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The Effect of Public Transportation on Crime: An Analysis of Durham’s Bull City Connector

By Jack Willoughby   The Effect of Public Transportation on Crime

I. Introduction

In response to population growth, scarce natural resources, and the growing atmospheric problems caused by greenhouse gasses, many cities have sought to reduce the number of drivers on the road to limit both traffic and fossil fuel consumption. One theoretical means of reducing the number of cars on the road is to expand the use of public transportation. Such an expansion can have unintended negative effects, however, if it results in an increase in the prevalence of crime near the new transit stations or bus stops. The goals of this paper are to present a model of how public transportation affects crime, to demonstrate how theory has been confirmed or challenged by previous studies, and to use data related to Durham’s Bull City Connector to test the model. Analysis of available crime data indicates that the Bull City Connector has had no noticeable effect on the total amount or distribution of crime near its route.

II. Theoretical Model

As first presented by Ihlanfeldt (2003), the effect of public transportation on crime can be spatially modeled and decomposed. The expected value of a crime (π) to the person who is committing the crime is a function of the expected payoff of the crime (w), the cost of committing the crime (c), and the expected punishment of being caught, which is equal to the product of the probability of being caught (p) and the expected penalty conditional on being caught (f). The expected value of committing a crime in neighborhood H is modeled in (1):

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Furthermore, (c) can be decomposed into the accounting costs of committing a crime, (b), the transportation costs to the crime (tc), and the opportunity costs of committing a crime. Accounting costs of committing a crime include the cost of buying a weapon or tool needed for the crime, the cost of buying disposable black clothes or a mask, the cost of paying someone for help, etc. The opportunity costs of committing a crime are the forgone welfare that a criminal could have earned through legitimate activities plus the value of improved mental wellbeing from not being a criminal (g). These forgone earnings are equal to the income from legal employment (e) minus the transportation costs of traveling to a job (tj). The costs of committing a crime are modeled in (2):

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Aggregating (1) and (2), the net expected value of committing a crime is (3):

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Transportation costs can also be broken down into accounting and opportunity costs. The accounting costs are the sun of the monetary costs of transportation, (m), which include the gas needed to drive, depreciation of a motor vehicle, and public transportation fare. The opportunity costs are the time it takes to make the journey, which can be decomposed into the value of time (v) times the amount of time traveled, which equals distance (d) divided by speed (s). This relationship is modeled in (4):

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Subscript (i) indicates the trip being taken. As noted above, subscript (j) will indicate the trip to a job, and subscript (c) will indicate the trip to commit a crime. Incorporating this into (3) yields (5):

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To determine the effect of public transportation on crime, we need to take the partial derivative of (5) with respect to transportation (T) and drop all terms that are not affected by a change in public transportation. The result of this marginalization is modeled in (6):

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Next, we can decompose crimes into two types: crimes committed in outside neighborhoods (O) and crimes committed in the neighborhood in which the criminal is a resident (R). Adding these geographical distinctions as superscripts, we can revise (6) into (7):

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An expansion in public transportation that results in its increased use implies that the marginal effect of the expansion is to decrease the total transportation costs of its riders, but it does not indicate if the mechanism through which it decreases total costs is through monetary or time costs. With one decision (whether or not to use public transportation) and two decision factors (monetary costs and time costs), the relative contributions of effects of public transportation on speed and money are unidentifiable. Theoretically, however, it is conceivable that improvements public transportation decrease both time and monetary costs. Public transportation is often highly subsidized and therefore cheap, and in cities where traffic and parking are time consuming, it is likely also faster than alternative methods of transportation. Given that the addition of new public transportation is both faster and cheaper than previous methods of existing transportation, its marginal effect on crime would be threefold. The addition of public transportation would:

(A) Decrease the transportation costs of committing crimes in distant neighborhoods that contain public transportation stops;

(B) Increase the relative transportation cost of committing crimes in criminals’ own neighborhoods relative to remote neighborhoods; and

(C) Decrease the transportation costs associated with having a legitimate job.
Therefore, assuming that the new public transportation is cheaper and faster than previous modes of transportation, its three theoretical marginal effects should be to decrease crime in areas in which criminals live, increase crime near public transportation stops in neighborhoods not previously populated with criminals, and decrease total crime levels as legitimate employment becomes a more attractive substitute to crime. The latter two effects operate in competing directions, so while crime should certainly decrease in areas currently populated by criminals, it theoretically will change ambiguously in areas not currently populated by criminals, depending on the relative contributions of the effects on crime due to (A) and (C).

III. Previous Research

The theoretical predictions of the effect of public transportation on crime have been tested empirically with mixed results. Plano (1993) found no significant relationship between proximity to rail transit stations and crime using data from the opening of stations in the Baltimore Metro system. Block and Block (2000) found a significant positive relationship between proximity to subway stations and street robberies in both the Bronx borough of New York City and the Northeast Side of Chicago. Liggett et al. (2003) found that the Green Line light rail transit system in Los Angeles had no effect on the overall levels of crime and the spatial distribution of crime in Los Angeles. Ihlandfeldt (2003) analyzed Atlanta’s MARTA system to find that the addition of rail transit stations resulted in the increase of crime in the center city, but did not affect crime levels in the outer limits of the city. Denver Regional Transportation District (2006) analyzed crime patterns near stations of the Central Corridor light rail transit line in Denver and found no evidence of an increase in crime as a result of its inception. SANDAG (2009) studied the expansion of the Green Line transit system in San Diego and found that both crime rates and distribution of crime were unaffected by the expansion of the public transportation system, as well as that residents did not feel more or less safe as a result of the expansion. Taken together, these studies indicate variation in the effects of public transportation, suggesting that specific cities respond differently to its expansion. Additionally, the rare statistical significance suggests that the magnitude of the effect of public transportation on crime is likely small.

IV. Case Study: Durham’s Bull City Connector

A. Introduction
On August 16, 2010, the Bull City Connector (henceforth BCC) began operation in the city

of Durham, NC. In conjunction with Duke University, Durham launched the BCC to more seamlessly connect Duke and downtown Durham. During operating hours, the bus runs every 15-20 minutes, depending on the time of day and day of the week, and it is completely free to the public. It stops 34 times on its loop bounded by Duke’s Central Campus on one end and Durham’s Golden Belt District on the other, passing through Brightleaf Square and downtown Durham along the way. The full route is shown in Appendix 1. Using available data, the effect on crime of this exogenous improvement in public transportation will hereafter be investigated.

B. Data and Empirical Method
First, regions of interest were identified, the boundary maps of which are contained in

Appendix 2. The theoretical model establishes a differential effect on crime of public transportation in neighborhoods in which criminals live versus neighborhoods not inhabited by criminals, so neighborhoods of different home values were studied. The first area studied was “Trinity South,” which is comprised of the neighborhood bordered by N Buchanan Blvd on the west, Urban Ave on the north, N Duke St on the east, and W Main St on the south. This region is directly to the north of the BCC route. Using estimates from Zillow.com, most houses in the region are valued between $300-450K. A control for this region, which is comprised of houses of similar value but is not immediately adjacent to the BCC’s route, is the “Trinity North” neighborhood. This region is to the north of Trinity South, and is bordered by N Buchanan Blvd to the west, W Club Blvd to the north, Ruffin St to the east, and Green St to the south. Next, low wealth neighborhoods were identified, both near and removed from the BCC route. Adjacent to the BCC’s route is the “Holloway” Neighborhood, which is comprised the residential areas surrounding Holloway St bordered by N Roxboro St to the east and N Alston St to the west. Using Zillow.com estimates, a large share of these houses are valued between 50-150K. Of similar value are the houses immediately north of North Carolina Central University, in the “North NCCU” neighborhood. This neighborhood is isolated from the BCC by Rt. 147, and is comprised of the residential areas bordered by Fayetteville St to the west, Linwood Ave and Simmons St to the north, S Alston St to the east, and Dupree St to the south.

Crime data on these four regions from the Durham Police Department Crime Mapper were used in analysis. The Crime Mapper displays spatially identified crimes across Durham for every month from January 2010 to the present. For this analysis, the total number of crimes in each month from January to July in both 2010 and 2011 was recorded for each of the four areas described above. The data from 2010 illustrate crime before the inception of the BCC, while the data from 2011 describe crime immediately after its inception. The analysis was confined to this limited dataset because no information on crimes before 2010 is available on the Crime Mapper.

A difference-in-difference analysis was used to identify the effect of the BCC on crime. The first stage of the difference-in-difference was to subtract the number of crimes in a region in a given month in 2010 from its corresponding month in 2011. Months were compared to corresponding months throughout the analysis to control for any seasonal variation in the amount of crime in Durham. This first difference yields the change in crime from before to after the launch of the BCC in each of the four regions. Next, a second difference is needed to control for any city-wide changes in crime that were occurring in Durham independent of the inception of the BCC. To control for these exogenous changes, changes in crime in areas geographically removed from the BCC were subtracted from the areas of interest adjacent to the BCC route. Furthermore, to attempt to balance the city-wide changes in crime as well as possible, control areas contained houses of similar value to the areas of interest. In keeping neighborhood quality constant, the relationship between holistic changes in crime in Durham and quality of neighborhood will be roughly accounted for.

C. Results
The theoretical model developed above predicts that crime should certainly decrease in areas

currently populated by criminals, and it theoretically will change ambiguously in areas not currently populated by criminals, depending on the relative effects on transportation costs to a job vs. to commit a crime. The BCC meets the assumptions of the model since it is free, so the monetary costs of travel will certainly decrease, and it travels to areas in which parking is often time-consuming, so it plausibly increases the speed with which people can travel. Assuming that criminals are more likely to live in lower value homes, more criminals per capita will reside in the poorer areas than the wealthier areas. Therefore, the theoretical model predicts that crime will increase in the wealthier area adjacent to the BCC, as compared to the control wealthy area. It predicts ambiguous results in the poorer area. The difference-in-difference results are displayed in table 1:

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As displayed in the table, there is no noticeable relationship between crime and the introduction of the BCC. In wealthier residential areas, crime in close proximity to BCC increased more after the launch of the BCC than in areas farther from the BCC in 3 out of 7 months, and in poorer areas it increased in 4 out of 7 months. Furthermore, neither result is statistically significant from zero (p- values = 0.9282 and 0.2378, respectively). Therefore, the theoretical hypotheses of the inception of the BCC are not confirmed by analysis of the available data.

D. Discussion
The lack of observed relationship between the introduction of the BCC and crime rates in

nearby neighborhoods could be due to the small sample of data used in analysis or the reality that the BCC actually does not affect crime in Durham. First, data were only used for 4 regions over 14 total months. The unavailability of data from before January 2010 confined the amount of data accessible for use in the study, but if more data were available, a potentially more significant result could be deduced. More likely, however, is that the BCC does not actually affect crime in Durham. The bus system shuts off at 10pm on Monday-Thursday, and at midnight every other day of the week, and is therefore not operational in the hours of the night when crime may conceivably take place. Also, from end to end, the BCC route is only about 3.5 miles long, so it may not provide criminals access to areas that were previously inaccessible. Duke University and Medical Center are at one end of the route, and I would suspect that people considering working at these employers would find a decrease in transportation costs, which might decrease overall crime as criminals turn to legitimate employment. An unlikely but possible explanation is that both of the regions studied were not home to criminals, and as theory would predict the shift in the distribution of crime toward these areas caused by the BCC was washed out by the decrease in the total number of criminals as former-criminals shifted to legitimate employment. Finally, as Durham’s once-impoverished areas improve, there may not be enough of a discrepancy between the number of criminals residing in the wealthier and poorer areas to establish any differential effect of the addition of the BCC.

V. Conclusion and Extensions

Many public officials believe that expanding public transportation is a solution to the existing problems of air pollution and excessive automobile traffic that current transportation methods have precipitated. Residents of currently peaceful neighborhoods counter this sentiment by arguing that the expansion of public transportation will provide criminals access to their homes. Theoretically, expansion of public transportation that results in a decrease in transportation costs should result in the shift of crime from neighborhoods in which criminals live to other neighborhoods, and also a shift from crime to legitimate employment as the travel costs associated with having a job decrease. Empirically, this relationship has rarely been found in the past, either due to a flaw in its theoretical development or a lack of available data. To test the application to Durham, data from the introduction of the Bull City Connector were collected and analyzed, but no relationship was found between the proximity to the Bull City Connector and change in crime rates after its inception.

The consideration of how changes in a city affect crime is a subject that has not been extensively researched, largely because isolating spatial effects on crime requires unique natural experiments and geo-coded data. As a result, there are many extensions for future research, even in Durham. For example, how did the construction of the Durham Bulls Park affect surrounding crime? How will the construction of Durham’s first skyscraper affect crime? How has the gentrification of Durham affected the spatial distribution of crime? Before policy decisions are made in the future, government officials should consider how their actions might change the incentives that prompt criminals to commit crimes. Only after assessing the externalities on crime, among other factors, can one truly value the effect of a change in Durham.

Appendix 1: Bull City Connector Route (photo taken from Google Maps)

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Appendix 2: Areas of Data Collection (photos taken from Google Maps)

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Appendix 3: Data

Data are on the number of crimes committed in the corresponding area and time period. Crimes reported are arson, assault, burglary, homicide, larceny, motor vehicle theft, robbery, and rape. Regions are defined in Appendix 2.

