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The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices

Bing Zhu, Roland Füss & Nico Rottke

abstract 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.
   
type journal paper
   
keywords Spatial regression – Hedonic price model – Anisotropic spatial correlation – Simultaneous autoregressive model – Housing market
   
language English
kind of paper journal article
date of appearance 1-5-2011
journal Journal of Real Estate Finance and Economics
publisher Springer Science (New York)
ISSN 0895-5638
ISSN (online) 1573-045X
DOI 10.1007/s11146-009-9209-8
volume of journal 42
number of issue 4
page(s) 542-565
review double-blind review
   
citation Zhu, B., Füss, R., & Rottke, N. (2011). The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices. Journal of Real Estate Finance and Economics, 42(4), 542-565, DOI:10.1007/s11146-009-9209-8.