By Melanie Green The Impact of Airport Development on Economic Development
Cities around the world are separated by physical distance, but individuals can travel relatively easily between cities using various forms of transportation. Air travel not only connects people but it connects economies to further develop the global economy. Airport development has also been linked with economic development. Much research has been done on this relationship, with focuses on different regions and cities around the world. In particular, studies that focus specifically on Chinese and Canadian airport economics, as well as metropolitan airport development in general, provide insight into this important economic relationship and the implications it can have on new airport development.
A new research study that stems from these previously explored relationships can look into the ongoing construction of the new terminal at Raleigh-Durham International Airport (RDU) and its relationship with economic development in the surrounding urban area. The correlation between airport and economic development is important, but determining a cause-and-effect relationship can be very useful in understanding the economics of the Triangle region as well as other regions around the country and the world. Thus, it is necessary to analyze similar situations worldwide to provide a background understanding of the issues at hand, as well as to develop models that can be used to analyze the specific situation at RDU.
Much of the discussion on the relationship between airport and economic development surrounds four key sub-topics: public finance, economic development, transportation and agglomerate economics, and airports in general. Airports can be considered impure public goods; therefore, in order to completely understand their worth, it is necessary to determine each individual’s marginal utility that results from the presence of a runway (Green, 2007). Economic development is often linked with infrastructure development, which means that airports are expected to further the development of the economies of the surrounding regions. Transportation in general affects the development of cities, with air travel having a large stake in both short and long distance transportation. Finally, airport economics have often included pricing and congestion issues in the past, but these issues can be combined with the economic impact of airports to gain a better understanding of urban development in the context of airport development (Green, 2007).
Economists have reached a general consensus that airports do share a relationship with economic development, but the exact cause-and-effect relationship is unclear and depends on many factors. For example, Yao and Yang (2008/07) found, based on a study of the Chinese economy, that a 10% population density increase in population density causes a 1.7% increase in air passenger volume and a 1.2% increase in air cargo. This specific relationship analyzes the effect that economic development has on airport development. On the other hand, according to a study performed by Benell and Prentice (1993), in order to create a one person-year of employment, the average number of additional air-travel passengers must increase by 1126, based on a sample set of airports in Canada. Each one of these additional passengers is expected to add a monetary value of approximately $78.08 to the economy (Benell and Prentice, 1993). In contrast with the previous relationship developed in the research focused on China, this Canadian research addresses the impact of airport development on economic development. A cause-and-effect relationship between airport and economic development is observed in both directions. For example, economic expansion can increase airport demand. An increase in airport capacity then raises productivity and/or demand in other sectors of the economy.
Methods and Findings of Economic and Airport Development Regression Analyses
In Airport Development and Regional Economic Growth in China, Yao and Yang (2008/07) perform a regression analysis to determine the effects of several variables on a dependent variable that is divided into two categories: the volume of passengers by air and the volume of cargo by air. The explanatory variables include GDP, population density, openness (trade/GDP), economic structure (share of employment accounted for by the tertiary industry), ground transportation (volume of rail and road transport), location dummy variables (east, northeast and west, using central as a base), and a time dummy for 1995-2001 (Yao and Yang, 2008/07). The data used in this study was collected from various data sources such as Statistical Data on Civil Aviation of China. The regression is represented mathematically by the following equation, with all variables evaluated in natural logarithms at 1995 price levels in China:
The researchers of this study concluded, based on the regression analysis, that economic growth and openness (measured by trade/GDP ratio) are principal determinants of airport development and air transportation volume. Further results imply that airport development is positively correlated with economic growth, industrial structure, population density, and openness. Airport development is negatively correlated with ground transportation development. The negative correlation with ground transportation reflects the substitutability between ground transportation options, such as railroads and highways, and air transportation. The less developed regions of China are the west and northeast regions. Yao and Yang’s results suggest an incentive to construct airports and promote air travel in these less-developed areas because substitutable forms of travel are costly to implement there due to the presence of vast, mountainous terrain (Yao and Yang, 2008/07). Furthermore, airport development in these less-developed regions can promote economic equality across the country, as airport development is positively correlated with economic growth. While the results of this research are only directly applicable to the Chinese economy, its methods and general findings can be transformed and applied to other urban economies.
