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Spatially Disaggregated Real Estate Indices

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  • Iversen, Edwin S, Jr

Abstract

A spatial-temporal Markov random-field model is used to produce indices for residential real estate from repeat home sale data. A set of regions is represented by a graph in which neighboring regions are linked. This graph, repeated consecutively a number of times, with each region linked to the same region at adjacent times, defines a spatial-temporal graph that connects regions over space and time in which each node represents a region at a particular time. An index is defined at each node to be the rate of appreciation of log sale price in a region during the preceding time interval. The indices are estimated from data consisting of repeat home sales. The Markov random-field model specifies spatial and temporal correlations between neighboring indices and relations between indices and individual repeat sales. A method is proposed for estimating various parameters in the model and for obtaining real-estate indices. Following this prescription, indices are calculated for the Dade County, Florida, residential real-estate market. Results are compared to those obtained using competing space-only models.

Suggested Citation

  • Iversen, Edwin S, Jr, 2001. "Spatially Disaggregated Real Estate Indices," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 341-357, July.
    • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:3:p:341-57
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    Cited by:

    1. Abhimanyu Gupta & Javier Hidalgo, 2020. "Nonparametric prediction with spatial data," Papers 2008.04269, arXiv.org, revised Nov 2021.
    2. Victor Medina-Olivares & Finn Lindgren & Raffaella Calabrese & Jonathan Crook, 2023. "Joint model for longitudinal and spatio-temporal survival data," Papers 2311.04008, arXiv.org.
    3. Kuangyu Wen & Ximing Wu & David J. Leatham, 2021. "Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 349-366, September.
    4. A Brint, 2009. "Predicting a house's selling price through inflating its previous selling price," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 339-347, March.

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