An Analysis of Spatial Dependence in Real Estate Prices

Abstract

Real estate properties are naturally location-fixed. When space related factors are not fully incorporated in a standard pricing equation, spatial autocorrelation is likely to exist. This results in inefficiencies in estimations and raises the need for more complex spatial models. This paper analyzes the determinants of spatial dependence and evaluates the performance of the hedonic regression equation when the determinants of spatial dependence are controlled for. Using a novel dataset for a metropolitan housing market, we document the spatial clustering of housing characteristics such as area, total number of floors and the building age. We find support for the hypotheses that the construction process, shared social services and high-rise residential complexes cause spatial correlation. Our findings show that spatial correlation is significantly reduced when the factors of spatial dependence and district level data is controlled for in the standard hedonic regression.