A Spatial Econometric Analysis of the Effects of Subsidized Housing and Urban Sprawl on Property Values
Type of Degreedissertation
DepartmentAgricultural Economics and Rural Sociology
MetadataShow full item record
Property owners often resist the idea of siting public or subsidized housing in their neighborhood. The notion that subsidized housing exerts a negative externality effect on adjacent properties has been investigated elsewhere in the US, particularly in the Northeast, with inconclusive findings. In the first two chapters of this dissertation, I analyze the spatial effects of two Federal Housing Programs, namely Section 8 Vouchers and Low-Income-Housing Tax Credits, on property values. The Census 2000 report estimates the Southeast to be among the fastest growing populations in the US. From 1990 to 2000 the population growth for the Southeastern States were Georgia (26.37%), Florida (23.57%), North Carolina (21.43%), South Carolina (15.07%), and Alabama (10.06%). The Southeast also has a higher concentration of minorities; this, coupled with high incidence of poverty, implies a higher demand for public housing in order to serve low income households. Policy makers need to be able to determine not only the housing needs of low income populations, but also how the provision of such affordable housing impacts surrounding property values. Chapter one offers an aggregated analysis of the impact of Section 8 and low-income housing tax credit programs on property values in Alabama. In chapter two, I perform a disaggregated analysis of the impact of Section 8 projects on proximate single family home sales in Fulton County, Georgia. Chapter three explores the nexus between property values, commuting, and urban sprawl in Birmingham metropolitan area, Alabama. There is a common methodological linkage between all three chapters. I employ spatial econometric estimation techniques, first, to account for spill-over effects in house prices, and secondly, to overcome spatial autocorrelation and spatial heterogeneity that may result in biases if the traditional OLS estimation is used. In chapter one, I specifically deal with the problem of causality between subsidized housing and property values. Two novel quasi-experimental methods—difference-in-difference and propensity score matching— are employed to test if there is actually a causal relationship between subsidized housing and property values. The last chapter also offers an innovative combination of principal component analysis and generalized spatial two stage least squares estimation techniques to investigate the relationship between urban sprawl, property values, and commute times.