Zestimate of $115,824 with a median of $108,780. On average they were built in 1950. (Figure 4)
The Holloway Historic District has 29 total homes. Seven of these had no information on Zillow and another three were considered multi-family. Of these 18 total homes, the average Zestimate was $165,739 with a median of $139,378. On average, the homes were built in 1931 with a median year of 1928. (Figure 5) The Holloway homes were compared with 84 surrounding homes. These homes had an average Zestimage of $69,912 with a median of $62043. They were, on average, built in 1934 with a median of 1925. (Figure 6)
For the following regressions, I used the formula lnZestimate=F(Historic, Characteristics) where Historic is a dummy variable for historic designation, and Characteristics include number of bedrooms and bathrooms, the square footage, lot size, and year. This hedonic model is in semi-log form, which implies that the coefficients for each explanatory variable are the percentage change in price with each one unit increase in that variable. From these variables, it is clear that there will be correlations between these explanatory variables, designation specifically being negatively correlated with year and positively correlated with other features of the house. Including all available Characteristics variables decreases the bias of the Historical variable.
For my initial regression, I only included Historic as an independent variable. In the Lakewood neighborhood, designation was found to have a positive influence on the Zestimate, with a coefficient of .317 and a t ratio of 5.52. (Figure 7) For homes in the Holloway district, designation had a positive influence on the Zestimate with a coefficient of .738 and a t ratio of 8.66. (Figure 8) Though both historic neighborhoods had a similar mean price, this initial regression reveals a significantly stronger influence of historic designation on the Holloway district. This could also be clearly seen through the mean Zestimates of the neighborhoods, as it is evident that the area surrounding the Holloway neighborhood contains considerably cheaper homes.
Following this regression, I included the rest of the independent variables for the characteristics of the home. In the Lakewood neighborhood, historic designation had a similarly high, statistically significant coefficient of .105 and a t ratio of 2.63. (Figure 9) This implies that, even after taking into account the structure of the home, historic designation brings a 10% increase in property value for the Lakewood neighborhood.
However, in the Holloway neighborhood, Designation had a coefficient of -.0019, though with a t ratio of -0.03. (Figure 10) Unfortunately, it is hard to draw any conclusions from this regression, as it is statistically not significant. Inclusion of a wider set of observations and explanatory variables could ultimately help this regression analysis.
From these regressions, I can conclude that the Lakewood historic designation significantly increases property value compared to the surrounding homes with the given data. Unfortunately, I cannot draw any conclusions after performing regressions on the Holloway District data.
If it is true that historic designation has lead to increased property values, then there are some potential policy issues that Durham will face. If these homes are in poorer areas, then an increase in property value could drive way poorer residents. If a neighborhood is locally designated, there is also an added burden to keep the home in its original form—requiring any change or fix to be approved by the HPC. In order to keep these homes affordable and less burdensome, there will need to be policy to keep residents in their historic home.
In order to conduct a better analysis of the impact of historic designation on home prices in Durham, this study would need more observations. As there are seven locally designated and fifteen nationally designated neighborhoods, there could be a large change in the outcomes with these homes included in the analysis.
This data set also did not include other variables about the features of the homes. Although they might not be significant, if a home has a garage, an attic, or basement could factor in to the property value as well as what the home is made of or even if it has been foreclosed on in the past. With these, the regressions might be more accurate.
One final inclusion to this data set could also be time. This regression does not show how the home prices have changed since the historic designation. A follow up study could look into whether or not these home prices have increased or decreased more rapidly than those homes in the surrounding blocks.
Map taken from: http://durhamnc.gov/ich/cb/ccpd/Documents/Historic%20Preservation%20Information/Historic_Resources_34x44_020312.pdf
Lakewood Historic Homes (N=62)
|Zestimate||Bed||Bath||Sq. Ft.||Lot||Year built|
Lakewood Non-historic Homes (N=48)
|Zestimate||Bed||Bath||Sq. Ft.||Lot||Year built|
Holloway Historic Homes (N=18)
|Zestimate||Bed||Bath||Sq. Ft.||Lot||Year built|
Holloway Non-historic Homes (N=84)
Lakewood Neighborhood, first regression
Holloway neighborhood, first regression
Lakewood Neighborhood, second regression
Holloway Neighborhood, second regression
“Historic Preservation.” City of Durham. N.p., n.d. Web. 29 Mar. 2013. http://durhamnc.gov/ich/cb/ccpd/Pages/HPC%20Items/Historic-Preservation.aspx
Individual Property Form for Holloway Street District. June 1984. Http://www.hpo.ncdcr.gov/nr/DH0188.pdf.
