Interpretable Machine Learning for Classifying Expansion and Deceleration Regimes in the U.S. Housing Market Using Construction Cost and Supply Indicators

Housing market research has traditionally emphasized forecasting continuous price levels, often overlooking the discrete regime shifts that more directly capture cyclical risk and market turning points relevant to construction planning and investment decisions. This study develops an interpretable machine learning framework to forecast monthly U.S. housing market expansion and deceleration regimes one month ahead by integrating macroeconomic, financial, and construction-related indicators with national housing price data spanning January 1993 through March 2025. Feature selection and hyperparameter tuning are conducted entirely within the training sample using time-series cross-validation, ensuring that all reported performance metrics reflect genuine out-of-sample generalization. Recursive feature elimination combined with variance inflation factor screening yields a compact, seven-variable predictor set, with no macro-financial variable contributing an incremental discriminatory signal. Five classification models spanning linear and tree-based ensemble families are benchmarked under a strict temporal 80%:20% train–test split. Tree-based ensemble models consistently outperform the linear baseline, with Random Forest achieving the highest holdout AUC (0.932) and the most consistent deceleration detection across nested cross-validation folds. To ensure methodological transparency, explainable AI techniques, including SHapley Additive exPlanations, partial dependence, and individual conditional expectation analyses, are employed to interpret both global and local predictive associations underlying regime classification. Construction-related indicators, particularly Construction Put in Place and Building Permits at short lag horizons, emerge as the dominant supply-side predictive signals, outperforming macro-financial variables in regime discrimination by a margin of 0.184 in training CV AUC. By shifting the analytical focus from price forecasting to one-month-ahead regime prediction and integrating predictive accuracy with economic interpretability, this study provides a transparent and scalable framework for monitoring housing market cycles with direct applications to construction risk management, procurement timing, and project planning.