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Spatial Dependence, Housing Submarkets, and House Price Prediction

  • Steven C. Bourassa
  • Eva Cantoni
  • Martin Hoesli
Article

Abstract

This paper compares alternative methods of controlling for the spatial dependence of house prices in a mass appraisal context. Explicit modeling of the error structure is characterized as a relatively fluid approach to defining housing submarkets. This approach allows the relevant submarket to vary from house to house and for transactions involving other dwellings in each submarket to have varying impacts depending on distance. We conclude that—for our Auckland, New Zealand, data—the gains in accuracy from including submarket variables in an ordinary least squares specification are greater than any benefits from using geostatistical or lattice methods. This conclusion is of practical importance, as a hedonic model with submarket dummy variables is substantially easier to implement than spatial statistical methods.

Keywords

Spatial dependence Hedonic price models Geostatistical models Lattice models Mass appraisal Housing submarkets 

Notes

Acknowledgments

We thank John Clapp, Xavier de Luna, and two anonymous referees for useful comments.

References

  1. Bai, Z., & Golub, G. H. (1997). Bounds for the trace of the inverse and the determinant of symmetric positive definite matrices. Annals of Numerical Mathematics, 4, 29–38.Google Scholar
  2. Basu, A., & 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., Hamelink, F., Hoesli, M., & MacGregor, B. D. (1999). Defining housing submarkets. Journal of Housing Economics, 8(2), 160–183.CrossRefGoogle Scholar
  4. Bourassa, S. C., Hoesli, M., & Peng, V. C. (2003). Do housing submarkets really matter? Journal of Housing Economics, 12(1), 12–28.CrossRefGoogle Scholar
  5. Can, A. (1992). Specification and estimation of hedonic housing price models. Regional Science and Urban Economics, 22(3), 453–474.CrossRefGoogle Scholar
  6. Can, A., & Megbolugbe, I. (1997). Spatial dependence and house price index construction. Journal of Real Estate Finance and Economics, 14(1/2), 203–222.CrossRefGoogle Scholar
  7. Case, B., Clapp, J., Dubin, R., & Rodriguez, M. (2004). Modeling spatial and temporal house price patterns: A comparison of four models. Journal of Real Estate Finance and Economics, 29(2), 167–191.CrossRefGoogle Scholar
  8. Clapp, J. M. (2003). A semiparametric method for valuing residential locations: Application to automated valuation. Journal of Real Estate Finance and Economics, 27(3), 303–320.CrossRefGoogle Scholar
  9. Colwell, P. F. (1998). A primer on piecewise parabolic multiple regression analysis via estimations of Chicago CBD land prices. Journal of Real Estate Finance and Economics, 17(1), 87–97.CrossRefGoogle Scholar
  10. Cressie, N. (1993). Statistics for spatial data. Wiley: New York.Google Scholar
  11. Cressie, N., & Hawkins, D. M. (1980). Robust estimation of the variogram, I. Journal of the International Association for Mathematical Geology, 12(2), 115–125.CrossRefGoogle Scholar
  12. Dubin, R. A. (1988). Estimation of regression coefficients in the presence of spatially autocorrelated error terms. Review of Economics and Statistics, 70(3), 466–474.CrossRefGoogle Scholar
  13. Dubin, R. A. (1998). Predicting house prices using multiple listings data. Journal of Real Estate Finance and Economics, 17(1), 35–59.CrossRefGoogle Scholar
  14. Dubin, R., Pace, R. K., & Thibodeau, T. G. (1999). Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 7(1), 79–95.CrossRefGoogle Scholar
  15. Fik, T. J., Ling, D. C., & Mulligan, G. F. (2003). Modeling spatial variation in housing prices: A variable interaction approach. Real Estate Economics, 31(4), 623–646.CrossRefGoogle Scholar
  16. Getis, A., & Aldstadt, J. (2004). Constructing the spatial weights matrix using a local statistic. Geographical Analysis, 36(2), 90–104.CrossRefGoogle Scholar
  17. Haining, R. (1990). Spatial data analysis for the social and environmental sciences. Cambridge: Cambridge University Press.Google Scholar
  18. LeSage, J. P., & Pace, R. K. (2004). Introduction. In J. P. LeSage & R. K. Pace (Eds.), Spatial and spatiotemporal econometrics. Advances in econometrics, Vol. 18 (pp. 1–32). Oxford: Elsevier.CrossRefGoogle Scholar
  19. Matheron, G. (1962). Traité de Géostatistique Appliquée, Tome I. Mémoires du Bureau de Recherches Géologiques et Minières, No. 14. Paris: Editions Technip.Google Scholar
  20. Militino, A. F., Ugarte, M. D., & García-Reinaldos, L. (2004). Alternative models for describing spatial dependence among dwelling selling prices. Journal of Real Estate Finance and Economics, 29(2), 193–209.CrossRefGoogle Scholar
  21. Pace, R. K., & Barry, R. (1997a). Quick computation of regressions with a spatially autoregressive dependent variable. Geographical Analysis, 29(3), 232–247.CrossRefGoogle Scholar
  22. Pace, R. K., & Barry, R. (1997b). Sparse spatial autoregressions. Statistics and probability letters, 33(2), 291–297.CrossRefGoogle Scholar
  23. Pace, R. K., Barry, R., Clapp, J. M., & Rodriguez, M. (1998). Spatiotemporal autoregressive models of neighborhood effects. Journal of Real Estate Finance and Economics, 17(1), 15–33.CrossRefGoogle Scholar
  24. Pace, R. K., Barry, R., & Sirmans, C. F. (1998). Spatial statistics and real estate. Journal of Real Estate Finance and Economics, 17(1), 5–13.CrossRefGoogle Scholar
  25. 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
  26. Pace, R. K., & Gilley, O. W. (1998). Generalizing the OLS and grid estimators. Real Estate Economics, 26(2), 331–347.CrossRefGoogle Scholar
  27. Palm, R. (1978). Spatial segmentation of the urban housing market. Economic Geography, 54(3), 210–221.CrossRefGoogle Scholar
  28. Reusken, A. (2002). Approximation of the determinant of large sparse symmetric positive definite matrices. SIAM Journal on Matrix Analysis and Applications, 23(3), 799–812.CrossRefGoogle Scholar
  29. Ripley, B. (1981). Spatial statistics. New York: Wiley.Google Scholar
  30. Ugarte, M. D., Goicoa, T., & Militino, A. F. (2004). “Searching for housing submarkets using mixtures of linear models”. In J. P. LeSage, & R. K. Pace (Eds.), Spatial and spatiotemporal econometrics. Advances in econometrics, Vol. 18 (pp. 259–276). Oxford: Elsevier.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Steven C. Bourassa
    • 1
  • Eva Cantoni
    • 2
  • Martin Hoesli
    • 3
    • 4
    • 5
  1. 1.School of Urban and Public AffairsUniversity of LouisvilleLouisvilleUSA
  2. 2.Department of EconometricsUniversity of GenevaGeneva 4Switzerland
  3. 3.HEC, University of GenevaGeneva 4Switzerland
  4. 4.University of Aberdeen Business SchoolUniversity of AberdeenAberdeenScotland
  5. 5.Bordeaux Business SchoolBordeauxFrance

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