Leichenko, Robin, N. Edward Coulson, and David Listokin. “Historic Preservation and Residential Property Values: An Analysis of Texas Cities.” Urban Studies 38.11 (2001): 1973-987. Sage Journals. Web. 4 Feb. 2013.
USDI/NPS Registration Form-Lakewood Park Historic District. 07 Mar. 2003. Http://www.hpo.ncdcr.gov/nr/DH2541.pdf.
By Valtcheva Katerina DP_ValtchevaKaterina
The part of Old North Durham around Foster St. and Geer St. has historically been an industrial neighborhood. It was generally considered a bad area of Durham despite its proximity to the central business district. In the past few years, this part of the city has been subject to some changes in development patterns, which have begun to transform the neighborhood. I will present my analysis of property-value movement in the area and examine it in the context of the new businesses that have been opening in the area. My hypothesis is that this recent commercial upturn, which was organic in origin, has had a positive impact on residential property prices in the part of Old North Durham that lies on and surrounds W Geer St. In order to confirm this hypothesis and quantify the impact of the new developments on existing structures, I have compared forty houses in Old North Durham (in proximity to Motorco Music Hall) with forty houses in East Durham and Edgemont over the last eight years. I have used Zillow estimates to obtain house value appraisals, collected annually, for the first month of each year starting in January 2006 and ending in January 2013.
Old North Durham initially developed with the construction of tobacco warehouses, as well as related commercial and industrial ventures nearby. North Durham Elementary School has had an influence on the area with respect to social development, while the Farmers market has also been influential in the area’s development as a center of Durham’s alternative lifestyle scene. However, for quite some time the area has been known as a rough part of town, and public perception to this effect has only recently begun to change. I believe that this trend started with the opening of several bars and restaurants in the area: Fullsteam Brewery was opened on August 13th 2010 on Rigsbee Ave; in 2011 a popular bar and music venue, Motorco Music Hall, was opened right across the street from Fullsteam; and a restaurant called Geer Street Garden, on the corned of Foster St. and Geer St., followed on May 5th the same year. Further, in the end of January 2013 a café called CocoaCinnamon—the area’s most recent addition—opened its doors across the street from Geer Street Garden. Interviews with the owners of these businesses revealed that a big part of what drew them to the area was not government policy, but a combination of its proximity to the city center and its industrial aesthetic: indeed, Fullsteam Brewery occupies a prior soda bottling facility, Motorco a car dealership, and CocoaCinnamon an automotive repair shop. The buildings that were chosen for these additions to Durham were not only located a short walk away from W Main St and the commercial heart of the city, but also happened to be particularly suitable for the type of bars, restaurants, and clubs that their owners were searching for. ,, 
The area I have chosen to compare Old North Durham to is similarly industrial, with its historical roots in milling and textiles. East Durham and Edgemont’s two textile mills have been turned into a retirement home and office spaces, which made them unavailable at the time when Old North Durham was chosen to host Motorco Brewery. Other reasons why I have chosen East Durham/Edgemont for my control area are that its houses are very similar to the ones around Motorco, built around the same time period, and that it is approximately the same distance away from the central business district as Old North Durham is. As of 2006, the area was more economically depressed than Old North Durham, but there are two reasons why I don’t believe that makes the results of this study less significant. The first is that, for the purpose of this study, I am interested in relative changes of values between the two neighborhoods pre- and post-2010. The second is that the difference would only be marginal to someone who is already taking the risk of opening a new business in Old North Durham. While the worse-off state the latter neighborhood does not distort the findings of the study, is nonetheless one of the factors that may have contributed to Geer St. and Rigsbee Ave’s housing Durham’s urban revival. However, this result is better explained by the fact that the industrial buildings existing in East Durham/Edgemont were either unavailable or poorly suited for housing breweries and cafes at the time Fullsteam opened.
