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A continuous spatio-temporal model for house prices in the USA

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  • Márcio Poletti Laurini

    (FEARP-USP, CNPQ)

Abstract

We revisit the studies on the evolution of house prices in the USA using a spatio-temporal model estimated using a Bayesian method. This method introduces a new specification of an error correction model with random effects measured continuously in space. This model allows observing the deviations from the co-integration relationship in each analyzed location and a clearer interpretation of the house price dynamics between 1975 and 2011 for 381 metropolitan areas in the USA. The results indicate the presence of a housing price cycle, consistent with the patterns observed in the analyzed period.

Suggested Citation

  • Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.
    • Handle: RePEc:spr:anresc:v:58:y:2017:i:1:d:10.1007_s00168-016-0801-6
    DOI: 10.1007/s00168-016-0801-6
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    References listed on IDEAS

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    Cited by:

    1. S. R. Johnson & S. E. Heaps & K. J. Wilson & D. J. Wilkinson, 2023. "A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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