Benell and Prentice (1993) conduct a related analysis focused on the consequences of Canadian airport expansion in their study titled A Regression Model for Predicting the Economic Impacts of CanadianAirports. The purpose of this research is to conduct an econometric analysis to estimate the relationship between indicators of airport activity and their economic impacts on the Canadian economy. This differs from the Chinese study because it looks at the opposite cause-and-effect relationship. They focus specifically on direct employment and revenue impacts as economic indicators and obtained most of the transportation data from Airports Group, Transport Canada. Benell and Prentice (1993) find that passenger traffic, a city’s commercial activity, air carrier maintenance bases, air traffic control towers, flight service stations, and selected aircraft movement statistics are key variables that determine the economic impact of a single airport. They run two regressions, one with a dependent variable of “person-years of employment that is directly attributable to the airport (E)” and the other with a dependent variable of “revenue, or economic output, that is the result of airport activity in one year (R)” (Benell and Prentice, 1993). The Ordinary Least Squares (OLS) regression is represented mathematically by the following equations:
Please refer to table 1 under references for an explanation of the variables in these equations.
The researchers find that revenue elasticity and labor elasticity can be developed from commercial airport activity. Both values of elasticity can then be used to determine the direct economic impact of an airport, using data such as passenger numbers and local economic conditions. Furthermore, it is apparent that direct labor counts are more reliable economic indicators than revenue indicators. When measuring revenues, it is difficult to avoid double counting, thus estimates become inaccurate. Numerically, Benell and Prentice (1993) find that a 1% increase in annual passenger traffic at a particular airport coincides with a .75% increase in direct employment and a .49% increase in direct revenues. The difference in the elasticity of direct employment and the elasticity of revenues is important for future planning and modeling of airport and economic development relationships.
How Airport Activity Affects Economic Development of Metropolitan United States
Similar studies have been conducted on various metropolitan areas in the United States. Specifically, research conducted by Richard Green (2007) addresses the hypothesis that activity at a metropolitan airport predicts employment and population growth. Green’s regression analysis is unique because he uses panels and two instruments to attempt to control for simultaneity and develop proof of causation rather than just correlation. This study uses four measures of airport activity that include boarding volume, passenger originations per capita, whether a city has an airport that is a hub for a major carrier, and cargo activity. The two economic development indicators are population and employment growth between 1990 and 2000.
Based on a numerical regression, using both OLS and Instrumental Variable (IV) tools, Green (2007) finds that boarding per capita, passenger originations, and the presence of a major airline hub have a significantly large influence on population growth. In fact, hub cities grew between 9% and 16% faster than non-hub cities between 1990 and 2000. However, the volume of cargo activity did not prove to cause economic development, according to this study. Green (2007) also explores the concept of negative externalities. While many argue that airport development is positively correlated with economic development, there exist negative externalities in building airports. For example, residents located within close proximity to airports often complain about the noise, pollution, and overall congestion that airports bring to the local neighborhoods. According to Green, airports can only be considered beneficial if the benefits of economic development outweigh the costs of the negative externalities.
Implications For Future Research
Each of the three aforementioned studies shares similar and dissimilar dependent and explanatory variables in completing regression analyses. This demonstrates the limitations of each study, as they define indicators such as economic activity or airport activity based on a set of individual variables that differ from study to study. It may be possible to combine parts of these individual regressions to create a more comprehensive study. The econometric models that have been created serve as effective templates for further research on this topic. Specifically, in looking at the relationship between the economy of the Raleigh-Durham region and the expansion of the RDU airport, it is possible to apply the specific economic and airport development indicators to create a distinct regression for this specified urban economy. Hypotheses for this specific research may include the following:
- RDUiscurrentlyundergoingexpansionbecauseoftheincreasedeconomicdevelopmentin the triangle region. Using airport activity as the dependent variable and various economic indicators as independent, or explanatory, variables can prove this prediction.
- RDU is currently undergoing expansion and this increase in airport activity will cause economic development in the triangle region in the future. One can support this prediction by running a regression with various indicators of airport activity (such as the ones used in the previous three studies) as the independent variables and an economic indicator such as GDP or employment levels as the dependent variable.
While generalizations can be developed regarding the economic impact of airports in general, individual urban economies are unique. A study of RDU and Raleigh-Durham specifically will bring new findings to complement pre-existing research. The study of an airport’s relationship to an economy is important because this relationship has the ability to have large implications for the future growth of a city. In addition, because airports connect cities throughout a country, airport development can even transform national economies, such as the example in China that predicts airport growth in less-developed regions can alleviate the income disparity across different regions in the country. Regression analysis is an important tool in determining these relationships and revealing the importance of airports in local economies.
Benell, Dave W; Prentice, Barry E, 1993. A regression model for predicting the economic impacts of Canadian airports. Logistics and Transportation Review; Jun 1993; 29, 2; ProQuest pg. 139
Green, Richard K. 2007, “Airports and Economic Development, Real Estate Economics; Spring 2007; 35, 1; ProQuest pg. 91
Yao, Shujie and Yang, Xiuyan. 2008, “Airport development and regional economic growth in China,” Nottingham, UK: University of Nottingham research paper 2008/07.
