The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices

The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices 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. The Journal of Real Estate Finance and Economics Springer Journals

The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices

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Springer US
Copyright © 2009 by Springer Science+Business Media, LLC
Economics; Regional/Spatial Science; Financial Services
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