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Spatial Dependence, Housing Submarkets, and House Prices

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  • Steven C. Bourassa

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  • Eva Cantoni

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  • Martin Hoesli

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Abstract

This paper compares the impacts of alternative models of spatial dependence on the accuracy of house price predictions in a mass appraisal context. Explicit modeling of spatial dependence is characterized as a more 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 compare the predictive ability of different specifications of both geostatistical and lattice models as well as a simpler model based on submarkets with fixed boundaries. We conclude that – for our data – no spatial statistics method does as well in terms of predictive ability as a simple OLS model that includes a series of dummy variables defining submarkets. However, of the spatial statistics methods, geostatistical models provide more accurate predictions than lattice models. We argue that this is due to the fact that the kriging procedure used to make predictions in a geostatistical framework directly incorporates spatial information about nearby properties. That is not possible in a lattice framework due to the reliance on a matrix of weights that incorporates relationships only for the sample of properties that transact.

Suggested Citation

  • Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2005. "Spatial Dependence, Housing Submarkets, and House Prices," FAME Research Paper Series rp151, International Center for Financial Asset Management and Engineering.
  • Handle: RePEc:fam:rpseri:rp151
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    File URL: http://www.swissfinanceinstitute.ch/rp151.pdf
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    References listed on IDEAS

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    1. R. Kelley Pace & James P. LeSage, 2004. "Spatial Statistics and Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 147-148, September.
    2. Dubin, Robin A, 1988. "Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms," The Review of Economics and Statistics, MIT Press, vol. 70(3), pages 466-474, August.
    3. Clapp, John M & Rodriguez, Mauricio, 1999. "Erratum: Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 19(1), pages 1-85, July.
    4. Bradford Case & John Clapp & Robin Dubin & Mauricio Rodriguez, 2004. "Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 167-191, September.
    5. Timothy J. Fik & David C. Ling & Gordon F. Mulligan, 2003. "Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(4), pages 623-646, December.
    6. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters,in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
    7. Bourassa, Steven C. & Hamelink, Foort & Hoesli, Martin & MacGregor, Bryan D., 1999. "Defining Housing Submarkets," Journal of Housing Economics, Elsevier, vol. 8(2), pages 160-183, June.
    8. Dubin, Robin A, 1998. "Predicting House Prices Using Multiple Listings Data," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 35-59, July.
    9. Can, Ayse, 1992. "Specification and estimation of hedonic housing price models," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 453-474, September.
    10. Basu, Sabyasachi & Thibodeau, Thomas G, 1998. "Analysis of Spatial Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 61-85, July.
    11. Can, Ayse & Megbolugbe, Isaac, 1997. "Spatial Dependence and House Price Index Construction," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 203-222, Jan.-Marc.
    12. Colwell, Peter F, 1998. "A Primer on Piecewise Parabolic Multiple Regression Analysis via Estimations of Chicago CBD Land Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 87-97, July.
    13. Pace, R Kelley & Gilley, Otis W, 1997. "Using the Spatial Configuration of the Data to Improve Estimation," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 333-340, May.
    14. Clapp, John M, 2003. "A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation," The Journal of Real Estate Finance and Economics, Springer, vol. 27(3), pages 303-320, November.
    15. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
    16. A. F. Militino & M. D. Ugarte & L. García-Reinaldos, 2004. "Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 193-209, September.
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    Cited by:

    1. Stefan Sebastian Fahrländer, 2006. "Semiparametric Construction of Spatial Generalized Hedonic Models for Private Properties," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 501-528, December.
    2. Takafumi Kato, 2008. "A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications," Journal of Regional Science, Wiley Blackwell, vol. 48(3), pages 615-639.
    3. Stefan S. Fahrlaender, 2006. "Indirect Construction of Hedonic Price Indexes: Empirical Evidence for Private Properties in Switzerland," Diskussionsschriften dp0601, Universitaet Bern, Departement Volkswirtschaft.
    4. Elif Alkay, 2008. "Housing Submarkets in Istanbul," International Real Estate Review, Asian Real Estate Society, vol. 11(1), pages 113-127.
    5. repec:asg:wpaper:1044 is not listed on IDEAS
    6. repec:asg:wpaper:1004 is not listed on IDEAS

    More about this item

    Keywords

    spatial dependence; hedonic price models; geostatistical models; lattice models; mass appraisal; housing submarkets;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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