By Emily Jorgens The association between urban sprawl and obesity- is it a two-way street?
I. Research Question
Plantinga and Burnell’s 2005 paper draft proposes a model that addresses how obesity and urban sprawl are related. This question arose due to the recent rise in obesity in the United States. There is ample research in the public health and urban planning realms on this topic. However, Plantinga and Bernell challenge the existing literature’s conventional assumption that sprawl causes obesity.
Urban economics is perhaps a surprising avenue by which to analyze the problem of obesity. However, the analytical model put forth in this paper finds meaningful results that could have profound public health and policy implications. Urban planning specialists have drawn links between urban sprawl patterns and demographic and lifestyle characteristics.
Specifically, urban sprawl and obesity are related in three main ways. First, poor street networks and low density lead to longer travel distances. Longer travel distances mean people are forced to travel by car rather than bike or foot. Second, low density means that public transportation systems are less effective and less likely to exist. Therefore, people are traveling by car and have longer commute times and thus less time for physical activity. Lastly, sprawling areas often have poor or unsafe public parks, which discourages exercise.
The existing research holds that poor infrastructure and land use, as outline above, ultimately cause weight issues. As a result, many cities are investing in projects to encourage healthy living. For example, the Atlanta Regional Commission recently invested $1.1 billion in bike and pedestrian infrastructure. Plantinga and Bernell’s model questions whether this will be effective. They assert that overweight people self-select for sprawling residential environments, and thus improving land use in these areas is futile.
Previous research has treated urban form as an exogenous variable. In other words, researchers have assumed that one’s Body Mass Index (BMI) has no influence on residential location choice. This study, however, poses that BMI indicates lifestyle choices that influence residential location choice. This distinguishes whether sprawl causes BMI to rise or whether high BMI individuals choose to live in sprawling locations. Treating BMI and location preference both as endogenous variables answers this question.
II. Theoretical Background
Plantinga and Bernell use the National Longitudinal Survey of Youth from 1979 (NLSY79) together with the sprawl index produced by McCann and Ewing (2003). The resulting dataset includes variables such as BMI, income, education, county of residence, and degree of sprawl. This paper builds on the conventional model for regressing BMI on locational attributes and a composite good.
The conventional function holds that utility is maximized by considering weight, attributes of the residential location (such as walkability), and a composite good. Weight (W) is given by:
Where W0 is initial weight of a person, N is a vector of locational attributes, and C is a composite good. Utility is maximized using the follow equation:
Given that p is a vector of prices for locational attributes and I is income. Using standard constrained optimization techniques, the following equations give the locational attributes (N*I ) and weight (W*I) that result on the greatest utility for an individual.
However, this paper argues that there would be codependence between the weight and locational attribute variables. Also, the researchers hold that a complete model would distinguish between people who recently moved versus have been in the same county for four years or more. This is because if land use does have an impact on weight, it would take some years to manifest. Therefore, the researchers propose a simultaneous equation model that would treat BMI and locational attributes as endogenous. They also create two different models that look at movers and non-movers separately.
III. Empirical Model
The BMI model used in this paper is as follows:
given that i (i=1,…,N) indicates specific individuals, B0 is the intercept term, Bj (j=1,…,14) are the variable coefficients, and ɛi is the error term. The explanation of variables is given in Table 1.
Due to the fact that migration is a separate decision and difficult to factor into the model, the researchers decided to define the decision to move to a county as whether it is high or low density, income, education, marital status, and more. Therefore, their model for adjusted BMI on all the other variables as a follows:
Where y*I is the latent variable describing choice of a low or high density county, that i (i=1,…,N) indicates specific individuals, y1 and y2 are parameters on ADJBMIi and SPRAWLi, X1i and X2i are vectors of the exogenous variables, B1 and B2 are conformable parameter vectors (like race, sex, smoking, age, education, and regional dummies), and ɛ1i and ɛ2i are the error terms.
Using least squares and a probit maximum likelihood model, they created a set of covariant matrices of expected values for the endogenous variables. These estimates were made using data from the year 2000 in the NLSY79 and the sprawl index. To separate out movers from non-movers, the model was run twice, each time with only individuals who had lived in their counties for 4 years or more versus less.
IV. Results and Conclusions
The results of the simultaneous equation model suggest that BMI does, in fact, have a negative effect on whether an individual moves into a dense county. This holds true for both movers (coefficient -.789) and non-movers (coefficient -1.182). The researchers also accounted for the fact that their arguably arbitrary cutoff for density or their year cutoff for being a mover versus non-mover may have skewed the results. However, even with more conservative and liberal estimations of these cutoffs, their results generally held true. The implications of these results are that current policies about land use and public health may be misguided. Increasing infrastructure that encourages an active lifestyle in sprawling areas could just result on obese-prone people moving elsewhere.
