Version 1
: Received: 20 September 2022 / Approved: 22 September 2022 / Online: 22 September 2022 (10:33:09 CEST)
How to cite:
Khosravi, M.; Arif, S.B.; Ghaseminejad, A.; Tohidi, H.; Shabanian, H. Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices. Preprints2022, 2022090341. https://doi.org/10.20944/preprints202209.0341.v1
Khosravi, M.; Arif, S.B.; Ghaseminejad, A.; Tohidi, H.; Shabanian, H. Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices. Preprints 2022, 2022090341. https://doi.org/10.20944/preprints202209.0341.v1
Khosravi, M.; Arif, S.B.; Ghaseminejad, A.; Tohidi, H.; Shabanian, H. Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices. Preprints2022, 2022090341. https://doi.org/10.20944/preprints202209.0341.v1
APA Style
Khosravi, M., Arif, S.B., Ghaseminejad, A., Tohidi, H., & Shabanian, H. (2022). Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices. Preprints. https://doi.org/10.20944/preprints202209.0341.v1
Chicago/Turabian Style
Khosravi, M., Hamed Tohidi and Hanieh Shabanian. 2022 "Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices" Preprints. https://doi.org/10.20944/preprints202209.0341.v1
Abstract
Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.
Keywords
Real State; Regressors; Artificial Intelligence; Machine Learning; Data-informed; Boston
Subject
Social Sciences, Decision Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
