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A comparison of data mining methods for mass real estate appraisal


  • del Cacho, Carlos


We compare the performance of both hedonic and non-hedonic pricing models applied to the problem of housing valuation in the city of Madrid. Urban areas pose several challenges in data mining because of the potential presence of different market segments originated from geospatial relations. Among the algorithms presented, ensembles of M5 model trees consistently showed superior correlation rates in out of sample data. Additionally, they improved the mean relative error rate by 23% when compared with the popular method of assessing the average price per square meter in each neighborhood, outperforming commonplace multiple linear regression models and artificial neural networks as well within our dataset, comprised of 25415 residential properties.

Suggested Citation

  • del Cacho, Carlos, 2010. "A comparison of data mining methods for mass real estate appraisal," MPRA Paper 27378, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:27378

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    References listed on IDEAS

    1. Acciani, Claudio & Fucilli, Vincenzo & Sardaro, Ruggiero, 2008. "Model Tree: An Application In Real Estate Appraisal," 109th Seminar, November 20-21, 2008, Viterbo, Italy 44853, European Association of Agricultural Economists.
    2. Bourassa, Steven C. & Hoesli, Martin & Peng, Vincent S., 2003. "Do housing submarkets really matter?," Journal of Housing Economics, Elsevier, vol. 12(1), pages 12-28, March.
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    More about this item


    mass appraisal; real estate; data mining;

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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