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.
Green Space and Property Values
Although many of the benefits associated with public green spaces are seemingly obvious and easy to describe, they are often much harder to quantify. Green spaces in urban and suburban areas have typically been publicly provided amenities that have no set market price, but it has become increasingly common to evaluate them in terms of their monetary contributions to their surrounding communities. There exists a need, therefore, to convert the many assumptions regarding the inherent benefits of green space into objective, quantitative estimates of their worth (Nicholls 2005). Recent trends towards increased land development, particularly in urban areas, makes the ability to determine the economic values of public parks and green spaces important in order to ensure their existence and designation. Early literature supports the notion that green space causes an increase in property values because home-owners and renters are willing to pay more for the perceived benefits of being close to green space (Crompton 2001). However, more recent studies have been able to use the hedonic pricing analysis as a more accurate means of demonstrating the variable effects. Because green space offers many different benefits, such as environmental, recreational, transportation, aesthetic and health-related nature, no one method exists to measure all such benefits simultaneously (Nicholls 2005). On a similar note, not all green spaces are the same or provide the same amenities, and thus their impact on property value may vary. Therefore, this literature survey will discuss one essay that provides a foundation establishing the importance of a quantifiable measure for green space benefits on property value, and two studies that use regression analysis to measure the different variables that impact the perceived benefits of green spaces and subsequent property values.
Some city planners, urban developers, and governmental officials believe that development brings prosperity through enhanced tax revenues, and hence any land left open or undeveloped is considered a wasted asset. Furthermore, opponents of green spaces have identified several negative externalities, such as the invasion of the privacy of those residents whose properties directly adjoin greenways, concern regarding the numbers of strangers who will be passing through local neighborhoods, and fears of increased noise, littering, trespass, and vandalism (Nicholls 2005). All of these factors can (and in some studies have) decrease the generally believed positive impact that green spaces have on home values (Nicholls 2005). Crompton (2001) combats this perception through the establishment of the proximate principle. This principle suggests that the value of a specified amenity, like green space, is at least partially captured in the price of residential properties “proximate” to it (2001). If it is anticipated that properties or homes located near an open green space are considered desirable, the additional money that homebuyers and renters are willing to pay for this location represents a “capitalization” of the land into proximate property values (2001). With an increase in property value comes and increase in property taxes, and in some cases the additional taxes paid for all proximate properties may cover or even exceed the annual cost of acquiring, developing, and even maintaining the green space (2001). As such, many public parks were originally created with the hopes of their direct and indirect economic contributions to city tax revenues, Central Park in New York City being a prime example (2001). As a result, the impact that green spaces can have on the economic development of an area makes them an important factor of consideration in urban and suburban planning. Twenty out of thirty previous studies that Crompton (2001) discusses support the proximate principal; however, several other factors can influence the relationship between green space and property values, such as the various forms of desirability associated with green space and the physical characteristics of the green space itself.
Nicholls (2005) uses hedonic pricing to operationalize and measure Crompton’s proximate principle in a specific location and takes into account two different desirability interpretations of green space: aesthetic appeal and physical proximity. The greenbelt chosen for the study is the Barton Creek Greenbelt and Wilderness Park in Austin, Texas, along with three major residential bordering neighborhoods: Barton, Lost Creek, and Travis. The greenbelt is a 1,771-acre natural area located to the west of downtown, and includes 7.5 miles of multi-use trails, as well as various parking and restroom facilities (2005). Each neighborhood is examined separately since each contains a different set of locational amenities for inclusion in the hedonic model, but since properties were located within the same geographic sub-areas (such as school and tax zones) neighborhood and community variations were not investigated (2005). Sales price is the dependent variable, and the independent variables include three groups of property value influence: structural, locational, and environmental (2005). The value of the greenbelt is measured in three ways: aesthetic value, which is shown using two variables, direct adjacency to the greenbelt and view of the greenbelt; and physical proximity, which is represented by a continuous measurement of the distance between each property and the closest entrance to the greenbelt (2005).
The results of Nicholls’ hedonic analysis show that adjacency to the greenbelt produced significant property value premiums in two of three neighborhoods (Barton and Travis), but in no case did visual or physical access to a greenway have a significant negative impact on surrounding property prices (2005). The lack of positive impact of greenbelt adjacency in the Lost Creek area may be a result of the dramatic topography and dense vegetation that dominates the area. Lost Creek homes directly adjacent to the greenbelt are typically located on the edges of deep, thickly vegetated ravines that lack recreational access or nice views (2005). Conversely, homes located farther away from the greenbelt boundary on a higher elevation level have widespread views of both Austin and the greenbelt, but this view often includes a high voltage power line (2005). Although proximity to a power line is usually seen to have negative or neutral impact on property values, in this case the result could be that the beauty of the green space in the majority of the view offsets the interference of the power line into a part of it (2005). The finding of significant positive impacts of greenbelt adjacency in the other two neighborhoods supports this argument that physical characteristics may be influential (2005). In both the Barton and Travis areas, the topography is less steep and the vegetation is less dense, which might provide more obvious visual benefits (2005).
While the Lost Creek area did demonstrate the expected relationship of a decline in property value with increased distance from the closest greenbelt entrance ($3.97 decrease with each foot from the nearest entrance), in Barton and Travis the coefficient on the distance variable appeared insignificant (2005). An explanation for the Travis area isn’t clear, but for Barton this could be a result of the neighborhood’s distance to the bridge to downtown Austin. Being the closest of the three neighborhoods to downtown, it is possible that Barton homeowners tend to be work downtown and enjoy walking or biking to work, making the distance to downtown an important element (2005). Moreover, the Barton neighborhood enjoys easy access to many green spaces besides the Barton Creek Greenbelt and Wilderness Park, weakening the value of proximity to this specific amenity (2005). The city of Austin is known for its many open space amenities and downtown with several outdoor recreational opportunities (2005). While this analysis does emphasize the influences that variables such as topography, vegetation, and use patterns may have on the value of a green space amenity to local residents, there are other important variables that have not been accounted for, such as the type of green space.
A study conducted by Anderson and West (2006) uses home transaction data from the Minneapolis–St. Paul metropolitan area to analyze the relationship between the proximity to several different types of green spaces and property values. As suggested by Crompton (2001), the type and purpose of green space is an important factor to take into consideration. Anderson and West (2006) analyze several types of green spaces, including neighborhood parks, special parks, golf courses, and cemeteries. Special parks are defined as national, state, and regional parks, arboretums, nature centers, natural areas, and wildlife refuges, in order to differentiate them from neighborhood parks, which are generally more urbanized and provide fewer recreational opportunities and natural amenities (2006). Furthermore, their hedonic analysis differs significantly from Nicholls (2005) in that it allows the effects of proximity to depend a completely different set of variables, including population density, income, crime, age of the population, and distance to the central business district. In addition, they control for neighborhood characteristics and potential omitted spatial variables using local fixed effects.
The most significant from the analysis were in relation to population density, distance to CBD, income, and crime rates. The effect of green space on sales price depends on a home’s location and neighborhood characteristics. On a broader scale, Anderson and West (2006) find that urban residents in more densely populated neighborhoods located near the CBD place a higher value on the proximity to green space than suburban residents located further away from the CBD and in less densely populated areas: in neighborhoods that are twice as dense on average, the amenity value of proximity to neighborhood parks is nearly three times higher than average, while the amenity value of special parks is two-thirds higher (2006). This finding suggests that estimates of green space benefits for the average home in a metropolitan area will over/under-estimate the values of properties in particular neighborhoods. Consequently, conclusions from studies analyzing city preferences should not be used to draw implications for suburban planning. Additional results from the Anderson and West (2006) analysis highlight the effect of income on green space and home values. In neighborhoods that are twice as wealthy on average, the amenity value of neighborhood parks is more than four times higher than average, while the amenity value of special parks is more than two times higher (2006). Crime rates also proved to be a significant factor impacting green space values, in fact the amenity value of proximity to neighborhood and special parks rises with crime rates, so it appears that both types of parks act as buffers against the negative effects of crime (2006). Although conclusions based on the other previously mentioned variables were also realized from this study, they were not as significant as the four discussed above.
While the findings of Nicholls (2005) and Anderson and West (2006) focus on distinctly different green space areas (one being more urban than the other), they both provide quantitative measures to unravel the many factors impacting the proximate principle established by Crompton (2001). As the decentralization of cities continues throughout the 21st century and cities keep growing at their peripheries, the tradeoff between developing and preserving green space becomes an increasingly important debate. Although development can help fulfill a population’s needs for additional housing and commercial space as well as increase tax base revenue, green spaces provide a number of benefits, many of which have been discussed throughout this survey. Understanding the impact that green space has on property value will not only help regional developers and government officials make better decisions regarding the provision, design, zoning, and use of these public goods, but also help the creation and development of better homes and more desirable communities.
