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 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 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 Mischa-von-Derek Aikman Gentrification’s Effect on Crime Rates
Many scholars have explored the behavior of crime rates within neighborhoods that are considered to have been completely gentrified, or are still currently undergoing the process of gentrification. They do this largely by studying the changes in crime trends in numerous neighborhoods that display typical characteristics of gentrification. This literature survey pays careful attention to the definition used to select examples of gentrified neighborhoods for examination. It will also exert the claim that crime rates of particular categories seem to rise on average within these neighborhoods upon the commencement of gentrification. It will look at the models used to normalize crime rates across neighborhoods of different populations and densities, as well as those that account for the issue of crime rates regressing towards the mean. Using these normalized statistics, the survey will outline conjectured reasons as to why crime rates seem to rise in gentrified neighborhoods.
Defining and Selecting Gentrifying Neighborhoods
The issue of neighborhood selection proves to be inherently complex as the literature quickly realizes that one of the root difficulties is that what is considered a “gentrified/gentrifying” neighborhood is subject to many different definitions and interpretations. When selecting “gentrifying” neighborhoods for study, it is important to differentiate between neighborhoods that are simply experiencing a cycle of appreciation, and those that are truly gentrifying (Taylor, 1989). In other words the average increase in dollar value of houses and land alone do not define a neighborhood that is gentrifying per se as this can be the result of inflation in the larger housing market (McDonald 1986). Additionally, McDonald (1986) distinguishes between gentrification and “incumbent upgrading” in which current residents improve housing stock, and there is no apparent population change. Rather, Taylor (1989) defines gentrification as “the migration of younger, middle-, and perhaps upper-income households into centrally located urban neighborhoods and the accompanying upgrading of the worn-out housing stock that previously had “filtered down” to lower-income occupants.” It is also commonly accepted that gentrification is accompanied by the inevitable displacement of lower-income residents who previously resided in these neighborhoods (Taylor 1989).
Even with this relatively common definition, methods of choosing neighborhoods for study vary between authors. McDonald (1986) selects a sample of fourteen neighborhoods in which “gentrification has been reported.” These neighborhoods were all located in Boston, New York, San Francisco, Seattle, and Washington, D.C. The literature studied these particular neighborhoods based on various principles. Most importantly, they were chosen due to the availability of time-series crime statistics between 1970 and 1984, as well as an attempt to capture neighborhoods that underwent both commercial and residential gentrification (McDonald, 1986). However, this methodology used by McDonald did not go on to compare gentrifying neighborhoods with non- gentrified neighborhoods, and was generally arbitrary in its selection process (Taylor 1989).
Negative Impacts of Displacement
Given the definition of gentrification used in this survey, displacement proves to be a necessary byproduct. Atkinson (2002) uses cross-sectional data in gentrified neighborhoods where population outflow exceeds citywide averages to determine the extent to which displacement becomes an issue. Atkinson (2002) argues that displacement is typically short lived, but may be prolonged depending on the rate of inflow of new residents. Although using less quantitative methods, Atkinson outlines issues that arise from displacement, which contribute to an environment conducive to increased crime. These include evictions due to the inability to afford the rising price of rent associated with gentrification. This inevitably leads to increased homelessness directly through the loss of Single Room Occupant (SRO) dwellings (Atkinson 2002). However, Atkinson admits that there was no conclusive evidence confirming that the loss of SRO’s were caused by gentrification directly, and not by occurrences in the wider housing market. Atkinson’s research on crime directly produced contradicting results (which we explore in more detail). While crime seemed to fall in some gentrified neighborhoods, others showed that crime actually increased within certain categories (Atkinson 2002). There is also the issue of social conflict sparked by the presence of new residents with “different cultural backgrounds” (Atkinson 2002).
Expectations Surrounding Gentrification’s Effect on Crime
Rational expectations about gentrification’s effect on crime can be made in either direction. We can expect a general decrease in crime due to the fact that statistically, middle to upper income residential spaces typically have lower crime rates (Taylor 1989). Additionally, the more affluent people migrating into the neighborhood are more likely to have more political influence, and can therefore successfully request a heightened police presence (Taylor 1989). We can also reasonably expect increased crime in gentrified areas due to the fact that displaced young adults may move to neighborhoods within close proximity of their original homes, and may view their wealthier replacers as more attractive targets (McDonald 1986). Another practical reason for crime rates to rise is that the presence of richer residents living among those who would typically be below the poverty line could feed an atmosphere of social conflict (McDonald 1986). This occurrence has the potential to manifest itself in physical violence between cohabitants.
As mentioned before, McDonald (1986) utilized the time-series data from 14 arbitrarily chosen gentrified neighborhoods to determine plausible effects of gentrification on crime. While his findings are also discussed in the ‘Results’ portion, this section will look more closely the approach used in Taylor’s (1989) study. Taylor (1989) utilizes census data available for all 277 Baltimore City neighborhoods, and 1979 – 1980 Part I crime data for the same neighborhoods. To obtain the ‘beginning of the decade’ and ‘end of the decade’ crime counts for each offense, 2-year averages were used (1970 and 1971 for beginning, and 1979 and 1980 for the end). Crime counts were then divided by respective total neighborhood population in order to calculate crime rates per 100,000 (except for burglary which was divided by number of households).
In order to capture neighborhood dynamics ‘in the context of what was happening in other neighborhoods,’ both the predictor and outcome scales were made relative. Therefore, both beginning and end of decade crime rates were transformed to weighted percentile scores. With this information, Taylor (1989) was able to rank all the neighborhoods (relative to one another) while accounting for each respective population size. This rank is essentially an ordinal representation of the various crime rates for all the neighborhoods. Taylor (1989) found this measurement attractive, as its skewness (measure of the asymmetry of the probability distribution) is lower than that of logged or raw crime rates.
As opposed to simply taking the difference between scores to determine change in crime rates from year to year, residualized change scores were used (Pt = A + BPt-1 + e). The residual itself (e) should theoretically represent the unexpected change from year to year as it was (as per the assumptions of regression) uncorrelated with the predicted scores. Additionally, each parameter value for 1980 was regressed on its respective 1970 score. The residual generated from this regression was used as the gauge of change (Taylor 1989). Ultimately, these regressions controlled for fluctuating population levels throughout the time period, as well as for each neighborhood’s respective initial level of crime.