The fact that Plantinga and Bernell challenged the assumption that sprawl causes obesity could have profound policy implications. With this discovery, money will be saved on fruitless or inefficient policies. Also, researchers are one step closer to discovering the root of obesity problems. Their research helps society edge closer to the true causes and possible solutions for obesity. This investigative research model could also be applied to other public issues related to urban sprawl. For example, one could research whether violent people move to sprawling or dense areas. Does density versus sprawl cause the violence or is it a result of the type of person who chooses to live there?
Andrew Plantinga and Stephanie Bernell, 2005, “The association between urban sprawl and obesity: is it a two-way street?”. Draft. americandreamcoalition.org *
Andrew Plantinga and Stephanie Bernell, 2007, “The association between urban sprawl and obesity: is it a two-way street?” Journal of Regional Science 47(5): 857-879.
*I used the 2005 draft because it explained which equations were used, while the 2007 version did not. The remainder of the article and analysis was largely the same.
By Emily Jorgens Energy and Urban Economics
Due to rising energy prices and recent attention surround energy consumption, an increasingly relevant area of academic interest is how urban systems are adapting. Historically, urban areas developed without constraints due to energy availability. The low price of energy led to highly dispersed urban landscapes. The value of clean air, larger properties, and the suburban lifestyle outweighed transportation and other energy costs. Today, in the face of energy constraints and rising energy prices, established cities have to adapt. This creates an opportunity to study the dynamics of urban development with respect to resource constraints. Research into this field can have many benefits, like shedding light on possible socioeconomic inequalities, as well as providing an efficient framework for developing cities to emulate.
Urban Form and Energy Consumption
There are two main points of view on how urban landscapes will respond to rising energy prices. Some authors argue that urban planning models indicate that the changing energy landscape will lead to a monocentric city. Other authors hold that semi-independent suburban centers of economic activity will result. The difference of opinion arises largely due to variation in the assumed cost of moving people versus goods. If goods are more expensive to move, a monocentric model will arise, and visa versa. Others argue that the reality seems to be something in the middle (Sharpe 1982).
Monocentric City Theory
A pertinent issue in the discussion of how energy and urban economics are related is whether different socioeconomic groups are impacted differently. Sharpe (1982) found that inefficiencies in urban planning do increase to socioeconomic inequalities. Specifically, he found that outer urban residents are affected most by rising energy costs. This is due to rising transportation costs and loss of value in property. Therefore, this model supports the shift to a monocentric city model due to rising energy prices.
Sharpe (1982) found that initially, rapid suburbanization, efficient public transport, rising wealth, and low energy prices led to sprawling development. Subsequently, as oil prices started increasing in the 1980s, evidence of socioeconomic discrepancies arose. In particular, low-income groups in outer areas who have low accessibility to public transportation carry most of the burden. This is because higher energy prices make it more attractive to live in urban areas, due largely to transportation costs. Low-income people are especially impacted by rising transportation costs because transportation takes up a larger percentage of their expenditures. Furthermore, low-income people are more likely to be burdened by changing land values across urban areas. Therefore, supporting the monocentric city theory, these socioeconomic groups are forced to move towards the city center for low-priced housing and short commute time (Sharpe 1982).
Semi-Independent Suburban Center Theory
In support of the semi-independent suburban center theory, one model holds that because energy reduction is easy, there is little incentive to relocate. A household’s energy costs can vary greatly based on commute distance, size of home, number of cars, age of household members, and number of people in a family (Small 1980). However, depending on the elasticity of demand for energy-using goods and services, consumers can adjust their consumption behaviors. Several studies demonstrate that “in those sectors most strongly affected by potential scarcity”, there are a variety of easy avenues to reduce energy consumption (Small 1980, 101). For example, a Resources for the Future study showed that simple home alterations can reduce heating costs by 20% (Schurr et al. 1979). Therefore, if energy prices rise, households can take simple steps to reduce their costs, rather than relocating to reduce costs. This supports the semi-independent suburban center theory because there is little incentive to move to a city center.
Rather than relying on theoretical frameworks, some economists assert that no conclusions can be made about location response to energy prices without quantitative evidence. They favor an empirical approach using data on energy consumption patterns together with cost data for city-suburban migration. This type of analysis more concretely sheds light on how energy scarcity might impact city form (Small 1980).
