This paper develops and estimates a model to explain the behaviour of house prices in the United States. The main finding is that over 70% of the increase in house prices relative to trend during the increase of house prices in the United States from 1995 to 2006 can be explained by a pricing mechanism where market participants are Fooled by Search. Trading frictions, also known as search frictions, have been argued to affect asset prices, so that asset markets are constrained efficient, with shocks to liquidity causing prices to temporarily deviate from long run fundamentals. In this paper a model is proposed and estimated that combines search frictions with a behavioural assumption where market participants incorrectly believe that the efficient market theory holds. In other words, households are Fooled by Search. Such a model is potentially fruitful because it can replicate the observation that real price growth and turnover are highly correlated at an annual frequency in the United States housing market. A linearized version of the model is estimated using standard OLS and annual data. In addition to explaining over 70% of the housing bubble in the United States, the model also predicts and estimation confirms that in regions with a low elasticity of supply, price growth should be more sensitive to turnover. Using the lens of turnover, a supply shock is identified and estimated that has been responsible for over 80% of the fall in real house prices from the peak in 2006 to 2010.