Furthermore, in an attempt to provide a more “clear-cut” method of identifying gentrified neighborhoods, Taylor (1989) utilizes a single (not multiple) indicator of gentrification. This measure uses a census-based item whereby households were polled, and asked to provide the current market value of the homes. Using this information, a ‘dynamic index’ representing the appreciation in neighborhood house values was constructed to determine a house-value percentile score for each neighborhood (Taylor 1989). This was done by using a regression model similar to that used in the development of percentile scores for the crime rates. Calculating the percentile changes in house values produced residuals that accounted for the unexpected increases or decreases given the neighborhood’s initial house-value score. Controlling for initial levels accounts for the “regression to the mean” issue, and since the only relevant factor is the ordered ranking of the neighborhoods at any given point in the time-series, inflation is also accounted for (Taylor 1989).
Finally, it was determined that neighborhoods with very high residualized relative house- value scores were those neighborhoods that truly “gentrified” (and not merely experienced appreciation). This obviously presented the issue of determining a “cut off point” (how far down that list would be considered as gentrified neighborhoods?). For this reason, the study was conducted with the top 15, as well as the top 20 scoring neighborhoods (Taylor 1989).
Using the regression analysis outlined in Taylor’s (1989) paper, it was concluded that gentrification was associated with unexpected increases in both larceny and robbery. This result held true both when using the top 20 gentrifying neighborhoods, as well as the top 15.
According to McDonald’s (1986) study on the 14 neighborhoods (See attached table for details on crime rates for each neighborhood), every gentrified neighborhood studied had total Index crime rates above the average of their respective cities. It most be noted, however, that these observations were based on ‘per capita’ crime rates. This poses an issue since the population of almost all these neighborhoods declined during the period of observation. Therefore, these rates could have been just as influenced by population fluctuations as they could be by the actual shift in number of crime incidents (McDonald 1986). More specifically, McDonald (1986) notes that presence of higher crime rates in the gentrified neighborhoods were actually lower for personal crimes, but higher for property crimes (with a few insignificant exceptions). Table 1 (attached in the appendix) indicates that the effect of gentrification on crime is not of a linear nature. The crime rates rise to a significant climax in 1980, and then subside again shortly after (McDonald 1986).
This tells us that the time frame of the observations plays a crucial role in the results one gets. In an attempt to correct for this, McDonald (1986) calculates each neighborhood’s crime rate as a ratio of their respective citywide rate (these values can be seen in Table 2). The results show significant declines in personal crime from what they were in 1970 in 6 of the 14 neighborhoods (McDonald 1986). The analysis of property crime rates showed just the opposite result. Property crime rates for all but one neighborhood showed a decline (McDonald 1986). Finally, it was the general observation that despite the apparent decline in personal crime rates, most of the gentrified neighborhoods maintained crime rates higher than their citywide averages (McDonald 1986).
Questions Moving Forward
Obviously, the results of these two studies produce slightly contradicting results (Atkinson 2002). Whereas McDonald observes a decline in personal crimes and an increase in property crimes, Taylor observes an increase in both. An interesting exercise would be to conduct McDonalds’ methodology of determining trends in crime rates on those neighborhoods selected using Taylor’s (1989) regression analysis. Additionally, studying time-series data for more than a decade could shed light on the results’ sensitivity to the span of time we noticed in McDonald’s piece. Lastly, it would be interesting to explore the existence of any implemented policies used as a response to heightened crime in gentrifying neighborhoods, as well as any influences these policies might have on the crime rates themselves.
Table 1: Table Showing Crime Rates in Selected Cities and Neighborhoods, 1970-84 (McDonald)
Table 2: Table Showing Crime Rates of Selected Neighborhoods, Indexed to the Crime Rates of Their Cities, 1970- 84 (McDonald)
- Atkinson, Rowland, Dr. “Does Gentrification Help or Harm Urban Neighbourhoods? An Assessment of the Evidence-Base in the Context of the New Urban Agenda.”ESRC Centre for Neighbourhood Research (2002): n. CNR. Web. 06 Feb. 2014.
- Covington, Jeanette, and Ralph B. Taylor. “GENTRIFICATION AND CRIME Robbery and Larceny Changes in Appreciating Baltimore Neighborhoods During the 1970s.” Urban Affairs Quarterly 25 (1989): n. pag. Sage Publications, Inc. Web. 06 Feb. 2014.
- McDonald, Scott C. “Does Gentrification Affect Crime Rates?” Chicago Journals(1986): n. The University of Chicago Press. Web. 06 Feb. 2014.
By Gabrielle Ware Which Characteristics of Schools Affect House Prices?
When looking for a new home, buyers consider many factors, including neighborhood appearances and demographics, proximity to city centers, safety, property tax rates, and the quality of local school districts and other public services. Specifically, buyers give considerable attention to public school quality and researchers have become increasingly interested in which school attributes are most valuable to home buyers, and hence, influence house prices most strongly. Traditionally, test scores have been used as a good indicator for school quality and have been associated with higher real estate prices; however, the relationship between school characteristics and local home values has been extremely difficult to quantify. The researchers of each of the following papers found unique methods to overcome various data shortcomings.
Downes and Zabel (2), Kane, Riegg, and Staiger (3), and Clapp, Nanda, and Ross (1) all examined the importance of district performance and demographic composition on property values in Chicago from 1997 until 2001, Mecklenburg County, North Carolina from 1994 until 2001, and Connecticut from 1994 until 2004, respectively. For each paper, the researches compiled data from multiple sources in order to eliminate bias from excluded variables and to quantify the effects of specific school attributes on house prices. Although there are similarities and differences in their methods, all three groups of researchers made conclusions largely consistent with one another.
While examining previous work, Downes and Zabel (2) identified two major shortcomings: the lack of controls for intra-jurisdictional variation in schools and for neighborhood quality when trying to determine which school attributes were relevant to home value. In order to combat these, Downes and Zabel (2) combined data from the Chicago Metropolitan Statistical Area (MSA) America Housing Survey (AHS) from 1897 until 1991, the Summary Tape Files (STF) from the 1980 and 1990 Decennial Censuses, and Illinois School Report Cards from 1987-88 until 1991-92. The AHS data include house characteristics and owner reported home information for a random sampling of homes, which eliminate the bias that arises from only using sales data and neighborhood characteristics that are available in the STF data. Additionally, Downes and Zabel sorted the Census and AHS data by census tracts, nearly homogenous areas of 2,500 to 8,000 occupants, enabling more accurate control over neighborhood variation.