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Block, R., & Block, C. “The Bronx and Chicago: Street robbery in the environs of rapid transit stations.” In V.

Goldsmith, P. McGuire, J. Mollenkopf, & T. Ross (Eds.), Analyzing crime patterns: Frontiers of practice. Thousand
Oaks, CA: SAGE Publications, Inc. (2000): 137-153.
Denver Regional Transportation District. “Technical Memorandum: Neighborhood vs. Station Crime Myths and Facts.”
Durham Police Department. “Crime Mapper Online Software.” Durham, 2010-2011. http://gisweb.durhamnc.gov/
GoTriangle. “Bull City Connector: First Year Report, August 2010-2011.” http://www.gotriangle.org/images/uploads/
Ihlanfeldt, Keith R. “Rail Transit and Neighborhood Crime: The Case of Atlanta, Georgia.” Southern Economic Journal 70.2
Liggett, Robin, Anastasia Loukaitou-Sideris, and Hiroyuki Iseki. “Journeys to crime: assessing the effects of a light rail
line on crime in the neighborhoods.” (2003).
Plano, Stephen L. “Transit-Generated Crime: Perception Versus Reality–A Sociogeographic Study Of Neighborhoods
Adjacent To Section B Of Baltimore Metro.” Transportation Research Record 1402 (1993).
SANDAG. “Understanding Transit’s Impact on Public Safety.” (2009).

Growth & Residential Integration Trends in Bull City


Durham is the fourth-largest city in North Carolina and the 83rd-largest in the United States by population, with 239,358 residents as of the 2012 United States Census. Known locally as “Bull City,” Durham currently serves as the home of an expanding community bolstered by the educational, research, healthcare, and business involvements of Duke University and Research Triangle Park. Since 2000, Durham’s individual city population grew an impressive 26.7 percent, or by more than double the national average. During the same time period, the population of the greater Raleigh-Durham metropolitan statistical area increased by a monumental 47.8 percent – the highest rate of all 52 metro areas in the U.S. with over 1 million residents. This rate exceeds more than three times the 12.7 percent average growth of those 52 metro regions. (Kotkin, 2013)

Several other cities, including Austin, Texas and Las Vegas, have also witnessed comparable population explosions surpassing 40 percent. Although these surges have been attributed chiefly to domestic migration and increases in foreign-born immigrant populaces, an interesting similarity identified among the Austin, Las Vegas, and the Raleigh-Durham metropolitan areas is a characteristically low population density relative to those of other metros. None of the ten fastest growing cities from 2010 to present have had urban core densities even half of those of cities like Boston, New York, or Los Angeles. Fast-growing regions such as Raleigh-Durham lack super high-density cores and instead possess populations that expand in a more spatially dispersed fashion than those of large concentrated metros (Kotkin, 2013). This pattern is observable in Figure 1, which compares Durham’s census tract population densities per square mile between the years 2000 and 2013.

The left map of Figure 1, which depicts population densities for the year 2000, illustrates a clearly identifiable main cluster in the geographic center of the city. The majority of the census tracts in this cluster are of analogous density and are located adjacent to one another. Additionally, these tracts are more heavily populated than those in the surrounding areas. The right map of Figure 1, which portrays population densities for the year 2013, shows two additional clusters that have emerged in the southern and western quadrants of Durham. These new clusters represent growth hotspots geographically displaced from the central urban core. Their appearance corroborates the theory that populations of swiftly evolving cities like Durham expand in a spatially dispersed manner.

Figure 1


Coloration ranges were determined using national quantiles. (Graphics generated using Simplymap.com; data obtained from the U.S. Census Bureau)

In conjunction with expansion patterns, housing affordability is an important factor to consider in analyzing quickly developing cities like Durham. Unlike New York and Los Angeles, many of the fastest-growing places in the U.S. boast significantly lower housing prices relative to their average incomes. In metropolitan regions such as Raleigh-Durham, housing cost as a percentage of income can be less than half of those of other more tightly regulated municipalities (Kotkin, 2013). In addition to its correlation with population gains, the affordability of housing generates a number of questions regarding residential diversity in the formation of cities. Specifically for Durham, how have low housing prices and the city’s robust population growth following the turn of the century affected its degree of racial segregation? Based on the inferences of Paul Courant’s urban housing search model and the trends visible in U.S. Census data, Durham’s level of racial integration has increased.

In his paper, titled “Racial Prejudice in a Search Model of the Urban Housing Market,” Paul Courant proposes a basic framework to explain the existence of housing segregation in cities. Courant’s simplified model makes several assumptions. First, the model assumes that a housing market is composed of only two sets of people with perfect information – whites and blacks. Second, it presupposes that some but not all whites have an inherent aversion to living with blacks. Third, it assumes that searching for housing is costly and that blacks’ searching costs are systematically affected by whites’ aversion to living in non-segregated communities. Following these assumptions, Courant’s model employs a utility maximization function to represent a black individual’s preferences at any stage of his housing search throughout n neighborhoods:

8(Courant, 1978, page 334)

Here, V represents the black searcher’s overall utility and V0 represents his utility from the best house visited up until the present. The parameter c denotes a constant search cost for both whites and blacks, while αj (assumed to vary across neighborhoods) denotes the probability that a black searcher will encounter an averse seller in a given neighborhood j. The function F(V0) is defined as the probability that a house’s utility will be below V0. Given these designations, each line of the maximization function below V0 is equal to the sum of the following:

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Where V*j represents the utility from the best house examined, a black searcher will browse for houses until the equilibrium condition is met. Assuming that preferences for housing types are the same across all neighborhoods, blacks will only search where the probability of encountering an averse seller is lowest, which would entail only searching in all black neighborhoods (α=0). Even if preferences for housing types vary, blacks will still prefer to search in neighborhoods with lower values of α because white seller aversion imposes greater search costs. This inclination to reduce discrimination-related search costs diminishes demand for housing in bigoted neighborhoods and increases demand for housing in predominantly black neighborhoods. Such shifts in demand create a price differential in which black neighborhood houses are purchased by black searchers at higher prices than those of houses located in white and integrated neighborhoods. (Courant, 1978)

In summary, Courant’s model posits that anti-black prejudice perpetuates segregation by creating higher search costs for blacks and disturbing competitive equilibria in urban housing markets. Such disturbances lead to inferior buying terms for black searchers in black neighborhoods, where price has been inflated due to boosted market demand; they also lead to inferior selling terms for suppliers of housing in bigoted neighborhoods, where price has been deflated due to suppressed market demand caused by reluctance to incur search costs. Courant’s model allows for a reversal of segregation once blacks begin purchasing homes in white neighborhoods. Immigration of blacks into a given white neighborhood induces the phenomenon of “tipping” by reducing the probability that other black searchers will encounter racist sellers there. As whites who are averse to living near blacks leave the neighborhood, black search costs further decline, total demand for housing increases, prices increase and provide bigoted whites incentive to sell, and other black searchers buy houses. Through the tipping cycle, Courant’s model accounts for the possibility of integration over time. (Courant, 1978)

Although Durham has had a long history of segregation as a city in the American South, recent demographics data provided by the United State Census Bureau indicate that the area’s level of racial integration has increased since the year 2000. These data are illustrated in Figure 2, which depicts the geospatial black-white racial composition of Durham in the years 2000 and 2010. In each outlined census tract, darker shades of color express higher percentages of households occupied by the target demographic and lighter shades express lower percentages of households occupied by the target demographic. Coloration categories are divided into five equal intervals, each spanning a 20 percent range. The blue color scheme corresponds to percentages of tract households occupied by blacks and the yellow color scheme corresponds to percentages of tract households occupied by whites. For example, a region shaded with the darkest blue communicates that between 80 and 100 percent of households in that specific area are inhabited by blacks. Furthermore, a census tract shaded with the lightest yellow communicates that between zero and 20 percent of households in that specific area are inhabited by whites.

In Figure 2, the top left map shows the percentage of households owned by blacks per tract for the year 2000, and the top right map shows the same data but for the year 2010.

Figure 2


Coloration categories are divided into five equal intervals, spanning 0% to 20%, 20% to 40%, 40% to 60%, etc.(Graphics generated using Simplymap.com; data obtained from the U.S. Census Bureau)

The top left map is marked by two solid tract clusters of the highest black percentage range and three clusters of the second highest range. Each cluster clearly contrasts with its surrounding tracts, most of which are shaded with the color for the second lowest range. In comparison, the top right map only contains one cluster of the highest black percentage range. Clusters of the second highest range still exist but they are smaller, more heterogeneous, and less contrastive with adjacent tracts. Also, a sizable swath of eastern areas have changed to the middle black percentage range. Moving downward, the bottom left map shows the percentage of households owned by whites per tract for 2000, and the bottom right map shows analogue data for 2010. The bottom left map generally reflects the inverse color gradient of the top left map, confirming the prevalence of black-white housing segregation in 2000. The bottom right map illustrates the persistence of  high white percentage tracts to the west but also shows the transition of eastern tracts to the middle white percentage range, which is mirrored by a similar black transition in the top right map. In graphical representations for both racial demographics, the visibility of extreme colors decreases from 2000 to 2010, signifying that the racial integration of residential housing in Durham has improved.

Evidence of greater integration achieved through Courant’s tipping cycle is revealed by additional census data detailing changes in the number of black and white housing units per housing value segments. Although the total numbers of houses owned by blacks and whites both increased by approximately 30 percent from 2000 to 2010, rates of change segmented by housing value vary by an enormous margin. Figure 3 displays percentage changes in the number of houses owned by blacks and whites grouped by housing value brackets. Growth in the number of houses owned per value segment is highlighted in green and decay is highlighted in red. Notably, the table reports that for all housing segments valued between $70,000 and $1,000,000, percent changes in units owned by blacks are unilaterally higher than percent changes in units owned by whites. In the $400,000 bracket, the rate of growth for blacks is even more than ten times that for whites.

Relatively larger percentage increases in the quantities of high-valued homes bought by blacks in Durham reflect increasing median income for blacks (see Figure 4). Since expensive homes are likely to be built in neighborhoods with houses of comparable value and whites currently occupy a majority of Durham’s expensive homes, a portion of affluent blacks who buy nice homes probably buy them in predominantly white neighborhoods. The immigration of a few affluent blacks into white neighborhoods, as suggested by superior percent change in the number of high-value homes owned by blacks, fits well within Courant’s tipping framework and explains how Durham has become a more integrated city since the year 2000. As the median income for blacks rose and more blacks began buying pricier homes in white neighborhoods, racist whites fled, black search costs declined, other blacks moved in, neighborhoods tipped, and Durham’s degree of integration increased over ten years.

Figure 3

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Figure 4


Durham’s growth over the past decades has been fierce and relentless, like a charging bull. With the city’s booming population and convenient housing costs, many people seeking a home already have much to love about Bull City. To make matters even better, residential segregation in Durham appears to be declining; using Paul Courant’s search model of urban housing markets to interpret observable trends in U.S. census data, one sees that the city’s residential communities have become increasingly integrated since the year 2000. Despite this positive trend, there are still many unanswered questions about how to best approach income and racial segregation in public environments, such as schools. Future research on such topics would be greatly beneficial to the lasting prosperity of Durham.


Courant, P. N. (1978). Racial prejudice in a search model of the urban housing market. Journal of Urban Economics5, 329-345.

Kotkin, J. (2013, March 18). America’s fastest-and slowest-growing cities. Forbes, Retrieved from http://www.forbes.com/sites/joelkotkin/2013/03/18/americas-fastest-and-slowest-growing-cities/

Simplymap.com; Census Data 2000 Geographies; Census Data 2010 Geographies. Retrieved from


U.S. Census Bureau; ACS_10_SF4_B25075; ACS_10_SF4_B25077; ACS_10_SF4_B992519; DEC_00_SF4_HCT066. Retrieved from



The Corner House and Relative Property Values: An Empirical Study in Durham’s Hope Valley

By Nathaniel Keating   The Corner House and Relative Property Values


   This paper analyzes the effect that the positioning of a single-family home on its block has on its open market price.  First, background information is presented and the arguments for and against owning a “corner house” are offered from a homebuyer’s perspectives.  The second part includes the results and discussion of an empirical study of homes in Durham’s Hope Valley neighborhood.  Drawing from data found at Zillow.com, a hedonic model for estimating property values was employed to determine the premium to which corner houses are valued with respect to houses located in the middle of the block.  This study found no evidence that corner homes in Hope Valley are priced any differently than homes located in the middle of the block.  Nevertheless paths for future research are recommended and outlined.


The determinants of the value of housing have long been studied and constitute a large portion of the literature published in journals of urban economics and real estate.  First and foremost, a house, as an asset, is a collection of various components, each of which add value to the whole.  These physical elements comprise the characteristics most often advertised on real estate websites such as lot size, living space, number of bedrooms, garage, etc.  Second, the value of a home is inextricably linked to overall market conditions, both within the region of sale by the law of supply and demand and nationally (or internationally) by interest rates and credit markets.  Finally, housing’s most unique feature as an asset, its positional permanence, profoundly affects its value and therefore, the price it might fetch on the open market.

