No abstract

In this paper, we use two comprehensive micro data sets to study how the distribution of mortgage debt evolved during the 2000s housing boom. We show that the allocation of mortgage debt across the income distribution remained stable, as did the allocation of real estate assets. Any theory of the boom must replicate these facts, and a general equilibrium model shows that doing so requires two elements: (1) an exogenous shock that increases expected house price growth or, alternatively, reduces interest rates and (2) financial markets that endogenously relax borrowing constraints in response to the shock. Empirically, the endogenous relaxation of constraints was largely accomplished with subprime lending, which allowed the mortgage debt of low-income households to increase at the same rate as that of high-income households.

The application of information technology to finance, or "fintech," is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loanprocessing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-toincome ratios than previous underwriting guidelines would have allowed. In this way, technology brought about an exogenous change in lending standards that was especially relevant for borrowers with low current incomes relative to their expected future incomes-in particular, young college graduates. By contrast, the data suggest that the credit expansion during the 2000s housing boom was an endogenous response to widespread expectations of higher future house prices, as average mortgage sizes rose for borrowers across the entire income distribution.

In this paper, we use two comprehensive micro datasets to study the evolution of the distribution of mortgage debt during the 2000s housing boom. We show that the allocation of mortgage debt remained stable, as did the distribution of real estate assets. We propose that any theory of the boom must replicate this fact. Using a general equilibrium model, we show that this requires two elements: (1) an exogenous shock to the economy that increases expected house price growth or, alternatively, reduces interest rates and (2) financial markets that endogenously relax constraints in response to the shock. The role played by subprime mortgage debt provides additional empirical evidence that this narrative mirrors reality. JEL codes: R31 and D3.

Prominent rent growth indices often give strikingly different measurements of rent inflation. We create new indices from Bureau of Labor Statistics (BLS) rent microdata using a repeat-rent index methodology and show that this discrepancy is almost entirely explained by differences in rent growth for new tenants relative to the average rent growth for all tenants. Rent inflation for new tenants leads the official BLS rent inflation by four quarters. As rent is the largest component of the consumer price index, this has implications for our understanding of aggregate inflation dynamics and guiding monetary policy. File is available with NTRR and ATRR indices through 2022q3.