To study the effects of a court-imposed desegregation order to redraw school boundaries in Mecklenburg County, North Carolina, Kane, Riegg, and Staiger (3) faced the same challenge faced by Downes and Zabel (2): distinguishing between the influence of school quality and other contributing factors on house prices. To combat this, they used home sales data from 1994 until 2001 from the county’s Property Assessment and Land Record Management Division. Furthermore, data from the tax assessor’s office was used to divide Mecklenburg County into 1,048 homogenous neighborhoods, which was combined with demographic information from the 1990 and 2000 Decennial Censuses and census tract information. Information about school boundaries throughout the rearrangement was obtained from the Charlotte Mecklenburg School District (CMS) from 1993 until 2001 and information on individual school performances and demographics was provided by the North Carolina Department of Public Instruction from 1997 though 2001.
Finally, Clapp, Nanda, and Ross (1) studied the influence of school district performance and demographic composition on home values in Connecticut, as well as whether or not the importance of these indicators varies over time. They used data from a sample of home sales from 1994 through 2004 purchased from Banker and Trademan, which was combined with census tract information from the 1990 Decennial Census to control for neighborhood variation as in the previous two studies mentioned. This data also included information to control for house characteristics. To account for the possibility that housing price appreciation or depreciation may vary regionally or by market, Clapp, Nanda, and Ross (1) included separate year and month fixed effects for each of the ten Labor Market Areas (LMAs) in Connecticut. Information on school attributes from 1994 until 2004 was provided by Connecticut public schools.
Methods and Results
For their work in Chicago, Downes and Zabel (2) first examined the correlation between school characteristics and neighborhood values. Many variables were significantly correlated suggesting that excluding neighborhood controls would bias the coefficient estimates for school characteristics when determining their influence on house prices. Next, Downes and Zabel (2) used multiple regressions, shown in Table 1, with the natural log of the owner estimated home value as the dependent variable to estimate the importance of six measures of school quality for both the district and school level data.
The six district/school quality measurements used were the proportions of African- American students, Hispanic students, limited English proficiency students, and subsidized lunch eligible students, as well as the natural logs of district per pupil expenditures and eighth grade reading tests. A comparison between the coefficients of the pooled regressions (with neighborhood variables included) estimated using the district level and the school level data revealed many biases that can arise from the use of district level data. First, homeowner’s sensitivity to the racial composition of the local school was hidden, and second, the effects of both the school cost variables were of a lower magnitude when district level data was used. There was no significant difference on the importance of test scores. After establishing that both neighborhood and intra- jurisdictional variations could not be excluded, the pooled regression with neighborhood variables included for the school level data became their favored regression, shown in the second results column of Table 1. From this specification, Downes and Zabel (2) concluded that both the proportions of African-American and Hispanic students in the local school had significantly negative effects on home values, while the proportion of limited English proficiency students and the natural log of district per pupil expenditures had significantly positive effects on home values. This is consistent with the argument that homeowners and researchers measure school quality differently. The first-difference regression results are similar, which shows that proper controls for neighborhood and house characteristics remove the need to control for temporally stable, unobserved house and neighborhood characteristics. And finally, the value-added regression tests the hypothesis that the relationship between house prices and standardized test scores is temporally stable. Although, they were unable to reject this null hypothesis, Downes and Zabel (2) note that the direction of change of the coefficient estimates is consistent with the expectation that as states have been more conscious of making school performances public, the correlation between housing prices and standardized test scores will strengthen.
Unlike Downes and Zabel (2), Kane, Riegg, and Staiger (3) were able to use the redistricting of schools to their advantage while trying to isolate the effects of school quality on home values. Their empirical strategy was two-fold, focusing first, on housing values near school boundaries (houses in the same neighborhood assigned to different schools) and second, on house values for homes affected by the court ordered redistricting. To study housing values near school boundaries, Kane, Riegg, and Staiger (3) ran a series of regressions to track the changes in coefficient estimates for elementary school test scores and distance to the elementary school while increasing controls for housing and neighborhood characteristics around each boundary. The natural log of the sales price was the dependent variable in these regressions. Shown in Table 2, all specifications supported the conclusions that mean test scores have a significant positive correlation to property values; however, this impact decreases in magnitude as more controls are added. The distance of a house from its school assignment was found to have a negative relationship with house price, but this relationship became insignificant as more controls for house and neighborhood fixed effects were included. These same specifications were also used for other measures of school quality, including the proportions proficient on the state test and African-American, the median household income, and the “value-added” test score between 1994 and 1999. The proportion of students scoring at the proficient level on the state test and medium income both had significant positive relationships with house prices, as expected, while the proportion of African-American students in the school had a significant negative relationship with house prices. The coefficients for “value-added” were not significantly different from zero in any of the specifications, implying that prospective buyers observe characteristics of potential peers, instead of “value-added” to measure school quality. This is consistent with the finding of Downes and Zabel (2) that homeowners measure school quality differently than researchers do.
Kane, Riegg, and Staiger (3) also examined the relationship between school characteristics and house prices using solely differences in redistricting. As shown in Table 3, all specifications included controls for housing and neighborhood characteristics, as well as fixed effects for every reassignment. The three measures of school quality included were the percent of African-American students, the median household income, and the percent of proficient students on the state test. The percent of African-American students had a significant negative impact on house prices while the average median income and percent proficient had positive effects on house prices at the high school level. At the middle school and elementary school levels, the measured effects were either insignificant or only marginally significant. Both empirical strategies Kane, Riegg, and Staiger (3) confirmed the presence of residential sorting as additional indirect impacts resulted because the population living in any given school boundary is itself a function of the school assignments.
Similarly, Clapp, Nanda, and Ross (1) used three specifications to observe the effects of school attributes using the same dependent variable as Kane, Riegg, and Staiger (3), the natural log of the transaction price. Shown in Table 4, the first results column uses the traditional hedonic regression without controlling for town or census tract effects, the second column presents the regression after controlling for town fixed effects, and the third column after controlling for census tract fixed effects. School attributes included in their study were Math test scores and the fractions of free lunch eligible, non-English speaking, African-American, and Hispanic students. Like the researchers who conducted studies in Chicago and Mecklenburg County, Clapp, Nanda, and Ross (1) found that the effects of school district attributes were sensitive to which specification was used.
