The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices

Article

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.

Keywords

Spatial regression Hedonic price model Anisotropic spatial correlation Simultaneous autoregressive model Housing market 

Reference

  1. Bao, H. X. H., & Wan, A. T. K. (2004). On the use of spline smoothing in estimating hedonic housing price models: empirical evidence using Hong Kong data. Real Estate Economics, 32(3), 487–507.CrossRefGoogle Scholar
  2. Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. Journal of Real Estate Finance and Economics, 17(1), 61–85.CrossRefGoogle Scholar
  3. Bourassa, S. C., Cantoni, E., & Hoesli, M. (2007). Spatial dependence, housing submarkets, and house price prediction. Journal of Real Estate Finance and Economics, 35(2), 143–160.CrossRefGoogle Scholar
  4. Bowen, W. M., Mikelbank, B. A., & Prestegaard, D. M. (2001). Theoretical and empirical considerations regarding space in hedonic housing price model applications. Growth and Change, 32(4), 466–490.CrossRefGoogle Scholar
  5. Can, A. (1992). Specification and estimation of hedonic housing price models. Regional Science and Urban Economics, 22, 453–474.CrossRefGoogle Scholar
  6. 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.
  7. Dubin, R. A. (1988). Estimation of regression coefficients in the presence of spatially autocorrelated error terms. Review of Economics and Statistics, 70, 446–474.CrossRefGoogle Scholar
  8. Dubin, R. A. (1998). Predicting house prices using multiple listing data. Journal of Real Estate Finance and Economics, 17(1), 35–59.CrossRefGoogle Scholar
  9. Gillen, K., Thibodeau, T. G., & Wachter, S. (2001). Anisotropic autocorrelation in house prices. Journal of Real Estate Finance and Economics, 23(1), 5–30.CrossRefGoogle Scholar
  10. Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. New York: Academic.Google Scholar
  11. LeSage, J. P. (1999). Spatial Econometrics. http://www.spatial-econometrics.com/html/wbook.pdf.
  12. LeSage, J. P., & Pace, R. K. (2004). Models for spatially dependent missing data. Journal of Real Estate Finance and Economics, 29(2), 233–254.CrossRefGoogle Scholar
  13. Pace, R. K., & Gilley, O. W. (1997). Using the spatial configuration of the data to improve estimation. Journal of Real Estate Finance and Economics, 14(3), 333–340.CrossRefGoogle Scholar
  14. Pace, R. K., & Gilley, O. W. (1998). Generalizing the OLS and grid estimators. Real Estate Economics, 26(2), 331–347.CrossRefGoogle Scholar
  15. Thorsnes, P., & McMillen, D. P. (1998). Land value and parcel size: a semiparametric analysis. Journal of Real Estate Finance and Economics, 17(3), 233–244.CrossRefGoogle Scholar
  16. 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
  17. Weickert, J. (1996). Anisotropic diffusion in image processing. Stuttgart: Teibner-Verlag.Google Scholar
  18. 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
  19. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics, 1, 80–83.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Real Estate Management Institute (REMI), European Business School (EBS)International University Schloss ReichartshausenWiesbadenGermany
  2. 2.Union Investment Chair of Asset Management and Real Estate Management Institute (REMI), European Business School (EBS)International University Schloss ReichartshausenOestrich-WinkelGermany

Personalised recommendations