Thus, the real estate mantra “location, location, location”, while almost laughably clichéd, does not lack for support from modern economic theory.  Studies have demonstrated that single-family homes with proximity to small neighborhood parks were valued as much as 13% higher, whereas those in close proximity to mobile home parks experienced negative effects on selling price, lending credence to the hackneyed expression.  However, the focus of much of this research has long been the value added to (or deducted from) a property due to nearby features and amenities.  This paper mitigates these effects as much as possible and adds controls for the physical differences among homes on the same street in order to examine the potential influence a home’s position on its block might have on its value.

A simple exploration into the value of corner houses—here defined as those homes built on lots at the intersection of two streets, and therefore with at least two sides exposed to the public view—yields a surprisingly deep fissure in consumer preferences.  Some Americans love the idea of owning a corner lot home, chiefly because of their generally larger lot size and accessibility and visibility from multiple sides.  On the other hand many claim they would never own a corner house, citing lack of privacy and additional maintenance and upkeep.  In addition, some states allow municipalities to assess double the taxes for two street “frontages”, though no inquiry into Durham’s policy regarding double assessment for corner lots was performed for this paper.  Realtors are also divided, some claiming corner lots are more likely to be burglarized, while others counter that a more easily visible house, like a corner home, is safer.  These contrasting views of alarming fervor and conviction provide an enticing opportunity to test Americans preferences empirically.

From an economic perspective, corner houses offer a notably distinct home ownership experience to a potential buyer.  Considering their uniqueness, homes on corner lots may fall within Haurin’s description of the atypicality effect.  Under this theory, exceptional houses will attract a higher variance in offers from potential buyers.  A homeowner’s rational reaction to a higher variance of offers is to leave the home on the market longer than the average house in order to attract a higher number of offers.  This effect was extended by Turnbull to include analysis of housing prices which the conclusion that an atypical home sells at a discount.  This follows intuitively considering atypical homes take longer to sell: controlling for time on the market, the atypical home will sell for less than a more average home.


            In this empirical study, data were gathered from Zillow.com, a leading online real estate database.  The data are accessible by the public and include the year each home was built, its lot size, square footage of living space, number of bedrooms and bathrooms, and each home’s Zestimate.  The Zestimate is an estimated market valuation for a home generated by Zillow’s formula with public and private information as inputs.  For the purposes of this study, the Zestimate will serve as a proxy for the value of each home.  The data set covers 32 homes selected from the Hope Valley neighborhood, and is subcategorized into 12 corner homes and 20 middle homes.  The homes were chosen at random using a random number generator from lists compiled of corner homes and middle homes in the appropriate area.  The means and standard deviations of the relevant variables can be found in Table 1 in the Appendix along with a map of Hope Valley.

The methodology undertaken involves a traditional hedonic pricing model implemented to isolate the corner house indicator variable’s effect.  This model assumes that the price, P, of a home—or Zestimate in this case—can be described as some function P = f(Xi), where Xi represents some combination of features of the home. Following convention, the semi-log form of housing price is used.  The equation used in evaluating the hedonic regression model is:

LogP­­­­i = bo + b1Bedi + b2Bathi + b3Sqfti + b4Corneri + ei

where Pi is the Zestimate and Bedi, Bathi, Sqfti, and Corneri  are respectively the number of bedrooms, number of bathrooms, square footage of living area, and an indicator variable equal to one if the house is on a corner and zero otherwise.  Also, the bi terms are parameters and ei  represents the error terms.  Each parameter bi represents the elasticity of housing price with respect to its associated variable.  Therefore,

 This model, while simple, includes some of the most powerful indicators of overall housing price to which the indicator variable Corneri was added.  In this way, the explanatory power of Corneri can be determined while controlling as best as possible for the most important, traditional variables.  In addition, it is worth noting that both the age of the home and the lot size were intentionally discarded as explanatory variables.  Further research into the Hope Valley region revealed that the neighborhood is split between very old and brand new homes with large variation in lot size.  Statistical testing revealed that both of these variables were surprisingly terrible at predicting home values for the randomly selected sample.


Table 2


The results of the hedonic pricing regression analysis are displayed above in Table 2.  The overall R2 of the regression was 0.7386 demonstrating that quite a large portion of the variation in price can be explained by variation in just a few variables.   However, a single variable regression conducting using price and square footage yields an R2 of 0.6782.  This would suggest that square footage is by far the best predictor of housing prices in Hope Valley.  Further study of table 2 reveals that square footage is the only variable with a significant t-statistic at the p < 0.01 level.  In fact, the t-statistic for the indicator variable Corneri is not significant at any reasonable p-level.  The model therefore provides no evidence that a corner home is treated any different on the market than a middle home.

            One such explanation for this is the nature of the Hope Valley neighborhood.  As one of the most secluded and affluent neighborhoods in Durham, fear of an increased chance of burglary and excessive noise and light from an additional side of the house are no longer issues to deter buyers.  Additionally, assuming a potential homebuyer in this affluent neighborhood would have a high annual income, such negatives as higher maintenance (e.g. more grass to cut, more gardening, more leaves to collect) might not exist for a homeowner that hires others for these tasks.  In this sense, perhaps the nature of the quiet neighborhood is masking what would normally be a more pronounced atypicality and therefore lower demand for corner homes.

Additionally, in Hope Valley there is much more space between homes than the average street in Durham, and the lot sizes are significantly larger as well.  In this way, perhaps the major benefits of owning a corner house in a more urban setting—larger lot size and less restricted positioning—might also be mitigated.  If so, the atypicality of corner homes would be further reduced to a negligible level.  It is therefore imperative that future research on this topic focus on a greater diversity of neighborhoods to determine the aggregate effect in Durham.



A hedonic pricing model was implemented to analyze the effect that the position of a house had on its price, specifically to determine if a corner house sells at a discount to its peers.  The results of the analysis produced the unexciting result that in Hope Valley no such considerations are taken, as there is no evidence that a house on a corner is priced any differently than a house in the middle of the block.

However, more research can certainly be done on this subject.  This study was severely limited in scope, collecting a very small sample size from only a single neighborhood in Durham.  Future attempts ought to include many hundreds more observations from a greater variety of cities and neighborhoods.  In addition, the hedonic model used included only a few explanatory variables.  Most notably, there was no control for the time each home was on the market before sale as estimates for housing prices were used.  In subsequent studies, greater care could be taken to determine a more robust model to predict housing prices, in particular a time-oriented analysis of sale prices.   Lastly, Zestimates were used as a proxy for housing prices.

The degree to which these estimates are accurate has been the subject of heated debate.  The consensus seems to be, however, that while most of the estimates are reasonably accurate (particularly given the lack of knowledge and inputs into Zillow’s proprietary formula), when the estimates are wrong, they can be extraordinarily incorrect.  If this is true, alternatives should be sought as use for housing prices.  Including these factors would certainly allow for more insightful discoveries and implications in the future.

Works Cited:

Espey, M., & Owusu-Edusei, K. (2001). Neighborhood parks and residential property values in

Greenville, South Carolina. Journal of Agricultural and Applied Economics33(3), 487-492.

 Haurin, D. (1988). The duration of marketing time of residential housing. Real Estate

Economics16(4), 396-410.

 Munneke, H. J., & Slawson, V. C. (1999). A housing price model with endogenous externality

location: A study of mobile home parks. The Journal of Real Estate Finance and Economics19(2), 113-131.

 Turnbull, G. K., Dombrow, J., & Sirmans, C. F. (2006). Big house, little house: relative size and

value. Real Estate Economics34(3), 439-456.


 Table 1


Map of Hope Valley Neighborhood





Homeownership in the Context of Durham’s Southside Revitalization: Why Purchase a Home in a Previously Undesirable Area?

By David Lillington   Homeownership in the Context of Durham’s Southside Revitalization


The American dream has been something of a national fixation rooted deep in our country’s mindset ever since the signing of the Declaration of Independence. The idea that one may go out and settle on his or her very own piece of land has fueled the fantasies of Americans and immigrants alike. The Wild West was built on this mantra, and America thrives today based on this expansionist history. Owning a home is still a dream in the hearts of many Americans. It is seen as a sort of gold medal in the course of one’s life; a lifetime achievement that proves they have “made it”.

One project, here in Durham, is attempting to provide some of its citizens with the opportunity to realize such a dream. The revitalization of the Southside neighborhood, a previously run-down community, plans to offer select Durhamites the chance to own their very own home in a brand- new development. Through various special mortgage packages, low- and moderate-income buyers will have the opportunity to purchase a home with some much-needed assistance. But is owning a home affordable for this demographic of residents? This paper will explore the benefits of homeownership and its affordability for qualified homebuyers within the context of Durham’s new Southside neighborhood.

Literature Review

The hallmark of homeownership is “creating the expectation of owning one’s home” (Shlay, 2006). This status sets owners apart from renters as the owner is obligated to “own” their home; that is, maintain and take responsibility for their property. Therefore the owner is taking on risk; but with risk can come reward. Such rewards might include various tax deductions and home appreciation. Anthropologist Constance Perin, cited in Shlay (2006), claims that homeownership is similar to citizenship. Just as a citizen pays taxes to live in his or her city, state, and country, the majority of homeowners will make payments to a bank through a mortgage. There is a sense of pride associated with such an accomplishment.

So how many people actually own a home in the United States? According to the 2013 U.S. Census Bureau News report (Callis and Kresin, 2014) the rate can be calculated as such,

Screen Shot 2014-04-09 at 12.40.16 AM


The document, published on January 31, 2014, reports that fourth quarter 2013 homeownership rates rest at 65.2%, down 0.2% from last year. Table 1, which is taken from this report, displays quarterly homeownership rates from Q1 1995 to Q4 2013. National homeownership rates appear to have reached a peak of 69.2% in Q2 and Q4 of 2004 and have been on a steady decline since then. According to Zillow.com’s local homes data (taken from the 2000 U.S. census), Durham has a much lower ownership rate at 49.0% (2014a). This is perhaps due to the younger population that lives in Durham. 65.3% of Durham’s population is aged 39 or younger (Zillow.com, 2014a; taken from 2000 U.S. Census), and only 36.8% of people in the U.S. under 35 own their home (Callis and Kresin,


Despite the fact that Durham’s homeownership rates are lower than the national average, it would behoove Durhamites to consider purchasing property. Shlay (2006) notes that there are a number of economic, social, political, and neighborhood benefits associated with owning a home. In the economic realm, homeownership leads to asset building, serves as an alternative form of investment, forces savings, and imposes what Shlay calls “created fixed housing costs” (2006). Asset building helps owners build equity in their homes; each time a mortgage payment is made, they own more and owe less. As an alternative form of investment, owners can expect a risk premium returned to them when selling their home, just as a 401K would pay interest rewarded in retirement. Forced savings is important in the sense that a portion of the owner’s income is automatically devoted to investment in a home. It ensures that money is not wasted. Finally, “created fixed housing costs” (in a fixed mortgage situation) allows owners some security in that their housing costs are predictable. Payments will not rise like rents may. Most owners are, however, responsible for their own repair costs (though HOAs may cover exterior repair and maintenance costs in some situations, such as if the repair is “communal”).

Shlay (2006) argues that homeownership brings about many social benefits as well. She cites studies by Rohe et al. (2002) and Rohe and Stegman (1994), both of which explain that “homeownership is believed to give people more control over their housing and, therefore, their lives”. In addition, other social advantages to owning a home include greater social stability, greater self-satisfaction, and greater voluntary civic participation (Shlay 2006). She also notes that if the household contains children, they will be less likely to experience juvenile delinquency and will feel physically and mentally healthier. Community involvement due to homeownership is a very important aspect in the Southside revitalization. This positive externality will help in creating a tight-knit community in a space that was otherwise dangerous and civically unengaged.

Political benefits from homeownership include decreased criminal activity, increased political participation, increased commitment to employment, and a larger tax base. Durham will benefit from new homeowners in Southside in this way. Futhermore, Shlay (2006) claims that neighborhood benefits seen as a result of homeownership may lead to higher property values, better care of property, more stability, less abandoned homes, and less signs of decline such as graffiti and litter. These issues were some of the same that plagued the Southside neighborhood noted in the Durham tour paper at the beginning of the semester. Many houses were boarded up, most had bars on their windows, and a broken umbrella was hanging from power lines. The Southside revitalization project will completely transform this area and will see most of these economic, social, political, and neighborhood benefits. With these benefits, however, comes a high cost. Will the new Southside area offer homeownership opportunities to low- and moderate-income Durhamites at an affordable price?


Quigley and Raphael (2004) claim that “public concern over the affordability of housing arises from two factors”. The first is that housing is most likely the most expensive item that an individual or family owns, equating to approximately a quarter of income devoted to annual housing payments. They note that low- and some moderate-income owners may devote half of their incomes to such expenditures. The second concern derives from the considerable increase in housing prices and rents in metropolitan areas. This creates greater barriers to entry for those who do not own a home and makes rent more unaffordable to those who rent.

The National Association of Realtors quantifies the affordability of homes in their Housing Affordability Index (HAI). As of January 2014, the HAI sits at 175.8 (2014a). This means that an individual or family at the median national income has 175.8% of the income needed to qualify for a purchase on a median-priced home. This assumes a 20% down payment. Figure 1 shows the performance of the index over a 10-year period from 2003 to 2013 (Hyman, 2014). There has been an overall increase in affordability of housing as evidenced in the chart. The National Association of

Realtors also provides data of the HAI by metropolitan area. Durham is reported as having a housing affordability score of 180.0 for 2013, a few points above the national index (2014b). It is safe to conclude that, according to the National Association of Realtors, housing is affordable in Durham.

