A comparison of data mining methods for mass real estate appraisal
AbstractWe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 27378.
Date of creation: 11 Dec 2010
Date of revision:
mass appraisal; real estate; data mining;
Find related papers by JEL classification:
- L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-12-18 (All new papers)
- NEP-CMP-2010-12-18 (Computational Economics)
- NEP-URE-2010-12-18 (Urban & Real Estate Economics)
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- 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.
- 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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