Anderson, Soren T. and West, Sarah E. “Open space, residential property values, and spatial context.” Regional Science and Urban Economics 36 (2006): 773–789. Web. http://www.macalester.edu/~wests/AndersonWestRSUE.pdf
Crompton, John L. “The Impact of Parks on Property Values: A Review of Empirical Evidence.” Journal of Leisure Research 33.1 (2001): 1-31. Web. http://www.actrees.org/files/Research/parks_on_property_values.pdf
Nicholls, Sarah. “The Impact of Greenways on Property Values: Evidence from Austin, Texas.” Journal of Leisure Research 37.3 (2005): 321-341. Web.
Introduction and Background
Extending my previous work, this paper explores the effects of opening and closing food retail stores on nearby housing prices in Durham, North Carolina. While the opening of a new grocer provides nearby residents with increased access to food and other goods, the full economic impacts of new stores are not always clear. In particular, there is often resistance to larger retail stores like Walmart being opened in a neighborhood, with arguments that a new supercenter will negatively affect local businesses and wage levels. Additionally, increased traffic, crime, noise pollution, and so on are also potential concerns regarding new stores. As such, it is not apparent how, if at all, new stores affect nearby home prices. Changes in housing prices can be used as a signal to evaluate these various competing effects and so the results of this study will help Durham residents and government officials evaluate whether to support or oppose new stores in their local areas.
Pope and Pope (2014) examined the effects of opening a Walmart on nearby home prices from 2000 to 2006, and this paper will use a similar framework to their study. Using a difference-in-differences specification, the authors found that houses located within 0.5 miles of a new Walmart saw an increase of 2-3 percent in sale price in the following two and a half years, and houses 0.5 to 1 mile away saw a 1-2 percent increase. These results would suggest a positive impact for new stores in Durham as well, though the authors are careful to note that their results only reflect a national average.
Publicly available home sale data for Durham extends ten years back (to 2005), so this study will analyze store openings and closings that have occurred in that time frame. This includes Walmart, Harris Teeter, TROSA Grocery, ALDI, and Save-a-lot stores. Stores like Target and Food Lion are not included in the study because their newest stores were opened 2004 or earlier.
This paper analyzes data provided by the Durham County Tax Administration on home sales in Durham, NC. Through the county’s online record search, I collected the electronic summary record of each single-family residential home sale in Durham since 2005, the earliest year for which data is provided. These searches yielded approximately 27,000 results, and an Excel VBA macro was used to access the details page for each sale and record characteristics of the property (bedrooms, bathrooms, and so on). This data were filtered to remove a small number—around one percent—of entries with incomplete information.
Next, I used the Rest-CSV interface provided through Geocoder.us to find the longitude and latitude of the homes in the dataset. A macro was written to query the Geocoder.us servers and record the geographic coordinates for the homes included in the service’s dataset. Geocoder.us uses TIGER/Line street and highway information provided by the US Census Bureau from 2004, and as a result, not all the addresses were able to be geocoded. Approximately 10,000 of the 27,000 home sales were not able to be located on the 2004 TIGER/Line maps; these are homes that were built on streets that did not exist a decade ago. This introduces a significant potential for bias in the data, and a re-examination of this paper’s analysis with fully geocoded data may prove insightful. Summary statistics for the full and geocoded datasets are provided below.
Table 1: Summary statistics for the full and geocoded datasets
As expected, the geocoded dataset contains homes that are older, and as a result this reduced dataset contains homes with a lower average sale price, heated area, number of bedrooms, and number of bathrooms. The below graph and table show the data in both datasets distributed by sale year, both in terms of absolute sale numbers and as a percentage of the overall dataset.
Figure 1: Distribution of sales by year for the full and geocoded datasets
Table 2: Distribution of sales by year for the full and geocoded datasets
Even though the geocoded dataset skews older and cheaper, the proportion of sales occurring in each calendar year is still roughly the same as the full dataset, indicating that the geocoded dataset is still very similar temporally to the full dataset. The effects of the subprime mortgage crisis on Durham home sales can be seen in both datasets; sales in 2008 and the years following are clearly depressed compared to 2005 through 2007. There is also evidence for a strong recovery in the past few years, and 2014 sales were at approximately the same level as the 2006 and 2007 peak.
We now run a hedonic regression on the logarithm of sale price for the geocoded data to examine the dataset. The regression used is below:
The sale price, heated area (in square feet), and bedrooms term are straightforward. The β3 is for the “bedroom/bathroom differential” which is the absolute value of the number of bedrooms minus the number of bathrooms for a house. This was motivated by a model developed by the National Association for Home Builders (NAHB) that aims to estimate home values. As Emrath (2006) describes, their data indicated that home buyers tend to prefer a rough balance between the number of bedrooms and the number of bathrooms. If a home has more bedrooms than bathrooms, an additional bathroom will increase the value of the home by a higher percentage than if the differential were smaller. The β4 is associated with a dummy variable for whether a property has a garage or not. The results of this regression are below.
Table 3: Regression on the geocoded dataset
The p-values for each of the variables is virtually zero. All else equal, an additional bedroom will add 15.3% to a home’s value and a garage will add 13.4%. The previously discussed bedroom/bathroom differential shows a fairly strong negative effect as expected. The NAHB model predicted that an additional full bath would add approximately 20 percent to a home’s value which is very consistent with the derived 19.8%. Heated area is measured in square feet and so an additional square foot has a very small positive effect as would be expected.
To make sure that there were no abnormal price trends present in the dataset, I established a price index using the data. This index is graphed below along with the Case-Shiller national home price index and the Case-Shiller home price index for Charlotte, NC, the nearest metropolitan area to Durham for which an index is provided. For each month, the Durham index is calculated as the average price per square foot of single-family residential home sales in the past three months; the Case-Shiller indices use a three-month moving average as well. The Case-Shiller indices use repeat sales of the same home to track changes, but the Durham market is not large enough to create an accurate index using this same method. As seen in the graph below, however, the price per square foot measure for Durham roughly approximates the Case-Shiller indices, suggesting that Durham home values have followed the same trends as other North Carolina cities (Charlotte) and the nation as a whole. It should be noted that it is merely a coincidence that the Durham index is similar in magnitude to the Case-Shiller indices.
Figure 2: Indices for home prices
This study analyzes the effects that the openings and closings of Walmart, Harris Teeter, TROSA Grocery, ALDI, and Save-a-lot grocery stores have had on nearby housing prices. The locations to be examined are listed below along with relevant dates. Other grocery stores can be found in Durham today, but the previously listed stores are the ones who have opened or closed stores in the past decade. These locations were manually geocoded using Bing Maps.
Table 4: Locations to be studied and relevant dates
The distance between each home and store was found using the Haversine formula which gives the great circle distance between two points on a sphere:
d = the distance between the two points
r = the radius of the Earth (3961 miles)
lat1, lat2, long1, long2 = the latitudes and longitudes of the two points
Borrowing from Pope and Pope (2014), the hedonic regression used was:
The logarithm of the sale price of a home can be explained through its property characteristics as previously defined and its geographic relation to a given store. Three indicator variables (D0.5, D1, D2) represent whether the given house is within 0.5 miles of a store, between 0.5 and 1 mile, or between 1 and 2 miles, and whether the home is between 2 and 4 miles away is the omitted indicator. Holmes (2011) in his research on Walmart considered a radius of two miles to properly constitute a store’s neighborhood, and this study will do the same for all the stores. The last group of homes 2 to 4 miles away will act as a control group for the homes inside the neighborhood that we are interested in examining.
The variable Post represents whether the sale occurred after the opening or closing of the given store. A sale occurring before the relevant date will have the relevant indicator (D0.5, D1, D2) set to 1, and the interaction between the Post term and the indicator variables inside the parentheses will lead to the corresponding indicator becoming zero. A sale after the relevant date will have both indicator variables at 1, and so the estimates for the second set of spatial estimators ( ) will provide information on whether the store impacted housing prices. This study uses home sales two years before and after each store’s opening or closing.
The difference-in-differences specification is used in order to remove the effects of omitted time-invariant variables that could bias estimates. Comparing homes sold before and after the store opening or closing allows one to more safely disregard neighborhood characteristics that would influence housing prices since these characteristics should more or less influence both the pre and post home sales in the same way. Holmes (2011) created and argued for a model in which Walmart stores are not placed based on characteristics of the nearby markets but rather on distribution and shipping costs. This would imply that the less than two and two to four mile ranges should be similar enough to properly control for broader housing market trends. For time-related variables, Figure 2 presented previously shows that the price per square foot for single-family residential homes in Durham has remained within a $20/sqft range for the past decade. While this is not an insignificant fluctuation, this index shows a reasonably flat trend as a whole so it is unlikely that including time-related variables would significantly influence the analysis.
