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Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices

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  • A. F. Militino

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  • M. D. Ugarte

    ()

  • L. García-Reinaldos
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    Abstract

    In this article different spatial statistics techniques to analyze the behavior of used dwelling market prices are compared. We fit two lattice models: simultaneous and conditional autoregressive, a geostatistical model, the so-called universal kriging and finally, a linear mixed-effect model. Different spatial neighborhood structures are considered, as well as different spatial weight matrices and covariance models. The results are illustrated through a real data set of 293 properties from Pamplona, Spain.

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    Bibliographic Info

    Article provided by Springer in its journal The Journal of Real Estate Finance and Economics.

    Volume (Year): 29 (2004)
    Issue (Month): 2 (09)
    Pages: 193-209

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    Handle: RePEc:kap:jrefec:v:29:y:2004:i:2:p:193-209

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    Web page: http://www.springerlink.com/link.asp?id=102945

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    Cited by:
    1. 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.
    2. José-María Montero-Lorenzo & Beatriz Larraz-Iribas & Antonio Páez, 2009. "Estimating commercial property prices: an application of cokriging with housing prices as ancillary information," Journal of Geographical Systems, Springer, vol. 11(4), pages 407-425, December.
    3. Ugarte, M.D. & Goicoa, T. & Militino, A.F. & Durbán, M., 2009. "Spline smoothing in small area trend estimation and forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3616-3629, August.
    4. Xiaolong Liu, 2013. "Spatial and Temporal Dependence in House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 341-369, August.
    5. Morito Tsutsumi & Hajime Seya, 2009. "Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits," Journal of Geographical Systems, Springer, vol. 11(4), pages 357-380, December.
    6. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256.
    7. Martellosio, Federico, 2008. "Testing for spatial autocorrelation: the regressors that make the power disappear," MPRA Paper 10542, University Library of Munich, Germany.
    8. Martellosio, Federico, 2008. "Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression," MPRA Paper 7255, University Library of Munich, Germany.
    9. Kato, Takafumi, 2012. "Prediction in the lognormal regression model with spatial error dependence," Journal of Housing Economics, Elsevier, vol. 21(1), pages 66-76.
    10. Victor Oliveira, 2012. "Bayesian analysis of conditional autoregressive models," Annals of the Institute of Statistical Mathematics, Springer, vol. 64(1), pages 107-133, February.
    11. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2007. "Spatial Dependence, Housing Submarkets, and House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 143-160, August.

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