Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean generates overreaction to forecast revisions, a modest response of forecasts to lagged realizations and a weak response to contemporaneous realizations, and forecast bias. Heterogeneity in signals and prior beliefs about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral biases, either diagnostic expectations or over-confidence, helps improve the model’s predictions for short-horizon overreaction and dispersion. Using a cross-section of forecasters and a term structure of forecasts are crucial for inference.

The aggregate matching function is at the core of structural search and matching models, but its micro-foundations remain elusive. We use granular and comprehensive data from the U.K. housing market to identify individual behaviour at different stages of the matching process (online search, physical meetings, final transactions). A Cobb-Douglas functional form finds broad support in the data, with an estimated demand elasticity of 0.2. We find constant returns to scale; different from other over-the-counter markets, frictions are not reduced as the market increases in size. Congestion effects primarily occur in physical meetings and when bargain- ing over prices. Information beyond market tightness, including pricing strategy and price revisions, helps to predict matches, consistent with an important role for seller optimization. We validate these insights using the 2022 “mini-budget” natural experiment.

We analyze the effects of central bank corporate debt purchases in a setting where the banking sector frictions they are supposed to address do not exist. We find that banks reallocate funding almost entirely to the real estate sector, which fuels real estate overvaluation and impairs nancial stability. Our results imply an elasticity of residential real estate prices to credit supply of 0.84, which is considerably higher than prior estimates in the literature. Our findings show that in economies that do not suffer from credit supply frictions, central bank policies that further stimulate loan provision come with substantial adverse effects.