This paper investigates a sequential social learning problem in which individuals face ambiguity about others’ signal structures and have max-min expected utility preferences, thereby exhibiting ambiguity aversion. Unlike previous findings, which suggest that learning outcomes depend on the specifics of the learning environment, this study establishes information cascades as a robust outcome under ambiguity. With sufficient ambiguity, cascades arise almost surely, regardless of the statistical properties of signal structures. Moreover, standard results predicting the absence of cascades can easily break down: Even minimal ambiguity can trigger cascades when signals are bounded and lead to incorrect herding when signals are unbounded.

We characterize the relationship between the rent-to-price ratio, expected returns, and expected rent growth in a dynamic housing valuation model, where the middle-aged-to-young (MY) ratio determines the slowly evolving mean of the rent-to-price ratio. The link between demographic trends and the housing market dynamics is established by analyzing how individuals’ housing demand varies across age cohorts using household-level survey data. Empirical results show that deviations of the rent-to-price ratio from its slowly evolving mean, which is determined by the MY ratio, exhibit pronounced predictive power for returns but weak predictive power for rent growth. Further analysis reveals heterogeneous evidence across sample cities.