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

dc.authorscopusid 57964038500
dc.authorscopusid 57963678400
dc.authorscopusid 6507328166
dc.contributor.author Ecevit, M.I.
dc.contributor.author Ecevit, Mert İlhan
dc.contributor.author Erdem, Z.
dc.contributor.author Dag, H.
dc.contributor.other Management Information Systems
dc.date.accessioned 2023-10-19T15:05:37Z
dc.date.available 2023-10-19T15:05:37Z
dc.date.issued 2022
dc.department-temp Ecevit, 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, Turkey en_US
dc.description 7th International Conference on Computer Science and Engineering, UBMK 2022 --14 September 2022 through 16 September 2022 -- --183844 en_US
dc.description.abstract In 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.citationcount 0
dc.identifier.doi 10.1109/UBMK55850.2022.9919540 en_US
dc.identifier.endpage 495 en_US
dc.identifier.isbn 9781665470100
dc.identifier.scopus 2-s2.0-85141867569 en_US
dc.identifier.startpage 490 en_US
dc.identifier.uri https://doi.org/10.1109/UBMK55850.2022.9919540
dc.identifier.uri https://hdl.handle.net/20.500.12469/4973
dc.khas 20231019-Scopus en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 1
dc.subject Apache-spark en_US
dc.subject decision tree en_US
dc.subject geocoding en_US
dc.subject logistic regression en_US
dc.subject random forest en_US
dc.subject real estate en_US
dc.subject spatial feature en_US
dc.subject Classification (of information) en_US
dc.subject Commerce en_US
dc.subject Forecasting en_US
dc.subject Logistic regression en_US
dc.subject Random forests en_US
dc.subject Apache-spark en_US
dc.subject Geo coding en_US
dc.subject House's prices en_US
dc.subject Istanbul en_US
dc.subject Logistics regressions en_US
dc.subject Price prediction en_US
dc.subject Random forests en_US
dc.subject Real estate market en_US
dc.subject Real-estates en_US
dc.subject Spatial features en_US
dc.subject Decision trees en_US
dc.title Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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