Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets

Real estate is crucial to the global economy, propelling economic and social development. This study examines the effects of dimensionality reduction through Recursive Feature Elimination (RFE), Random Forest (RF), and Boruta on real estate price prediction, as-sessing ensemble models like Bagging, Random Forest, Gradient Boosting, AdaBoost, Stacking, Voting, and Extra Trees. The findings indicate that the Stacking model achieved the best performance with an MAE of 14,092.30, an MSE of 5.34 × 10⁸, an RMSE of 23,104.19, and an R² of 0.9241, followed closely by Gradient Boosting (MAE = 14,536.52, R² = 0.9197). However, applying RFE for a variable reduction in Gradient Boosting led to a 16.9% increase in MAE and a 1.6% decrease in R². A similar trend was noted in Stacking, where the complete version had a 14.6% lower MAE. RF also displayed variable impacts: Gradient Boosting and Stacking saw MAE increases of 19.1% and 17.7%, respectively, whereas RF combined with AdaBoost enhanced performance with a 5.3% reduction in MAE. Boruta enabled the reduction of variables to 16 without significantly impacting ac-curacy; in Stacking, the MAE only increased by 9.8%, while R² decreased to 0.9082. Beyond accuracy, dimensionality reduction enhances computational efficiency, promoting scala-bility in real applications. Future research should investigate hyperparameter optimiza-tion and hybrid strategies to boost performance in complex settings.