Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case
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Date
2022
Authors
Ecevit, M.I.
Erdem, Z.
Dag, H.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
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.
Description
7th International Conference on Computer Science and Engineering, UBMK 2022 --14 September 2022 through 16 September 2022 -- --183844
Keywords
Apache-spark, decision tree, geocoding, logistic regression, random forest, real estate, spatial feature, Classification (of information), Commerce, Forecasting, Logistic regression, Random forests, Apache-spark, Geo coding, House's prices, Istanbul, Logistics regressions, Price prediction, Random forests, Real estate market, Real-estates, Spatial features, Decision trees
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Citation
0
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Source
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
Volume
Issue
Start Page
490
End Page
495