The Southside revitalization project plans to make more housing affordable to the Durham community. Although it appears that housing is already affordable, the City of Durham and the North Carolina Housing Finance Agency (NCHFA) are offering down payment and mortgage assistance to those who may need more assistance (City of Durham, 2014). They state that individuals and families over the age of 18 may qualify if they have an income less than or equal to 80 percent of the area’s median income. For a family size of 1 the maximum household annual income is set at $37,950. A family of 2 may have a household income no more than $43,350; a family of 3 may have a household income no more than $48,750, and so on. A federal requirement dictates that at least 51% of homes sold in the new Southside community must be sold to buyers falling within these thresholds (City of Durham 2013). This will allow those who might otherwise not have the opportunity to own a home to take ownership in a brand new piece of property and enjoy some of the benefits of homeownership as discussed previously.

There exists a wide range of other benefits to owning a home particular to Durham’s Southside community. Builder B. Wallace (2014a) notes that the City of Durham has purchased lots in this area at very low prices. Therefore the lots on which new homes will be built will be sold to buyers at below-market value. In addition, the Southside community is just a couple blocks from downtown Durham and offers convenient access to gourmet dining and shopping experiences. Such amenities are valuable to many, especially younger buyers who might fall into Southside’s target audience. She claims that some similar homes in similar parts of Durham have been selling as much as $25,000 more than the $162,000 to $198,900 Southside prices (City of Durham, 2014). Very similar houses on Redfern Way, a cul-de-sac off Bivins St., built by builder B. Wallace, sold in 2012 for prices ranging from $176,000 to $203,000 (Zillow.com, 2014b). Lots were similar sizes as well. In fact, according to her website, builder B. Wallace is offering the exact same floor plans in the Southside neighborhood as she did in the Redfern Way cul-de-sac in 2012 (B. Wallace, 2012, 2014b). The area of Bivins St., though not as deprived as the Southside area, still had seen some rough times, as evidenced in the Durham tour paper. The area has picked up since then, and we can use Redfern Way and Bivins St. as an example of what to expect in the Southside area after construction has been completed. Figure 2, taken while doing research for the Durham tour paper, provides a visual of the Redfern Way cul-de-sac.

Furthermore, builder B. Wallace (2014a) provides information on the different loan packages offered for the new Southside community. Four opportunities are being offered to assist with down payment and mortgage costs. The first is from the City of Durham through a forgivable mortgage program. A $20,000 loan is given to qualifying buyers and is “forgiven” at a rate of 3.33% per annum. This means that the loan is essentially a grant; however the amount of principal owed decreases at this yearly rate. No interest is charged, but the remaining balance must be paid if the owner decides to sell before the loan has been completely forgiven. The second option is provided by the NCHFA and gives up to $18,000 in the form of a deferred mortgage loan (City of Durham, 2013). This money is loaned at 0% interest without any repayment until the house is sold. The third option offers a second mortgage program from the City of Durham (B. Wallace, 2014a). In this loan, the buyer takes out a second amortizing loan of up to $20,000 (different from the private bank loan to pay for the majority of the house). This loan charges a 2.00% interest rate and must be paid in 30 years. Finally, Duke University is offering $10,000 through the form of a forgivable loan to 10 of its employees.

Data Analysis

How affordable will new Southside homes be to qualifying buyers after these incentives? Using information provided by B. Wallace (2014a) and the City of Durham (2013, 2014), this paper presents various mortgage scenarios created in Microsoft Excel to calculate how much monthly mortgage payments would be for a basic new home in the Southside community. We use the most affordable housing option at $162,000 and subtract from it the City of Durham forgivable loan of $20,000 and the deferred NCHFA loan of $18,000. This leaves $124,000 to be borrowed. The City of Durham also offers a loan of up to $20,000 in the form of a 2% fixed 30-year loan which is at more than half the current interest rate. Separating this loan from the remaining balance, we are left with $104,000 to be borrowed at the federal interest rate of 4.50% for 30 years (Bankrate.com, 2014). We leave out the subsidy provided by Duke since it does not apply to all buyers. In Excel we set up the spreadsheet to include the principal of $104,000 to be loaned to the buyer at a rate of 4.50% over a period of 30 years or 360 monthly payments. Using the Excel formula,

=PPMT(rate, per, nper, pv),

we are able to calculate the monthly principal payment for this particular loan where rate stands for interest rate divided over a period of 12 months, per stands for the particular period (in this case month 1), nper is the total number of periods of the loan (360), and pv is the present value of the loan ($104,000). We arrive at a principal payment in month 1 of $136.95. A similar calculation is done to

calculate monthly interest payment. Using the formula,

=IPMT(rate, per, nper, pv),

Excel calculates month 1’s interest payment to be $390.00. This adds up to a total payment of $526.95 per month. The same method is used to calculate the City of Durham’s 2% amortized 30- year loan of $20,000. The monthly payment for this particular loan is calculated to be $73.92. Adding these together, we get a total monthly payment of $600.88. This, however, does not include association fees of $20.00 per month as well as taxes and insurance estimated to be $250.00 per month (B. Wallace, 2014a). With these estimates, total monthly homeownership costs sum to be $870.88 per month.

To see the positive effects of these subsidies, payment schedules were calculated without any sort of incentives to act as a comparison to the first scenario. One schedule was calculated assuming a standard 20% down payment on the house, or $32,400. The other schedule, though unrealistic, presents a scenario in which there is no down payment; all of the money is borrowed from a lender. The subsidized scenario is much like this, as the NCHFA and City of Durham are helping the buyer with the down payment. With 20% down, homeowners would pay a monthly payment of $656.66 a month; a difference of only $55.78 more per month. This may seem to be small, however when purchasing the house, a down payment of $32,400 had to be made. This amount of money may equate or come close to equating the yearly salary of a qualified buyer for the Southside project. It is unlikely that one would have this sort of money disposable if they qualified for subsidies. If one were to eliminate the down payment completely, monthly payments would rise to $820.83 per month, not even including HOA fees, taxes, and insurance payments. This is a difference of $219.95. This translates into savings made through the subsidies offered by the NCHFA and the City of Durham. The only cost not included in the subsidized scenario is the repayment of the $18,000 NCHFA deferred loan upon the sale of the house. When adding this back in to the total amount paid by the owner plus a required contribution of $500, we get a total loan cost of $234,815.58. Compared to the other scenarios at $268,799.10 and $295.498.87 respectively, the subsidies offer qualified buyers the opportunity to take advantage of owning a home at a much more affordable price.

In addition, subsidized payments plus taxes and HOA fees amount to 27.5% of the maximum income allowed for one individual: [(870.88*12)/37950]*100 = 27.5%. This is near the 25% target of portion of income devoted to homeownership expenses as discussed by Quigley and Raphael (2004). Subsidized payments plus HOA fees, taxes, and insurance also amount to nearly three-quarters of the rent required to occupy the same home. According to Zillow.com (2014b), a similar house valued in the neighborhood at $167,000 would rent for $1,200 per month. Therefore, it is safe to conclude that purchasing a house and taking advantage of the subsidies offered in Southside proves to be a financially wise and affordable option.


This paper explores the affordability of new homes in Durham’s revitalizing Southside neighborhood. Through various subsidies offered by the City of Durham and the North Carolina Housing Finance Agency, people who would otherwise struggle to find the capital needed to purchase a home may take advantage of an opportunity to do so. This will leave them with many of the positive benefits associated with being a homeowner, as discussed by Shlay (2006). It will also allow select Durhamites a cheaper, smarter alternative to renting in their beloved city. For $870 per month qualified people might have the chance to start owning a home. Southside will surely see a welcomed, dramatic transformation after construction is completed, and both new owners and the City of Durham will see many positive benefits as a result of these new properties.

Works Cited
B. Wallace, 2014a, “Southside: Why investing in a new home in Southside is a great idea and how to do it”. B.

Wallace. http://bwallacebuilt.com/files/2014/03/SouthsidePresentationV2forPDF.pdf B. Wallace, 2014b, “Southside-Downtown: Property Description” B. Wallace.


B. Wallace, 2012, “Redfern Way: Property Description” B. Wallace. http://bwallacebuilt.com/communities/redfern-way-2/

Bankrate.com, 2014, “Current Mortgage Interest Rates” Bankrate.com. http://www.bankrate.com/finance/mortgages/current-interest-rates.aspx

Callis and Kresin, 2014, “Residential Vacancies and Homeownership in the Fourth Quarter 2013” U.S. Census Bureau News: 1-12.

City of Durham, 2013, “Southside Homebuyer Update, July 3, 2013” Durhamnc.gov, City of Durham Community Development Department. http://durhamnc.gov/ich/cb/cdd/Documents/SSBuyerUpdate.pdf

City of Durham, 2014, “Homeownership” Durhamnc.gov. http://durhamnc.gov/ich/cb/cdd/Pages/FTHB.aspx

Hyman, Michael, 2014, “NAR Affordability Index: Composite, Fixed + ARM” Economists’ Outlook Blog. http://economistsoutlook.blogs.realtor.org/2013/12/18/latest-housing-affordability-data-4/

John Quigley and Stephen Raphael, 2004, “Is housing unaffordable? Why isn’t it more affordable?” Journal of Economic Perspectives 18(1): 129-152.

National Association of Realtors, 2014a, “Housing Affordability Index” Realtor.org. http://www.realtor.org/sites/default/files/reports/2014/embargoes/hai-01-2014/hai-01-2014-housing- affordability-index-03-14-2014.pdf

National Association of Realtors, 2014b, “Affordability Index of Existing Single-Family Homes for Metropolitan Areas” Realtor.org. http://www.realtor.org/sites/default/files/reports/2014/embargoes/metro-affordability-2013-existing- single-family-2014-02-11.pdf

Shlay, Anne B., 2006, “Low-Income Homeownership: American Dream or Delusion?” Urban Studies 43(3): 511-531.

Zillow.com, 2014a, “Durham Local Information” Zillow.com. http://www.zillow.com/local-info/NC- Durham/r_24457/

Zillow.com, 2014b, “1008 Redfern Way, Durham, NC 27707” Zillow.com. http://www.zillow.com/homedetails/1008-Redfern-Way-Durham-NC-27707/114315049_zpid/

Tables and Figures 

Table 1 

Screen Shot 2014-04-09 at 12.44.04 AM


Table 2

See attached Excel file Lillington David_Southside+Mortgage+Scenarios

Figure 1 

Screen Shot 2014-04-09 at 12.45.31 AM

Blog. http://economistsoutlook.blogs.realtor.org/2013/12/18/latest-housing-affordability-data-4/

Figure 2 – B. Wallace’s 2012 Redfern Way Community

Screen Shot 2014-04-09 at 12.46.46 AM

Source: Author’s own

Northgate Mall’s Effect on Surrounding Property Values

By James Seago   Northgate Mall’s Effect on Surrounding Property Values

I. Introduction & Motivation

Over the course of the last few decades economists and scholars have produced a significant amount of research on the various factors influencing the value of residential properties. Major determinants of property values include the physical characteristics of a property, the environmental and amenity attributes, the financial conditions of the sale and, most importantly, the location of a property. Homebuyers will consider many different facets of a property when determining the price they’d be willing to pay for their new home. The number of bathrooms, number of bedrooms, size of the lot, square footage of the property and additional amenities are all essential components that factor into their decision. However, as the saying goes, only three things matter in the real estate industry: location, location, location. This is the homebuyer’s most important decision.

The effects of certain locational determinants, such as proximity to public transport, highways, schools and churches, on the value of property are well documented. However, despite the acknowledgement of the role that nearby commercial land uses have on pricing homes, current studies and literature on the topic suggest that there is a clear lack of consensus on whether the externalities created by commercial land developments negatively or positively influence surrounding property values. Shopping centers, in particular, represent a very unique influence in the impact they have on housing prices. The benefits associated with close proximity to a variety of retail stores and restaurants, are arguably offset by increased levels of traffic, noise pollution and crime. These attributes have led to many economists and real estate professionals debating the true bearing that shopping centers can have on local neighborhood housing prices.

In this paper I will introduce and examine my findings on how the prices of residential properties are affected by their distance from Northgate Mall. Through my research I hope to provide a broader understanding of the direct influence a neighborhood shopping center can have on property values in the surrounding area. The conclusion of this paper will then aim to compare these results to those of similar studies that have previously been conducted.

II. Literature Review

Despite the large quantity of studies and literature that cover the effect of commercial land use on the value of neighboring properties, there is a distinct division of opinion on the conclusions that have been made. Stull (1995), for example, found a quadratic relationship between home values and the amount of commercial development in an overall residential area. The studies’ results suggested that small quantities of commercial development in a local area would have a positive impact on home values. However, once commercial development exceeded 5% of the total neighborhood land, property prices would begin to experience a substantial decline. A more recent study by Song and Knaap (2004) drew similar conclusions, showing that commercial development had no negative effect on the property values that they had assessed. The findings showed that housing prices increased as their distance from neighborhood commercial land uses shortened. Furthermore, homeowners that lived within walking distance from the commercial development were likely to pay an additional premium due to improved accessibility. Despite these results, the paper does conclude that the size of a particular commercial development can have powerful effects on neighboring home values and that larger commercial developments are more likely to create a negative impact.