The results for the first Walmart store located at 1010 Martin Luther Jr Pkwy showed a positive 14.4% effect on homes 0.5 to 1 mile away and positive 8.3% effect on homes 1 to 2 miles away after the store was opened. The Walmart store located at 1525 Glenn School Rd showed no significant effects at any distance, both before and after the store opened. This means that the Walmart was placed into a neighborhood which was similar price-wise to its surrounding area and that the opening of the Walmart did not influence nearby prices. It should be noted that this Walmart featured fewer home sales in its relevant subset of the dataset; its opening was in 2009 during the housing crisis when fewer homes were being sold, and it is located in the northeastern part of the city which is less dense. This Walmart had 1343 sales within four miles in its four year window compared to 2281 for the first Walmart store. This smaller dataset perhaps contributed to the lack of significant coefficients found in the regression.
The Walmart store at 3500 Roxboro Rd closed around the same time the previous Walmart opened. Before the closing, homes 0.5 to 1 mile away sold for 12.4% more than homes 2 to 4 miles away and homes 1 to 2 miles away sold for 23.0% more. The coefficients associated with the post-closing indicator variables were not statistically significant, so the closing of the store did not change nearby housing prices from the previous baselines. Only one of the three Walmart stores openings/closings in Durham caused nearby housing prices to change, indicating that the positive average effects found in Pope and Pope (2014) are not necessarily applicable to the stores local Durham area.
The three Harris Teeter store openings had significant results in terms of the neighborhoods they were placed in though none of the three showed any significant effects on nearby housing prices after the stores were opened. The Harris Teeter store at 2017 Hillsborough Rd was placed in a location where houses 1 to 2 miles away were worth 9.3% less than homes slightly farther away. The 1501 Horton Rd store was placed where homes 0.5 to 1 mile away were worth 10.2% less and homes 1 to 2 miles away were worth 9.2% less. The 1125 West NC 54 Hwy store was placed where homes 0.5 to 1 mile away were worth 11.8% more and homes 1 to 2 miles away were worth 8.5% more. While the coefficients are similar in magnitude, the differing signs suggest that there was no deliberate effort by Harris Teeter to locate its stores in certain kinds of neighborhoods. The regressions also show that none of the three stores had an impact on surrounding housing prices after they were opened. Homes near the first two Harris Teeter stores were still worth approximately 10% less and homes near the third store were still worth about 10% more.
TROSA, Triangle Residential Options for Substance Abusers, Inc., opened a grocery store in East Durham, at 2104 Angier Ave, in 2010. East Durham is a significantly poorer area of the city, and the regression quantified this economic difference. Before the store opened, homes within 0.5 miles of the location were worth 74.7% less, homes 0.5 to 1 mile away were worth 70.0% less, and homes 1 to 2 miles away were worth 53.6% less as compared to homes slightly farther away. After the store opened, prices of homes within 0.5 miles fell 65.3% further, and homes 0.5 to 1 mile away fell a further 24.3%. Due to low traffic and high operating costs, the store closed in 2012. As expected from the previous regression, before the closing, the homes near the store were worth significantly less than homes farther away. After the store closed, home prices near the store actually increased; homes less than 0.5 miles away experienced a 35.9% increase from the previous baseline, and homes 1 to 2 miles away experienced a 33.3% increase.
These results do not necessarily mean that the grocery store exacerbated the poor economic conditions of the East Durham area. The store was located two miles from downtown Durham, an area that has seen significant investment and growth in recent years. Since the control group for the regression uses home sales two to four miles away, increasing prices in the downtown area would lead to homes within two miles becoming worth relatively less. While the grocery store was the first in fifty years to open in the neighborhood, offering nearby residents a source of fresh produce within walking distance, these results show that any positive effects that this increased food access provided were not significant when comparing East Durham’s poor economic conditions to downtown.
The ALDI store that opened at 7906 NC 751 South showed no significance for any of the distance indicator coefficients. This store is located near Southpoint Mall, and so it is unsurprising that the store opening did not change nearby housing prices given the very large number of shopping options already available in the area.
The regression for the Save-a-lot store opening was unique in that it showed significance for all of the distance coefficients. Before the store opening, homes within 0.5 miles were worth 75.6% less than farther homes, homes 0.5 to 1 mile away were worth 78.8% less, and homes 1 to 2 miles away were worth 8.4% less. Save-a-lot is located about a mile away from downtown Durham in the same East Durham area as the TROSA Grocery store was, and so these results make sense considering the much higher value of homes downtown compared to East Durham. After the store opened, homes within 0.5 miles experienced a 45.4% price increase, homes 0.5 to 1 mile away experienced a 33.2% increase, and homes 1 to 2 miles away experienced an 11.4% increase. It is unlikely that the opening of a single discount grocery store single-handedly led to these dramatic price increases, and so these results need to be interpreted with caution. We should note that the closing of the TROSA Grocery store led to similar increases; both the opening of the Save-a-lot and closing of the TROSA Grocery occurred in 2012, and we can hypothesize that East Durham experienced some sort of revitalization during this time that led to these results.
This paper shows varying results for the impacts of store openings and closings in Durham. While Pope and Pope (2014) found that new Walmart stores slightly increase nearby housing prices, this study did not find conclusive support for their conclusion. As a whole, the results for the different stores were difficult to interpret, and no retailer as a whole showed a definitively positive causal impact. This study shows that while there may be beneficial trends on a larger scale from introducing a new store into a neighborhood, the impacts of individual stores on home prices can be difficult to predict.
Emrath, P (2006). “How Much is a Bathroom Worth?” National Association of Home Builders. Retrieved from http://www.nahb.org/generic.aspx?genericContentID=62422.
Holmes, M. (2011). “The Diffusion of Wal-Mart and Economies of Density.” Econometrica 79: 253-302.
Pope, D. and J. Pope (2014). “When Walmart comes to town: Always low housing prices? Always?” Journal of Urban Economics 87: 1-13.
Over the last two decades, Durham has undergone a significant revitalization of its downtown area. Both private and public investments have contributed to a large number of redevelopment projects that have transformed the face of a once decaying tobacco town into what is now considered one of the nation’s most desirable cities to live in (Carmichael). The transformation first began in the 1980s and 90s with the original conversion of Brightleaf Square, the completion of a downtown Civic Center, the restoration of the Carolina Theater, and the construction of the Durham Bulls Park Stadium – but the major turning point came in 2004 with the start of the American Tobacco Campus redevelopment project (Wong and Wolf, 9). The American Tobacco Campus, along with several simultaneous and subsequent large-scale downtown projects, led to the proliferation of new businesses, restaurants, bars, and shops that have contributed to the city’s “live, work, play” environment and growing economy.
Durham’s promising future has continued to attract both commercial and residential investors and developers who are seeking to capitalize on this growing market. More recently, however, there has been a rapid growth in the number of hotel development/redevelopment projects over the last five years. Much of downtown’s rebirth is the result of a strategic plan led by Durham’s city and county participation in public-private partnerships, and the recent influx of hotel developments is no exception. Four out of the five new hotels that will be opening their doors in downtown within the coming year are funded by city and county incentives. Although public sector financing in support of urban developments is a common practice, the trend in relation to the hotel industry is relatively new, particularly in Durham. For these reasons, my paper seeks to provide a two-part analysis explaining the new growth in public-private partnerships funding Durham’s downtown hotel development.
I will begin by providing a brief overview of the specific downtown area and the new hotels that have been approved for funding and development, followed by a general discussion about the roles of hotels within the city and the recent national trend towards public sector funding. Then I will analyze the trend in relation to Durham specifically: first, by discussing the motivating factors behind Durham’s city and county incentives for these new hotel projects – including the current supply shortage, perceived economic benefits, and historic preservation; and secondly, by addressing the concern of a potential oversupply – including demand indicators and generators and product differentiation. I will conclude with a brief overview of the analysis and final considerations regarding the future success of these investments. All numerical and statistical data used throughout this paper come from the Durham Convention and Visitor Bureau and the Durham Chamber of Commerce unless otherwise cited.
