Comparison of different machine learning models for mass appraisal of real estate

dc.authorid0000-0002-0881-0396
dc.authorid0000-0002-9725-5792
dc.contributor.authorBilgilioğlu, Süleyman Sefa
dc.contributor.authorYılmaz, Hacı Murat
dc.date.accessioned2023-03-09T11:32:50Z
dc.date.available2023-03-09T11:32:50Z
dc.date.issued2023
dc.departmentMühendislik Fakültesi
dc.description.abstractThe present study aimed to compare five machine learning techniques, namely, artificial neural network (ANN), support vector machine (SVM), chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and random forest (RF) for mass appraisal of real estate. Firstly, 1982 precedent data was collected throughout the entire study area for train and test models. Secondly, a total of 68 variables were considered for the mass appraisal. Subsequently, the five machine learning techniques were applied. Finally, the receiver operating characteristic (ROC) and various statistical methods were applied to compare five machine learning techniques.
dc.identifier.doi10.1080/00396265.2021.1996799
dc.identifier.endpage43en_US
dc.identifier.issn0039-6265
dc.identifier.issue388en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage32en_US
dc.identifier.urihttps:/dx.doi.org/10.1080/00396265.2021.1996799
dc.identifier.urihttps://hdl.handle.net/20.500.12451/10322
dc.identifier.volume55en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.ispartofSurvey Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectMachine Learning
dc.subjectMass Appraisal
dc.subjectArtificial Neural Network
dc.subjectSupport Vector Machine
dc.subjectChi-square Automatic Interaction Detection
dc.subjectClassification and Regression Tree
dc.subjectRandom Forest
dc.titleComparison of different machine learning models for mass appraisal of real estate
dc.typeArticle

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