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Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging

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  • Jorge Chica-Olmo

    () (Universidad de Granada, Facultad de Ciencias Económicas y Empresariale, Campus Cartuja s/n. 18011-Granada. Spain.)

Abstract

Cokriging is a multivariate spatial method to estimate spatial correlated variables. This method allows spatial estimations to be made and interpolated maps of house price to be created. These maps are interesting for appraisers, real estate companies, and bureaus because they provide an overview of location prices. Kriging uses one variable of interest (house price) to make estimates at unsampled locations, and cokriging uses the variable of interest and auxiliary correlated variables. In this paper, housing location price is estimated using kriging methods, isotopic data cokriging, and heterotopic data cokriging methods. The results of these methods are then compared.

Suggested Citation

  • Jorge Chica-Olmo, 2007. "Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging," Journal of Real Estate Research, American Real Estate Society, vol. 29(1), pages 95-114.
  • Handle: RePEc:jre:issued:v:29:n:1:2007:p:95-114
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    File URL: http://pages.jh.edu/jrer/papers/pdf/past/vol29n01/05.91_114.pdf
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    References listed on IDEAS

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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. John M. Clapp & Hyon-Jung Kim & Alan E. Gelfand, 2002. "Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(4), pages 505-532.
    3. 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.
    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. Clapp, John M. & Rodriguez, Mauricio & Thrall, Grant, 1997. "How GIS Can Put Urban Economic Analysis on the Map," Journal of Housing Economics, Elsevier, vol. 6(4), pages 368-386, December.
    6. Gatzlaff, Dean H & Haurin, Donald R, 1997. "Sample Selection Bias and Repeat-Sales Index Estimates," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 33-50, Jan.-Marc.
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    Cited by:

    1. José-María Montero-Lorenzo & Beatriz Larraz-Iribas, 2012. "Space-time approach to commercial property prices valuation," Applied Economics, Taylor & Francis Journals, vol. 44(28), pages 3705-3715, October.
    2. Beatriz Larraz, 2011. "An Expert System for Online Residential Properties Valuation," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 69-82, April.
    3. Narula, Subhash C. & Wellington, John F. & Lewis, Stephen A., 2012. "Valuating residential real estate using parametric programming," European Journal of Operational Research, Elsevier, vol. 217(1), pages 120-128.
    4. Simlai, Prodosh, 2014. "Estimation of variance of housing prices using spatial conditional heteroskedasticity (SARCH) model with an application to Boston housing price data," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 17-30.
    5. repec:kap:jgeosy:v:20:y:2018:i:1:d:10.1007_s10109-017-0257-y is not listed on IDEAS
    6. Pierrette Chagneau & Frédéric Mortier & Nicolas Picard & Jean-Noël Bacro, 2011. "A Hierarchical Bayesian Model for Spatial Prediction of Multivariate Non-Gaussian Random Fields," Biometrics, The International Biometric Society, vol. 67(1), pages 97-105, March.

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