Quantities and prices in the US housing market exhibit an unusually high degree of volatility. I argue that the expectations channel not only is relevant but also serves as a key factor in explaining this volatility. In this paper, I incorporate Diagnostic Expectations as an amplification mechanism within a TANK model featuring housing and banking sectors. Using Sequential Monte Carlo methods to estimate the model, I find that the size of the housing preference shock is reduced by at least one-third under Diagnostic Expectations compared to Rational Expectations. Specifically, the diagnostic model relies on smaller shocks, whether agents’ imperfect memory is based on recent or three-year past experiences. This highlights the importance of the expectation formation process in explaining a significant portion of what Iacoviello and Neri (2010) refer to as “genuine shifts in tastes for housing or unmodeled disturbances affecting housing demand”. Furthermore, when the expectations channel is removed -i.e., when agents are rational- the model fails to generate the high volatility in house prices observed in the data.