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dc.creatorCeh, Marjan
dc.creatorKilibarda, Milan
dc.creatorLisec, Anka
dc.creatorBajat, Branislav
dc.date.accessioned2019-04-19T14:30:04Z
dc.date.available2019-04-19T14:30:04Z
dc.date.issued2018
dc.identifier.issn2220-9964
dc.identifier.urihttp://grafar.grf.bg.ac.rs/handle/123456789/959
dc.description.abstractThe goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008-2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R-2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.en
dc.publisherMDPI AG
dc.relationSlovenian-Serbian bilateral research project 451-03-3095/2014-09/34
dc.rightsopenAccess
dc.sourceIsprs International Journal of Geo-Information
dc.subjectrandom foresten
dc.subjectOLSen
dc.subjecthedonic price modelen
dc.subjectPCAen
dc.subjectLjubljanaen
dc.titleEstimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartmentsen
dc.typearticle
dc.rights.licenseBY
dc.citation.issue5
dc.citation.other7(5): -
dc.citation.rankM22
dc.citation.volume7
dc.identifier.doi10.3390/ijgi7050168
dc.identifier.fulltexthttp://grafar.grf.bg.ac.rs//bitstream/id/4308/957.pdf
dc.identifier.rcubconv_1974
dc.identifier.scopus2-s2.0-85047141908
dc.identifier.wos000435194700008
dc.type.versionpublishedVersion


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