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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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    References listed on IDEAS

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    Cited by:

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    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. Salvati, Luca & Ciommi, Maria Teresa & Serra, Pere & Chelli, Francesco M., 2019. "Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy," Land Use Policy, Elsevier, vol. 81(C), pages 143-152.
    4. 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.
    5. Fan Liu & Min Min & Ke Zhao & Weiyan Hu, 2020. "Spatial-Temporal Variation in the Impacts of Urban Infrastructure on Housing Prices in Wuhan, China," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    6. 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.
    7. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    8. Jamie Spinney & Pavlos Kanaroglou & Darren Scott, 2011. "Exploring Spatial Dynamics with Land Price Indexes," Urban Studies, Urban Studies Journal Limited, vol. 48(4), pages 719-735, March.
    9. Jorge Chica-Olmo & Rafael Cano-Guervos & Mario Chica-Rivas, 2019. "Estimation of Housing Price Variations Using Spatio-Temporal Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    10. José-María Montero & Román Mínguez & Gema Fernández-Avilés, 2018. "Housing price prediction: parametric versus semi-parametric spatial hedonic models," Journal of Geographical Systems, Springer, vol. 20(1), pages 27-55, January.
    11. Zuo Zhang & Xinhai Lu & Min Zhou & Yan Song & Xiang Luo & Bing Kuang, 2019. "Complex Spatial Morphology of Urban Housing Price Based on Digital Elevation Model: A Case Study of Wuhan City, China," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
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