On the other hand, a study by Grether and Mieszkowski (1978) indicated that there is a small positive correlation between housing prices and distance from industrial activity and public housing zones. The findings are based on a hedonic pricing model that is used to evaluate the effect that proximity to industrial land uses has upon home values.

The aforementioned empirical research exhibits differing results on the impact of commercial developments on property prices because in these cases, and in many others, the research has failed to recognize the tremendously localized character of the effect. Colwell, Gujral and Coley (1985) showed, in their comparison of how house prices in a neighborhood changed from before and after the introduction of the local shopping center, that property values within 1500 feet of the development decreased as proximity increased. However, after this critical distance was reached houses displayed an increase in value the closer they were situated to shopping and other development amenities. Li and Brown (1980) also explored the idea that commercial developments can have both positive and negative consequences on the surrounding region. The study assessed both the potential negative effects generated on housing prices, as a result of the aesthetics and noise pollution created by the commercial development, and also the positive influence of “accessibility” to the shopping center. Closer proximity to the industrial land improves access to shopping, various developmental amenities and to work places for homeowners. The empirical research that they conducted suggested that homes within 1760 feet (one third of a mile) of the commercial development diminish in value the closer they are to the development site. Similar to the studies produced by Colwell, Gujral and Coley (1985) once this 1760 feet threshold is passed, residential homes begin to command higher prices the closer they are to the development. The conclusions of the paper add that the positive impact created by the “accessibility effect” outweighs the effects that are realized by the negative externalities.

Aydin, Crawford and Smith (2011) sought to expand on these findings and applied them to a large commercial development in the Town Center Improvement District in Montgomery County. The primary goal of their research was to assess the negative externalities incurred by residential properties that would be generated through “Commercial Development Spillover”. Once again the results from this study, despite a much larger commercial development and a distinctly different location, shared many commonalities. The research demonstrated that any negative impacts that were generated from commercial developments were limited to areas of very close proximity. The increased size of the commercial property, compared to those from the aforementioned literature, resulted in an increased radius of negative spillover effects (approximately one mile past the TCID’s boundary). However, this increased size also led to a far greater rise in positive externalities past this point and thus these positive externalities far outweighed the negative.

The objective of this paper will be to assess whether similar patterns arise in areas surrounding Northgate Mall through the use of a hedonic regression model that shares many characteristics to the model implemented by Aydin, Crawford and Smith (2011).

III. Data  & Methodology

This paper utilizes a hedonic regression model to interpret the effect of Northgate Mall on the value of nearby housing. Hedonic regression provides the most apt method to statistically estimate the relationship between the market value of a property and the property’s characteristics, including distance from the shopping center. The model was comprised of the following features that play the most significant role in determining the value of a property: Lot area (Loti), Square Footage of property (SFi), number of bedrooms (Bedi), number of bathrooms (Bathi) and the distance from Northgate Mall is split up into five segments (see appendix) that are represented by five dummy variables in the model (D1i, D2i… D5i).

The paper categorizes 250 different houses that have been sold since 2012 into 5 different distance segments.1 These segments represent a portion of land that is situated within certain radius (0.5 miles, 1.0 miles, 2.0 miles, 3.0 miles and 4.0 miles) from the shopping center. The data for these different property characteristics were accumulated from Zillow.com2 and individually collected based on whether homes were placed within the required segment. A sample size of 50 different houses was chosen from each geographic segment to represent the overall population of houses from within that area. Collection of data ensured that a minimal of 12 different properties were selected from each the North, South, East and West to account for any anomalous results or other confounding factors that could influence housing prices. For example segment (D3) contained properties in the Southeast that are in close vicinity to Brightleaf Square and 9th Street. Once the data was successfully collected OLS regressions were run with the log of housing price as the response variable. The three models that were created are shown below. The first model is the most standard model, the second seeks to improve the fit of the model with the incorporation of square footage squared and the third assesses the size of the lot squared:

Screen Shot 2014-04-09 at 12.34.35 AM

IV. Empirical Review

The results of the regression are shown in Table 1 (see appendix). The coefficients of the number of bedrooms, numbers of bathrooms, housing size and lot size are predictably all positive. Irrespective of a property’s distance from Northgate Mall one would anticipate that an increase in any of the aforementioned factors would lead to a positive rise in the value of home. Houses that were situated within a 0.5-mile radius of the shopping center appeared to suffer from negative externalities as predicted in our hypothesis. The coefficient displayed by D1 under the first model was -.05, which indicates that houses suffer a small drop in prices when within a 0.5-mile radius from Northgate Mall. One limitation of the model is the small sample size that has been selected to represent a much wider set of homes and this is a potential explanation for the low t-statistic demonstrated by D1.

The most interesting findings from running these models is that this data has shown almost identical patterns to the results shown by both Aydin and Crowel. D2 (segment of 1.0-mile radius from Northgate) has the strongest positive coefficient of all variables from the model. A coefficient of 0.25 and strong t-statistic (3.04) suggests that properties within this area experience a strong increase in price as a consequence of their distance from the commercial development. The coefficients of D3 and D4, 0.155 and 0.002 respectively, further give credence to the results produced by Aydin and Crowel. The coefficients demonstrate that house prices in both areas are still positively impacted by their relative proximity to Northgate Mall, however once the critical point is reached (in our case 0.5-mile radius) property prices begin to fall the further one moves away from the commercial development.

V. Conclusions

There are several limitations that constrict the true power of the results provided by the research done in this paper. With more time the empirical research would have ideally included a much wider array of datasets and would have attempted to differentiate certain house prices based on other confounding factors such as additional areas of influence.

Despite the limitations of the model the results of this research provide further backing for the proposed theory that negative externalities of commercial developments are realized in a very localized surrounding area. Once this area is passed properties experience positive changes to their prices and their values increase the closer they are to, in this case, the shopping center. The fact that these results are similar across a diverse range of commercial developments and different cities suggests that this theory has very powerful implications.

VI. Works Cited

Aydin, Recai, Evert Crawford, and Barton A. Smith. “Commercial Development Spillover Effects Upon Residential Values.” Southwestern Economic Review 37 (2011): 47–62.

Colwell, Peter, Surinder Gurjral and Christopher A. Coley. 1985. “The Impact of a Shopping Center on the Value of Surrounding Properties.” Real Estate Issues 10 (1): 35-39

Grether, David M. and Peter Mieszkowski. 1980. “The Effects of Non-residential Land Uses on the Prices of Adjacent Housing: Some Estimates of Proximity Effects.” Journal of Urban Economics 8 (1): 1-15.

Li, Mingche M. and James H. Brown. 1980. “Micro-Neighborhood Externalities and Hedonic Housing Prices.” Land Economics 56 (2): 125-141.

Song, Yan and Gerrit-Jan Knaap. 2004. “Measuring the Effects of Mixed Land Uses on Housing Values.” Regional Science and Urban Economics 34 (6): 663-680.

Stull, William. 1975. “Community Environment, Zoning, and the Market Value of Single- Family Homes.” The Journal of Law and Economics 18 (2): 535-557.

Appendix I 

Table 1

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Appendix II 

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            Mobile home units and trailers are often perceived as ugly in today’s society, and the people who live there have been accused of not paying their fair share of taxes. There are also many negative stereotypes associated with being poor, such as higher crime rates, but there is very little research on mobile home communities and virtually no research on crime within mobile home communities. This research explores the relationship between crime and the existence of mobile home communities in the city of Durham, North Carolina. Through the use of Geographical Information Systems (GIS) and publically available crime data, I provide an outline of the crime rates in select mobile home communities in Durham and explain why it appears that incidents of crime are smaller in them.

Crime near mobile home communities appears to be much lower than crime near non-mobile home communities in Durham, NC. Taking into account the fact that mobile home communities tend to be located in the outskirts of Durham, it may be the case that mobile home communities have less crime because crime tends to be focused in the center of the city. This means that if a mobile home community which currently has less crime were to be relocated to the center of the city, its crime rate might increase to match or even exceed the area around it. Further economic analysis in a different location is necessary to determine whether crime rate is lower in mobile home communities, because there are no centrally located mobile home communities in Durham, NC.


A mobile home community can be defined as a multiunit community of prefabricated housing units. This places them somewhere in between apartment buildings and traditional single-family homes (Becker). Mobile homes began to appear in the United States in the 1920’s and 1930’s and became more popular when the government installed them during World War II for the use of temporary employees. After World War II, the typical resident of a mobile home unit shifted from temporary mobile workers to younger, less educated individuals. Today, there are at least 8.8 million mobile homes in the United States. This accounts for 8.4% of all housing units (McCarty).

There are many negative stereotypes associated with living in a mobile home park that are similar to those associated with being poor, but 8.8 million families still choose to live in one. The main benefit to living in a mobile home park is that it provides an affordable housing option. They are lower cost with more square footage, and according to the 2011 American Housing Survey (AHS), 25% fewer mobile home owners have a mortgage than do traditional homeowners. Since the median income of a mobile home owner is $30,000 less than a traditional homeowner and they have fewer mortgages, it seems likely that mobile homeowners finance the purchase of a mobile home through personal property or chattel loans (Becker). This means that mobile home units are cheaper and can be financed in different ways than the traditional home. Some of the other benefits for the mobile homeowner include the ability to relocate, the feeling of homeownership that is not felt with renting an apartment, and the community which the mobile home is located in (although, as with any form of housing, this could be a negative depending on the community).

The stereotype associated with mobile home parks that I am going to examine is that they are associated with higher crime rates. There is an abundance of research that affirms a statistical correlation between crime and poverty (Blau, Kelly, and Ludwig to name a few), but hardly any on mobile home parks. Since mobile home parks are more affordable and tend to house individuals with a lower median income, the logic follows that there is or may also be a statistical correlation between crime and mobile home parks. It is possible however, that mobile home communities have lower or comparable incidents of crime. Perhaps individuals in mobile home communities are different (have different beliefs or personalities) than those of similar socio-economic status who do not live in a mobile home community; or that mobile home parks create a community that leads to residents being more familiar with each other and are therefore more likely to report and deter suspicious activity on behalf of their neighbors. It could also be that there are fewer entrances into a mobile home community and that this deters criminals.

Little academic research exists regarding criminal activity with relation to mobile home parks, and though it may be difficult to understand the amount of safety benefit or dis-benefit of living in or near a mobile home community, it is beneficial to look at recent criminal activity in Durham to better understand the situation of crime in Durham mobile home communities.


According to City Data, both violent crime (murder, rape, robbery, and aggravated assault) and property crime (burglary, larceny, and motor vehicle theft) in Durham have steadily decreased from 2002-2012. Furthermore, The Herald Sun reports a 20% decrease in crime from 2013-2014. The City Data statistics can be found in figure 1 in the appendix. Even though property crime accounts for the majority of all reported crime in Durham at about 80% (shown for data in 2012 in a pie chart as figure 2 in the appendix), property crime is at its lowest level in 2012 and since 2002 has decreased by 44%. This is mainly due to the decrease in larceny and burglary and can be seen in the statistics reported in the appendix in figure 1.

I will be analyzing the number of crimes within a one mile radius of mobile home communities and include a map of where crimes have occurred in the past two years. On this map, I will highlight the locations of mobile home communities to provide a visual that will help determine whether more crime occurs near mobile home communities in Durham, NC.



            Using Google Maps, I began by identifying 13 mobile home communities in Durham. I was able to find mobile home communities from four different zip codes: 27703, 27704, 27705, and 27707. The majority of mobile home communities were located in the northwest region of Durham in the zip code 27705 and therefore had overlapping sets of data. I used a random number generator to select half of them for analysis, but still include the location of the others on the map in figure 4 in the appendix. I then used both the Durham Police Department and their Geographical Information Systems (GIS) software, and Busted Maps to locate each of the 13 mobile home communities and pinpoint any property crime that had occurred within a mile of it in the last five years. By using multiple resources, I had hoped to find more accurate data, but unfortunately the data from 2012 and 2013 seemed to be inconsistent with the City Data I mentioned earlier.

City Data says that crime has consistently decreased in Durham for the past ten years, but GIS and Busted Maps show lower crime levels before 2012, a spike in crime in 2012, and a 10% decrease thereafter. This leads me to believe that there is either insufficient crime data in GIS and Busted Maps in Durham before 2012, or that there is very little crime in the outskirts of Durham where a majority of the mobile home communities are located. Figure 3 in the appendix shows the crime data from 2012-2013, figure 4 in the appendix shows a crime map of Durham from 2012-2014, and figure 5 shows the 13 mobile home communities assessed, and a number of other mobile home communities in Durham.

This approach is not ideal because if fails to show changes over time (due to using only data from the last two years), it only provides information about crime near a mobile home community as opposed to within one (which means data gathered for non-mobile home communities that are also on the outskirts of the city would tend to almost completely overlap with data for mobile home communities; this would lead to mobile home communities and non-mobile home communities having the same data and provide no insight about safety differences), and it lacks data from a mobile home community in the center of the city where most of the crime is located. With this being said, I chose not to find data from non-mobile home communities and instead include a crime map and a map with the location of mobile home communities drawn on it. The table of crime near mobile home communities accompanied by the crime map and map of mobile home communities provide valuable information about crime near mobile home communities in Durham, NC.


