A Multi-Strategy Machine Learning Framework for Real-Estate Price Prediction: Pooled, Contextual, and Segmented Approaches

The primary contributions of this research are outlined below:
Empirical Comparison: An empirical comparison of three modeling strategies—a pooled baseline (Model A), a pooled contextual model (Model B), and dedicated per-segment models (Model C)—on 30,472 Bangladeshi property listings spanning four market segments, using five regression algorithms under a shared preprocessing and five-fold cross-validation protocol.
Statistical Testing: Paired, fold-level statistical testing with Holm–Bonferroni correction, confirming that the observed strategy differences are statistically supported.
SHAP-Based Interpretation: A SHAP-based model interpretation showing that the added market-context features are informative and that price determinants differ across the four segments.
Strategy Preference Evidence: Evidence on when each strategy is preferable: a pooled contextual model is strongest overall and on the sale segments, whereas dedicated segment models perform best on the rental segments.