Using data on 50 million home sales from the last U.S. housing cycle, we document that much of the variation in volume came from the rise and fall in speculation. Cities with larger speculative booms have larger price booms, sharper increases in unsold listings as the market turns, and more severe busts. We present a model in which predictable price increases endogenously attract short-term buyers more than long-term buyers. Short-term buyers amplify volume by selling faster and destabilize prices through positive feedback. Our model matches key aggregate patterns, including the lead–lag price–volume relation and a sharp rise in inventories.

Information asymmetries are known in theory to lead to inefficiently low credit provision, yet empirical estimates of the resulting welfare losses are scarce. This paper leverages a randomized experiment conducted by a large fintech lender to estimate welfare losses arising from asymmetric information in the market for online consumer credit. Building on methods from the insurance literature, we show how exogenous variation in interest rates can be used to estimate borrower demand and lender cost curves and recover implied welfare losses. While asymmetric information generates large equilibrium price distortions, we find only small overall welfare losses, particularly for high-credit-score borrowers.