            The data is summarized in the appendix in figure 3. In total, there were 74 property crimes committed within a mile of the 13 selected mobile home communities. There was a 10% decrease in crime in the selected communities from 2012-2013, and higher crime on average in the zip code 27703 which has mobile home communities closest to the center of the city. The 74 property crimes committed within the selected communities reflect less than 1% of the total crime committed in Durham. This shows that crime is not evenly dispersed throughout Durham and should not necessarily be attributed to mobile homes being safer; they may just not be located in crime dense regions of the city (such as in the center).

I did not analyze data for Durham communities that were not near a mobile home community, because they tend to be located in the center of the city or included in the data already generated for mobile home communities. This means that most of the data I could gather for non-mobile home communities would be likely to reflect their center locations instead of the fact that it is a non-mobile home community, or that the data would appear to be the same for mobile home communities and non-mobile home communities.

Figure 4 in the appendix shows the trend of higher crime toward the center of the city, figure 5 highlights the location of the selected mobile home communities and other mobile home communities in Durham. It is clear that mobile home communities form a ring around Durham and that much of the crime is within that ring. This tells us that it is safer to live in one of the mapped mobile home communities in Durham if the alternative is somewhere in the center of the city; but it is not necessarily safer to live in a mobile home community if the alternative living option is also in the outskirts of the city. In other cities it may be possible to find a mobile home community located in the center of the city where crime rates appear to be higher, and this data would tell us nothing about whether that mobile home community located in the center of the city is safer than alternative housing options also located in the center of the city.



            The data I’ve gathered tells us that mobile home communities in Durham are much safer than the average location in Durham, but correlation does not equal causality and this data (figure 3 in the appendix), does not include other variables such as where the mobile home is located. The data in figure 3 shows crime within a mile of the listed mobile home community. This makes it difficult to compare non-mobile home communities that are also on the outskirts of the city because the data already encompasses such a large area and the two different communities would overlap. It is also difficult to compare non-mobile home communities that are nearby but in the center of the city because being in the center of the city seems to already be a large factor in contributing to higher crime.

While figure 3 tells us nothing about the safety of mobile homes in general, it does provide insight into the fact that living in a mobile home community is far less of a predictor of safety than where you live in that city. If you’re looking for a safe home above all other factors, do not look for one in the center of the city; a mobile home community on the outskirts of the city appears to be safer than renting an apartment or buying a home in the center.

Further economic analysis is needed in a different city to be able to say whether mobile home communities themselves are safer or not. It could be that because residents in a mobile home park live closer together, form a tight-knit community and are therefore more likely to report and deter suspicious activity on behalf of their neighbors. It could also be that those living in a mobile home community are self-selecting and seek the benefits and style of living that is associated with living in a mobile home community. With all the negative stereotypes about mobile home living, this may at first seem unlikely, but perhaps for those of a lower socio-economic status, living in a mobile home is very desirable compared to renting on a low income street or in the center of a city. In some cases, mobile home communities are even in the better school district or may seem more family oriented than other options and this could lead to the aforementioned self-seeking behavior. Mobile home communities also tend to have minimal entrances which could deter criminals.

It is extremely valuable for individuals to understand the safety consequences of where they decide to live, and since there are 8.8 million mobile homes in the United States, and an increasingly limited selection of affordable housing options, more research on the safety in mobile home communities is necessary. Durham is not the target city for looking at mobile home community safety because there are no mobile home units in the center of the city and there is not enough data on crime in the outer ring of the city. It is also likely that not all crime is reported within a mobile home community because cops are not required to do regular patrols since the land is owned and rented out by an individual. The ideal research would involve an analysis in a city with more mobile home communities, with each individual mobile home community providing data for crimes. Perhaps neighborhood polls of how safe individuals feel could prove to be useful as well.


            The responsible public policy strategist should continue to use GIS data to better understand spatial differences related to mobile home communities and other spatial features. Public policy should also include further research in other cities in North Carolina, and work with specific mobile home communities on identifying the types and rates of crime and comparing it to areas of similar socio-economic status. After analyzing Durham, it is also clear that in order to compare crime in mobile home communities to non-mobile home communities, they must be of similar distance to the center of the city, and perhaps even next door to one another. Crime analysis is very important and the ultimate goal is to perform better policing and reduce crime.


Figure 1: A table taken from City Data on crime in Durham, NC.

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Figure 2: Crime breakdown for Durham in 2012 taken from City Data



Figure 3: A table of select mobile home parks, their zip codes, and the number of crimes committed in 2012 and 2013.

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Figure 4: A map of crime in Durham using GIS software



Figure 5: A map of mobile homes in Durham, NC reveals that the majority of them are in the Northern region of Durham. When compared to figure 4, it is apparent that mobile homes fall outside of the crime heavy region.




Becker, Charles. “The Price of Tornado Proofing: A Hedonic Model for Pricing Manufactured Homes.”

“Crime Rate in Durham, North Carolina (NC): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map.” City Data. N.p., n.d. Web. 20 Mar 2014. <http://www.city-data.com/crime/crime-Durham-North-Carolina.html>.

Durham Crime Map, retrieved on Mar 20, 2014 from bustedmaps.com

Durham Police Department. Crime Mapper Online Software. Durham, 2012. http://gisweb.durhamnc.gov/gis_apps/crimedata/dsp_entryform.cfm

Map of Durham, retrieved on Mar 20, 2014 from website www.maps.google.com

McCarty, William. “Trailers and Trouble? An Examination of Crime in Mobile Home Communities.” Cityscape: A Journal of Policy Development and Research. 12.2 (2010): 127-44. Web. 20 Mar. 2014. <http://www.jstor.org/stable/20868748>.

Platt, Wes. “Sheriff Reports Crime Decrease in Unincorporated Durham.” Herald Sun. 12 JAN 2014: n. page. Web. 20 Mar. 2014

Interactions Between Crime and Schooling in the Housing Market

By Billy Marsden    Interactions Between Crime and Schooling in the Housing Market


Arguably the largest and most important purchase that a consumer will make in his or her lifetime is a house. Housing costs make up a considerable portion of one’s annual spending. According to the Wall Street Journal, Americans spend an average of around 30% of their income on housing. Thus, a lot of time and energy goes into determining exactly what one wants in a new home. A homebuyer must determine exactly what qualities they want to pay a premium for, and which to forego. The buyer must figure out their preferences regarding the location of the house, including the city, neighborhood, and proximity to certain amenities, as well as the desired physical attributes, including size, number of bedrooms and bathrooms, and general aesthetic style.

Thus, significant research has gone into determining exactly what characteristics affect housing prices. Homeowners, construction companies, real estate firms, and the government alike could all benefit from better understanding the preferences of homebuyers, whether that be knowing exactly how much they’ll pay for an extra 300 square feet or from the addition of a pool into the backyard. However, due to the complexities of housing characteristics and the pure number of different traits that go into a purchasing decision, it becomes much more difficult to pinpoint exactly how much consumers will pay for different attributes.

Technical Review

Two such characteristics that have received a significant amount of attention in academic and technical papers are crime and school quality. Theoretically, homebuyers would pay a premium in order to live in a physically safe environment to avoid crime, as well as the mental security of knowing that they are safe. Additionally, parents will pay a premium to live within the district boundaries of the best public schools, allowing their children free access to quality education. While these make sense theoretically, many economists and psychologists have attempted to quantify these effects.

One study conducted by Stephanie Swift of the University of Troy examines the effect of crime on housing prices in Jacksonville, Florida. Segmenting by violent and non-violent crime, the researcher runs a simple linear regression using certain housing characteristics, including square

footage and other basic amenities, and local crime rates to predict housing prices. Based on the regression output, both violent and nonviolent crime rates had a significant effect on housing prices. However, nonviolent crime actually had a negative effect on housing prices. The paper addresses the fact that this could potentially be due to the fact robberies and property damage occur in areas with more expensive real estate.

A second study that addresses this dual relationship between nonviolent crimes and housing prices is a study published by Keith Ihlanfeldt and Tom Mayock of the Economics Department at Florida State University. The 2009 analysis focuses on the effects of crime on housing prices in Miami-Dade County. Through a nine-year time-series regression analysis, the researchers attempt to investigate the effect of changes of crime rates, segmented by violent and property crimes, on changes in property values. As the researchers argue, crime is never treated as an endogenous variable when regressed with housing prices, despite the fact that housing prices may affect crime rates, specifically nonviolent ones. Thus, they not only use a simple OLS regression, but also an instrumentation approach to attempt to derive the causality of crime rates on housing prices.

Their regressions provide overwhelming evidence that property crime has little impact on housing prices, while violent crime rates have a significant impact. Homeowners pay a significant premium to avoid living in violent crime-ridden areas. For a 1% increase in violent crime rates, holding all other factors constant, housing prices decreased by .25%.

While crime is one significant factor that homebuyers may consider when thinking about a new purchase, local public school quality is another key factor, and one that has also been the subject of significant academic research. Much like crime, school quality is highly correlated with other unobservable characteristics, including quality of the neighborhood, so it is difficult to determine causality via regression analysis.

One such paper that attempts to quantify the effect of school quality on housing prices is Which School Attributes Matter, a paper by John Clapp, Anupam Nanda, and Stephen Ross of the University of Connecticut. Using housing and schooling data from the state of Connecticut, along with neighborhood demographic and socio-economic qualities, the researchers attempt to determine the true effect of school quality on the housing market. A shortfall of past studies, according to these researchers, is their inability to control for unobservable neighborhood characteristics. Thus, this analysis incorporates a fixed effects model, allowing them to control for all unobservable neighborhood traits that may have been masked into the school effect in previous models. Comparing the general OLS model to the neighborhood fixed effects model, the effect of school

quality, as measured by math test scores, is reduced to 20% of its original value. The magnitude of the school quality coefficient reduces from .074 to .013 when the neighborhood fixed effect is introduced. Without controlling for neighborhood characteristics, the importance of test scores is overstated by a factor of five. However, the effect is positive and statistically significant in both cases, implying the quality of schools is a significant factor when purchasing a home.

While many studies have looked at both of these elements separately, or have even incorporated both in the same regression, no analysis has been done connecting the two. It could be possible that those who put a premium on schooling put a different premium on crime than those who choose not to put a premium on schooling. Thus, my regression will not only incorporate schooling and crime data and their effect on housing prices, but an interaction between the two. Based on the data collection and regression output, we will be able to determine the premium on crime that homebuyers who live in high performing schooling areas, low performing schooling areas, and areas without any schools have, and whether any difference exists among these groups.

Data and Methodology

The data used in the analysis were collected from a variety of online sources. The housing data, including housing price, number of bedrooms, number of bathrooms, housing square footage, and lot size were collected from Zillow. The 98 analyzed houses were a random collection of houses sold in the last month in Durham.

Houses were then segmented into 3 different groups based on their proximity to schools. Schools given a School Category rating of 0 did not have any schools within a half-mile. Schools with a School Category rating of 1 were in close proximity to a low performing school, and those within a half mile of a high performing school were given a School Category rating of 2.

The quality of the school was determined through a rating from greatschools.org. The website aggregates publicly available test score data and assigns all US public schools a rating between 1 and 10. Schools that received a rating between 1 and 5 were assigned to the low performing group, and schools with a rating between 6 and 10 were deemed high performing. Due to the fact that test score information is only available for public schools, private schools and houses located near private schools were omitted.

Crime statistics were collected through the Durham Crime Mapper website. For each house, the total number of crimes that occurred within a half-mile of the house between January and March of 2014 were counted. Violent and nonviolent crimes were bundled together, which includes arson, assault, burglary, homicide, larceny, motor vehicle theft, robbery, and rape.

With the collected data, OLS regressions were run with the log of housing price as the response variable. A combination of number of bedrooms, number of bathrooms, housing square footage, housing square footage squared, lot square footage, crime rates, school category, and an interaction between crime rates and school category were the explanatory variables. The 3 primary models are shown below. The first model is the most basic, while the second model incorporates the interaction term of interest, and the third model includes squared footage squared to help with the fit of the model.

