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Learning from Search in the Housing Market

Author

Listed:
  • Irina Telyukova

    (UC San Diego)

  • Leena Rudanko

    (Boston University)

Abstract

House prices fall as the time on the market passes. We document this negative duration dependence for the US housing market using house-level data on listed prices. We interpret the pattern as a result of sellers' imperfect information about the "appeal" of houses to potential buyers. When listing a house sellers have beliefs about this appeal, but these beliefs get downgraded as the house remains on the market. We formalize these ideas in an equilibrium model of search and learning in the housing market, which builds on the work of Gonzalez and Shi (2009). We use the model to derive further testable predictions relating the degree of duration dependence in prices to cross-sectional variation in the activity-level of different housing markets. In the model learning takes place faster in more active markets, implying stronger duration dependence in prices.

Suggested Citation

  • Irina Telyukova & Leena Rudanko, 2013. "Learning from Search in the Housing Market," 2013 Meeting Papers 490, Society for Economic Dynamics.
    • Handle: RePEc:red:sed013:490
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