Now I would like to focus on how I selected properties for use in a quantitative comparison of development in these two areas. House values representative of the Motorco area in Old North Durham come from forty homes that are located on one of the following streets: W Geer St., North St., Hargrove St., Glendale Ave, Northwood Cir, and N Mangum St. The average house size of this sample is 1375 ft2 (± 523 ft2.) The average lot size is 6121 ft2 (±1640 ft2.) The year these properties were built ranges between 1910 and 2004. The average year built is 1937 (±20) years. For my control group in East Durham and Edgemont I have chosen houses on one of the following streets: Hart St., S Driver St., Roberson St., Angier Ave, Vale St., S Plum St., E Main St., Clay St., and Ashe St. The average home size in the control area is 1432 ft2 (±476 ft2.) The average lot size is 7047 ft2 (±1413 ft2.) The year the homes were built ranges from 1900 to 1992 and averages to 1929 (± 21) years. Although the area I have chosen for the controlled sample closely resembles North Durham in many aspects, the house lots there were generally bigger than the ones in North Durham. For the purpose of creating a better group of comparable properties, I have excluded some houses with significantly larger lots. Moreover, the majority of houses in East Durham and Edgemont were built between 1900 and 1910. Therefore, I included in my sample as many of the later-built houses in the controlled area as possible in order to create a sample that more closely resembles the properties I had chosen in Old North Durham. With this changes, the group I control for has an average lot size of about 900 sq. ft. more than the sample of the North Durham area. The statistical analyses discussed below demonstrate that this is not a big enough difference to significantly distort the finding of my study, as the relationship between house prices and house size and amenities is much stronger than the relationship with lot size. This is especially noticeable in a town such as Durham where land is not a scarce commodity, as opposed to a place such as New York City, where such a difference in lot sizes would have been significant.
Figure 1: Property value information collected from zillow.com
To explore the trends within home values in Old North Durham and East Durham/Edgemont, I first normalized property values of each house by the square footage of the house. I also normalized each property value by the historic average price of housing in Durham at large for January of the corresponding year (fig. 1) in order to ignore the effects of the housing bubble and bust. For the time being, I have ignored the effects of lot size, as normalizing by that value would imply a linear relationship between lot size and overall property value—a false assumption in this environment. I first plotted mean property values in the two areas prior to 2010, in order to see how house prices in the two areas have been moving prior Old North Durham’s recent commercial development. I found the best-fitting line between the points for each group and I observed a positive change in house prices for this time period with a difference in their slopes of .0113 in favor of the control area (fig. 2 and 3). This does not indicate a significant difference in the house price movements between the two areas prior to 2010.
When plotting the data for all eight years, the picture changes dramatically (fig. 4 and 5). House prices continue to rise in Old North Durham through the entire time period (though, with the addition of datapoints through 2013, the slope of the price curve falls from .0206 to .0108), while the overall trend in house values in East Durham/Edgemont has become negative. This means that the recent fall of house values in the area has been so great that the slope of the line describing the overall trend of house prices went from positive .0319 (between 2006-2010) to negative .0275 (when datapoints are included through 2013); a total change five times bigger difference between the two areas prior to 2010. Running a Student’s t test comparing mean home prices on a year-by-year basis, it was observed that the difference in mean home values between the two neighborhoods was not statistically significant until 2010, from which point it became consistently significant (p<<0.001) (fig. 6). These findings suggest that the rise of new businesses in Old North Durham have correlated with a buoying of property values in the surrounding area, while a similar area in Durham has experienced a statistically significant fall in residential property values.
In order to get a fuller idea of how the relationship between house prices in the area have changed, I ran a total of sixteen linear regressions of house prices versus home size, lot size, and home age in each area over the years investigated. These regressions gave me a measure of how much house price depends on these parameters. My findings are summarized in Figure 7. From the first graph we observe a substantial marginal change in home value in Old North Durham with square footage. While the result in 2009 appears to be an outlier, all other years show a difference between the price dependence on square footage between the two areas that has been stable up to 2009 and then diverging since then (with only a small change in Jan 2013). The divergence we see since 2009 shows that the new developments in Old North Durham have increased the gap between the willingness of people to pay more for extra square footage in between these two neighborhoods. The graph also illustrated that this divergence is not due to a significant increase in the customers’ willingness to pay for additional sq. ft. of a house around the Motorco Area as much as a decline in their willingness to pay for the same sq. footage in the control area. This is consistent with what we saw in Figures 4 and 5.
The second graph on figure 7 shows the marginal change of house prices with lot square footage. This does not represent a strong or clear relationship in these areas, either within each neighborhood over time, nor between them. One way to interpret this is the possibility that residents’ decision to buy a house in a poorer and worse neighborhood may not be influenced by the lot size as much as the ones in nicer neighborhoods. However, the small correlation between lot size and home value supports the idea that lot size does not really influence a residential property’s price in a city like Durham where land is abundant.