The most basic OLS model, controlling only for neighborhood observables, overestimated the effect of test scores on housing prices, and gave coefficient estimates for the effects of the fractions of non-English speaking, African-American, and Hispanic students that were inconsistent with previous findings. As seen before, this regression overestimated the effect of school quality of housing prices. In the second and third columns, the coefficient estimates are not statistically different from one another; however, Clapp, Nanda, and Ross (1) favor the regression that includes census tract fixed effects. This model implies that test scores have a significant positive effect of home values while greater fractions of African-American and Hispanic students have a significant negative effect on home values. These effects are of similar size as the ones measured by Downes and Zabel (2) and Kane, Riegg, and Staiger (3). Clapp, Nanda, and Ross (1) took their study one step further to explore whether or not the effects of key district attributes have changed over time. They found that the effects of the fraction of Hispanic students and test scores are changing over time, becoming less negative and more positive, respectively. This is the type of change that Downes and Zabel (2) suggested, but were unable to confirm statistically.
The three studies surveyed in this review yield many similar conclusions regarding which school attributes influence house prices. First, homebuyers are concerned about changes in the demographic makeup of the local school, in addition to, and sometimes more than, test scores when deciding how much to spend on a home. Second, two sources agreed that prospective homebuyers do not measure school quality in the same value-added way that researchers might. Instead, they rely on many observable factors, such as demographics of their peer groups, to measure school quality. And third, it would be interesting to see if the way homebuyers measure school quality will change as school performances on standardized tests are made more public. One source was able to confirm that this is the case, while yet another suggested it without providing statistical verification. All of this leads to the question of whether homebuyers actually cared more about certain observable demographic factors or if they just used them as indicators because they were more easily accessed. The decline in the use of certain demographic factors as key indicators may also suggest changing opinions regarding race that would be interesting to explore.
- Clapp, John M., Anupam Nanda, and Stephen L. Ross. “Which school attributes matter? The influence of school district performance and demographic composition on property values.” Journal of Urban Economics63.2 (2008): 451-466. http://digitalcommons.uconn.edu/cgi/viewcontent.cgi?article=1094&context=econ_wpape rs
- Downes,ThomasA.,andJeffreyE.Zabel.”Theimpactofschoolcharacteristicsonhouse prices: Chicago 1987–1991.” Journal of Urban Economics 52.1 (2002): 1-25. http://theunbrokenwindow.com/Research%20Methods/Hedonic_school.pdf
- Kane, Thomas J., Stephanie K. Riegg, and Douglas O. Staiger. “School quality, neighborhoods, and housing prices.” American Law and Economics Review 8.2 (2006): 183-212. http://www .dartmouth.edu/~dstaiger/Papers/KaneRieggStaiger%20NBERwp11347.pdf
Table 1: Downes and Thomas (2) – Regressions Using School level Data and the Natural Log of Owner Estimated House Value as the dependent Variable
Note: controls for variations house and neighborhood and neighborhoods characteristics were included in this regression but are not pictured below
Table 2: Kane, Riegg, and Staiger (3) – Sensitivity of Regression Estimates to Neighborhood and Housing Characteristic Controls
By Peter Struckmeyer Mass Transit and Its Externalities
Within urban economics, public infrastructure is an area of interest when evaluating a city’s economic potential. In particular, in larger cities, mass transit plays an important role in the overall economic development of a region, since it is the most basic means of connecting individuals and businesses together. Despite this, the literature regarding the benefit of mass transit in urban development suggests that mass transit can bring more harm than good. This literature survey will examine several arguments regarding the effectiveness of mass transit on urban economic development, and it will analyze the gaps that exist in the current discussion regarding urban mass transit.
One criticism regarding mass transit is its overall social cost. The argument that Clifford Winston and Vikram Maheshri (2007) present regarding the relationship between mass transit and social cost is that the addition of urban transit systems reduces social welfare more than it benefits urban areas. In order to investigate this hypothesis, Winston and Maheshri use a model of supply and demand to explain the welfare effects associated with mass transit. On the demand side, the quantity demanded is hypothesized to depend on the average fare, as well as exogenous variables explaining the rail network (including density, average line length, and number of links) and city characteristics. On the short run costs side, their model breaks down short run costs based on output (in passenger miles), factor prices, and exogenous variables for network variables and other influences on cost. The study uses twenty-five different urban transit systems in the U.S. as its sample, though it should be noted that roughly two-thirds of rail miles analyzed come from New York City’s transit system, since its traffic greatly exceeds all other urban transit systems in the study (Winston and Maheshri 2007).
After testing the significance of the variables comprising the demand and short run cost curves, Winston and Maheshri compare the consumer surplus and the deficits created by urban mass transit systems by integrating the difference between the inverse demand function and the cost function, where the resulting area represents the net benefits associated with an urban rail system. The benefits demonstrated pale in comparison to the costs associated with creating and maintaining the transit systems; the aggregate net benefits for all cities study was -4496 (in millions of 2000 dollars). In fact, with the exception of San Francisco’s BART system, social net benefits are negative
for all transit systems (Winston and Maheshri 2007). A contributing factor to this phenomenon is that urban transit systems are largely funded through taxation, which creates a deadweight loss that is measured by the model. Additionally, there is insufficient evidence to suggest that other externalities – such as increased safety and commercial development – are caused by developments in urban mass transit. Winston and Maheshri argue that overall, urban transit systems continue to be an investment only because of the prestige and image associated with having a mass transit system in a city, since all other effects on social welfare are negative.
While Winston and Maheshri’s study constructs a quantitative framework to evaluate the effects of an urban mass transit system, the slant of study’s sample blurs the accuracy of its claims. The study uses New York City as its primary example, and though it explores several other urban transit systems, its use of New York City, easily the largest urban transit system of the sample, as its primary example exaggerates the effect that an urban mass transit system can have on social welfare. Because of this, it is difficult to conclude that adding an urban mass transit system would be harmful for any city, since the effects found in New York City are not representative of the effects in all cities. Other studies, by contrast, are more even in their coverage of their samples. Nathaniel Baum- Snow and Matthew Kahn (2005) apply a more balanced analysis when evaluating the effects of urban transit expansion. Their study investigates trends in overall transit ridership over time, and it explores possible benefits associated with increased ridership.
