The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices
- First Online:
- 227 Downloads
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.
KeywordsSpatial regression Hedonic price model Anisotropic spatial correlation Simultaneous autoregressive model Housing market
- Colwell, P. F., & Munneke, H. J. (2009). Directional land value gradients. Journal of Real Estate Finance and Economics, 39(1), doi:10.1007/s11146-007-9104-0.
- Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. New York: Academic.Google Scholar
- LeSage, J. P. (1999). Spatial Econometrics. http://www.spatial-econometrics.com/html/wbook.pdf.
- Valente, J., Wu, S., Gelfand, A. E., & Sirmans, C. F. (2005). Apartment rent prediction using spatial modeling. Journal of Real Estate Research, 27(1), 105–136.Google Scholar
- Weickert, J. (1996). Anisotropic diffusion in image processing. Stuttgart: Teibner-Verlag.Google Scholar
- Weickert, J. (1997). A review of non-linear diffusion filtering. In B. ter Haar Romeny, et al. (Eds.), Scale-space theory in computer vision, lecture notes in computer science (pp. 3–28). Berlin: Springer.Google Scholar