[Because most mortgages in the United States are securitized in agency mortgage-backed securities (MBS), yield spreads on MBS are a key determinant of homeowners’ funding costs. We study variation in MBS spreads in the time series and across securities and document that MBS spreads show a pronounced cross-sectional smile with respect to the securities’ coupon rates. We present a new pricing model that uses “stripped” MBS prices to identify the contribution of non-interest-rate prepayment risk to spreads and find that this risk explains the smile, whereas the time-series spread variation is mostly accounted for by nonprepayment risk factors.]

Abstract 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.

Abstract Because most mortgages in the United States are securitized in agency mortgage-backed securities (MBS), yield spreads on MBS are a key determinant of homeowners’ funding costs. We study variation in MBS spreads in the time series and across securities and document that MBS spreads show a pronounced cross-sectional smile with respect to the securities’ coupon rates. We present a new pricing model that uses “stripped” MBS prices to identify the contribution of non-interest-rate prepayment risk to spreads and find that this risk explains the smile, whereas the time-series spread variation is mostly accounted for by nonprepayment risk factors. Received March 30, 2015; editorial decision November 21, 2018 by Editor Leonid Kogan. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We argue that the time-varying regional distribution of housing equity influences the aggregate consequences of monetary policy through its effects on mortgage refinancing. Using detailed loan-level data, we show that regional differences in housing equity affect refinancing and spending responses to interest rate cuts, but these effects vary over time with changes in the regional distribution of house price growth. We build a heterogeneous household model of refinancing with mortgage borrowers and lenders and use it to explore the monetary policy implications arising from our regional evidence. We find that the 2008 equity distribution made spending in depressed regions less responsive to interest rate cuts, thus dampening aggregate stimulus and increasing regional consumption inequality, whereas the opposite occurred in some earlier recessions. Taken together, our results strongly suggest that monetary policy makers should track the regional distribution of equity over time.

Technology-based (“FinTech”) lenders increased their market share of U.S. mortgage lending from 2% to 8% from 2010 to 2016. Using loan-level data on mortgage applications and originations, we show that FinTech lenders process mortgage applications 20% faster than other lenders, controlling for observable characteristics. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than do other lenders in response to exogenous mortgage demand shocks. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest. We find no evidence that FinTech lenders target borrowers with low access to finance.Received June 1, 2017; editorial decision November 5, 2018 by Editor Wei Jiang. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.