Beyond Prediction: A Generalized Explainable Hybrid Econometric-Machine Learning Framework for Real Estate Valuation

Real estate valuation increasingly requires methodologies that combine predictive accuracy, economic interpretability, and model transparency. This study proposes a Generalized Explainable Hybrid Econometric–Machine Learning Framework integrating econometric modelling, machine learning, and Explainable Artificial Intelligence (XAI) within a unified analytical architecture. The framework is organized around a generalized conceptual valuation equation and implemented through a workflow comprising data quality assessment, econometric benchmarking, hyperparameter optimization, nonlinear machine-learning modelling, five-fold cross-validation, and SHAP-based explainability. The methodology is demonstrated using an internally constructed Azerbaijan commercial real estate dataset and independently assessed using the publicly available UCI residential benchmark dataset through an identical Python-based computational pipeline. Under the complete income-capitalization benchmark, the Artificial Neural Network achieved the highest predictive performance for the Azerbaijan dataset, whereas Random Forest performed best for the external benchmark. A leakage-reduced robustness analysis further showed that the framework retained meaningful predictive capability after excluding Net Operating Income and the Capitalization Rate, with XGBoost providing the strongest performance under the reduced specification. SHAP analysis identified economically meaningful valuation drivers, highlighting income generation in the commercial market and transport accessibility in the residential benchmark. The proposed framework provides a transparent, explainable, and transferable decision-support methodology for modern real estate valuation.