Unsurprisingly, the study echoes the sentiment presented by Winston and Maheshri – based on historical data, overall ridership has declined over time both due to the number of alternatives to public transportation and the decentralization of cities. But unlike Winston and Maheshri, Baum- Snow and Kahn approach their conclusions regarding urban mass transit’s utility by evaluating commute time. Their model focuses on bid-rent curves, which track real estate prices as one moves further away from the center of a city, in order to evaluate the benefit associated with living closer or further away from a central business district (CBD). Taking into account the price of the land, the study evaluates the added benefit of time saved by living closer to the city center. Of the cities surveyed, Washington has the highest amount of hours saved, with more than 50,000 hours saved per workday. When put into a monetary value, the estimated worth of Washington’s metro system exceeds $1 million a day (Baum-Snow and Kahn 2005). As a part of the overall discussion regarding mass transit, Baum-Snow and Kahn’s finding adds valuable insight to the conversation by recognizing the value that mass transit can have outside of strictly monetary terms.
Despite this reported benefit, the study also acknowledges that not every aspect of mass transit is beneficial. In particular, Baum-Snow and Kahn argue that there is insufficient evidence to suggest that pollution and congestion, two negative externalities associated with vehicular transit, have decreased due to urban rail transit systems. As a policy recommendation, the study suggests that future investments be more focused on increasing bus access rather than rail investments, since bus routes are less costly to establish and connect suburb areas better than rail systems. Additionally, because pollution and congestion were not found to be significant, fears about buses causing one or both of those factors to increase are unfounded (Baum-Snow and Kahn 2005). In total, Baum-Snow and Kahn’s analysis furthers the discussion regarding urban mass transit by offering both the pros and the cons to mass transit systems. By evaluating both sides of the argument, the study demonstrates why mass transit is still a debated topic today, since there are both advantages and disadvantages to its presence in a city.
Other studies suggest that urban rail transit systems may actually lead to increased centralization of poverty in cities. In their analysis, Edward Glaeser, Matthew Kahn, and Jordan Rappaport (2008) investigate why poor populations tend to live in urban areas. Using geocoded census data, the study finds that the poverty rate is 14.5% in the city center, while further away from the CBD, the poverty rate is only 8.3% (Glaeser, Kahn, and Rappaport 2008). The study attributes high concentrations of centralized poverty in cities to the notion that richer individuals migrate further away from the city where land is cheaper so they can own bigger homes, while the poor live in the city center instead. In order to test the hypothesis regarding poverty sorting in cities, the study regresses the log of land size and the log of income. The study then regresses time to work and the distance from work in order to test if transportation is a contributing factor toward the phenomenon of urban poverty sorting. The result of this is that nearly three-quarters of income sorting of poor individuals to the city center is due to public transportation (Glaeser, Kahn, and Rappaport 2008). The study concludes by suggesting that transportation-mode choice is a determining factor in poverty sorting; thus, mass transit systems will contribute to the phenomenon of centralized urban poverty because of its appeal as a mode of transportation to lower-income individuals. It is important to note that although mass transit systems may caused centralized urban poverty, the existence of centralized urban poverty may still be preferable to other alternatives. Regardless, Glaeser, Kahn, and Rappaport’s investigation is significant toward the overall discussion regarding mass transit, because it establishes a causal link between mass transit and heightened levels of urban poverty.
The three studies mentioned provide several arguments to suggest that mass transit systems have negative externalities associated with them. In particular, the studies focus on trends associated with income, commute times, and overall cost of construction and maintenance as the primary phenomena associated with mass transit systems. What the literature does not adequately explain, however, is business development surrounding metro stations. The three papers analyze the issue of mass rail transit based on the costs to create the infrastructure, the commuting cost, the cost of land, and the income of nearby residents. Although all of these factors could be negatively affected by the development of metro lines and other forms of mass transit, these negative externalities could also be offset by positive externalities created by new business that mass transit brings to an area. Metro stations typically exist near high traffic, high business areas of cities. By establishing a rail transit line to an area, the cost may outweigh the business the rail itself generates, but if business near the metro station were to increase substantially because more individuals were able to access it, this could contribute to the overall benefit of the metro line, which, if large enough, could offset the overall cost of the project. Overall, as a next step in the discussion regarding mass transit, a separate analysis on the effect of additional metro stations on levels of business generated in surrounding areas of a city would need to be conducted in order to fully evaluate the consequences of mass transit systems.
Clifford Winston and Vikram Maheshri, 2007, “On the social desirability of urban rail transit systems,” Journal of Urban Economics 62: 362-382.
Edward Glaeser, Matthew Kahn, and Jordan Rappaport, 2008, “Why do the poor live in cities? The role of public transportation,” Journal of Urban Economics 63: 1-24.
Nathaniel Baum-Snow and Matthew Kahn, 2005. “Effects of urban rail expansions: evidence from sixteen cities, 1970-2000,” BWPUA 2005: 147-197.
By David Lillington Do Gays Influence Property Values?
This literature survey begins with an exploration of Richard Florida and Gary Gates’ (2001) work in creating what they call the “Gay Index”, an index that shows the relationship between high-tech cities and their concentrations of gay inhabitants. It then discusses the findings of a study carried out by Florida and Charlotta Mellander (2009) that details the relationship between their “Bohemian-Gay Index” and regional property values. Finally, this survey summarizes the work of David Christafore and Susane Leguizamon (2011) in their study of the influence of gay and lesbian couples on house prices in liberal and conservative neighborhoods of Columbus, OH. Through these three studies this survey establishes that gay populations do affect property prices and through a variety of methods (Florida and Gates 2001, Florida and Mellander 2009, Christafore and Leguizamon 2011) thus setting the grounds for further research on this topic.
