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Hedonic Predicted House Price Indices Using Time-Varying Hedonic Models with Spatial Autocorrelation

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Abstract

Hedonic housing price indices are computed from estimated hedonic pricing models. The commonly used time dummy hedonic model and the rolling window hedonic model fail to account for changing consumer preferences over hedonic characteristics and typically these models do not account for the presence of spatial correlation in prices reflecting the role of locational characteristics. This paper develops a class of models with time-varying hedonic coefficients and spatially correlated errors, provides an assessment of the predictive performance of these compared to the commonly used hedonic models, and constructs and compares corresponding price index series. Alternative weighting systems, plutocratic versus democratic, are considered for the class of hedonic imputed price indices. Accounting for seasonality in house sales data, monthly chained indices and annual chained indices based on averages of year-on-year monthly indexes are presented. The empirical results are based on property sales data for Brisbane, Australia over the period 1985 to 2005. On the basis of root mean square prediction error criterion the time-varying parameter with spatial errors is found to be the best performing model and the rolling-window model to be the worst performing model.

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  • Alicia Rambaldi & Prasada Rao, 2011. "Hedonic Predicted House Price Indices Using Time-Varying Hedonic Models with Spatial Autocorrelation," Discussion Papers Series 432, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uq2004:432
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    File URL: http://www.uq.edu.au/economics/abstract/432.pdf
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    1. Göran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
    2. Robert J. Hill & Daniel Melser, 2008. "Hedonic Imputation And The Price Index Problem: An Application To Housing," Economic Inquiry, Western Economic Association International, vol. 46(4), pages 593-609, October.
    3. W. Erwin Diewert & Jan de Haan & Rens Hendriks, 2015. "Hedonic Regressions and the Decomposition of a House Price Index into Land and Structure Components," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 106-126, February.
    4. R. Carter Hill & J. R. Knight & C. F. Sirmans, 1997. "Estimating Capital Asset Price Indexes," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 226-233, May.
    5. James Hansen, 2009. "Australian House Prices: A Comparison of Hedonic and Repeat-Sales Measures," The Economic Record, The Economic Society of Australia, vol. 85(269), pages 132-145, June.
    6. Iqbal Syed & Robert J. Hill & Daniel Melser, 2008. "Flexible Spatial and Temporal Hedonic Price Indexes for Housing in the Presence of Missing Data," Discussion Papers 2008-14, School of Economics, The University of New South Wales.
    7. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    8. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
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    Cited by:

    1. Tao Chen & John P. Harding, 2016. "Changing Tastes: Estimating Changing Attribute Prices in Hedonic and Repeat Sales Models," The Journal of Real Estate Finance and Economics, Springer, vol. 52(2), pages 141-175, February.

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