The downtown area of Durham, according to the Downtown Durham Inc., is approximately .751 square miles. As shown in Figure 1.1, the 6-distrcit area encompasses 14 x 12 blocks that span from the intersection of Buchanan Boulevard and W. Main Street to the intersection of Fayetteville Street and E. Main Street. The map in Figure 1.2 depicts the approximate downtown locations for each of the five new hotels that have secured funding and are scheduled to open their doors by the end of next year. They include the 21c Museum Hotel (opening March 2015), an Aloft Hotel (opening April 2015), the Durham Hotel (opening May 2015), a Residence Inn by Marriott (opening June 2015), and the Jack Tar Hotel (opening Q1 2016). While several other hotels have been developed in the surrounding neighborhoods, these are the only hotels that will be directly located in the delineated downtown area. The map also shows the only hotel that currently serves the downtown, the Marriot City Center, as well as several completed downtown development projects (ATC, DPAC, and the Durham Bulls Park) that have attracted many real estate developers to the area. With the Aloft Hotel being the only exception, all of downtown’s new hotel projects have been backed by some degree of city and county funding (data was taken from official City and County contracts and agreements from www.durhamnc.gov):
- 21c Museum Hotel: 125 room project estimated cost is $48 million; received $5.7 million incentive from the city and $2 million from the county
- The Durham Hotel: 54 room project estimated cost is $11 million; received $1.2 million incentive from city and county
- Residence Inn by Marriott: 143 room project estimated cost is $29.5 million; received $1.3 million incentive from city and $400,00 incentive from the county
- Jack Tar Hotel (74 room hotel included in the larger City Center development project): project estimated cost is $85 million; received $3.9 million incentive from city and county
*Note that this hotel is unique in that its development is in support of the lager 26-story mixed-use City Center project
It should be noted that all of these incentives are guided by specific requirements, including start/finish construction dates, and are “performance-based,” meaning the contracted firm will not receive any payments from the city or county until after the project construction is complete. In other words, public dollars will follow private performance and investment.
Hotels, Cities, and Public Investment
Before I can analyze specifically why Durham’s local government approved a total of $10.6 million towards the development of five new downtown hotels opening within one year, it is important to understand the general role that hotels play in relation to the city, and why the public sector has increasingly become a major contributor to the funding of large hospitality projects. With regard to the hotel’s role within the city, McNeil (2008) asserts that hotels have become a crucial component to urban renewal strategies and the notion of the “circulatory” city. He notes that changes in hotel developments are part of a broader reconfiguration of the central business district, in which hotel location is increasingly driven by proximity to the “diverse workforce of office markets.” Hotels locate themselves in accordance to their market, and in turn they become nodes of circulation, “operating between fixity and flow, locating and refreshing mobile bodies, embedding them in relatively fixed networks within particular cities.”
In recent years, the public sector, either on its own or in partnership with the private sector, has gradually become a major contributor in the urban renewal of cities across the nation. Tress (2003) emphasizes that a key component of urban revitalization often begins with or incorporates the development of a large hospitality projects. However, the high risk of hotel development, primarily due to high replacement costs, often results in less available returns on investment than those desired by private investors. Furthermore, Tress points out that private sector investment in the hotel industry has become increasingly conservative – loan to value (LTV) ratios have shifted downward and debt service coverage (DSC) ratios upward, further emphasizing the associated high risk with the hotel industry. As a result of these two trends, the public sector has emerged by providing incentives to alleviate some of the risk and bridge the gap between return on investments.
Public Sector Motivations
There are several reasons that motivate the local public sector to provide the financial support of hotel development projects. In the context of Durham, the most obvious reason is the significant shortage in the supply of hotels directly servicing the downtown area. Until very recently, the only hotel located in downtown Durham has been the 190-room Marriott City Center, which opened in 1986 and was last renovated in 2008 (Oleniacz). Adjacent to the hotel is the 40,00 square foot Durham Convention Center. The convention center is equally co-owned by Durham City and Durham County and until 2011 was managed by Shaner Hotels Group, the owner-operator of the Marriott City Center (Oleniacz). Historically, convention centers have acted as loss leaders, but several factors contributed to the DCC’s largely increasing operating deficit. Under new management by Global Spectrum, the center’s operating deficit has significantly decreased (Oleniacz). However, the fact remains that the Marriott City Center hotel only has 20% of the necessary guest rooms for a convention center of its size (Arai). One of the primary motivations, thus, behind Durham’s public sector funding of these new hotel developments is to provide the necessary additional hotel rooms that would help generate convention center business.
Aside from the obvious supply shortage of hotel rooms in the downtown area, another important motivating factor behind public sector funding is the economic benefits that are expected to come from these developments. The economic impact of hotels is most commonly assessed by the fiscal (tax base revenue), direct (job creation), and induced (visitor spending) impact (Tress). In each one of the incentive agreements signed by the city and hotel development companies, approval was given because of the likely expansion to Durham’s tax base from new property, sales and occupancy taxes and the creation of new jobs in accordance with the Durham-Based Business Plan and Durham Workforce Development Plan (which stipulate good faith efforts to engage Durham-based firms in the construction work for the project and engage the Durham JobLink Career Center System in efforts to hire temporary and permanent staff related to the project). Based on each hotel’s proposed agreements and contracts, below are numerical calculations of their respective estimated fiscal and direct impacts:
- 21c Museum Hotel
- Fiscal Impact: over $1.9 million in incremental property taxes and $8.5 million in occupancy and retail sales taxes would be generated as a result of this project, yielding a net revenue gain to the City of over $2.6 million over 20 years
- Direct Impact: project is slated to create more than 150 permanent jobs with a substantial percentage paying in excess of livable wage
- The Durham Hotel
- Fiscal Impact: over $107,363 in incremental property taxes and $686,213 in occupancy and retail sales taxes, yielding a net revenue gain to the City of over $188,576 over 7 years
- Direct Impact: project is slated to create 91 new permanent downtown-based jobs and over 100 temporary construction jobs
- Residence Inn by Marriott
- Fiscal Impact: over $640,736 in incremental property taxes and $1,138,000 in occupancy and retail sales taxes would be generated as a result of this project, yielding a net revenue gain to the City of $446,634 over 8 years
- Direct Impact: new jobs, expected to be created by the project, would consist of 14 part-time positions and 31 new full-time paying jobs with benefits; including 8 salaried positions
- Jack Tar Hotel
- Fiscal Impact (included in the larger City Center development project): over $4,911,118 in incremental property taxes and $3,402,472 in occupancy and retail sales taxes, yielding a net revenue gain to the City of $4,340,446 over 15 years
- Direct Impact: new jobs, expected to be created by the project, would consist of 250 construction jobs, 500 office workers, 75 retail-based employees; and 10 hotel jobs with benefits
While the fiscal and direct impact of a hotel can be numerically calculated and predicted, the induced impact associated with the “spillover effect” – the money that visitors spend on services in other downtown industries – is often more difficult to assess. According to the DCVB’s data on Visitors’ Economic Impact on Durham in 2014, the number of visitors in Durham has steadily increased of the last decade with approximately 9 million visitors spending a collective total of $765.8 million each year. Most visitors spend the largest proportion (26.7%) their money in the food & beverage industry – reinforcing downtown Durham’s vibrant and prolific local restaurant industry. Consequently, all of the city’s and county’s approvals for the public funding of these projects were made with the belief that they will bring more visitors to downtown, thereby increasing visitors’ net economic benefit for Durham.
One final motivating factor behind Durham’s public sector support for downtown’s recent hotel development is the city’s emphasis on historic preservation. Many of downtown Durham’s older buildings lend themselves to this nature, and in fact all of the publicly incentivized hotels in downtown are planned to either preserve or include some historical element unique to Durham in their design. The Residence Inn by Marriott is of particular importance because its public funding was needed primarily to cover the increased costs associated with preserving certain architectural elements of the historic McPherson hospital into the hotel structure (Hoyle). Similarly, the Jack Tar Hotel project would also involve the rehabilitation of a contributing building that is part of the Downtown Durham Historic District. The developer of the boutique hotel has agreed to maintain as much original material as possible while designing the project in order to preserve the building’s unique connection to Durham’s past (Hoyle). On a similar note, both the 21c Museum Hotel and the Durham Hotel are boutique hotels that have structured their design to reflect and incorporate the art, culture, and history of Durham in some form. McNeil (2008) points out that boutique hotels are often located “within the discourse of urban renaissance fostered through the refurbishment of historic buildings (including ex-factories and warehouses),” which is very much the type of landscape that defines downtown Durham.
Concern for Oversupply
The second part of my analysis regarding the new hotel development occurring in downtown Durham involves addressing the primary concern of a potential oversupply to the area. Upon completion of the five new downtown hotels, the inventory of hotel rooms in Durham will nearly triple in size, jumping from 190 to 530 rooms in less than a year. Although the need for more hotel rooms in downtown was previously stated as a primary motivation for public investment in these projects, this drastic increase has, nonetheless, caught the concerning eye of government officials, industry leaders, and local residents.
The belief of many Durham leaders in support of the development, however, is similar to the “Field of Dreams” theory suggested by Culligan (1990), which is, “If you build it, they will come.” After looking at both supply and demand in the hotel industry from 1965 to 1990, Culligan noted that growth in demand occurred in remarkably close relationship to growth in supply, and in some cases, such as with new niche hotels, supply could actually stimulate demand growth. His conclusions are based on the simple assumption that the market tends toward equilibrium, but his evidence comes only from historical examples in which demand growth followed supply booms.