The “Gay Index”
Florida and Gates’ (2001) “Gay Index” presents a method for measuring populations of gay male couples in metropolitan areas. In creating this index, Florida and Gates (2001) collected data of males who identified themselves as “same sex unmarried partners” on the 1990 U.S. Census. They then ranked each city according to this count to create the Gay Index. They examined the Gay Index’s relationship with high-tech cities finding it to be positive and significant. That is to say, cities ranked highly on the Milken Institute’s “Tech-Pole Index” also ranked rather highly on the Gay Index. They define the Tech-Pole Index as a measure of the concentration and growth of high-technology industries. Conversely, cities that ranked at the bottom of the Tech-Pole Index also ranked quite poorly on the Gay Index (Florida and Gates 2001). They claim that these highly ranked regions were also shown to have low barriers to entry for human capital due to their open and accepting nature, a trait that is crucial for “technology-based” growth. Furthermore, where there is growth there is almost certainly increases in cost of living. In fact, these gay “urban pioneers” have been found to have “substantial positive effects” on property values (Florida and Mellander 2009), thus putting forward the theory that gays are “harbingers of redevelopment and gentrification” (Florida and Gates 2001).
Florida furthers his claims of gay gentrification and increasing property values in his empirical study with Mellander (2009). They suggest that gays increase property values in two methods. First, they claim that gays and bohemians produce amenities by trade, attract talent, and appreciate authenticity and aesthetics, thus the neighborhoods in which they live demand a premium (Florida and Mellander 2009, Christafore and Leguizamon 2011, Florida and Gates 2001). The second argument they make is that bohemians and gays “reflect a tolerance or open culture premium” (Florida and Mellander 2009, Florida and Gates 2001). This promotes the generation of ideas and entrepreneurship, which in turn influence incomes and thus home values. Florida and Mellander (2009) relate regional income, regional amenities, and regional openness to regional housing values. Therefore if there is an increase in any of these variables housing values increase.
Is there empirical evidence of gay influences on property values?
Florida and Mellander (2009) began their empirical test by creating a model that examined the direct and indirect relationships between certain variables and housing value. The variables they used included median housing value, income, wages, technology, human capital, creative class (creative occupational groups), the Bohemian-Gay Index (similar to the Gay Index but includes “bohemian” artists, such as Durham’s “foodies”), population, changes in income per capita 1990-2000, change in employed civilian population 1990-2000, annual patent growth 1975-2000, and other control variables (to control regional size and development level). Using structural equation models (SEM) and ordinary least squares regression, Florida and Mellander (2009) were first able to create a correlation matrix for all of the variables they tested. Structural equation models are essentially “an extension of regression analysis and factor analysis” based on variances and co-variances that show the interrelationship between variables (Florida and Mellander, 2009). According to them, this method eliminates multicollinearity. Ordinary least squares regression is a linear model that shows the response of a variable based on one or multiple explanatory variables (Hutcheson, 2011). Florida and Mellander (2009) then produced scatter plots of housing versus certain variables and proceeded to present path models detailing their findings. They reported that the highest correlation coefficient that they had calculated was between income and housing at 0.747. Not far behind, they note, was the Bohemian-Gay Index with a correlation coefficient of 0.731. They also found that the scatter plots for the income vs. housing value and Bohemian-Gay Index vs. housing value behaved similarly. Based on calculated variance inflation values they concluded that the Bohemian-Gay Index was independent of income. Florida and Mellander (2009) provided other permutations of their path analyses using the SEM method and the Bohemian-Gay Index performed strongly in all of them. They also ran the SEM dividing it into four region sizes based on population, finding the index to be positive and significant for all sizes but one. Overall, they concluded that a relationship exists between gays and bohemians and housing values (also confirmed by Christafore and Leguizamon in 2011). But is this relationship always positive?
Do gays always have a positive influence on property values?
Christafore and Leguizamon (2011) took Florida and Mellander’s (2009) findings to the next level by exploring how gay and lesbian couples affected house prices in liberal and conservative areas of Columbus, Ohio. They had hypothesized that liberal areas would not discriminate against gay couples while conservative areas would discriminate in terms of house values. Adopting the practices of studies that have used the hedonic pricing model to explore racial discrimination in the housing market, Christafore and Leguizamon (2011) used a similar method to calculate the influence of LGBT discrimination on home prices. However, instead of a variable that denoted race, they introduced a sexual orientation variable into a spatial autoregressive hedonic price model to measure its relationship with home prices. They note that they used this model to take into account that house prices are related due to their spatial proximity. This dependent variable vector of home prices was multiplied by an n x n weight matrix to represent the special relationship in prices. A one was placed in the matrix if two houses were neighbors. They then divided all non-zero numbers across each row to create the matrix. Christafore and Leguizamon (2011) also introduced an independent variable of the average house prices of neighboring houses to control for this interrelationship.
Data were still needed to measure the liberalness and conservativeness of an area even after obtaining sexual orientation and property transaction data. This was found by using voting results for the Defense of Marriage Act divided into county subdivisions (Christafore and Leguizamon 2011). They assumed those subdivisions that had a majority vote in favor of DOMA were socially conservative, and those that did not were socially liberal. Because the act concerned LGBT couples, Christafore and Leguizamon (2011) felt that it would a safe measure of who would discriminate against gay couples and who would not. They note that the other variables included in the model concerned housing characteristics (i.e. home size, number of bedrooms, number of bathrooms, lot size etc) and they were placed into a matrix, X.
Christafore and Leguizamon (2011) first ran an OLS regression and obtained results that they had expected: the coefficient for the same sex couple variable and house value was positive and significant, as Florida and Mellander (2009) had suggested. However, they found the coefficient on the “interaction term” of conservative subdivisions and number of gay couples versus housing values to be negative and significant. This provided “evidence of prejudice in socially conservative neighborhoods” (Christafore and Leguizamon 2011). To calculate the “marginal associated effect” of adding one more gay couple to a neighborhood they added the coefficient of the gay couples variable and the coefficient of the interaction term (gay couples and conservative area) and multiplied this value by the percent DOMA vote. They found that in the addition of 1 gay couple to every 1000 households there would be a positive increase of 1.1% in housing values in extremely liberal areas (areas with a percent DOMA vote of around 31%). For an extremely conservative area they reported the opposite: an area with a DOMA vote of 84% would experience a drop of 1% in home values if 1 gay couple were to be added for every 1000 households. It must be considered, however, that gay populations bring with them amenities that positively affect home values (Christafore and Leguizamon 2011, Florida and Mellander 2009, Florida and Gates 2001). Christafore and Leguizamon (2011) suggest that the prejudice might be greater than perceived in this research due to this positive amenity affect. In other words, the depression of house prices in conservative areas might be slightly relieved by the premium of extra amenities brought into the neighborhood by gay couples. The same could be said for ultra liberal areas; that some of the increase in value might be due to the positive amenities a gay household brings into the neighborhood. The numbers were also run representing only gay male households and another time representing only lesbian households (Christafore and Leguizamon 2011). They discovered only gay male households to produce “significant coefficients” representing impact on house prices; lesbian households did not. Overall, they found that homosexual couples did impact neighborhoods’ house values both positively and negatively.
