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Estimation of house prices in regions with small sample sizes

Author

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  • Luis Pereira
  • Pedro Coelho

Abstract

House price indexes had become important economic indicators worldwide, since movements in house prices have been closely correlated with the economic cycle. In order to compute these kind of indexes it is imperative to produce reliable estimates of the average transaction price of houses, not only at the macrolevel (e.g. national and state level), but also at the microlevel (e.g. district, municipalities or further disaggregate regional level). In Portugal, there is a rapidly growing demand of such microlevel statistics since the beginning of the recent financial and economic crisis. The Portuguese Statistical Office provides a range of invaluable data at national level; however, this data cannot be used directly to produce reliable regional-level estimates due to small sample sizes. In this paper we employ small area estimation techniques to produce design and model-based estimates of average transaction price of houses for Portuguese regions with small sample sizes. Our results show that the model-based estimates based on spatial and temporal models are more accurate than the traditional direct design-based estimates. The use of these techniques allows the production of information at disaggregated regional levels that would not be available under the traditional direct estimation approaches. Furthermore, it is even possible to produce reliable model-based estimates for geographical areas without sample. The estimates are expected to provide invaluable information to policy-analysts and decision-making. Copyright Springer-Verlag 2013

Suggested Citation

  • Luis Pereira & Pedro Coelho, 2013. "Estimation of house prices in regions with small sample sizes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(2), pages 603-621, April.
    • Handle: RePEc:spr:anresc:v:50:y:2013:i:2:p:603-621
    DOI: 10.1007/s00168-012-0507-3
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    References listed on IDEAS

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    1. Gauri Sankar Datta & J. N. K. Rao & David Daniel Smith, 2005. "On measuring the variability of small area estimators under a basic area level model," Biometrika, Biometrika Trust, vol. 92(1), pages 183-196, March.
    2. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, February.
    3. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    4. Nathalie Girouard & Sveinbjörn Blöndal, 2001. "House Prices and Economic Activity," OECD Economics Department Working Papers 279, OECD Publishing.
    5. Isabel Molina & Nicola Salvati & Monica Pratesi, 2009. "Bootstrap for estimating the MSE of the Spatial EBLUP," Computational Statistics, Springer, vol. 24(3), pages 441-458, August.
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    Citations

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    Cited by:

    1. Eilers, Lea, 2017. "Is my rental price overestimated? A small area index for Germany," Ruhr Economic Papers 734, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.

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    More about this item

    Keywords

    C21; C89; R39;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

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