Enhancing real estate investment trust return forecasts using machine learning

Abstract

We extend the emerging literature on machine learning empirical asset pricing by analyzing a comprehensive set of return prediction factors for real estate investment trusts (REITs). We show that machine learning models are superior to traditional ordinary least squares models and find that REIT investors experience significant economic gains when using machine learning forecasts. In particular, we show that REITs are more predictable than stocks and that their higher predictability is stable over time and across industries.