Hedonic regression analysis of single family homes typically includes structural variables, locational variables and neighborhood quality characteristics. When nearby properties are related, Dubin (1988) reports that error terms are spatially autocorrelated. Estimation methods for these spatially autocorrelated error terms or hereafter, spatial approaches, include maximum likelihood estimation (MLE) and kriging techniques such as kriged maximum likelihood estimation (KMLE). Unfortunately these spatial methods require massive computer resources and are limited to significantly fewer observations than traditional ordinary least squares (OLS). This paper investigates the combination of spatial approaches and Monte Carlo analysis, a method that approximates large data sets. A question arises for researchers interested in the relationship between environmental characteristics and single family property values whether spatial approaches are preferable to a traditional hedonic approach. Using 550 sample sets, each containing 750 random observations from a data set of 16,155 single family residential properties, we find spatial consistently outperforms traditional.
Dwellings; Housing – Prices; Neighborhoods; Nevada – Clark County; Real property – Valuation; Spatial analysis (Statistics)
Probability | Real Estate
Neill, H. R.,
Hassenzahl, D. M.,
Assane, D. D.
A Monte Carlo analysis of hedonic models using traditional and spatial approaches.
American Environmental and Resource Economics