Using machine learning algorithms for predicting real estate values in tourism centers

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dc.contributor.authorAlkan, Tansu
dc.contributor.authorDokuz, Yesim
dc.contributor.authorEcemis, Alper
dc.contributor.authorBozdas, Asli
dc.contributor.authorDurduran, S. Savas
dc.date.accessioned2024-02-23T13:43:53Z
dc.date.available2024-02-23T13:43:53Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractAlong with the development of technology in recent years, artificial intelligence (machine learning) techniques that perform operations, such as learning, classification, association, optimization, and prediction, have started to be used on data on real estate according to the criteria affecting the value. Using artificial intelligence (machine learning) techniques, valuation processes are performed objectively and scientifically. In this study, machine learning techniques were employed to balance the real estate market, affected by the tourism sector in Alanya district of Antalya province, Turkey, and examine changes in value objectively and scientifically. First, the criteria affecting the real estate value were determined as structural and spatial, and data on real estate were obtained from the online real estate website. Then, the values of the real estate in the selected application area were predicted using machine learning algorithms (k-nearest neighbors, random forest, and support vector machines). Unlike studies in the literature, algorithm-based valuation using machine learning algorithms was performed instead of mathematical modeling. When analyzed for performance metrics, the best result was achieved with the support vector machines algorithm (0.73). Objective methods should be used to balance the exorbitant differences between real estate values, to regulate market conditions and to carry out a real estate valuation process free from speculative effects in coastal areas where tourism factor is effective. This study indicated the applicability of algorithm-based machine learning techniques in real estate valuation.en_US
dc.identifier.doi10.1007/s00500-022-07579-7
dc.identifier.endpage2613en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85140995507en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2601en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-022-07579-7
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10964
dc.identifier.volume27en_US
dc.identifier.wosWOS:000876924600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectReal Estate Valuationen_US
dc.subjectMachine Learning Algorithmen_US
dc.subjectK-Nearest Neighborsen_US
dc.subjectRandom Foresten_US
dc.subjectSupport Vector Machinesen_US
dc.subjectGeographic Information Systemsen_US
dc.titleUsing machine learning algorithms for predicting real estate values in tourism centersen_US
dc.typeArticleen_US

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