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The mass appraisal of the real estate by computational intelligence
Department of Information Systems, Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
2011 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 11, no 1, p. 443-448Article in journal (Refereed) Published
Abstract [en]

Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches - support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center. (C) 2009 Elsevier B.V. All rights reserved

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2011. Vol. 11, no 1, p. 443-448
Keywords [en]
Ordinary least squares regression, Support vector regression, Multilayer perceptron, Committee, Self-organizing map, Mass appraisal of real estate
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-5453DOI: 10.1016/j.asoc.2009.12.003ISI: 000281591300045Scopus ID: 2-s2.0-77957891329OAI: oai:DiVA.org:hh-5453DiVA, id: diva2:345741
Available from: 2010-08-26 Created: 2010-08-26 Last updated: 2018-01-12Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
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  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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