John Mondragon

I use an episode of relatively tight credit supply in the jumbo mortgage market to quantify the importance of price and non-price credit supply mechanisms in explaining changes in borrowing. Following market disruptions in March 2020, borrowers with jumbo loans saw significantly tighter credit supply conditions relative to borrowers with conforming loans. As a result, jumbo borrowers were 50 percent less likely to refinance and when they refinanced they borrowed 4-6 percent less than counterfactual borrowers facing looser credit conditions. The reduction in borrowing may have been caused by both an increase in the price and a change in a non-price mechanism, a decline in the availability of cash-out refinances. Decomposing the total effect into a price and cash-out channel, I find that that the cash-out channel accounts for two to three times as much of the decline as the price effect, and together both explain 70-80 percent of the total decline. This suggests that non-price mechanisms can be least as important as prices in clearing credit markets, a fact which is not adequately explained by current models of credit markets.

Using a nationally-representative panel of consumer credit records for the US from 1999 to 2021, we document a positive correlation between child and parent homeownership. We propose a new causal mechanism behind this relationship: parents extract home equity to help finance their child’s home purchase. To identify the mechanism, we use fixed effect, event study, local projection and matching methods. We find that children whose parents extract equity: (i) are 60-80% more likely to become homeowners; (ii) have lower leverage at origination; and (iii) buy higher-valued homes and at a younger age. The effects are stronger when housing affordability is worse and children’s financial constraints are more likely to bind. Using a simple structural model, we find that in a counterfactual economy with no role for parental equity, intergenerational homeownership mobility increases.

What explains record U.S. house price growth since late 2019? We show that the shift to remote work explains over one half of the 23.8 percent national house price increase over this period. Using variation in remote work exposure across U.S. metropolitan areas we estimate that an additional percentage point of remote work causes a 0.93 percent increase in house prices after controlling for negative spillovers from migration. This cross-sectional estimate combined with the aggregate shift to remote work implies that remote work raised aggregate U.S. house prices by 15.1 percent. Using a model of remote work and location choice we argue that this estimate is a lower bound on the aggregate effect. Our results imply a fundamentals-based explanation for the recent increases in housing costs over speculation or financial factors, and that the evolution of remote work is likely to have large effects on the future path of house prices and inflation.

We compare survey-based measures of work from home (WFH) in the cross section. While the surveys differ in how comprehensively they measure WFH, they are highly correlated in the cross section of US states, suggesting that they will yield similar causal effects. Researchers should carefully consider the trade-off between how comprehensively WFH is measured and measurement error, with the American Community Survey well suited for low levels of aggregation and the Survey of Working Arrangements and Attitudes and Real-Time Population Survey well suited to measuring distinct types of WFH. We also document that the experimental 2020 ACS replicate weights produce errors that are too small.

We study how employment documentation requirements and out‐of‐pocket closing costs constrain mortgage refinancing. These frictions, which bind most severely during recessions, may significantly inhibit monetary policy pass‐through. To study their effects on refinancing, we exploit an FHA policy change that excluded unemployed borrowers from refinancing and increased others’ out‐of‐pocket costs substantially. These changes dramatically reduced refinancing rates, particularly among the likely unemployed and those facing new out‐of‐pocket costs. Our results imply that unemployed and liquidity‐constrained borrowers have a high latent demand for refinancing. Cyclical variation in these factors may therefore affect both the aggregate and distributional consequences of monetary policy.

This article studies how credit markets respond to policy constraints on household leverage. Exploiting a sharp policy-induced discontinuity in the cost of originating certain high-leverage mortgages, we study how the Dodd-Frank “Ability-to-Repay” rule affected the price and availability of credit in the U.S. mortgage market. Our estimates show that the policy had only moderate effects on prices, increasing interest rates on affected loans by 10-15 basis points. The effect on quantities, however, was significantly larger; we estimate that the policy eliminated 15% of the affected market completely and reduced leverage for another 20% of remaining borrowers. This reduction in quantities is much greater than would be implied by plausible demand elasticities and indicates that lenders responded to the policy not only by raising prices but also by exiting the regulated portion of the market. Heterogeneity in the quantity response across lenders suggests that agency costs may have been one particularly important market friction contributing to the large overall effect as the fall in lending was substantially larger among lenders relying on third-parties to originate loans. Finally, while the policy succeeded in reducing leverage, our estimates suggest this effect would have only slightly reduced aggregate default rates during the housing crisis.

Using household-level debt data over 2000-2012 and local variation in inequality, we show that low-income households in high-inequality regions (zip codes, counties, states) accumulated less debt relative to their income than low-income households in lower inequality regions. We also find evidence that low-income households face higher credit prices and reduced access to credit as inequality increases. We argue that these patterns are consistent with inequality tilting credit supply away from low-income households and toward high-income households, which may have long-run implications for outcomes like homeownership or entrepreneurship.