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Models for Spatially Dependent Missing Data

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Author Info

  • James P. LeSage

    ()

  • R. Kelley Pace

    ()

Abstract

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known characteristics. In fact, explanatory variable information on house characteristics for all properties, sold and unsold, are often available from assessors. We set forth an estimation approach that predicts missing values of the dependent variable when the sample data exhibit spatial dependence. Employing information on the housing characteristics of both sold and unsold properties can improve prediction, increase estimation efficiency for the missing-at-random case, and reduce self-selection bias in the non-missing-at-random case. We demonstrate these advantages with a Monte Carlo experiment as well as with actual housing data.

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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: 233-254

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Handle: RePEc:kap:jrefec:v:29:y:2004:i:2:p:233-254

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

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Cited by:
  1. Seya, Hajime & Yamagata, Yoshiki & Tsutsumi, Morito, 2013. "Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 429-444.
  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. Kato, Takafumi, 2012. "Prediction in the lognormal regression model with spatial error dependence," Journal of Housing Economics, Elsevier, vol. 21(1), pages 66-76.
  4. Marc Baudry & Masha Maslianskaia-Pautrel, 2012. "Revisiting the hedonic price method to assess the implicit price of environmental quality with market segmentation," EconomiX Working Papers 2012-45, University of Paris West - Nanterre la Défense, EconomiX.
  5. 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.
  6. Thomas-Agnan, Christine & Laurent, Thibault & Goulard, Michel, 2013. "About predictions in spatial autoregressive models: Optimal and almost optimal strategies," TSE Working Papers 13-452, Toulouse School of Economics (TSE).
  7. Bernard Fingleton, 2008. "Prediction Using Panel Data Regression with Spatial Random Effects," SERC Discussion Papers 0007, Spatial Economics Research Centre, LSE.
  8. Bin Zhou & Kara Kockelman, 2008. "Neighborhood impacts on land use change: a multinomial logit model of spatial relationships," The Annals of Regional Science, Springer, vol. 42(2), pages 321-340, June.
  9. Alicia N. Rambaldi & D.S. Prasada Rao & K. Renuka Ganegodage, 2009. "Spatial Autocorrelation and Extrapolation of Purchasing Power Parities. Modelling and Sensitivity Analysis," CEPA Working Papers Series WP012009, School of Economics, University of Queensland, Australia.
  10. Takafumi Kato, 2013. "Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 169-195, July.
  11. Harry Kelejian & Ingmar Prucha, 2010. "Spatial models with spatially lagged dependent variables and incomplete data," Journal of Geographical Systems, Springer, vol. 12(3), pages 241-257, September.
  12. Wang, Wei & Lee, Lung-fei, 2013. "Estimation of spatial panel data models with randomly missing data in the dependent variable," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 521-538.
  13. Antonio Páez, 2009. "Recent research in spatial real estate hedonic analysis," Journal of Geographical Systems, Springer, vol. 11(4), pages 311-316, December.
  14. Bing Zhu & Roland Füss & Nico Rottke, 2011. "The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(4), pages 542-565, May.
  15. E.-H. Yoo & P. Kyriakidis, 2009. "Area-to-point Kriging in spatial hedonic pricing models," Journal of Geographical Systems, Springer, vol. 11(4), pages 381-406, December.
  16. Olivier Parent & Rainer Hofe, 2013. "Understanding the impact of trails on residential property values in the presence of spatial dependence," The Annals of Regional Science, Springer, vol. 51(2), pages 355-375, October.

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