Using ensemble methods of machine learning to predict real estate prices

• URL: http://arxiv.org/abs/2504.04303v1
• Date: Sat, 05 Apr 2025 23:53:38 GMT
• Title: Using ensemble methods of machine learning to predict real estate prices
• Authors: Oleh Pastukh, Viktor Khomyshyn,
• Abstract summary: This study helps to gain a deeper understanding of how effective and accurate ensemble machine learning methods are in predicting real estate values.<n>The results obtained in the work are quite accurate, as can be seen from the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) calculated for each model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, machine learning (ML) techniques have become a powerful tool for improving the accuracy of predictions and decision-making. Machine learning technologies have begun to penetrate all areas, including the real estate sector. Correct forecasting of real estate value plays an important role in the buyer-seller chain, because it ensures reasonableness of price expectations based on the offers available in the market and helps to avoid financial risks for both parties of the transaction. Accurate forecasting is also important for real estate investors to make an informed decision on a specific property. This study helps to gain a deeper understanding of how effective and accurate ensemble machine learning methods are in predicting real estate values. The results obtained in the work are quite accurate, as can be seen from the coefficient of determination (R^2), root mean square error (RMSE) and mean absolute error (MAE) calculated for each model. The Gradient Boosting Regressor model provides the highest accuracy, the Extra Trees Regressor, Hist Gradient Boosting Regressor and Random Forest Regressor models give good results. In general, ensemble machine learning techniques can be effectively used to solve real estate valuation. This work forms ideas for future research, which consist in the preliminary processing of the data set by searching and extracting anomalous values, as well as the practical implementation of the obtained results.

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