Macroeconomic Predictability and Machine Learning in REIT Returns: Out-of-Sample Evidence and Property-Sector Heterogeneity
This study finds that while nonlinear machine learning models offer economically meaningful out-of-sample forecasting gains for medium-horizon U.S. REIT returns—particularly in the health-care sector and driven by macroeconomic factors—they do not achieve statistically significant aggregate superiority over linear benchmarks after multiple-testing adjustments, with predictive signals concentrated in common time-series components rather than cross-sectional selection.