The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices
Posted: 9 Feb 2011
Date Written: February 8, 2011
This paper develops a method to capture anisotropic spatial autocorrelation in the context of the simultaneous autoregressive model. Standard isotropic models assume that spatial correlation is a homogeneous function of distance. This assumption, however, is oversimplified if spatial dependence changes with direction. We thus propose a local anisotropic approach based on non-linear scale-space image processing. We illustrate the methodology by using data on single-family house transactions in Lucas County, Ohio. The empirical results suggest that the anisotropic modeling technique can reduce both in-sample and out-of-sample forecast errors. Moreover, it can easily be applied to other spatial econometric functional and kernel forms.
Keywords: Spatial regression, Hedonic price model, Anisotropic spatial correlation, Simultaneous autoregressive model, Housing market
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