Spatial analysis of the relationship between transportation infrastructure and metropolitan housing values : integrating traditional, spatial, and geostatistical approaches

Urban accessibility has long been thought to play a role in house price determination. Traditionally, this has been captured in house-price studies by including one or several variables, representing the distance to urban "attractions", such as the central business district. This distance is typically measured over the transportation network. Meanwhile, a separate body of work has been rapidly developing, concerned with the impacts o f public infrastructure stocks, and public infrastructure investments. These latter studies have been regional in scope, and have focused on economic growth and development impacts. The local impact of infrastructure investments on the housing market has gone relatively unstudied. This is especially curious, given the links between transportation infrastructure and accessibility on one hand, and accessibility and house price on the other. This dissertation examines such impacts by merging two spatio-temporal data sets. The first contains records of all single family detached houses that sold during calendar year 1990 in Franklin and Delaware Coimties. in the state o f Ohio. The second data set contains information on accessibility changing transportation investments made in the same area since 1978. through 1990. Within a standard hedonic price function, the relationship between house price and several different formulations o f transportation infrastructure investment is estimated. The various categories o f investment impacts center around the type, timing and location o f the investment, with respect to the house transaction. With the housing and transportation infrastructure investment data in hand, the next relevant question is how best capture the spatial relationships between them. Specification o f house price models has begun to incorporate the spatial forces that operate in housing markets, yet. as is the case with functional form, there is not a "right" way to do so. This dissertation estimates and evaluates four alternative approaches to capturing spatial process in housing market models. The first uses ordinary least squares as its base, but departs from traditional specifications in that no neighborhood or accessibility variables are included. Ordinary kriging is then employed on the residuals, thus modeling the spatial information in this second step. The second and third models use generalized least squares as their base, which include neighborhood, accessibility, and the above mentioned transportation variables. The second specification models the residuals using ordinary kriging. while specification three uses co-kriging as its final step. The fourth model is a maximum likelihood spatial lag model, which incorporates spatial structure by including neighboring house prices in the estimation. Results indicate that both current and past investments play a role in the determination o f house prices. These effects, on a house-by-house basis, are small, but are worthy of notice when an investment's "impact area" is considered. Major investments in transportation infrastructure can have serious local market implications.