When looking at the third graph on figure 7, one must note that this the regression is performed on year built rather than on age, which means that the graph shows the relative change in home value for each additional year you add to a home’s age. Interpreting the relationship we see that on average older houses in the Motorco, or Old North Durham, area cost more than older houses in the control group. This is an interesting observation that could mean that there are more renovations in Old North Durham, while more renovations mean that the area has been taking a turn for the better since the new businesses came in.
There is more evidence that Old North Durham has begun to transform in the recent years. From my interview with Fullsteam’s owner I learned that he has noticed his clientele becoming more diverse though the years, and that a lot more students are comfortable stopping by than before. This is evidence that Old North Durham has improved in terms of safety and could be attributed to the opening of the new businesses—not only the new bars and restaurants, but also the food trucks, which are now stopping by regularly, make the area around Old North Durham a lot more vibrant than it used to be. The increased attention to this part of town is also evident from a dispute over a nearby area that is currently used as a soccer field. The disagreement comes from the fact there seems to be a conflict of interests between the members of the community over the use of the land after a proposed renovation. Some residents are using this area to practice soccer and claim Durham does not have enough soccer fields based on national standards. However, others believe that the area should host a smaller soccer field and some playgrounds. “The park has been included in two Capital Improvements Plans, first in 2001 and again in 2005, but the city has not funded any upgrades there, saying money was better spent in other parks.” The increased attention towards the soccer field debate today supports the idea that the area has began to take a turn for the better and could even suggest that gentrification is occurring.
The outcomes of this analysis provide strong evidence towards the positive effect of the new businesses in Old North Durham. The area’s house prices show stable growth in the past eight years, while a similar area such as East Durham/Edgemont has suffered a decline in house prices. This comes to show that risky investments in bad neighborhoods could pay dividends for residents as well as local business owners. The information in this analysis can be used as an example of how strategic reclamation of industrial spaces can prove an economic boon through enticement of the “alternative lifestyle” market. Basing its actions on the positive changes Old North Durham, the local governments may be able to stimulate the revival of other areas in Durham (as well as other cities with a ample vacant industrial space) by creating incentives for entrepreneurs to develop such spaces.
 Holloway, Carson. Personal Interview. 28 03 2013.
 Wilson, Sean. Personal Interview. 25 03 2013.
 Barrera de Grodski, Leon. Personal Interview. 23 03 2013.
 Holloway, Carson. Personal Interview. 25 03 2013.
 Schwartz, Joe. “Bad blood brewing over Old North Durham Park.” Indyweek.com. N.p., 13 4 2011. Web. 6 Apr 2013. <http://www.indyweek.com/indyweek/bad-blood-brewing-over-old-north-durham-park/Content?oid=2361969>.
Heterogeneous Effects in the Housing Market’s Reactions to Sex Offenders: Evidence from Durham, North Carolina
by Yuan (Ingrid) Zhuang DP_ZHUANGINGRID
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.
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 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 and Durham neighborhood demographics (age, race, sex and education level) data from City Data. 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*SOnt+β1*Rt+β2*SOntRt+γ1*racent*SOnt+γ2*sexnt*SOnt+γ3*agent* SOnt+ γ4*incoment*SOnt+X+εnt (Equation 4.1)
Hnt=α+β0*SOnt+β1*Rt+β2*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.
Table 5.1 Impact of Sex Offenders’ Arrival on Housing Value and Recession’s Effect
|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.
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.
 The use of 0.1 miles radius is based upon the reasoning Linden and Rockoff (2006) gave in their paper.
 Data obtained from http://www.census.gov/2010census/data/
 Data obtained from http://www.city-data.com/nbmaps/neigh-Durham-North-Carolina.html
By Stephanie Xu DP_XuStephanie
A key debate during North Carolina’s 2012 election cycle revolved around the proposed development of 164 acres in southwest Durham County, an area now widely referred to as 751 South. The development called into question zoning practices and decisions, as the Durham Planning Commission finally approved 751 South to be rezoned from its former Rural Residential designation to a Low-Medium Density Residential designation. Yet even as Southern Durham Development (SDD), the company spearheading the project, moves forward with its plans, environmental activists and local community leaders have spoken up against the development, citing an irreversible environmental impact, including damage to Jordan Lake and its aquifer.
