This paper investigates how market participants form risk perspectives through a sequence of information shocks. Guided by a theoretical Bayesian learning model, we exploit a natural experiment afforded by the fracking boom in Pennsylvania in the late-2000s. We empirically examine whether familiarity with historical conventional gas explorations affects the willingness to pay for houses near fracking wells. We find the local real estate market is very efficient with participants rapidly collecting and processing market–relevant new information. We also find that participants discount historical events and rely on current information to estimate the risk of a change in market conditions.
All Science Journal Classification (ASJC) codes
- Economics and Econometrics
- Urban Studies