Geoff Durham, president of Downtown Durham Inc., said he believes demand will exceed expectations for the new downtown hotels because of the “success and growth” in the entertainment, restaurant, and retail venues downtown and “to a large extent, these new hotel developments are playing catch-up to this demand,” (Oleniacz). On the same note, Shelly Green, President & CEO of the Durham Convention and Visitors Bureau, told City Council that the economic and domestic tourism demand in Durham could accommodate 1,200 rooms (Oleniacz). She further explained that greater marketing efforts by the DCVB will help fill the additional hotel rooms. The DCVB receives a third of the occupancy tax revenue collected by hotels and uses that for marketing to attract more visitors: “As the inventory of hotel rooms grows we have more marketing dollars to fuel that demand and visitation,” (Oleniacz).
The evidence in support of this increased visitor demand comes largely from the city’s two major leisure attractions, the recently built Durham Performing Arts Center and the Durham Bulls Park Stadium. Built in 2008, DPAC is a 2,800-seat multi-use performance theater that houses nationally renowned Nederlander productions, including Broadway shows and various other performances throughout the year. A year-round schedule of national entertainment acts supplemented with local entertainment events has added marketing and entertainment cache to the thriving Durham arts scene. According to the Durham Convention and Visitors Bureau, total visitor spending by DPAC guests in Durham exceeded $66.3 million last year, with an economic impact of $48.4 million. The Durham Bulls Athletic Park was originally built in 1995 through public funding and has served as an integral catalyst to downtown’s revitalization. The stadium, home to the Durham Bulls as well as the Duke Blue Devils’ and North Carolina Central Eagles’ college baseball teams, was renovated this past year in preparation to host the 2014 Triple-A All-Star Game. This nationally televised game brought in visitors from across the country for a 5-day festival, resulting in an estimated $3.3 million in visitor spending to the area. The $20 million renovation has made an enormous impact on the stadium’s success, with last year’s final paid attendance equaling a record high of 533,033. The amount of visitors that come to Durham to attend events hosted by DPAC and the Bulls Stadium indicates that there is an unmet demand ready to fill the new supply of hotel rooms in downtown. Developers of these new hotels have strategically placed them in the downtown location to be in close proximity to Durham’s demand generators, hoping to capitalize on a market of visitors who would stay longer/overnight – and thereby spend more money in downtown – if more hotel options were available.
In addition to these demand generators that target leisure travelers (who make up 73% of Durham’s visitors according), Durham also has several indicators that support an increase in the business traveler demand. Several studies show that a strong economy and employment rate are signs of increases in demand for the hospitality industry (O’Neill). The 2014 edition of PKF Hospitality Research states that rising levels of employment, combined with increased geographic expansion of the national economic recovery, will result in revenue per available room growth in excess of long-run averages for all hotel markets from 2014 through 2017. In fact, PFK projected demand growth to outpace changes in supply in the U.S. through 2016. Durham currently has an unemployment rate of 4.4%, which is below the national average of 6.3%. Over $1.3 billion dollars in private and public investment have been put forth into new development projects that will generate even more employment opportunities for downtown. Downtown’s proximity to Duke University and Medical Center, major global companies, and a wealth of entrepreneurial activity ensure further employment growth. According to Durham’s Chamber of Commerce, Durham County has enjoyed a 10.2% increase in jobs located within the county since 2000, far exceeding North Carolina’s 2.7% growth. Another important factor to consider is that hotel demand in university towns has generally proven to be more stable than national averages, which has proven to be true for Durham for the past two decades (O’Neill).
One final point should be made addressing the potential concern for oversupply, and that is the product differentiation in the types of hotels that will be developed. From boutique to branded, select- to full-service, each of the five hotels caters to a relatively different market of customers with little overlap. The 21c Museum Hotel and the Durham Hotel provide truly unique “boutique” experiences for guests that might be willing to pay a higher price for greater amenities and personalized service, while the Residence Inn and Aloft Hotel primarily cater to travelers who value convenience and practicality over luxury. This creates a variance in the price scale for hotel rooms serving the area, making sure that the new supply of hotel rooms reaches all spectrums of the market.
In summary, whether built by the public sector or private sector, a crucial element in deciding to build a hotel is evaluating and ensuring sufficient demand for the rooms once built. When the private sector provides a significant portion of the financing needed to build a hotel, this is a good indication the demand is there. Public sector funding for urban development purposes is a common practice, but has not been as common for hotel development projects until recently, especially in Durham. Throughout this paper, I have provided reasons explaining the new growth in public-private partnerships funding of hotels specifically in Durham’s downtown, focusing on the motivations on behalf of the public provide these incentives, and addressing the potential concern for an oversupply of hotel rooms in the area. Although the success of these new development projects can not be properly assessed until hotel operations have been underway for a substantial amount of time, many other cities across the United States have provided significant public funding and financing for downtown hotel projects when faced with similar challenges — the lack of downtown hotel rooms. Nonetheless, the key factor to ensuring that the supply of these new hotel rooms is met relies heavily on the effective marketing strategies for each hotel. None of these new hotel development projects (aside from the Aloft Hotel) would have been feasible without the financial support of Durham’s city and council. Public sector involvement has been crucial in the revitalization of downtown Durham, and the development of these new hotel projects will further enhance the city’s goal for continued growth and a strengthening economy.
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Assessing the Economic Impact of Sports Facilities on Property Values: A Spatial Hedonic Approach By: Xia Feng and Brad R. Humphreys
I. Research Question
Brad Humphreys, Dennis Coates and many other urban economists have conducted research in the field of sports arenas and urban development. However, most research has focused on identifying and analyzing tangible, economic benefits of sports arenas on cities. Differentiating itself from prior research on the intangible benefits of sports arenas on cities, Xia Feng and Brad Humphreys’ paper proposes a spatial hedonic model that estimates the intangible benefits of two sports facilities in Columbus, Ohio on residential property values.
This discussion of the benefits of sports stadiums stems from the willingness of cities and towns to subsidize construction of expensive sports stadiums. As the rise in the size of these subsidies has coincided with the boom in the construction of new stadiums, urban economists conducted research on the costs and benefits of construction of new stadiums and arenas. Proponents of these subsidies posit income increases, job creation and multiplier effects (due to new spending) as tangible, positive impacts of building new sports stadiums. However, contrary to the aforementioned claims, made mostly by consulting firms (usually hired by the respective sports franchises), the findings from years of economic research have shown no positive impact of building new stadiums on cities. In fact, econometric evidence has shown that professional sports facilities can have little effect to net negative effects on the local economy.
Regardless of these well-respected and well-supported research projects, cities continue to subsidize the construction of sports stadiums. The continuation of this policy decision, which research finds in general to be neither cost-effective for cities nor beneficial to cities, forces consideration of intangible benefits. Few papers have empirically estimated the intangible benefits, such as the increased civic pride, increased city attractiveness or increased cultural benefits, of building sports stadiums. A couple papers have examined the impact of sports facilities on property values with varying results, and this study adds to the literature by providing new evidence based on data from different locations and different sports. Most importantly, this study does not ignore spatial effects. Spatial autocorrelation is the correlation among values of a single variable due to their close locational positions on a two-dimensional (2-D) surface. Spatial autocorrelation could have caused biased estimates and model misspecification in the few earlier models on the subject of stadium presence’s impact on housing prices
II. Theoretical Background
Because of the difficulty of measuring “intangible benefits or costs”, Feng and Humphreys assume that the presence of a stadium would be viewed as an intangible characteristic and the presence of a sports stadium would be capitalized in housing prices. Housing prices tend to be spatially correlated due to common neighborhood characteristics.
Feng and Humphreys use an adaptation of the spatial lag hedonic model:
(I − ρW y) −1 = I + ρW + ρ 2W2 + . . .
This model links each observation of the dependent variable to all observations of the explanatory variables through a spatial multiplier.
Using transactions data, containing observations on 9,504 single-family housing units, for the year 2000, Feng and Humphreys analyze the values of residential housing around Nationwide Arena and Crew Stadium in Columbus. The data set includes housing and neighborhood characteristics such as lot size, school quality, environmental quality and number of fireplaces.
To account for aspects of the model that were not incorporated into the adapted spatial lag hedonic model, certain modifications were made to the model. To account for the presence of Ohio Stadium, dummy variables were created. To control for the effects of businesses on housing values, Feng and Humphreys controlled for the number of commercial establishments in each zip code, which allowed the business-related variables to capture some of the effects of business location on residential property values.
III. Empirical Model
Known as a spatial weighting matrix, this symmetric matrix is used to define the locations for which the values of the random variables are correlated, and the rows in the weights matrix are standardized. The features of both housing markets and individual housing data make the definition of the spatial weights matrix W especially important. The aforementioned matrices specify “neighborhood sets”, and these neighborhood sets capture spatial interaction. Feng and Humphreys use GeoDa to specify the neighborhoods and to define the spatial weights matrix, and begin by using four different spatial weights to create the matrices. Next, Feng and Humphreys use the log-log form of the hedonic housing price with the appropriate spatial lags to best estimate the parameters.