Amidst the political debate, this paper seeks to examine whether or not the 751 development will be beneficial to Durham using economic analyses. We first review the origins of zoning policies to gain a better understanding of the sensitivity of rezoning. Then, we look more deeply into 751 South and the research that has been conducted about the project, including an independent economic impact analysis and a cost-benefit analysis by Durham County. From the data available, 751 South is a viable and necessary development that will bring a great deal of benefits to Durham County, ranging from job creation to privately-funded highway improvements.
The practice of zoning as a method of urban planning and land use regulation has been surrounded by equity and segregation debates over the past few decades. A wide body of literature about the topic exists, with a range of focuses, from its early implementation in the early 1900s to its efficiency to its effect upon race and class segregation. This paper examines the history and progression of zoning practices and policies, as they are crucial to understanding the associated controversies. We then look into the impact of zoning upon income distribution and equity, first through a more dated but resourceful paper by Fernandez and Rogerson (1997), and then through a 2006 paper by Calabrese, Epple, and Romano. Lastly, we explore literature concerning the relationship between zoning laws and public health initiatives.
Fischel (2004) offered a comprehensive overview of zoning from its early 1900s origins to modern day policy reforms to address the exclusionary products of such regulations. Fischel posited that zoning was not a necessary practice until the advent of buses and trucks that allowed businesses to locate farther from streetcar tracks and stations. Until then, workers were able to buy houses in the suburbs without fear of encroaching business. However, companies’ newfound capability to transport resources threatened the residential environment that workers found so valuable. Thus, zoning was the only way to give homeowners security and assurance that their neighboring land wouldn’t become a noisy business district. Several decades later, the problem was exacerbated by a sudden increase in highway construction in the 1960s. Firms could now move out of high-density urban areas even more easily. Failing or struggling cities and suburbs welcomed the influx of businesses for the accompanying fiscal benefits. Meanwhile, only well-off communities could afford to keep businesses out. Fischel argued that it was in this environment that income distribution began to play such a strong role. In this paper, he sought to find a way to keep home values stable, as zoning was initially created to accomplish, without causing the exclusion with which we are faced today. To resolve this, he proposed selective home equity insurance, despite his acknowledgement of the administrative problems and risks of moral hazard and adverse selection.
Fernandez and Rogerson (1997) and Calabrese et al. (2006) investigated the impact of zoning upon equity, but differed in their approaches in a critical way. Both used a two-community model with two different income distributions. Both also made a point of stratification as a natural occurrence, with or without zoning laws. But the former required in their model that individuals committed to one community or another before any voting took place, and before they bought any property. Calabrese et al., on the other hand, based their argument upon the Tiebout rationale that people could “vote with their feet” and choose to reside in towns that align with their personal preferences.
Thus they arrived at different conclusions. Fernandez and Rogerson found that in general, zoning made the richest better off and the poorest worse off, but left a great deal of ambiguity about those in the middle. Poor individuals who moved from a more affluent community to a lesser one because of zoning policies tended to be better off, while individuals who move to a richer community tend to be worse off. Calabrese et al. finds an overall increase in efficiency and welfare as a result of zoning, due to their assumptions under the Tiebout model. They conclude that because residents can move after a community votes on tax rates and local public goods, “aggregate welfare gains arise from better Tiebout matching of preferences to levels of public-good provision” (Calabrese et al. 2006, 4). Comparing these two scholarly works highlights a decisive factor in zoning theory: that the order in which individuals vote on taxes and public goods, buy property, and choose their community affects the predictions of the ensuing welfare gains or losses.
751 South: Background
Until July 2012, the 751 South region was designated as Rural Residential, which permits four or fewer units per acre of land. Since the Durham Planning Commission approved the rezoning application by a vote of 8-5 in favor, 751 South is now a Low-Medium Density Residential area and is allowed anywhere from four to eight units per acre of land.
SDD’s plan for the development allocates 300,000 square feet to retail space, 300,000 square feet to office space, 645 apartments, 358 condominiums, 170 townhomes, and 107 single-family houses. Their plan also includes a 26-acre donation to Durham to build a new elementary or middle school, a new fire department, and a new sheriff’s office. Finally, SDD has committed to investing the full $6 million needed to improve Highway 751 to expand its capacity and relieve congestion in the area.
