Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103465
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dc.contributorDepartment of Building and Real Estate-
dc.creatorAbidoye, RBen_US
dc.creatorChan, APCen_US
dc.date.accessioned2023-12-11T00:34:09Z-
dc.date.available2023-12-11T00:34:09Z-
dc.identifier.issn0263-7472en_US
dc.identifier.urihttp://hdl.handle.net/10397/103465-
dc.language.isoenen_US
dc.publisherEmerald Publishing Limiteden_US
dc.rights© Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.en_US
dc.rightsThe following publication Abidoye, R.B. and Chan, A.P.C. (2017), "Artificial neural network in property valuation: application framework and research trend", Property Management, Vol. 35 No. 5, pp. 554-571 is published by Emerald and is available at https://doi.org/10.1108/PM-06-2016-0027.en_US
dc.subjectArtificial neural networken_US
dc.subjectDeveloped countriesen_US
dc.subjectDeveloping countriesen_US
dc.subjectProperty marketen_US
dc.subjectProperty valuationen_US
dc.subjectReviewen_US
dc.titleArtificial neural network in property valuation : application framework and research trenden_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage554en_US
dc.identifier.epage571en_US
dc.identifier.volume35en_US
dc.identifier.issue5en_US
dc.identifier.doi10.1108/PM-06-2016-0027en_US
dcterms.abstractPurpose: The predictive accuracy and reliability of artificial intelligence models, such as the artificial neural network (ANN), has led to its application in property valuation studies. However, a large percentage of such previous studies have focused on the property markets in developed economies, and at the same time, effort has not been put into documenting its research trend in the real estate domain. The purpose of this paper is to critically review the studies that adopted ANN for property valuation in order to present an application guide for researchers and practitioners, and also establish the trend in this research area.-
dcterms.abstractDesign/methodology/approach: Relevant articles were retrieved from online databases and search engines and were systematically analyzed. First, the background, the construction and the strengths and weaknesses of the technique were highlighted. In addition, the trend in this research area was established in terms of the country of origin of the articles, the year of publication, the affiliations of the authors, the sample size of the data, the number of the variables used to develop the models, the training and testing ratio, the model architecture and the software used to develop the models.-
dcterms.abstractFindings: The analysis of the retrieved articles shows that the first study that applied ANN in property valuation was published in 1991. Thereafter, the technique received more attention from 2000. While a quarter of the articles reviewed emanated from the USA, the rest were conducted in mostly developed countries. Most of the studies were conducted by universities scholars, while very few industry practitioners participated in the research works. Also, the predictive accuracy of the ANN technique was reported in most of the papers reviewed, but a few reported otherwise.-
dcterms.abstractResearch limitations/implications: The articles that are not indexed in the search engines and databases searched and also not available in the public domain might not have been captured in this study.-
dcterms.abstractPractical implications: The findings of this study reveal a gap between the valuation practice in developed and developing property markets and also the contributions of real estate practitioners and universities scholars to real estate research. A paradigm shift in the valuation practice in developing nations could lead to achieving a sustainable international valuation practice.-
dcterms.abstractOriginality/value-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProperty management, 2017, v. 35, no. 5, p. 554-571en_US
dcterms.isPartOfProperty managementen_US
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85037354653-
dc.identifier.eissn1758-731Xen_US
dc.description.validate202312 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberBRE-0993-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6984856-
dc.description.oaCategoryGreen (AAM)en_US
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