IV. Results and Discussion
The results of the research of Feng and Humphreys suggest that the presence of sports facilities in Columbus have a significant positive distance-decaying effect on surrounding house values. For Nationwide Arena, at the average, all else equal, for each 1% decrease in the distance to the arena is associated with a 0.175% increase in the price of the average house. In dollar terms, a 1% decrease in distance from each house to the arena, on average, increases the price of an average house by $222. The primary variable used to evaluate the effects of sports facilities on surrounding housing values is the distance between each house and the sports facility, and analysis of this parameter shows that the presence of sports facilities has positive effects (though they diminish with distance) on housing values. Importantly, Feng and Humphreys also show that prior OLS models, which did not account for spatial autocorrelation, overestimated the distance parameters, and did not correct for heteroskedasticity when present.
This paper elevated the credibility of the larger economic argument by finding the general importance of factoring spatial autocorrelation into property value modeling. With regard to policy decisions, professional sports facilities generate intangible benefits in the local economy, and cities do have a rational economic argument to lodge in support of provision of subsidies to sports stadiums. While the costs of public support rarely exceed the cost of public funding for the stadiums directly, the subsequent rise in property values can set the foundation for more substantial growth in adjacent areas, and give the city’s business community the confidence necessary to invest. Feng and Humphreys offered a more precise method of analyzing costs and benefits, and show that there are positive effects (contrary to most research) of building sports facilities at least in this one example. This paper offers answers, and poses new questions. What other benefits can be discovered? How close can economists make it to quantifying the efficient subsidy level for stadiums and arenas?
 Humphreys, Brad & Feng, Xia. “Assessing the Economic Impact of Sports Facilities on Property Values: A Spatial Hedonic Approach.” LASE/NAASE Working Paper Series 8.12 (2008): 1-20. Web. 25 March 2015.
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/>.
A fascinating, age-old U.S. public policy question surrounds the relationship between educational outcomes and segregation (e.g., income, racial). Over the years, growing economic literature continues to add nuanced perspectives to the issue of the interaction between school finances and the makeup of local communities. Social science researchers emphasize the importance of studying residential segregation, whether by income or race-ethnic groups, because of potential neighborhood effects on the long run educational outlook for young students. Since public education in the U.S. is largely financed by local property taxes, there is a large disparity between funding for schools in different communities.
Thomas Nechyba, “School finance, spatial income segregation, and the nature of communities”:
Although many education policy workers focus almost solely on the impacts of disparate per pupil expenditures across schools, a large body of economic research shows strong evidence that there are broader factors affecting educational inequities, and vice versa. For instance, since public school systems are based in local districts, residential segregation by income is more likely to occur—and over time this greater residential segregation feeds even larger inequalities, thus leading to a relentless cycle. Differences in education quality are capitalized into housing prices (i.e., property values often further decline in poor areas with inadequate schools).
Nechyba (2003) questions why the contemporary educational inequalities discussion is so restricted to per pupil spending gaps; rather, he calls for a more general examination of different school finance institutions and their various equilibrium effects. Thus, the paper sets up a structural model representing a decentralized economy in which households select their place of residence, where to send kids for education, and the degree of support to provide public schools; the model includes the most causal potential factors leading to income segregation. Using observations from New Jersey districts, Nechyba (2003) adjusts the underlying structural parameters until his model simulates realistic features from the data. Then, holding these parameters constant, policy simulations can be conducted. The framework accounts for a couple main sources of residential segregation by income: 1) different neighborhoods are historically endowed with disparate housing quality and neighborhood characteristics; 2) places of residence affect a child’s school quality since public education systems require households to live within exogenously outlined boundaries. However, the inclusion of private schools into this model complicates matters because households that send their children to private educations care less about public school quality near their homes; alternatively, they would be even motivated to live in a subpar public school district with lower housing costs. Since private school households are often wealthier, this counterintuitive phenomenon pushes the local economy toward income desegregation.
Subsequently, Nechyba (2003) use the structural model to find that state financing of school districts indeed dampens residential income segregation in an area.  This effect is expected because school systems that are purely locally financed give wealthier households motivation to segregate by income and thus shape better schools. However, two less intuitive findings are that the existence of a private school market leads to considerable residential income desegregation, and that in the presence of private schools, the existence of public schools actually tends to lower residential segregation—even though, by themselves, public school systems are associated with higher spatial segregation. However, a notable caveat to this study is that the model equilibrium overestimates the movement of private school households into poorer neighborhoods simply because the structural model does not account for non-school characteristics of such communities.
Raquel Fernandez and Richard Rogerson, “Keeping people out: income distribution, zoning, and the quality of public education”:
Meanwhile, Fernandez and Rogerson (1993) investigate the effects of community zoning regulations on per pupil spending, particularly with respect to property taxes and the formation of communities with average income differences. The analysis relies on simulations using a two-community model, where each community determines tax rates by majority vote, and households can choose their place of residence. Without zoning, the equilibrium of this model results in a poor neighborhood with a low tax rate (i.e., low per pupil expenditure) and a wealthier community with a higher tax rate. The study found that the existence of zoning regulations, meaning that households have to purchase a minimum level of housing in order to live in a predetermined area, is associated with the wealthy community decreasing in size and becoming richer, while the poorer neighborhood grows larger. The increased exclusivity of the rich neighborhood causes the lowest income households of that neighborhood to enter a poorer one, thus raising average income in both areas. As a result, the poorer community sees greater per pupil spending, but the change in education quality across the two areas is ambiguous.
Sarah Reber, “School desegregation and educational attainment for blacks”:
Although the first two studies mentioned did not delve into racial segregation, it is important to gauge the relationship between segregation and educational attainment for certain race-ethnic groups. Reber (2007) looks into the effects of the desegregation process after the Supreme Court’s 1954 Brown v. Board of Education decision, specifically focusing on schools in Louisiana. Previous studies showed that the desegregation policy had two effects: 1) increasing black students’ exposure to white peers in school and 2) raising the level of federal and state funding such that the average spending in predominantly black schools increased. Given that desegregation essentially eliminated disparities in student-teacher ratios for black and white students within previously segregated districts, Reber (2007) sought to determine whether greater educational attainment by blacks following desegregation resulted more from greater exposure to white peers or increased funding.
The simplest specification in this paper is a univariate regression that looks for an association between a county’s initial share of black enrollment and change in educational attainment from before and after desegregation; in this model, the dependent variable of interest is mean attainment for 1970-1975, minus the average attainment for 1960-1965. Later, the paper considers metrics such as the 12th grade continuation rate and adds controls for other characteristics such as change in county’s employment. These regressions on high school grade continuation and graduation rates indicate that greater educational attainment increased more for black students in districts with higher rates of black enrollment following desegregation—thus implying that, following desegregation, increased funding played a larger role than higher exposure to white students in raising attainment.
Growing literature contribute to U.S. policies designed to match educational opportunities of students coming from vastly different racial and socio-economic backgrounds. Whether through investigating the means by which certain communities are able to make use of taxes and zoning as instruments to keep specific segments of the population out of a school district, or by evaluating the effect of private schooling on income segregation, valuable insights can be drawn from these types of analyses. A thorough look at the institutional set-up of education can reveal the role that segregation and various school finance mechanisms play in long-run inequality.
Raquel Fernandez and Richard Rogerson, 1997, “Keeping people out: income distribution, zoning, and the quality of public education,” International Economic Review 38(1): 23-42.
Sarah Reber, 2007, “School desegregation and educational attainment for blacks,” Cambridge, MA: NBER working paper 13193.
Thomas Nechyba, 2003. “School Finance, Spatial Income Segregation and the Nature of Communities,” Journal of Urban Economics 54(1), 61-88, July.
 Thomas Nechyba, School finance, spatial income segregation, and the nature of communities, 66: It is notable that although this analysis focuses on income segregation, it can apply to problems involving racial segregation as well, “only to the extent that such segregation is driven by income differences.”
 Nechyba, 65
 Nechyba, 74: “While it might be expected that state financing will lead to less segregation than local financing, the relatively small magnitude of this effect compared to the huge effect of private schools is surprising, as is the different effect of public schools in a world with and without private school markets.”
 Raquel Fernandez and Richard Rogerson, “Keeping people out: Income distribution, zoning, and the quality of public education,” 1
 Fernandez and Rogerson, 32
 Sarah J. Reber, “School desegregation and educational attainment for blacks,” 3
 Reber, 6: To prevent white flight, schools with larger proportions of black students received more funding to “level up to the levels previously experienced only in the white schools”
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.
In “Mortgage Lending in Chicago and Los Angeles: a paired-testing study of the pre-application process”, Ross et al. (2008) used paired testing to measure discrimination against African-American and Hispanic homebuyers in the mortgage lending process. Many studies have provided evidence that minority buyers are less likely to receive mortgage loans than white buyers and, if successful, receive less favorable loan amounts and terms. There is debate, however, on how much of this outcome can be attributed to discrimination. Due to differences in creditworthiness, it is not typically straightforward to isolate the effects of differences in racial and ethnic treatment. Most work done on the topic of race in lending has used HMDA data which does not contain many important lender and loan attributes such as credit history and lending ratios.