The 1980 Small paper empirically analyzes how energy use for urban versus suburban dwellers vary in terms of work travel, nonwork travel, and home heating/cooling. In order to quantify incentives for relocation based on cost differentials for work travel, Small (1980) used data from the 1975 Travel-to-Work Supplement of the Annual Housing Survey. The data allows for comparison of whether city or suburb residences are employed in the city or suburbs. The increased cost of the commute was calculated as 5 cents extra per mile for 240 round-trips per year. The data revealed that absolute increases are quite small for all four categories of commuters. Specifically, “city locations are less attractive by $33 per year per worker compared to the average suburb” (Small 1980, 108). Therefore, the data does provide weak evidence that more centralized suburban locations are more popular due to rising energy prices.
Small acquired non-work travel data from anther author who used household survey data to estimate car ownership and use. The cost differential was estimated using variables like single family versus multifamily home, owner-occupied versus rented, and suburban versus central city location. The cost difference for an urban versus city resident was estimated as $113 per car.
Finally, heating and cooling cost differences in suburban versus city centers were found using engineering studies on housing unit types in the four regions of the U.S. The calculation used weighted averages for energy consumption based on region and home type. Small (1980) found that, with a 75 cent per gallon increase, the cost differential ranges from $63 to $136 per year amongst the different groups.
One shortcoming that is acknowledged in Small’s 1980 paper is that this study does not completely quantify the net incentives for relocations. In addition to household modifications and buying more fuel-efficient cars, consumers can opt to carpool, etc. Quantifying all of these options would be arduous. Instead, Small chooses to assume that these alterations would impact different locations proportionally. Therefore, excluding these factors from the analysis gives the upper bound on relocation incentive.
Qualitatively these cost differentials total about $256 annually for households. It is important to remember that this study excludes any considerations of energy-saving behavior, like carpooling, that might result from increased energy prices. Small (1980) extrapolates on these findings by adapting a model that predicts household migration response to taxation disparities in cities versus suburbs. The multiple regression equation is not given in this paper. However, Small says that the model shows that if households reacted to energy price differentials in the same way as taxes, a $256 increase would cause a net outmigration of .56% from cities. Small (1980) concludes that location shifts within suburbs and cities may occur but that overall density and form will not change as a result of energy prices.
Urban Energy Footprint Model
Continued research in the field of urban and energy economics has lead to more robust models. For example, one of the most recent and comprehensive methods, developed by Larson et al. (2012), is the Urban Energy Footprint Model (UEFM). It shows how land use and transportation policy affect housing markets and transportation. Unlike most models in the field, Larson et al. (2012) includes income groups and traffic congestion as factors.
Congestion is found by v(k) = 1/(a-bV(k)c, where v(k) is the commuting speed, V(k) is the traffic volume through location k, and a, b, and c are parameters that reflect the severity of the congestion function. In order to determine the spatial structure of the housing market, the authors drew variables from the American Housing Survey, 2000 Census of Population database, and the Internal Revenue Service. In order to add the energy consumption variables to the spatial housing market data, this model drew from the Housing Assistance Supply Experiment (HASE) and estimates from household energy consumption equations, which are based on the 2005 Residential Energy Consumption Surveys (RECS) (Larson et al. 2012).
The paper concludes that the relationship between energy prices and urban form are significant. Specifically, Larson et al. (2012) hold that with rising fuel prices, the city becomes more compact in terms of reduced area and increased density. Also, low-income households are more impacted than higher income households due to a difference in income elasticity of demand. Interestingly, the increased fuel prices save more energy due to the shifts in the housing market than the reduction in driving. This bold conclusion differs from other research, but is perhaps more valid because of the inclusion of more variables (Larson et al 2012).
Conclusions and Further Research
Inspired by the findings in the vast literature surrounding energy and urban economics, there is room for further research into the socioeconomic differences in urban form response to energy. A combination of models that exists in the field today seems like the most viable way to answer the question of whether low income groups are more effected by rising energy prices and how that influences their movement in an urban sprawl.
Some limitations of the existing literature include that some studies focus mostly on oil prices rather than coal and natural gas because historically oil has been more scarce than the other two (Sharpe 1982). Another major shortcoming of in this field is that most of the research doesn’t factor in the differences in energy use across cities. Energy use can vary substantially based on the quality of public transportation, state gas taxes, environmental sympathy of the population, technology, and land use policies. However, the current models rely heavily on national averages for energy consumption. This leads to broad conclusions, rather than city-specific results.
Furthermore, the fact that the global energy landscape is so fast-paced must be taken into account. Many studies were conducted in the 1980s, which was a time of great pessimism towards energy resources. Today, we have improved extraction technologies for conventional resources, like oil and gas, as well as development of alternative and renewable energy sources (Small 1980).
It is important to understand the dynamics between urban and energy economics because, for example, energy policies may have unintended impacts on urban form and visa versa. The UEFM model lacks inclusion of technological improvements. Technological improvements can expect to impact the energy industry by reducing the need to travel, creating more energy efficient products, and by producing alternative forms of energy. Therefore, I think the model should include a variable that captures the increasing likelihood that these advancements will happen in the future. A proxy could be made for the state of technological advancement by comparing two similar cities in a developed versus developing nation.