751 South: Economic Analysis
The economic impact of the 751 project come in two phases: the construction phase and the permanent operating phase. Benefits, as outlined by the ERA Economic Impact Analysis, come in many forms, the most significant being in job creation and increased property and sales tax revenue. Other benefits include SDD’s full funding of the highway renovations and planned donation of 26 acres of land for the purpose of constructing a new public school, and new sites for the fire and police departments. In total, the value of these contributions exceeds $11.4 million, saving the expense of taxpayer money. The costs that have been examined include the costs to construct, the cost to equip and staff new fire and police departments, and other operating costs of the new landscape. Overall, without any reliable data on environmental costs, the benefits to developing the 751 South project exceed the costs by a tremendous amount.
Phase 1: Construction
This building phase of the 751 South project brings changes in three components: direct, indirect, and induced impacts. Direct impacts refer to the initial changes in industries; indirect impacts refer to changes in inter-industry transactions as demand grows; and finally, induced impacts refer to changes in local spending that result from the changes in the industry sectors. Intuitively, any development dollars spent and re-spent in Durham County will generate additional income for both companies and their employees as money circulates through a cycle of spending. ERA acknowledges that there will be some leakages, so this multiplier effect will not be entirely contained within Durham County, but overall the benefits are significant. Figure 1 breaks down the impacts into three construction phases over ten years, noting the three types of effects upon output, employment, and labor outcome.
ERA has predicted that the direct $414.5 million investment in the project will bring an additional $107.4 million in indirect output and $44.5 million in induced output. The project is also set to create an estimated 2,419 jobs purely for the development of the site as well as another 1,333 jobs in indirect and induced impacts. In turn, the job creation results in an increase in labor income in Durham County totaling $166.9 million during the ten-year construction phase
Phase 2: Permanent Operations
The economic impact of the operating activities of retail, office, and consumer spenders is permanent and proves more substantial than the Phase 1 impacts. The benefits can be broken down into jobs and income, household spending, and tax revenue. First, using data from the American Planning Association, the new retail and office buildings will house 1,050 new professional office jobs and 683 new retail jobs. Additionally, the new occupants who will occupy the housing at 751 South will create demand for 714 other jobs for a total of 2,980 new jobs at build-out with an estimated annual labor income of $154.2 million. Figure 2 provides an exact breakdown of the predicted permanent jobs, which range from construction positions to legal personnel.
Second, 751 South is predicted to house 1,280 new households with a total estimated disposable income of over $98.9 million that will contribute to the new industries and businesses in the area.
Finally, the new development is forecast to bring in about $5.7 million in annual property taxes to Durham and additional revenue in sales taxes. Data based upon local experience and internal market research examined similar communities such as Colvard Farms and Meadowmont and yielded estimates of $3.2 million for Durham County with an additional $2.4 million for the city if the project area is annexed.
The two economic analyses provided suggest that development in full of SDD’s project for 751 South would be the appropriate policy decision, but fail to acknowledge any environmental impact research that may reduce the perceived benefits of the development. The economic impact analysis by ERA explores the various benefits that will come from 751 South, ranging from sales and property tax revenues to thousands of permanent jobs to privately-donated land for Durham Public Schools, the Fire Department, and the Police Department. The cost-benefit analysis by BMS demonstrates that full completion of SDD’s proposal would bring profits sooner than any alternative scenario. Intuitively, completion of only 48.1% or 21.9% of the project yields slower returns and fewer returns for a lower investment.
The policy question comes down to a balance between environmental risks of developing 751 South and other needs that 751 South would help fulfill for Durham. While maintaining the status quo would eliminate the environmental concerns, this option leaves Durham with many of its problems unsolved. Overcrowding in Durham Public Schools will remain an issue, as will the traffic congestion along the 751 and NC-54-I-40 corridors. While the existing economic studies have revealed a number of economic benefits that would accompany the 751 South project, there has yet to be conducted an official environmental impact assessment that would lend credence to the environmental objections to it. Without such evidence, a fully informed policy choice cannot be formed.
Economic Research Associates. Project Report: 751 South Economic Impact Analysis,
Prepared for Southern Durham Development (March 29, 2009), www.era.aecom.com.
Budget and Management Services. Cost Benefit Report: 751 South Project Voluntary
Annexaction Petition (March 31, 2011), ww2.durhamnc.gov/annexation/751_Report/pdf/04.pdf.