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Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches

Doumpos Michail, Papastamos Dimitrios, Andritsos Dimitrios, Zopounidis Konstantinos

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URIhttp://purl.tuc.gr/dl/dias/9AF5107E-EC6A-42D8-AFDC-3C8C2F296371-
Identifierhttps://doi.org/10.1007/s10479-020-03556-1-
Identifierhttps://link.springer.com/article/10.1007/s10479-020-03556-1-
Languageen-
Extent19 pagesen
TitleDeveloping automated valuation models for estimating property values: a comparison of global and locally weighted approachesen
CreatorDoumpos Michailen
CreatorΔουμπος Μιχαηλel
CreatorPapastamos Dimitriosen
CreatorAndritsos Dimitriosen
CreatorZopounidis Konstantinosen
CreatorΖοπουνιδης Κωνσταντινοςel
PublisherSpringer Natureen
Content SummaryAutomated valuation models are widely used in real estate to provide estimates for property prices. Such models are typically developed through regression approaches. This study presents a comparative analysis about the performance of parametric and non-parametric regression techniques for developing reliable automated valuation models for residential properties. Different approaches are explored to incorporate spatial effects into the valuation process, covering both global and locally weighted models. The analysis is based on a large sample of properties from Greece during the period 2012–2016. The results demonstrate that linear regression models developed with a weighted spatial (local) scheme provide the best results, outperforming machine learning approaches and models that do not consider spatial effects.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-06-09-
Date of Publication2021-
SubjectReal estateen
SubjectAutomated valuation modelsen
SubjectNon-parametric regressionen
Bibliographic CitationM. Doumpos, D. Papastamos, D. Andritsos, and C. Zopounidis, “Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches,” Ann. Oper. Res., vol. 306, no. 1–2, pp. 415–433, Nov. 2021, doi: 10.1007/s10479-020-03556-1.en

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