William Larson, Feng Liu, Anthony Yezer. Energy footprint of the city: Effects of urban land use and transportation policies. Journal of Urban Economics. Volume 72. Issues 2–3. September–November 2012. Pages 147-159. (http://www.sciencedirect.com/science/article/pii/S0094119012000332)
Kenneth A. Small. Energy Scarcity and Urban Development Patterns. International Regional Science Review. Volume 5. Issue 2. August 1980. Pages 97-117. (http://irx.sagepub.com/content/5/2/97.refs?patientinform-links=yes&legid=spirx;5/2/97)
R. Sharpe. Energy efficiency and equity of various urban land use patterns. Urban Ecology. Volume 7. Issue 1. September 1982. Pages 1-18.
References within articles:
A. Davies and G. Glazebrook. Transport energy and equity: winners and losers. Australian Transport Research Forum Papers. 6th Meeting. October 1980. Brisbane. Brisbane Metropolitan Transit Authority. Page 227-247.
S. H. Shurr, J. Darmstadter, H. Perry, W. Ramsey, and M. Russell. Energy in America’s Future: the choices before us. 1979. A study by the staff of the Resources for the Future National Energy Strategies Project. Baltimore: the Johns Hopkins University Press.
By Jack Willoughby The Effect of Public Transportation on Crime
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):
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):
Aggregating (1) and (2), the net expected value of committing a crime is (3):
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):
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):
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):
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):
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
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.
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:
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.
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)
Appendix 2: Areas of Data Collection (photos taken from Google Maps)
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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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/
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line on crime in the neighborhoods.” (2003).
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Adjacent To Section B Of Baltimore Metro.” Transportation Research Record 1402 (1993).
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By Lisa Wang Urban Development and the Rise of China
The monocentric urban model has long generated conclusions about the correlations between urban expansion and the fundamental building blocks of economies. As the past few decades have been accompanied by the rapid evolution of developing countries, economists have relied heavily on the monocentric model to uncover trends in these urbanizing landscapes. In particular, the scale of urbanization in China is without precedent in history. Sixty years ago, merely 15% of people in China lived in cities. Today, urban settlers comprise of 45% of the overall population, with a projection of 60% by 2030. China’s economic boom has led to a drastic transformation in its urban landscape, and with a bright economic future ahead, there is no doubt that further transformations must take place in order for China to continue its journey towards becoming a developed country.
In examining the evolution of urban landscape in China, I will focus on three studies that explore the topic from different perspectives. In the first study, Land and residential property markets in a booming: New evidence from Beijing, Siqi Zheng and Matthew E. Kahn use the urban monocentric model to examine Beijing, additionally exploring how the capitalization of local public goods contributes to urban development. In the second study, Growth, Population and Industrialization and Urban Land Expansion of China, Xiangzheng Deng, Jikun Huang, Scott Rozelle and Emi Uchida, adapts the basic empirical monocentric model to urban China, determining some of the key variables driving the country’s urban expansion. Both studies introduced above conclude that the monocentric model does align with the urban development of cities throughout China. Finally, the last paper I refer to explores the rise of “megacities,” such as Beijing, and the necessary path towards polycentricity in order to achieve continued and balanced urban development. In Beijing-the Forming of a Polycentric Megacity, Dong Zhi and Kong Chen provide a thought-provoking analysis of the problems of a monocentric Beijing, and leave us with suggestions of how rapid urban development in China can be sustained without negative effects if Beijing becomes a polycentric megacity through the inclusion of its neighbors Tianjin and Tangshan.
1.1. Urban Expansion in Beijing
China’s economic boom has sparked explosive growth of new reconstruction in Beijing’s housing market. As the capital city of China, as well as the political and cultural hub of the nation, Beijing’s population grew by 40.6% between 1991 and 2005. Consequently, this steep rise in demand for housing in Beijing has sparked escalating real estate development, instigated by long-term leases from the government (urban land is owned by Chinese government), both through negotiations as well as rigorous auctions (Zheng et al., 744). Having been born in China and personally experienced what it was like before the real estate policy changes that took place in 1988, traditionally, Beijing’s urban land was assigned through a central planning system, where housing units were built in accordance to the location of workplaces, to provide subsidized housing for employees. After the reforms, old homes in Beijing were taken down to make way for more luxurious housing and commercial projects, significantly increasing the spread of the Central Business District, which represents the area surrounding the historic TianAnMen Square. In addition, it is important to note that after the implementation of the open-door policy, the structure of the Chinese economy transformed from an agriculture-based sector to a predominantly manufacture/service-based sector, which evidently affected urban land development.
