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.
Similar content being viewed by others
References
Barry, R., and R. K. Pace. (1999). "A Monte Carlo Estimator of the Log Determinant of Large Sparse Matrices," Linear Algebra and its Applications 289, 41-54.
Dempster, A. P., N. M. Laird, and D. B. Rubin. (1977). "Maximum Likelihood From Incomplete Data Via the EM Algorithm," Journal of the Royal Statistical Society, Series B 39, 1-38.
Gelfand, A. E., and A. F. M. Smith. (1990). "Sampling-based Approaches to Calculating Marginal Densities," Journal of the American Statistical Association 85, 398-409.
Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin (1995). Bayesian Data Analysis. New York: Chapman Hall/CRC.
Gilley, O. W., and R. K. Pace. (1996). "On the Harrison and Rubinfeld Data," Journal of Environmental Economics and Management 31, 403-405.
Harrison, D., and D. L. Rubinfeld. (1978). "Hedonic Prices and the Demand for Clean Air," Journal of Environmental Economics and Management 5, 81-102.
Kibria, G. B. M., L. Sun, J. V. Zidek, and N. D. Le. (2002). "Bayesian Spatial Prediction of Random Space-Time Fields With Application to Mapping PM2.5 Exposure," Journal of the American Statistical Association 97, 112-124.
Knight, J. R., C. F. Sirmans, A. E. Gelfand, and S. K. Ghosh. (1998). "Analyzing Real Estate Data Problems Using the Gibbs Sampler," Real Estate Economics 26, 469-492.
LeSage, J. P. (1997). "Bayesian Estimation of Spatial Autoregressive Models," International Regional Science Review 20, 113-129.
LeSage, J. P. (1999). Spatial Econometrics. Web Book of Regional Science http://www.rri.wvu.edu/regscweb.htm.
LeSage, J. P. (2000). "Bayesian Estimation of Limited Dependent variable Spatial Autoregressive Models," Geographical Analysis 32, 19-35.
Little, R. J. A., and D. B. Rubin. (2002). Statistical Analysis with Missing Data, 2nd edn. New York: Wiley.
Muirhead, R. J. (1982). Aspects of Multivariate Statistical Theory. New York: Wiley.
Pace, R. K., and R. Barry. (1997). "Quick Computation of Spatial Autoregressive Estimators," Geographical Analysis 29, 232-246.
Pace, R. K., and J. LeSage. (2004). "Chebyshev Approximation of Log-determinants of Spatial Weight Matrices," Computational Statistics and Data Analysis 45, 179-196.
Poirier, D. J. (1995). Intermediate Statistics and Econometrics. Cambridge, MA: MIT Press.
Rao, C. R., and H. Toutenburg (1995). Linear Models: Least Squares and Alternatives. New York: Springer-Verlag.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
LeSage, J.P., Pace, R.K. Models for Spatially Dependent Missing Data. The Journal of Real Estate Finance and Economics 29, 233–254 (2004). https://doi.org/10.1023/B:REAL.0000035312.82241.e4
Issue Date:
DOI: https://doi.org/10.1023/B:REAL.0000035312.82241.e4