Model 1:

LogP_i = beta_0+beta_1*Bed_i+beta_2*Bath_i+beta_3*SF_i+beta_4*Log(Lot_i)+


Model 2:

LogP_i = beta_0+beta_1*Bed_i+beta_2*Bath_i+beta_3*SF_i+beta_4*Log(Lot_i)+


Model 3:

LogP_i = beta_0+beta_1*Bed_i+beta_2*Bath_i+beta_3*SF_i+beta_4*SF^2+



The results of the model are shown in Table 1. Model 3 appeared to provide the best fit for the data based on the r-squared and residual plots. Interpreting the coefficients from model 3 yielded insights into which characteristics affect housing prices. Unsurprisingly, bedrooms, bathrooms, and housing size all have positive coefficients. The log of lot size has a slightly negative but statistically insignificant coefficient, rendering it not useful in predicting housing price. In all models, crime has a negative and statistical significant coefficient. This implies that higher levels of crime are a predictor for lower housing prices. Additionally, the category 1 and 2 school variables have positive and mostly statistically significant values. The category 2 coefficient is generally larger than the category 1 coefficient, implying that homebuyers will pay a larger premium for high performing schools than low performing schools. In model 3, the School: Category 1 coefficient is .15 and the School: Category 2 coefficient is .16. The interpretation of this coefficient is that on average, holding all other factors constant, the log of housing price will increase by .15 for low performing schools and .16 for high performing schools in relation to houses with no schools in nearby proximity.
Finally, when interpreting the coefficient for the interactions, we receive a counterintuitive result. For model 3, the interaction between crime and school quality has a positive coefficient. This means to determine the true coefficient of crime on housing prices for houses sold near schools, the sum of both the original crime coefficient and the interaction must be taken. The true coefficients of crime on housing prices for each category of school from model 3 are shown below.
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For houses not near any type of school, crime is negatively associated with housing price, an expected result. However, for houses near schools, higher levels of crime predict higher property values. Thus, the model predicts that homeowners purchasing homes near schools are actually less sensitive to crime than those not purchasing near schools.
While our model predicts that homeowners buying near schools appear to put a premium on crime, this may point out a flaw in the model. It is very difficult to imply causality in a regression model. Instead, it is necessary to control for all factors that would affect housing prices in order to imply the causality of crime on housing prices. For example, in the paper Which School Attributes Matter, the researchers controlled for unobservable neighborhood characteristics, which diminished the importance of school effect. The model in this paper does not incorporate fixed effects, meaning the unobservable neighborhood characteristics that would normally affect housing prices are bundled into other coefficients.
When looking at the crime coefficients for houses in category 1 and category 2 schools, we see a positive coefficient. This probably does not imply that homebuyers put a premium on crime, but instead that high amounts of crime are positively correlated with wealthier neighborhoods.
Another flaw in the model is the fact that violent and nonviolent crimes are not segmented. If they had been segmented, then we could have more appropriately determined the effect of violent crimes (homicide, rape, assault) and nonviolent and property-based crime on housing prices.
A potential method to imply causality of crime on housing prices is to use a time-series regression, something not utilized in these models. Using crime data from the previous year, we could attempt to view the causal relationship on that year’s housing sales data. However, this would require a more complex time-dependent linear model and a large sample size. The model could look something like the formula shown below.

LogP_i = beta_0+beta_1*Bed_i+beta_2*Bath_i+beta_3*SF_i+beta_4*SF^2+



An additional flaw is the sample size used in this analysis. 98 data points is relatively small to achieve statistically significant results given the number of explanatory variables used, especially when interactions are incorporated. So, while many of the results were not statistically significant, more coefficients may have been with a larger sample size.
Given our model creation it is plausible to imply correlation between housing and crime. Crime is negative and statistically significant when on analyzing housing that is not within a half-mile radius of a school. However, the picture becomes more complex when analyzing homes near schools. The coefficient turns positive, though not statistically significant. Instead of assuming that homebuyers near schools put a premium on crime when making a purchasing decision, it is more likely to assume that higher crime rates are correlated with higher housing prices due to the fact that they are more prone to nonviolent property crimes, including robberies, motor vehicle theft, and larceny. With a more sophisticated model and larger dataset discussed in the limitations above, it would be possible to more correctly derive the sensitivities of homebuyers to crime given their proximity to schools. However, with the simple regression model derived in this paper, we can only imply correlation.


Table 1
Screen Shot 2014-04-09 at 12.02.11 AM

Works Cited:
1. Clapp, John M., Anupam Nanda, and Stephen L. Ross. “Which School Attributes Matter? The Influence of School District Performance and Demographic Composition on Property Values.” Journal of Urban Economics 63.2 (2008): 451-66. Print.
2. “Durham Crime Mapper.” Durham Crime Mapper. N.p., n.d. Web. 24 Mar. 2014.
3. “How Much You Should Spend on a Home.” Personal Finance RSS. Wall Street Journal, n.d. Web. 24 Mar. 2014.
4. Ihlanfeldt, Keith, and Tom Mayock. “Crime and Housing Prices.” Department of Economics and DeVoe Moore Center: Florida State University (2009): n. pag. Web.
5. “Join GreatSchools.” GreatSchools. N.p., n.d. Web. 23 Mar. 2014.
Swift, Stephanie. “Do Crime Rates Affect Housing Prices?” Troy University Economics Department (n.d.): n. pag. Abstract. (2005): n. pag. Print.
6. “Zillow: Real Estate, Apartments, Mortgage & Home Values in the US.” Zillow. N.p., n.d. Web. 23
Mar. 2014.

The Millennial Generation and Durham’s Housing Market

By Gabrielle M. Ware  The Millennial Generation and Durham’s Housing Market


In the most recent decade, Durham has experienced a large influx of members of Generation Y, otherwise known as the Millennials. Millennials, defined throughout this paper as those born between 1980 and 1998 are currently between ages 16 and 34 and described as educated, diverse, creative, connected and open to change. Forecasted to be the most educated generation in the United States’ history, greater than one-third, or 37%, are unemployed or out of the workforce as a result of the nation’s 2008 Financial Crisis1. In the search for financial stability, members of this generation are drawn to Durham and its surrounding area because of the high caliber universities located there, as well as the promising jobs in healthcare, pharmaceuticals, and other professions found in Research Triangle Park. There is definite incentive for Durham officials to continue to attract and retain members of Generation Y to sustain the county’s high growth rate. The rise of an educated, and hopefully successful, generation will not only bring more young professionals into Durham, but will also benefit businesses and Durham’s overall economy. Typically, Millennials want to live in high-density neighborhoods, accessible to centers of business and employment either by walking or public transportation, and desire affordable urban rental spaces as they  try to achieve financial stability. As Durham’s housing market becomes dominated by Millennials, demand for the aforementioned types of housing will certainly increase and it will be interesting to examine how Durham officials will respond to this changing demand.

The goal of this paper is to explore which communities and neighborhoods Millennials are most attracted to and what factors determine their housing choice. To achieve this goal I will compare neighborhoods with the highest and lowest proportions of Millennials in Durham County and will go over the study of Millennials in Philadelphia conducted by the PEW Charitable Trusts. Because patterns have shown that members of this generation are more concerned with the quality of their surroundings than of their housing itself, I will focus primarily on which neighborhood and county qualities will play the largest role in these buyers’ decisions.

Millennials and the Housing Market

Over the past five years, as the majority of members of Generation Y have reached their twenties and thirties, there has been increasing concern about the housing market. In the decades of their lives in which previous generations have driven growth in homeownership, many Millennials are putting off owning their own home due to massive amounts of student loans they must pay back and uncertainty in the job market. Additionally, members of this generation have been known to switch jobs more often than their parents, spending only about two years in each position before moving on to a new one. This creates another deterrent to homeownership, as Millennials prefer not to be tied down in any sense, and many members of Generation Y are even more hesitant after witnessing the suffering of their slightly older friends, many of whom had just bought their first home when the housing bubble burst. Furthermore, Millennials are marrying much later than previous generations with 75% still single between the ages of 18 and 28, compared to 67% of Generation X and only 52% of their baby boomer parents at the same age. This could also be working to slow home purchases, as marriage is a milestone that often coincides with homeownership. However, this must be taken in stride as members of Generation Y are also much more open to couples living together before marriage, and much more social in general, than previous generations were. This combination of both financial and preferential reasons against owning a home has led to largest drop in homeownership of any age group in the United States for 24 to 33 years old between 2005 and 2011. Despite this decline, approximately 93% of Millennial renters still plan to own their own home at some point in the future, indicating that this decline is driven by lack of funds and may only be temporary. Still, many experts point out that this reluctance to purchase now may leave members of Generation Y stuck in a far less favorable real estate market down the road and unable to reverse the trend. Although there are many benefits to purchasing a home now, with Trulia estimating that it is 35% cheaper to own a home than rent in America’s largest cities due to slightly undervalued homes and interest rates that are still historically low, many Millennials have insufficient funds and are too afraid of the commitment to one place to make a down payment.

In addition to whether or not they will rent or buy, another important factor to consider when assessing the housing needs of this generation is whether or not needs are likely to change in the long term and by how much. While it is true that Millennials are currently seeking out small and affordable housing in high-density city centers, with only 14% living in rural areas, these needs may change in the next five to ten years. A look at the population between ages 20 to 34 in Philadelphia gives some insight into how this generation’s needs may be rapidly changing. During the period between 2006 and 2012, Philadelphia experienced a larger increase, totaling 6.1%, in members of Generation Y as a percentage of its total population than any other US city; however, this growth may only be short lived as slightly over half of the Millennials living in Philadelphia plan to relocate in the next five to ten years and would definitely not recommend the city as a good place to raise children. The there main considerations survey participants cited for planning to leave the city were jobs/career, school quality/child upbringing, and the crime/safety/drugs. A full list and more detailed comparison between Millennials and older generations can be found in Table 1 (Appendix at end). From the case of Philadelphia, it is implied that successful policy to attract and retain Millennials will need to be adapted as the generation’s needs change and that Durham may benefit from incorporating both long-term and short-term strategies.

Millennials in Durham

As shown in Figure 1, Millennials have located throughout Durham in accordance with what is predicted by patterns and preferences of the generation. The map shows Durham County, divided into 60 smaller census tracts as defined by the US Census Bureau for more uniform and complete data. The five tracts with the highest proportions of 16 to 34 year olds are marked with green diamonds, while the five tracts with the lowest proportions of 16 to 34 years olds are marked with red circles. Even from a quick glance, it is apparent that the five tracts with the highest proportions of Millennials are clustered near the city-center while the five tracts with the lowest proportions of Millennials are scattered around the Durham County’s edges. Table 2, created by data obtained from the US Census Bureau American Community Survey five year estimates from 2008 to 2012, show some of the most relevant characteristics of each of these ten tracts and the differences between the two groups can be used to demonstrate which factors have led Millennials to distribute themselves as they have throughout Durham.

When examining the five census tracts with the highest proportions of Generation Y members living in them, it is not surprising that two, 15.03 and 15.01, contain Duke’s East and West Campuses, respectively, two, 15.02 and 4.02, correspond with areas touching Duke’s Campuses and the final tract, 13.03, contains North Carolina Central University’s campus. When examining the eight included characteristics of each tract, including population density, number of occupied rental units, median gross rent, median household income, proportion of residents with at least some college education, median commuting time to work, and the proportions of residents that are black and Hispanic, any extreme outliers can be explained by the presence of specific institutions in or adjacent to the tracts. For example, 100 percent of the population in tract 15.03 has at least some college education and the median household income, $67,454, is uncharacteristically higher than the surrounding area as a result of the presence of Duke’s East Campus, filling almost the entire tract.  This fact also explains the lack of data to estimate the median gross rent. When examining the tracts with lowest proportions of Generation Y members, it is also not surprising that four out of five tracts feature either a golf or country club or luxury development, while the tract with the absolute lowest proportion of Millennials, 9801, is fairly non-residential including two major highways and Research Triangle Park. Again, the aforementioned features of each tract can explain away any outliers, such as the extremely low population density and proportion of Millennials in tract 9801, as young adults commute to work in RTP, but do not live there.

The differences between the groups with high and low proportions of Millennials were also not surprising. Because of their documented affinity for high-density city centers, the clustering seen in Figure 1 and higher population densities of the tracts with high proportions of Millennials were to be expected. All of these areas are within walking distance from some sort of city center and are accessible by public transportation. This desire to be close to some sort of community is also reflected in the relatively shorter commuting times to work places in tracts with higher proportions of Millennials. Additionally, it is consistent with trends in other cities that tracts with high proportions of Generation Y members have many units available for rent at affordable prices, as many Millennials are burdened with student loans and trying to be debt free and secure some sort of emergency fund before purchasing a home10. It can also be seen that tracts with higher proportions of Millennials are more diverse, another defining characteristic of this generation.

What Does this Mean for Durham?

Because of the great universities located in and surrounding Durham County, as well as the promising professional opportunities in Research Triangle Park, Durham has already begun to attract members of Generation Y at high rates. Still, Durham has not experienced an influx of the same magnitude as cities like Philadelphia, Boston, Nashville, and Baltimore11. Because of this, policy for Durham should take a two-sided approach. First, in the short-run, Durham authorities should learn from and attempt to mimic some of the conditions in the aforementioned cities and parts of Durham that have already attracted high numbers of Millennials. Understandably, it is impossible for Durham to match the size, density, and connectedness of a city like Boston in the short run, but it can take some steps in the right direction. As seen in Figure 1, the most important factor for members of Generation Y when deciding whether or not to relocate is for job or career opportunities. It is clear that in order to attract Millennials, the city must make it worth it for them professionally. Durham’s government can help achieve this by offering incentives for companies that provide professional opportunities located in Durham or within reach of the city’s public transportation.

Once attracting Millennials to the opportunities Durham has to offer, the city must accommodate the living needs of Generation Y. Durham authorities must make an effort either to expand upon community centers already in existence with more affordable housing or to plan additional centers that will be walkable to some degree, and have access to centers of employment. As an extremely connected and collaborative generation, members will also be attracted to high concentrations of young adults, as well as youthful, liberal, and creative ambiances. The addition of a 26-foot sky-scraper at the corner of Main and Parrish Streets, a redevelopment project led by Austin Lawrence Partners set to break ground in the Fall, is a step in the right direction for Durham. The high-rise, which will consist of a mix of retail, office, and residential space and is already in surrounded by award-winning restaurants, theaters, and sports centers, will be certain to attract young and professional adults and solidify the city’s downtown area, creating a high energy and urban environment.