Another critical factor that needs to be taken into consideration to clearly understand the role of Beijing is that it is one of four cities (known as municipalities) in China that act as provincial entities, with the autonomous right to govern all social and economic development within their jurisdictions. Furthermore, recent reforms promoting urbanization have resulted in the implementation of trial areas known as “Special Economic Zones,” where cities like Shenzhen and Tianjin receive privileges such as tax exemption, infrastructure construction and international trade. Since the 1980s, the gradual expansion of SEZs in China has created “windows” for foreign direct investment, generating foreign exchanges through exporting products and importing advanced technologies, ultimately accelerating the process of inland economic development.
1.2 Data Sets used in Beijing
Zheng and Kahn use three data sets to test the monocentric city model. The first is a housing project data set that contains a record of 920 new housing projects, with an average of 791 housing units in each, between 2004 and 2005. The second is a land parcel data set, which includes information about land parcel auctions from 2004 to 2006 (Zheng et al., 746). Both of the data sets above are used to analyze the prices of Beijing’s land and housing projects as a function of distance to the CBD. The third data set contains information on housing projects and their proximity to local public goods such as public transportation, educational institutions, crime levels and environmental sustainability, depicting how public goods capitalize housing prices. The results of the study conclude that the monocentric model is a good representation of Beijing’s urban development.
1.3 Testing the Monocentric Model in Beijing and Variable Specification
The empirical analysis is done through estimating hedonic pricing regressions. For housing projects, j represents a project at location q in year t. For land parcels, j represents a parcel at location q in year t (Zheng et al., 751). The regressions are controlled for the region of Beijing in which the land is located, partitioning the city center into four quadrants, with TianAnMen Square as the point of origin. This ensures that the differences in the various regions that result from factors aside from distance to CBD are captured. The estimation equation run for the land parcel data found that an extra kilometer of distance from TianAnMen Square decreases the price of residential and commercial land by 4.8%. When the regression was run for residential housing, the land price gradient dropped to 4.3%, indicating a higher value of land for commercial purchases. The second estimation run for housing projects predicted a 2% decrease in price per kilometer away from the CBD (Zheng et al., 751).
Furthermore, Zheng and Kahn’s inclusion of local public goods in determining housing prices is advantageous because the location of public goods is determined exogenously in China, due to the former central planning system. After running multiple regressions, it was found that the explanatory power increases when controlling for distance to local public goods, where air quality, parks, universities and schools have an impact on home prices, while transit and crime have no significant effect (Zheng et al., 754). Since this paper was published in 2005, it would be interesting to see whether the transformation of the public subway system, which has expanded to 14 lines and now ranked fourth in the world, would be significantly relevant today.
2.1 Monocentric Model of China and Measures of Urbanization
Deng et al. tested the hypotheses of the monocentric model throughout China, through a unique three-period panel data set of high-resolution satellite imagery data and socioeconomic data for entire area of coterminous China. The testing of the model utilizes four key determinants: income, population, agricultural rental, and transportation costs. Included is also a time trend variable to control for five decades worth of data to capture the time-variant unobservable factors. Methodologically, the study relied on the OLS estimator (Deng et al.,6).
The unit of measure in the study is the county, which is the third level in the administrative hierarchy in China, below province and prefecture. With over 2000 counties in China, this analytical unit can be regarded as both an administrative and economic region, which has the power to determine its own land usage (Deng et al., 8). Within the county, areas are broken up in the urban core, rural settlements and other built-up areas, where counties with more than one urban settlement make up the urban core. Further, the expansion of the urban core throughout time is defined as the built up area. Rural settlement refers to built-up areas in small villages (Deng et al., 9).
The focus of analysis is on the urban core, and to overcome the administrative shifts in county boundaries, two counties that had been subject to border shifts would be combined into a single unit. China’s four provincial municipalities would encompass all cities within its administrative region, which resulted in a total of 2,348 analytical units (Deng et al., 11). Two key control variables are the measure of distance in kilometers from a county to the provincial capital, and the measure of distance between a county and the nearest port city. Data showed that changes in urban core was significantly associated with changes in GDP, as well as the rate of growth in industry and rate of growth of the service sector, which is consistent with the monocentric model (Deng et al., 15).
2.2 Empirical Model and Variable Specification
The equation above models the differences in the spatial scale of cities across space and over time, where UrbanCore is the total area found in the ithcounty in year t. The explanatory variables include GDP, Population, AgriInvest (measure of investment allocated to agriculture is proxy for rent) and HwyDensity (proxy for commuting costs). The measure of industrialization is constructed as the value of GDP from the industrial sector divided by total GDP (GDP2_share), and the same measure
was created for the service sector (GDP3_share). Control variables include climate, elevation, terrain and distances from provincial capitals and port cities (Deng et al., 23).
