Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case

dc.authorscopusid57964038500
dc.authorscopusid57963678400
dc.authorscopusid6507328166
dc.contributor.authorEcevit, M.I.
dc.contributor.authorErdem, Z.
dc.contributor.authorDag, H.
dc.date.accessioned2023-10-19T15:05:37Z
dc.date.available2023-10-19T15:05:37Z
dc.date.issued2022
dc.department-tempEcevit, M.I., Information Technologies Fmv Işk University, İstanbul, Turkey, Management Information Systems Kadir Has University, İstanbul, Turkey; Erdem, Z., Management Information Systems Kadir Has University, İstanbul, Turkey; Dag, H., Management Information Systems Kadir Has University, İstanbul, Turkeyen_US
dc.description7th International Conference on Computer Science and Engineering, UBMK 2022 --14 September 2022 through 16 September 2022 -- --183844en_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. © 2022 IEEE.en_US
dc.identifier.citation0
dc.identifier.doi10.1109/UBMK55850.2022.9919540en_US
dc.identifier.endpage495en_US
dc.identifier.isbn9781665470100
dc.identifier.scopus2-s2.0-85141867569en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage490en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK55850.2022.9919540
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4973
dc.identifier.wosqualityN/A
dc.khas20231019-Scopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022en_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.subjectLogistic regressionen_US
dc.subjectRandom forestsen_US
dc.subjectApache-sparken_US
dc.subjectGeo codingen_US
dc.subjectHouse's pricesen_US
dc.subjectIstanbulen_US
dc.subjectLogistics regressionsen_US
dc.subjectPrice predictionen_US
dc.subjectRandom forestsen_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
dspace.entity.typePublication

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