In addition to meeting the needs of Millennials today, Durham officials should learn from the case of Philadelphia and try to anticipate how the needs of Generation Y members will change in the next five to ten years. As shown in Table 1, young adults between the ages of 20 and 34 are concerned about living in a family oriented city that has good school districts and is safe in addition to having good career opportunities in the future. It is also true, that members of this age group still plan to invest in a home at some point down the road. Durham authorities should plan ahead for this need, so when the time comes to make the more permanent decision of buying a home and settling down with a family, Millennials do not choose to locate elsewhere. To do this, officials must work to ensure a safer and cleaner city, as well as school quality that rivals not only neighboring districts, but is competitive on a national level. The low quality of Durham Public schools in relation to Orange and Chatham County school districts will be one of Durham’s biggest challenges in retaining Millennials in the next decade. Possible solutions include raising teacher salaries as a way to attract better teachers, which may enhance school quality, or increasing per pupil spending; however, these solutions will also make Durham a less affordable place to live. Additionally, there is reasonable doubt to believe that increasing per pupil spending will translate into increased school quality in the homebuyer’s eyes12. Furthermore, Durham officials must work to create clean and safe neighborhoods consisting of affordable homes so that educated professionals will make the more permanent decision to stay in Durham. Over time, this may translate into better school quality for Durham, as the children of educated parents will be far less expensive to teach.

Overall, Durham’s goals to create a better living environment for Millennials are complementary to one another and cannot be achieved alone. Professional job opportunities and urban centers must exist to attract members of Generation Y initially, and then safe and affordable housing communities must follow to retain them. Better school quality will hopefully ensue as Durham begins to consist of a more educated demographic. Policy throughout the next decade will be critical in whether or not Durham will continue to sustain high growth levels.


Table 1: Main Reasons for Expecting to Leave Philadelphia in the Next 5-10 Years for Millennials in Contrast to Older Adults

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Figure 1: 5 Census Tracts Least and Most Populated by Millennials

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Table 2: The 5 Census Tracts in Durham with the Most and Least Millennials and their Characteristics

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1. Bissonnette, Zac. “Homeownership: The Elusive American Dream for Millennials.”CNBC.com. N.p., 28 Nov. 2013. Web. 23 Mar. 2014.


2. Bracken, David. “Report: Triangle Home Prices Kept Rising in January.”&newsobserver.com (n.d.): n. pag. Web. 20 Mar. 2014.


3. Clapp, John M., Anupam Nanda, and Stephen L. Ross. “Which school attributes matter? The influence of school district performance and demographic composition on property values.” Journal of Urban Economics63.2 (2008): 451-466.

http://digitalcommons.uconn.edu/cgi/viewcontent.cgi?article=1094&context=econ_wpap ers

4. DeBruyn, Jason. “Pew: Millennials with a College Degree Earn 38% More than Those without.” Triangle Business Journal. N.p., 12 Feb. 2014. Web. 20 Mar. 2014.


5. Durham, North Carolina Government. Durham City-County Planning Department.

Forecasting Land Use and Trends. By Daniel Band and Holli Safi. N.p., Apr. 2013. Web. Mar.2014.


6. “Millennials in Philadelphia: A Promising but Fragile Boom.” The PEW Charitable Trust(2014): n. pag. Web. 20 Mar. 2014.

http://www .pewtrusts.org/our_work_report_detail_full.aspx?id=85899534472>.

7. Millennials: Confident. Connected. Open to Change. Rep. Pew Research Centers Social Demographic Trends Project RSS, 24 Feb. 2010. Web. 21 Mar. 2014.

<http://www .pewsocialtrends.org/2010/02/24/millennials-confident-connected-open-to-change/>.

8. Roth, Bryan. “The Future of Duke’s Workforce.” DukeToday (2014): n. pag. Web. 20 Mar. 2014.


Does Living Near a University Boost Home Prices? Duke and Durham As a Case Study

By Mischa-von-Derek Aikman  Does Living Near a University Boost Home Prices?

The purpose of this paper is to explore the possible existence of a correlation between the proximity of one’s home to a higher education institution (such as Duke University), and the monetary value of that respective home. I have decided to use the residential structures surrounding Duke University’s East Campus as the sample population for this study. More specifically, I analyze and contrast the historical price trends for those homes located within one block of the perimeter of Duke’s East Campus, with homes located two blocks away from the same perimeter. The details of the specific geographic locations of these homes are discussed more thoroughly in the paper’s analysis. I propose that there is increased property value associated with living closer to the physical location of the University relative to living farther away. This difference in value appears to be apparent even in homes that are within one block of each other.


As mentioned above, the homes selected for the study were those located within a 2-block radius of Duke University’s East Campus. The annual historic prices of each of these residential homes between 2004 and 2014 were gathered using Zillow.com’s respective “Zestimate.” The “Zestimate” value is the median Zillow estimate of prices of all the houses in a given geographic location. As of May 2010, the index had tracked over 200 metropolitan areas, and had successfully calculated the index for 120 of these locations. Therefore, the extensive nature of the index made it suitable for the purposes of this study. The homes were divided into two groups; one for those located within a one-block radius of East Campus, and the other for those located within the second block of the East Campus perimeter. The reasoning behind this division was to determine if there is a significant price gap on average between homes located more closely to the University relative to those that are farther away. The specific monetary values for each home can be found in the appendix. The Zestimate at the Durham County level was also collected for this time period to be used as a benchmark. It is important to note that commercial buildings and apartment complexes within the given radius were excluded in an attempt to control for the types of residential structures being observed. The table and annotated map below show which streets the two groups spanned.

Table Showing the Streets each Respective Group Spanned 

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Figure 1: Annotated Map Used for Study

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The homes that fall between the green and blue borders constitute those placed in Group 1 (located closer to Duke), while those that fall between the blue and red borders constitute Group 2 (located farther from Duke). Pricing information was gathered for a total of 485 homes over the 10 years.

Observed Trends and Analysis

The mean home prices for each year was then calculated for both respective groups, and were then plotted against each other along with the Durham County level Zestimate.

Figure 2: Mean Home Prices for Group 1 and Group 2

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It is obvious that there is a significant and consistent price gap between those homes located within one block of Duke’s campus, and those situated two blocks away. It is also very interesting to notice that despite the price gap, both housing groups seem to have been appreciating at more or less the same rate over the past decade. Plotting the price gap itself, as we do in Figure 3 on the next page, shows an unequivocal spike in the price gap between 2007 and 2008. This can be attributed to the culmination, and subsequent burst of the national housing bubble in 2009. Trulia’s chief economist asserted that “geographical home prices was widest in 2007, the peak of the housing bubble.” This may have translated into the unusual widening of the gap at that particular point in time within this subsector of the wider housing market.

Figure 3: Price Gap Between Groups 1 and 2 

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Regarding the significant price gap between the two defined housing groups, we can look to the apparent economic implications of being located within close proximity of a prestigious University. Although extensive literature does not exist on these effects, it is common knowledge that Universities impact communities socially, culturally and economically.


First, is the factor of employment. As of 2006, Duke University was the second largest private employer in the state. A significant portion of the population living within immediate proximity of the University (i.e. within the one block radius) will tend to be well-paid members of faculty and staff within some facet of the University such as health care workers and professors. Hence, these residents are likely to be more financially stable, and equipped to pay more rent than their average counterpart. While it is also probable that some percentage of these employees also live within the two block radius of campus as well, the desired real estate will be that which is more convenient, and therefore, closer to one’s place of employment. This augmented demand within a targeted group of individuals may contribute to the higher prices found within this geographic region.

Investment Incentive

Second, is the very attractive opportunity to invest in real estate near Universities. Zillow’s chief economist, Stan Humphries, asserts that “a lot of students will live off campus, there’s built-in rental demand.”4 The very high flow of students and faculty from year to year lowers the risk investors run by renting homes to tenants near Duke, or any university for that matter. Vacancy rates will be much lower relative to other areas given the continuous demand for housing. Therefore, the heightened demand from the faculty and staff’s perspective, feeds the investor’s growth of demand, who are more confident in the long-term returns on their investment, as well as the short-term security of it. This might be another reason those houses in Group 1 were consistently valued higher than those in Group 2.

Location, Location, Location…

Just as acquiring a beachfront property will typically cost more than the average home, it can be argued that the same is true with purchasing a home close to a University. Housing is an asset that, despite crashes like that of 2008, ultimately appreciates over time. Being located near Duke University, one of three major points in the Research Triangle, intrinsically implies that one is located in an “established” neighborhood. It is very unlikely for the ‘status’ of this neighborhood to decline over time, as the physical University is essentially an immovable asset. This point is further supported if we take a closer look at Figure 2, which plots the Annual Mean House Price for both groups. Although the housing bubble burst in 2008, Durham real estate prices in both groups did not experience a dip in prices until late 2012, leading into 2013. This three-year lag in the reaction of housing prices suggests that residencies located near a powerhouse University such as Duke may be privy to some level of insulation from national market occurrences. This supports the paper’s rationale even further as to why properties in Group 1 would be more desirable, and therefore, more expensive compared to those in Group 2.

Isolating the Outliers

Another interesting observation was that while the house values were cheaper on average in Group 2 than they were in Group 1, there were a few outliers. More specifically, there were occasional strips of Group 1 homes that were far cheaper than quite a number of Group 2 homes. In order to isolate the streets along which these outliers existed, the historical mean price of homes were calculated for each street within each individual group (see appendix for Group 2 data).

Figure 4: Iredell as Outlier for Group 1 Housing 

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For the streets covered within Group 1, the majority of the mean historical prices were range bound between approximately $175,000 and $250,000. The means for Broad St., Minerva St. and Watts St. were all higher relative to the others with a maximum mean of $531,529. The obvious outliers in this case were those houses located along Iredell St, whose historical means floated very consistently around $75,000 throughout the entire decade. Why are these houses valued so much lower than the others found in its group? If one were to look closely at the annotated map in Figure 1, he will notice that Iredell Street was on the furthest most point of the boundary used to confine houses in Group 1. It is possible that the ‘one-block vs. two-block’ measure might have been too neat of a divide, and that the very outskirts of Group 1’s boundary had already transitioned into homes which fit the characteristics of Group 2 more appropriately. However, this outlier still did not cause for the hypothesis to be rejected.


While the results of this study were informative, and supported the original hypothesis, there were a few facets of the experiment that may have limited the level of conclusiveness. First, is the use of the Zillow estimate to gather the historical prices for the homes. It was a suitable index to use within the scope of this experiment since it uses public data on house attributes and actual sales prices to develop its model. However, the academic community often criticizes it for its lack of publically available historical time series.

Second, it is clear that the sample size used in the experiment is relatively small. Having surveyed 4,850 historical prices for 485 homes in Durham provides a nice picture for the community immediately surrounding Duke University. However, there would be great value in expanding the boundaries throughout a larger geographical spread within Durham.

The final limitation is concerned with the method used to define the boundaries that divided the residential homes into two groups. As was seen in the “Isolating the Outliers” portion, the evidence suggests that the border may have been too rigid of a split. Perhaps one could observe more accurate price correlations using the metric distances from the center point of the university and each respective home. This allows the distance factor of the model to be continuous, not discrete, and can speak to even more meaningful relationships.

Summary and Conclusions

Using Duke University and Durham as a case study, we were able to observe significant relationships between historical housing prices for homes located closer to the campus (Group1) relative to those located farther away (Group 2). We noticed that while the houses in both these groups appreciated at rates that were relatively very similar, there was a consistent price gap between houses located within one block of Duke’s campus, and those located two blocks away. More specifically, the homes within the first block were consistently more expensive than those in the second block by significant amounts. Various reasons that could potentially contribute to the existence of this gap were discussed. These included the impact that Duke University has on the employment of those who live near campus, the attributes of homes situated near a university that attract investors, and what seems to be some kind of cushion against larger market phenomena such as the housing crash in 2008. All of these supported the hypothesis that homes located closer the Duke’s East campus, were consistently more expensive than those located farther away over the past 10 years.

Moving forward, it would be very interesting to conduct the same study on a larger scale for numerous universities across the United States. The differences in results between private Universities and State Schools, or between Universities whose campuses are compactly designed (such as Duke University) vs. those that are dispersed throughout a city (such as North Carolina State University) would prove to be very useful within this topic.

Work Cited:

  1. Dougherty, Conor. “Gap Between Most, Least Expensive Housing Market Still Wide.”Real Time Economics RSS. The Wall Street Journal, n.d. Web. 27 Mar. 2014.
  2. Duke and Durham:AnAnalysisofDukeUniversity’sEstimatedTotalAnnualEconomicImpactontheCityandCountyof Durham. Rep. Durham: Office of Public Affairs, 2006-2007. Print.
  3. The Identification and Estimation of A University’s Economic Impacts.G.GeoffreyBoothandJeffreyE.Jarrett.The Journal of Higher Education. Vol. 47, No. 5, pp.565-576
  4. TrackingtheHousingBubbleAcrossMetropolitanAreas–ASpatio-TemporalComparisonofHousePriceIndices.Laurie Schintler and Emilia Istrate. Cityscape. Vol. 13, No. 1, Discovering Homelessness (2011), pp. 165-182)
  5. Woolley, Suzanne. “Real Estate: Investing in College Towns: A Degree in Real Estate”. Bloomberg.com. Bloomberg, 5 Nov. 2012. Web. 20 Mar. 2014.

Appendix: [you may find all the data in Appendix here Does Living Near a University Boost Home Prices? ] 

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