The results of the multivariate analysis show that growth in GDP is highly correlated to the expansion of the urban core, with a coefficient of 0.397, representing at 3.97% expansion of the urban core for every 10% growth in GDP. Population is significant and positive with a coefficient of 0.057 while AgriInvest is negative, which is in accordance with the monocentric model. The coefficient on transportation cost is also positive, indicating that when transportation networks are well developed and commuting costs are low, the urban core should expand more. Further, when geophysical factors are included in the model, the coefficients of the variables retain their same signs (Deng et al., 26). Ultimately, the paper concludes that when the monocentric model is applied to cities in China, there is fairly high explanatory power, highlighting that if China wants to continue growing at high rates, urban expansion is essential.
3.1 Beijing’s Path to Polycentricity
In contrast to the studies above, Dong and Kong examine the transformation of Beijing into a “megacity,” an emergent concept that falls under a subgroup of a metropolitan region with over ten million people (Dong & Kong, 13). In the recent decades, the emergence of Asian megacities have surpassed the growth rate of those in developed regions, with an average population density of 8,800 persons per km2, double that of developed countries. The paper delineates the advantage of transforming Beijing from a monocentric megacity with a primary CBD, to a polycentric region comprised of Beijing, Tianjin and Tangshan (BTT) (Dong & Kong, 10). The monocentric Beijing megacity model has many flaws, including the capital city’s unique restrictions on development, the intensification of traffic jams, decreased quality of life due to high population density, and the destruction of historical monuments (Dong & Kong, 48). The polycentric transformation of Beijing is a natural advantage that will increase the quality of labor force, release traffic congestion, create access to more raw resources, halt the urban development contaminating the vicinity of the historic center, and ultimately balance urban growth through each region’s specializations. The original plan of transforming Beijing into an international exchange center aligns with the development of a polycentric city, where economic functions can be transferred to areas within the polycentric Beijing and ease the excessive functional pressure within the city (Dong & Kong, 53). The urbanization of many Chinese regions similar to the BTT will also have to adopt a polycentric model in order to achieve balanced development.
In an age of rapid growth and expansion in the developing world, it is imperative to evaluate the efficacy of smart growth conditions to ensure sustainable urban development. After two decades of rapid economic growth, urbanization in China threatens to produce damage to the environment, shortage of land resources, and social inequality. Through the investigation of the various studies presented above, I aim to uncover some of the fundamental factors that impact urban growth in Chinese cities, in accordance to classical urban economic models. To further this discourse, I have touched upon some of the concerns illuminated by Dong & Kong, regarding the sustainability of the monocentric model in megacities such as Beijing, and the benefits of moving towards a polycentric model. As large megacities such as Beijing continue to expand, polycentric urban development seems to be a natural transition, improving commuting patterns, reducing congestion, lowering development costs and increasing administrative efficiency.
1. Deng, Xiangzheng, Jikun Huang, Scott Rozelle, and Emi Uchida. “Growth, Population and Industrialization and Urban Land Expansion of China.” (2006): 1-39. Print.
2. Zheng, Siqi, and Matthew E. Kahn. “Land and Residential property markets in a booming economy: New evidence from Beijing.”Journal of Urban Economics 63 (2007): 743-57. Print.
3. Zhi, Dong, and Kong Chen. “Beijing- the Forming of a Polycentric Megacity.” (2011): 1-73. Print.
By Matthew P. Lee 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.
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:
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:
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.
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.
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 Economics, 5, 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
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.
II. DATA & METHODOLOGY
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.
III. EMPIRICAL RESULTS & ANALYSIS
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.
IV. CONCLUSIONS & LIMITATIONS
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.
Espey, M., & Owusu-Edusei, K. (2001). Neighborhood parks and residential property values in
Greenville, South Carolina. Journal of Agricultural and Applied Economics, 33(3), 487-492.
Haurin, D. (1988). The duration of marketing time of residential housing. Real Estate
Economics, 16(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 Economics, 19(2), 113-131.
Turnbull, G. K., Dombrow, J., & Sirmans, C. F. (2006). Big house, little house: relative size and
value. Real Estate Economics, 34(3), 439-456.
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.
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,
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.
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.
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
See attached Excel file Lillington David_Southside+Mortgage+Scenarios
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:
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.
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.
By Emma Etheridge CRIME IN MOBILE HOME COMMUNITIES IN DURHAM, NC
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.
CURRENT MOBILE HOME RESEARCH:
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.
CRIMINAL ACTIVITY IN DURHAM:
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.
FINDINGS AND ANALYSIS
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.
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.
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