In this article, I study the predictability of bond risk premia by means of expectations to future business conditions using survey forecasts from the Survey of Professional Forecasters. I show that expected business conditions consistently affect excess bond returns and that the inclusion of expected business conditions in standard predictive regressions improve forecast performance relative to models using information derived from the current term structure or macroeconomic variables. The results are confirmed in a real-time out-of-sample exercise, where the predictive accuracy of the models is evaluated both statistically and from the perspective of a mean-variance investor that trades in the bond market.

We study U.S. house price movements using a variance decomposition based on subjective expectations data from the University of Michigan’s Survey of Consumers. We find that households’ subjective cash flow (income) expectations account for the dominant share of the overall variation in house prices, whereas subjective discount rate (return) expectations are insignificant. This finding is robust across different samples and subgroups based on home ownership, census regions, income, and age. This contrasts previous evidence from VAR-based models for rational expectations. Households’ ex post forecast errors and ex ante expectational errors are predictable from housing market information and credit conditions.