I examine the effects of seller uncertainty over their home value on the housing market. Using evidence from home listings and transactions data, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic microsearch model of the home selling problem with Bayesian learning. The estimated model highlights how information frictions help to explain the microdecisions of sellers and how these microdecisions affect aggregate market dynamics. The model generates a significant microfounded momentum effect in short‐run aggregate price appreciation rates.