Using data from a recent paired test study of discrimination in lending, Ross et al. examine the effects of race and ethnicity on mortgage lending. Using paired testing, two individuals, one white and one minority, separately pose as homebuyers with equal qualifications for borrowing. Both members of the pair ask about the availability and terms for the same home mortgage loan. Since the two borrowers are constructed to be equal in every regard other than race or ethnicity, differences in the responses received by the two can provide direct evidence for differing treatment of minorities. It should be noted that this methodology will only focus on the first part of the lending process, the pre-application stage (which involves a loan officer that can observe the race of the applicant) rather than the approval stage (with an underwriter who typically does not).
Paired Testing Methodology
The study included approximately 250 paired tests of a representative sample of mortgage lenders in Los Angeles and Chicago. Testers posed as first-time homebuyers with limited assets making general requests for information from lenders about their mortgage loan options. The testers were given profiles that qualified them for loans targeted towards A- credit quality borrowers in their respective housing markets. Each tester was assigned sufficient income to purchase a median-priced home in the area (with a 30 year fixed-rate loan and 5% down payment) and randomly assigned one or two minor credit issues, mostly late payments. Each pair was given almost identical financial and household characteristics with the minority in the pair receiving slightly better qualifications. These pairs, it should be noted, were not permanent—a tester could be paired with multiple partners if more than one partner was available that also generally matched in gender, age, and appearance.
Table 1 below provides data on the lending institutions in the study. The study looked only at lenders that reported under the Home Mortgage Disclosure act, accepted at least 90 loan applications in 1998, and had reasonably located offices for a first-time homebuyer. 67 lenders in Los Angeles and 106 lenders in Chicago qualified under these criteria, and in order to draw a market representative sample, lenders were selected (with replacement) with a probability of selection based on loan volume. This provided 35 lenders for black-white testing in Los Angeles, 34 for Hispanic-Anglo in LA, and so on as indicated in the table.
The basic testing protocol involved five steps:
- Obtain an appointment – testers called to arrange in person visits with lenders
- Make the initial request – testers requested help in figuring out a price range of housing they could afford and an estimated loan amount that they would qualify for
- Exchange personal/financial information – testers provided all requested information on income, debts, assets, credit history, etc.
- Record information on recommendations – testers noted suggested home price range, estimated loan amount, and financing options recommended
- End the visit – testers thanked the lender and allowed them to suggest follow-up contact
The testers then completed a test report form which allowed the study to gather information on the following six questions:
- Did the testers receive the information they requested about loan amounts and house prices they could afford
- How much were testers told they could afford to borrow and/or buy?
- How many specific products were discussed with the tester?
- How much “coaching”, such as offers of advice on paying down debts, down payment assistance, or a prequalification letter, did testers receive to help them qualify for a loan?
- Did testers receive follow-up calls from lenders?
- Were testers encouraged to consider FHA loans as an option?
Statistical Analysis Methodology
The paired tests each generate a series of treatments t for the white and minority testers, designated as Wit and Mit respectively. An incidence measure i is derived by comparing the experiences of the two testers and classifying the test as majority favored, equal treatment, or minority favored. For loan amounts or house prices, a test is considered favored one way or the other if a tester receives an estimate that is 5% higher than their counterpart. Gross majority favored treatment is defined as the fraction of tests classified as majority favored, and likewise for gross minority favored treatment. The net measure of adverse treatment, Nt is then defined as
which is gross majority favored treatment minus gross minority favored treatment. Probability (Pr) in this case is solely a measure of sample frequency. Also, a severity measure for a treatment is defined as the difference in the treatment experienced by the two testers
where the expected value (E) is captured by the sample mean of the difference of the two series of treatments. These two measures, Nt and St, are commonly used estimates of systematic discrimination towards minorities. Statistical tests are performed on these two variables to determine if they differ significantly from zero using a two sided test. While it would be very unlikely to find unfavorable treatment for whites based on past studies, the authors decided to use the two-sided test as it was more conservative.
To address the potential issue of bias arising from using the normal distribution for small sample sizes, the authors use Fisher’s exact (permutation) tests, writing the null hypothesis for Nt as
For St the null hypothesis is
Table 5 below summarizes the patterns of findings. Significant differences between the white favored and minority favored are indicated, with * representing significance at the 5% level and ** for the 1% level. The last row of the table shows that in Chicago, Hispanics and blacks received significant differential treatment from whites in three and four of the six categories, respectively. For both minority groups in Chicago, this leads to a rejection of the null hypothesis of equal treatment for whites and minorities at the 0.01 level. In Los Angeles, the data taken as a whole is consistent with the null hypothesis of equal treatment.
In summary, the paper finds strong evidence of adverse treatment of Hispanics and blacks compared to whites in Chicago in the pre-application stages of the mortgage lending process. In the study, Hispanics were quoted lower loan amounts and house prices, were given less information about products, and received less coaching. African Americans were provided less information, received information about fewer products, received less coaching, and were less likely to experience follow-up contact. Los Angeles, on the other hand, showed no statistically significant differences in overall treatment of its white and minority borrowers. While minorities received worse treatment in some specific categories, this was not indicative of an overall pattern in LA.
Discriminatory treatment at this early stage in the mortgage lending process, though subtle,can have effects on the rest of the mortgage application. Minority homeseekers may be discouraged from applying for a mortgage due to their treatment by a lender, either abandoning their search completely or applying through the costlier subprime mortgage market instead. Also, loan officers provide more support and information to white applicants in certain circumstances which gives them a better chance of acceptance than a similarly qualified minority applicant.
Federal law, through the Equal Credit Opportunity Act (ECOA) and Fair Housing Act (FHA), forbids credit discrimination and real-estate related discrimination. The results from this study show that discrimination in these aspects is an unfortunate reality for minorities seeking home mortgage loans. Further study could be done on the reasons behind the different levels of discrimination found in Chicago and Los Angeles in the study. This research could then be used to help implement policies and effect change on a broader scale to help fight against unfair lending treatments and practices.
Stephen Ross, Margery Austin Turner, Erin Godfrey, and Robin Smith, 2008, “Mortgage lending in Chicago and Los Angeles: a paired-testing study of the pre-application process,” Journal of Urban Economics 63: 902-919.
Tables 3 and 4 below provide information on the proportions of each test that were favored for white or minority testers.
Durham Tour: January 18th, 2015
Cole Mill Road is a lengthy road in the northernmost section of Durham that branches off to numerous neighborhoods of various affluence levels. Neighborhoods along the road share the commonalities of a heavily wooded environment and expansive land space. However, there are some other aspects which highlight stark differences between neighborhoods. Stoneybrook for example (more on this subdivision in the next paragraph) is probably the nicest neighborhood I’ve seen in Durham. The neighborhood itself is flanked by a large golf course that looked surprisingly empty. It seemed more like a private golf course for Stoneybrook residents than anything else. On the other hand, the subdivision off of Jefferson Dr. has the look of a loose settlement in the mountains. The hilly area is dotted with wood cabin type establishments, but the houses virtually have no lawn as they are smothered by the dense forest. The houses also look worn and there are large amounts of trash lying around.
Stoneybrook Drive is a small road off Cole Mill Road which leads to very nice looking neighborhood. Having first observed the neighborhood on Jefferson Dr. off of Cole Mill Rd., I was not expecting to see anything too enticing in terms of housing. However, as we drive in to check out the real estate, the first thing we see is a flashy new BMW coming out of a driveway. The status of the car certainly did not outdo the status of the household from which it came. The uniquely constructed brick house was gated, had elaborate landscaping, and also spanned upon a rather large piece of land. The backyard was spacious and led right into the golf course. Further down the drive was a sign warning of a neighborhood security watch that would report any suspicious activity. This was one of two neighborhoods that I saw this sign. (The other is East Forest Hills Blvd.)There was another house that had two BMW’s in the driveway. The house that stood out the most is pictured below. Its appearance did not fit in with its surrounding at all. The house had modern architecture with a wild-west style. Pine trees and a western designed mailbox surrounded by cacti distinguished its landscaping. What stood out about this neighborhood was that every house seemed to have been uniquely designed by the owner as opposed to a cookie-cutter neighborhood. This aspect speaks to the upper class nature of the neighborhood as well as the relatively recent construction of the houses.
A modern, stylish house of the sort usually seen in southern California rather than in North Carolina.