This literature survey explores influence of gay populations on property values. Using research by Gates and Florida (2002) it was established that gays attract high-tech industries and talent and also gentrify areas. This, in theory, would be a phenomenon that would cause property values to rise. This claim was then empirically tested by Florida and Mellander (2009), who found there to be a strong positive correlation between a “Bohemian-Gay Index” and housing values, suggesting that gay populations do affect these values (Christafore and Leguizamon (2011) confirmed this). The Christafore and Leguizamon (2011) study delved a bit deeper and explored gay couple’s influences on property values in both conservative and liberal neighborhoods. They found that gay couples cause property values to increase in liberal neighborhoods and decrease in conservative neighborhoods. They also found gay male couples to be the driving force behind this phenomenon.
There is evidence to show that gay populations bring to their neighborhoods amenities and also demand them (Florida and Gates 2001, Florida and Mellander 2009, Christafore and Leguizamon 2011). It would be interesting to explore what these amenities are and how they are valued. Perhaps it would then be possible to determine values for these premiums. It might also be worthwhile to parallel some of the articles on the reading list about racial discrimination in urban economics and LGBT discrimination. Perhaps there is a model used to evaluate racial discrimination that could be applied in the LGBT context as done in the Christafore and Leguizamon (2011) study. It may be a challenge, however, to collect data on LGBT populations. I would also like to find out if a gay population has ever priced itself out of a neighborhood; that is to say has a gay population gentrified a neighborhood so much to the point that it becomes unaffordable for the original gay population to live in? This phenomenon may by occurring in the Cedar Springs Road area of Dallas. Some gay residents are beginning to move to more affordable yet still artsy Oak Cliff, an area just south of downtown Dallas, due to the gentrification near Cedar Springs Road (Dallas’ gay “strip”). I was just in Dallas recently and noticed a lot of multistory apartment complexes being built in this area. I imagine that rents will be rising if they have not already. I will have to do further research to see if these sorts of papers are feasible. It would be hard to do any research without a reliable data set.
Christafore, David and Leguizamon, Susane, 2011, “The Influence of Gay and Lesbian Coupled Households on House Prices in Conservative and Liberal Neighborhoods” Journal of Urban Economics (accepted manuscript, September) pp. 1-28. http://econ.tulane.edu/LeguizamonSJ_Influence.pdf
Florida, Richard and Gates, Gary 2001, “Technology and Tolerance: The Importance of Diversity to High-Technology Growth” The Brookings Institution – The Center of Urban and Metropolitan Policy, Washington, D.C (June) pp. 1-12. http://www.brookings.edu/~/media/research/files/reports/2001/6/technology%20florida/techtol.pdf
Florida, Richard and Mellander, Charlotta, 2009, “There goes the metro: how and why bohemians, artists and gays affect regional housing values” Journal of Economic Geography pp. 1-22. http://creativeclass.com/rfcgdb/articles/oxford%20journal.pdf
Hutcheson, G. D. (2011). Ordinary Least-Squares Regression. In L. Moutinho and G. D. Hutcheson, The SAGE Dictionary of Quantitative Management Research pp. 224-228. http://www.research-training.net/addedfiles/READING/SAGEdictionaryOLSregression.pdf
Measuring Speculation in Housing Bubbles By Cecilia Ju
Literature Survey: Measuring Speculation in Housing Bubbles
A housing bubble is defined by rapid increases in property values to the point of unsustainable levels followed by a steep decline to the point in which the mortgage debt exceeds the value of the property itself (Bianco, 2008). Just as financial crises are not identified until a downward spiral has occurred, the housing bubble was not recognized until 2006 when market correction was already in stride.
The impact of the housing bubble exceeded experts’ forecasts. As a national average, house prices in the United States grew 6.5% per year in real terms between the late ‘90s and early 2000s (Goodman and Thibodeau, 2008). However, these price growths were especially prominent in cities along the East and West Coasts; California cited an average annual increase of 15% between 2000 and 2005 (Goodman and Thibodeau, 2008). However, the meltdown was quick to reverse the prices just as quickly as they had risen. Most economists believed that the crisis would be contained within the housing market – particularly among mortgage issuers. As it turned out, the subprime crisis that led to the collapse of the housing bubble was the prime factor for the most recent recession, a recession that has spread well beyond the US economy and into economies worldwide. Domestically, the pop of the housing bubble led to a flurry of federal regulations for the financial industry, a drastic decrease in state and local budgets due to a fall in property tax revenues, as well as homelessness by those who have lost their homes to foreclosure or landlord defaults (Bianco, 2008).
While the general public blames speculators for driving prices up to artificial levels, Goodman and Thibodeau (2008) claim that much of the price increase can be attributed to changes in fundamental economic determinants. Across all literature surveyed, researchers recognized that the housing bubble was concentrated along coastal states. Galeser, Gyourko, and Saiz (2008) examine housing supply and housing bubbles in their working paper, and identified elasticity in the housing supply as the main independent variable affecting housing price increase, bubble frequency, and bubble duration. Utilizing data from the two most significant housing market bubbles in the past 25 years, Galeser et al. (2008) constructed a model of housing bubbles with supply as an endogenous variable. During the two most recent housing crises (the 1982-1996 Cycle and the Post-1996 Boom), this model indicated that building infrastructure on steep topography created inelasticity of supply, which created higher price booms (Galeser et al., 2008).
During the ‘80s, areas with elastic supply were hardly affected by the housing bubble occurring in supply elastic places (Galeser et al, 2008). When they did experience housing price booms, the duration of the bubble in these areas was also significantly shorter (Galeser et al, 2008). The model predicted that locations with inelastic housing supply experience greater increases in price compared to those with more elastic supply (all else held constant) (Galeser et al, 2008). Furthermore, inelasticity in housing supply is also positively correlated with bubble frequency and duration (Galeser et al, 2008).
