with Vimal Balasubramaniam and Tarun Ramadorai
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). This allows us to make progress along four dimensions: First, a Cobb-Douglas functional form finds broad support in the data, with an estimated demand elasticity of 0.2. Second, we find constant returns to scale -- different from other over-the-counter markets, frictions are not reduced as the market increases in size. Third, congestion effects primarily occur in physical meetings and when bargaining over prices. Finally, information beyond market tightness, including pricing strategy and price revisions helps to predict matches, consistent with an important role for seller optimization. We verify and extend these findings by studying search and matching during the 2022 "mini-budget" natural experiment.

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