Reviewing the effects of spatial features on price prediction for real estate market: Istanbul case

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dc.authorid0000-0002-3852-0840
dc.authorid0000-0001-6252-1870
dc.contributor.authorEcevit, Mert İlhanen_US
dc.contributor.authorErdem, Zekien_US
dc.contributor.authorDağ, Hasanen_US
dc.date.accessioned2023-02-09T10:44:38Z
dc.date.available2023-02-09T10:44:38Z
dc.date.issued2022-09-16
dc.departmentIşık Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Enformasyon Teknolojileri Bölümüen_US
dc.departmentIşık University, Faculty of Economics, Administrative and Social Sciences, Department of Information Technologiesen_US
dc.description.abstractIn the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house price ranges. It is considered that the results can contribute to making decisions more accurately for the appraisal in the real estate industry.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEcevit, M. İ., Erdem, Z. & Dağ, H. (2022). Reviewing the effects of spatial features on price prediction for real estate market: Istanbul case. Paper presented at the 2022 7th International Conference on Computer Science and Engineering (UBMK), 490-495. doi:10.1109/UBMK55850.2022.9919540en_US
dc.identifier.doi10.1109/UBMK55850.2022.9919540
dc.identifier.endpage495
dc.identifier.isbn9781665470100
dc.identifier.isbn9781665470094
dc.identifier.isbn9781665470117
dc.identifier.issn2521-1641
dc.identifier.issn2768-0592
dc.identifier.startpage490
dc.identifier.urihttps://hdl.handle.net/11729/5362
dc.identifier.urihttp://dx.doi.org/10.1109/UBMK55850.2022.9919540
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEcevit, Mert İlhanen_US
dc.institutionauthorid0000-0002-3852-0840
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 7th International Conference on Computer Science and Engineering (UBMK)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectApache-sparken_US
dc.subjectDecision treeen_US
dc.subjectGeocodingen_US
dc.subjectLogistic regressionen_US
dc.subjectRandom foresten_US
dc.subjectReal estateen_US
dc.subjectSpatial featureen_US
dc.subjectClassification (of information)en_US
dc.subjectCommerceen_US
dc.subjectForecastingen_US
dc.subjectRandom forestsen_US
dc.subjectGeo codingen_US
dc.subjectHouse's pricesen_US
dc.subjectIstanbulen_US
dc.subjectLogistics regressionsen_US
dc.subjectPrice predictionen_US
dc.subjectReal estate marketen_US
dc.subjectReal-estatesen_US
dc.subjectSpatial featuresen_US
dc.subjectDecision treesen_US
dc.titleReviewing the effects of spatial features on price prediction for real estate market: Istanbul caseen_US
dc.typeConference Objecten_US

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