Ensemble Machine Learning-Based Real Estate Price Prediction with Explainable Artificial Intelligence Methods for Determinant Analysis
Ensemble Machine Learning-Based Real Estate Price Prediction with Explainable Artificial Intelligence Methods for Determinant Analysis |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-10 |
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| Year of Publication : 2025 | ||
| Author : Matthew C. Okoronkwo Ugochukwu E. Orji, Chikodili H. Ugwuishiwu, Caroline N. Asogwa, Emenike C. Ugwuagbo, Bande S. Ponsak | ||
| DOI : 10.14445/22315381/IJETT-V73I10P106 | ||
How to Cite?
Matthew C. Okoronkwo Ugochukwu E. Orji, Chikodili H. Ugwuishiwu, Caroline N. Asogwa, Emenike C. Ugwuagbo, Bande S. Ponsak,"Ensemble Machine Learning-Based Real Estate Price Prediction with Explainable Artificial Intelligence Methods for Determinant Analysis", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.79-94, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P106
Abstract
The Real Estate (RE) industry is an essential part of many countries’ economies, and accurately forecasting housing prices is beneficial to buyers, real estate agents, and the government. However, multiple factors influence the prices of RE properties, which are difficult to measure; the relationship between housing prices and housing characteristics is complex and nonlinear, requiring a flexible algorithm and tools. Three regression-based models were developed using Neural Network (NN), Random Forest (RF), and Extreme Gradient Boosting (XGB) algorithms to predict house prices. Explainable Artificial Intelligence (XAI) methods were deployed to explain the key factors influencing RE prices. The dataset used has 923,159 records, available on Kaggle. The models were evaluated using four zip codes, and the house size influenced the price prediction for the RF model. For efficient RE price performance evaluation, the following metrics were computed: squared (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). These metrics were applied to evaluate the machine learning models adopted in the research, and the results show that the XGB model performed better with R2 = 0.817011. The XGB model, SHapley Additive exPlanations (SHAP) plot showed that acre lot and bath are the most influential determinants in predicting the price of houses, while the Individual Conditional Expectation (ICE) plots showed that bath, by the traditional evaluation methods. The result promises a better decision support to potential RE buyers in selecting houses that meet their specific needs.
Keywords
Explainable AI, Ensemble Learning, Housing Price Prediction, Random Forest, Gradient Boosting Analysis.
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