Technology-based ("FinTech") lenders increased their market share of U.S. mortgage lending from 2 percent to 8 percent from 2010 to 2016. Using market-wide, loan-level data on U.S. mortgage applications and originations, we show that FinTech lenders process mortgage applications about 20 percent faster than other lenders, even when controlling for detailed loan, borrower, and geographic observables. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than other lenders in response to exogenous mortgage demand shocks, thereby alleviating capacity constraints associated with traditional mortgage lending. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest to do so. We find no evidence that FinTech lenders target marginal borrowers. Our results suggest that technological innovation has improved the efficiency of financial intermediation in the U.S. mortgage market.
Home price expectations are believed to play an important role in housing dynamics, yet we have limited understanding of how they are formed and how they affect behaviour. Using a unique “information experiment” embedded in an online survey, this article investigates how consumers’ home price expectations respond to past home price growth, and how they impact investment decisions. After eliciting respondents’ priors about past and future local home price changes, we present a random subset of them with factual information about past (one- or five-year) changes, and then re-elicit expectations. This unique “panel” data allows us to identify causal effects of the information, and provides insights on the expectation formation process. We find that, on average, year-ahead home price expectations are revised in a way consistent with short-term momentum in home price growth, though respondents tend to underpredict the strength of momentum. Revisions of longer-term expectations show that respondents do not expect the empirically-occurring mean reversion in home price growth. These patterns are in line with recent behavioural models of housing cycles. Finally, we show that home price expectations causally affect investment decisions in a portfolio choice experiment embedded in the survey.
A large body of empirical evidence suggests that beliefs systematically deviate from perfect rationality. Much of the evidence implies that economic agents tend to form forecasts that are excessively influenced by recent changes. We present a parsimonious quasi-rational model that we call natural expectations, which falls between rational expectations and (na�ve) intuitive expectations. (Intuitive expectations are formed by running growth regressions with a limited number of right-hand-side variables, and this leads to excessively extrapolative beliefs in certain classes of environments). Natural expectations overstate the long-run persistence of economic shocks. In other words, agents with natural expectations turn out to form beliefs that don't sufficiently account for the fact that good times (or bad times) won't last forever. We embed natural expectations in a simple dynamic macroeconomic model and compare the simulated properties of the model to the available empirical evidence. The model's predictions match many patterns observed in macroeconomic and financial time series, such as high volatility of asset prices, predictable up-and-down cycles in equity returns, and a negative relationship between current consumption growth and future equity returns.
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