Guess Road led the tour back into a commercial area nearer the central part of Durham. Guess Road is primarily lined with cheaper food, retail, and service options. I noticed that the street also exhibited greater diversity in commercial options than most other streets I’ve driven around in Durham. For example, almost half of the stores have Spanish in their names. There is also a Chinese dim sum restaurant (this kind of Chinese food is rare) nestled between a hot dog shack and a BBQ joint. The condition and aesthetic of the buildings/plazas matches the economic state of the area. The buildings appear old and worn, and while the road itself sees relatively busy traffic, the parking lots along the road are often barren.
Northgate Mall is old and a bit rundown, and it shows. A large Sears (a true mark of any ancient mall) towers in the forefront of the strip mall. The parking lot, on a sunny Saturday afternoon, is hardly a third filled. Both the exterior and interior of the mall clearly aren’t the most inviting commercial setups, and I feel that most people come to this mall for necessities rather than retail therapy. Inside the mall is a grand carousel that must have been a major highlight back when Northgate was built, but now it seems out of place. I noticed that many of the stores are either low end brands or small cheap shops set up by independent vendors. As the number of cars in the parking lot suggested, the traffic within the mall was eerily low. Also, some members of my group were walking around with fancy cameras, and we got stopped by mall police warning us that taking any photos of the mall property is strictly prohibited. I’ve read that Northgate Mall has received publicity for the wrong reasons in the past, and I wonder if the mall considers unnecessary documentation to be threatening.
Old North Durham neighborhood was a mixed bag of old and new and seemed like one of the areas undergoing a transition. We parked in the empty lot for the Grace Baptist Church which also housed the Durham Nativity School, a non-profit effort which serves to bring higher quality education to financially underprivileged boys in the area. In the backyard of this church was a shabby playground that looked to be falling apart. Surrounding this block, however, was a surprisingly eclectic array of large and fairly well established residences. Every house seemed to be painted a different color – from green to yellow to bright red. Each house also stood out because of its starkly contrasting architectural style from the rest of its neighbors. Interestingly, I did not notice very many vehicles at all in the driveways, and cars rarely passed through this area. There were a few pedestrians, and I watched one elderly lady board a DATA bus which had a stop right next to the Old North Durham Inn. Perhaps bus transportation is especially common in this area since it is so close to downtown Durham. The juxtaposition of the old church and playground within a finer residential area suggests that this neighborhood is in a state of transition, but traces of poverty are still evident.
Abandoned toys are strewn about on a grassless playground.
One of the unique establishments around the Old North Durham neighborhood.
East Main Street was by far the most rundown of all places I visited for this tour. The scenes along the entire route of North Alston Ave. departing from Old North Durham just seemed to become more and more entrenched in poverty the closer we got to East Main Street. At first, the houses just seemed old, then they had constructional damage, and finally entire units were boarded up and entire streets seemingly unoccupied. The neighborhoods around East Main St. are the ones that are currently undergoing some serious renovations. We passed by a house that is currently being rebuilt by Habitat for Humanity. If there are often houses here being rebuilt by Habitat for Humanity, then the renovation process for this area may be terribly slow and drawn out. It does not appear that there is any institutionalized reconstruction project in place, and some houses that are boarded up may have been empty for years.
We stopped along a small street that branched off East Main to get a closer look at these boarded up houses. I was surprised to note that on one side of the street were strictly unoccupied houses while the other side of the street had cars and looked perfectly occupied. This atmosphere of this area was quite strange, and although it was broad daylight, I had the urge to get back in the car quickly. My friend was in the process of taking a photo of one of the occupied houses when the homeowner actually opened the door to see what we were doing. Although my friend successfully made small talk with the woman, I could tell that she was a bit suspicious of our activities, rightfully so. Many of the boarded up buildings had “Private Property” or “No Trespassing” signs bolted into the front, but I was surprised to see that the woman’s house also had one such sign right next to her “Life is Short, Eat Cookies” sign. Usually buildings have these signs when they are owned by someone or some entity that is not personally there to monitor the property. It just seemed odd that an occupied building would have one too.
Most of the area around East Main Street is really empty which can certainly be attributed to the lack of suitable housing. The houses are small and bland and nothing about the housing structure stood out other than its poor state. There were often mixes of single unit buildings and town homes (3 units to a building) along a single street. Lawns were unkempt and strewn with tarps and other construction related trash. Other than residential spaces, we saw a learning center that offers vocational type training for those that perhaps struggle to achieve higher education. Adjacent to this learning center is a performance learning center for young students who are in danger of dropping out of school.
A house that has every door and window boarded up. The household across the street sees this sight every time they exit their home.
Another view of the same house.
East Pettigrew Street showed us a bit of the manufacturing side of Durham. Two sets of railroad tracks run parallel alongside the street. We passed by a number of large old manufacturing plants including Delta Gypsum and Holcim that looked to be producing cement. The area surrounding East Pettigrew is very expansive and underdeveloped, and this serves the ease of production and transportation well. Having these plants situated right next to the railroad also certainly aids the efficiency of the businesses. However, certain parts along the road suggest that manufacturing industries may be slowly phasing out of Durham, or at least in this area. There were junkyards with old trucks and cars, and some factory parts were rusting and falling apart, or all together abandoned. A gem we found on this tour is the Durham Green Flea Market which happened to be right on the side of East Pettigrew Street. Relative to its small area, the activity at this market was much more bustling than at Northgate Mall. Locals come to buy cheaper fresh produce, trinkets and even electronics from a predominantly Hispanic vendor base.
A plant along East Pettigrew that is falling apart and probably no longer in use.
Hayti is a small area that is more commercial than residential. A couple of large roads cut right through the district, and newer shopping plazas with small businesses and restaurants populate the space. The centerpiece of Hayti is the large and modernly architected Hayti Heritage Center, a locus of performance and gatherings for the historically African American community. Only a few cul-de-sacs surround the Heritage Center, and from what I saw, the majority of residents still seem to be African American. I stopped at an adjacent commercial plaza (one of the older plazas), but there was not a ton of variety in the shops. Four out of six shops were some form of barber shop or hair salon. The others were a flower shop and a Southern soul food restaurant. We opted to stop for a bite at the restaurant, and the chicken & waffles were every bit as good as Dame’s, but without the recognition, it happened to be much cheaper as well! Given the relatively modern look of Hayti, I would guess that Durham is trying to establish the area to become more commercially focused.
East Forest Hills Boulevard along with Stoneybrook are by far the two wealthiest residential areas we visited on the tour. This neighborhood is essentially nested within the Forest Hills Park. As the name implies, the park has numerous shallow rolling hills, and many houses are built at the top or into the side of these hills. Also due to the geography of the land, houses are scattered and spaced very generously apart from one another. Like Stoneybrook, each building is strikingly unique – a large Georgian brick house is neighbored by a quaint German style house. Since East Forest Hills runs right along the edge of the park, houses look right upon the dense green landscape, and residents have easy access to the park’s trails. Kids (the only time we saw children outside on this tour) played soccer on the open fields while joggers and bikers traversed the trails. Although the housing density is low, we saw many more people outdoors than in any other area of Durham. This characteristic attests to the safety, recreational value, and affluence of the East Forest Hills neighborhood.
A cozy well maintained German style house in East Forest Hills Boulevard. It’s hard to see, but in front is a sign that alerts outsiders that the house is security alarm protected.
North Carolina Central University is a compact urban campus distinguished by the blend of classic Georgian architecture and modern glass-heavy additions. The residential halls, which are not numerous, appear to have been recently renovated and look clean and comfortable. It is probable that the majority of students reside nearby the university or live in off campus housing. The landscaping was not remarkable given the time of year, but I could imagine that in the summer time, the campus would feel quite homely. We ventured into one of the science buildings, and both exterior and interior were similar to Duke’s own French Family Science Center. NCCU of course lacks the research and laboratory facilities that Duke possesses. A busy street (Fayetteville Rd.) cuts right through a segment of the campus, and I wonder if this presents a dangerous crossing at night. The streets around NCCU actually seem really similar to the neighborhoods off Duke’s East Campus. The small houses and apartment units are modest establishments and may serve as affordable off campus housing options for NCCU students.
Weaver Street runs along the southern part of Durham isolated away from the denser quarters of downtown much like Cole Mill Rd. is in the northern part of the city. Most of the neighborhoods along East Weaver streets had large 1-story homes with spacious lawns set against a wooded terrain. The houses weren’t fancy, but were structurally sound, and lawns were clean. This space was also particularly empty as we did not see any people outside nor did we pass by any cars on this street (the only street where we could drive at 5 mph without guilt). Continuing on to West Weaver Street, we saw a completely different housing landscape. For about a third mile up until the end of Weaver Street, there were perhaps 30 identical plain town homes, each housing at least 2 units. The layout of this area as well as the institutionalized appearance of this complex suggests that it may involve publicly subsidized housing. I saw clothes hanging out on drying lines, so perhaps the housing units are so basic that they do not even include laundry utilities. The complex also had its own recreational center and seemed to function as an independent community.
One of many identical town homes that populate West Weaver Street. The housing units are very simple, but appear clean and relatively new.