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In the context of an ever-fluctuating real estate market, accurate price forecasting is critical for investors and consumers. This study proposes a robust methodology for building and evaluating high-performance real estate price prediction models. The approach incorporates advanced data preprocessing, multi-dimensional feature engineering, and a hybrid modeling framework with optimal market segmentation. Through comprehensive statistical analysis including Elbow method, Silhouette score, Davies-Bouldin Index, and Calinski-Harabasz Index, we demonstrate that three price clusters (Low, Medium, High) provide optimal market segmentation with 85% stability. Utilizing a public dataset spanning 2018–2025 with locational, socio-economic, and property-specific features, the research segments data into these three distinct pricing tiers using K-Means clustering, then trains supervised machine learning models—Random Forest, XGBoost, LightGBM, and CatBoost—for each segment. Initial results showed unrealistically high accuracy (R \(^2\) > 0.99) indicating data leakage. After implementing rigorous validation protocols and removing 23 leaky features, LightGBM achieves the best performance with R \(^2\) = 0.843, demonstrating significant improvement over baseline methods while maintaining realistic performance within academic literature benchmarks.

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Predicting Real Estate Prices Using Ensemble Learning and Advanced Feature Engineering with Optimal Market Segmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Tra-Giang Vo Thi,
  • Long-Phuoc Tôn

摘要

In the context of an ever-fluctuating real estate market, accurate price forecasting is critical for investors and consumers. This study proposes a robust methodology for building and evaluating high-performance real estate price prediction models. The approach incorporates advanced data preprocessing, multi-dimensional feature engineering, and a hybrid modeling framework with optimal market segmentation. Through comprehensive statistical analysis including Elbow method, Silhouette score, Davies-Bouldin Index, and Calinski-Harabasz Index, we demonstrate that three price clusters (Low, Medium, High) provide optimal market segmentation with 85% stability. Utilizing a public dataset spanning 2018–2025 with locational, socio-economic, and property-specific features, the research segments data into these three distinct pricing tiers using K-Means clustering, then trains supervised machine learning models—Random Forest, XGBoost, LightGBM, and CatBoost—for each segment. Initial results showed unrealistically high accuracy (R \(^2\) > 0.99) indicating data leakage. After implementing rigorous validation protocols and removing 23 leaky features, LightGBM achieves the best performance with R \(^2\) = 0.843, demonstrating significant improvement over baseline methods while maintaining realistic performance within academic literature benchmarks.