We analyse the ability of a newspaper-based metric of uncertainty of the United States in predicting housing market movements using daily data over the period 2nd August, 2007 to 24th June, 2020. For our purpose, we use a k-th order nonparametric causality-in-quantiles test, which allows us to test for predictability over the entire conditional distribution of not only housing returns but also volatility by controlling for misspecification due to nonlinearity and structural breaks – both of which we show to exist between housing returns and the uncertainty index. Our results show that uncertainty does indeed predict housing returns and volatility, barring the extreme upper end of the respective conditional distributions. Our results are robust to eight other popular measures of uncertainty, as well as an alternative data set involving daily housing prices of the US and ten major metropolitan statistical areas (MSAs). Our findings have important implications for academics, investors, and policymakers.