The role of investor sentiment in forecasting housing returns in China: A machine learning approach

Abstract

This paper analyzes the predictive ability of aggregate and disaggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. We find that our new aligned investor sentiment index has greater predictive power for housing returns than the principal component analysis (PCA)-based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the disaggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the disaggregate proxies of investor sentiment. Furthermore, when we let the machine learning models to choose from all key control variables and the aligned sentiment index, the forecasting accuracy is improved at all forecasting horizons, rather than just the short-run as witnessed under standard predictive regressions. This result suggests that machine learning methods are flexible enough to capture both structural change and time-varying information in a set of predictors simultaneously to forecast housing returns of China in a precise manner. Given the role of the real estate market in China's economic growth, our result of accurate forecasting of housing returns has important implications for both investors and policymakers.