Goodman and Thibodeau’s research also took a supply side approach and supports the idea that expected rate of appreciation in house prices is highly contingent upon housing supply elasticity. However, in their deconstruction of the recent housing bubble between 2000 and 2005, Goodman and Thibodeau also incorporated the effects of demand. Their analysis utilized data to parse out the portion of price appreciation attributable to fundamental economic determinants for house prices.
First, Goodman and Thibodeau (2008) addressed the increase in demand and its effect on homeownership rates: between the years of 1999 and 2006, the rate of homeownership in the US increased from 66.8 percent to 69 percent. While it may seem that the 2.2 percentage point increase is rather unimpressive, it is important to keep in mind that each percentage point in homeownership rate raises demand for owner-occupied housing by approximately one million units (Goodman and Thibodeau, 2008). The increase in demand is also attributed to an increase in real estate investment as well as to speculation in “continued house appreciation” (Goodman and Thibodeau, 2008). In terms of real estate investment, historically low nominal interest rates and the subsequent virtual removal of wealth and income as a barrier to homeownership in the US was cited as a main reason (Goodman and Thibodeau, 2008). Another reason was the cultural and political shift from renting to homeownership amongst single-family households (Goodman and Thibodeau, 2008). On the speculative side, rise in demand was attributed to the continuous development of the home-equity market. On the supply side, land prices and housing construction costs increased. The demand for homeownership by households that had historically rented and by preexisting homeowners alike rose at a more rapid pace than did the rise in supply of housing (Goodman and Thibodeau, 2008). Thus, due to the shortage in houses on the market, the real price of homes increased (Goodman and Thibodeau, 2008). A two-pronged approach was applied to answer the question of how much of this appreciation was driven by the justified economic fundamentals of local housing markets and what fraction was driven by speculation. The relationship between house price appreciation rates and supply elasticities were investigated via a simulation model of the housing market and estimates of metropolitan area housing supply elasticities were produced using cross-sectional place data of the non-bubble 1990-2000 period (Goodman and Thibodeau, 2008). The empirical analysis revealed statistically significant positive supply elasticities for 84 metropolitan statistical areas (MSAs) (Goodman and Thibodeau, 2008). Then, using the American Community Survey for 2000-2005 changes, Goodman and Thibodeau (2008) used computed expected rates of appreciation for these MSAs and compared the expected appreciation rates to the rates observed over the 2000-2005 period. The results indicated that the expected rate of appreciation in house prices is very sensitive to the assumed supply elasticity (Goodman and Thibodeau, 2008). Given that 30% of over the expected increase based on the data was used as the housing bubble threshold, only 25 of the 84 metropolitan areas with significantly positive supply elasticities exceed this threshold (Goodman and Thibodeau, 2008). Furthermore, these cities, with the exception of Las Vegas, were all coastal and within 75 miles of either the east or west coast. This led to the conclusion that speculative activity was extraordinarily localized to coastal areas where housing supply was inelastic (Goodman and Thibodeau, 2008).
Despite strong evidence of a relationship between supply elasticity and housing price increase and stronger speculative pricing effects, Wheaton et al. offers … In their research, Wheaton and Nechayev (2008) also examined the inflation of house prices of the most recent bubble, albeit of a slightly earlier timeframe (1998 – 2005) and investigated its relationship to increases in demand fundamentals (population, income growth, decline in interest rates) over this period. Then, Wheaton and Nechayev (2008) assessed and predicted patterns for housing price correction in the years following 2005.
The research incorporated data of 59 MSAs from 1998 through 2005, and constructed time series models to estimate markets and price changes (Wheaton and Nechayev, 2008). The aforementioned economic fundamentals were utilized to drive the models (Wheaton and Nechayev, 2008). Results from the models found that in all 59 markets, actual price growth between 1998 and 2005 was actually significantly higher than those forecasted (Wheaton and Nechayev, 2008). Wheaton and Nechayev (2008) were able to find that forecast errors are most prevalent in several types of MSAs: larger MSAs, MSAs where second home and speculative buying was prevalent, and MSAs where indicators suggest the sub-prime mortgage market was most active. Wheaton and Nechayev (2008) found two major factors that explained the most recent bubble and the commonly seen “excess” price increase in coastal cities: widespread availability of risk-priced mortgage credit and the unusually strong purchase of houses as second homes and investments. As a caveat, they noted the importance in inferring causality and thus stated that it is difficult to determine depth and duration of the housing correction. Since these factors are all unique to the recent housing market, assessing potential price “correction” after 2005 could not be done without inferring causality (something Wheaton and Nechayev were reluctant to do) (Wheaton and Nechayev, 2008). Thus, determine depth and duration estimates of the housing correction were not formulated.
Most of the literature focuses on identifying reasons behind recent housing booms, presumably for the purpose of understanding, identifying, and avoiding future bubbles. Most of the sources attributed the latest housing bubble to changes in market factors, particularly low interest mortgages and a trend in real estate investment and homeownership. Geospatial analysis of house prices over the time span of the last bubble also revealed a negative correlation between housing supply elasticity and susceptibility to speculative forces. The research reports a lack of optimism in correctly projecting correction terms as well as the nature of the next bubble. Moreover, since such projections and models are based solely on the most recent bubbles, the unique characteristics of the last case may interfere with prediction capabilities. Perhaps an in-depth analysis of the most significant bubbles (domestic and international) from the past century may allow researchers to weed out the unique characteristics of each case and identify several classic traits of real estate bubbles.
Allen C. Goodman, and Thomas G. Thibodeau. “Where are the Speculative Bubbles in the US Housing Markets?”Journal of Housing Economics 17 (2008): 117-37. Print.
Edward L. Galeser, Joseph Gyourko, and Albert Saiz. “Housing Supply and Housing Bubbles” National Bureau of Economics Research (2008). Print.
Katalina M. Bianco. “The Subprime Lending Crisis: Causes and Effects of the Mortgage Meltdown” www.consejomexicano.org
William C. Wheaton, and Gleb Nechayev. “The 1998-2005 Housing “Bubble” and the Current “Correction”: What’s Different this Time?” JRER 30 (2008). Print.