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Bayesian Spatial Modeling of Housing Prices Subject to a Localized Externality

Author

Listed:
  • Mark D. Ecker

    (University of Northern Iowa)

  • Victor De Oliveira

    (University of Texas at San Antonio)

Abstract

This work proposes a non-stationary ramdom field model to describe the spatial variability of housing prices that are affected by a localized externality. The model allows for the effect of the localized externality on house prices to be represented in the mean function and/or the covariance function of the random field. The correlation function of the proposed model is a mixture of an isotropic correlation function and a correlation function that depends on the distances between home sales and the localized externality. The model is fit using a Bayesian approach via a Markov chain Monte Carlo Algorithm. A dataset of 437 single family home sales during 2001 in the city of Cedar Falls, Iowa, is used to illustrate the model.

Suggested Citation

  • Mark D. Ecker & Victor De Oliveira, 2007. "Bayesian Spatial Modeling of Housing Prices Subject to a Localized Externality," Working Papers 0030, College of Business, University of Texas at San Antonio.
    • Handle: RePEc:tsa:wpaper:0071mss
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    File URL: http://interim.business.utsa.edu/wps/MSS/0030MSS-600-2007.pdf
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    References listed on IDEAS

    as
    1. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
    2. 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.
    3. Hughes-Oliver, Jacqueline M. & Gonzalez-Farias, Graciela & Lu, Jye-Chyi & Chen, Di, 1998. "Parametric nonstationary correlation models," Statistics & Probability Letters, Elsevier, vol. 40(3), pages 267-278, October.
    4. William M. Bowen & Brian A. Mikelbank & Dean M. Prestegaard, 2001. "Theoretical and Empirical Considerations Regarding Space in Hedonic Housing Price Model Applications," Growth and Change, Wiley Blackwell, vol. 32(4), pages 466-490.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Won Kim, Chong & Phipps, Tim T. & Anselin, Luc, 2003. "Measuring the benefits of air quality improvement: a spatial hedonic approach," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 24-39, January.
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    More about this item

    Keywords

    Geostatistics; Hedonic regression; Monte Carlo; Random field; Real estate data.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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