We study the joint evolution of prices and rents of residential property. We construct indices for both rents and prices of renter-occupied properties and for prices of owner-occupied properties. We then decompose the change in the price of occupant-owned property into three components: (1) changes in rent, (2) changes in the relative prices of investor- and occupant-owned properties, and (3) changes in the price-rent ratio. We use a simple model to link our decomposition to different sources of variation in house prices. We argue that while the 2000s boom was plausibly driven by exuberant expectations, the boom of the 2020s more likely resulted from a preference shock.
Mortgage borrowers who have experienced employment disruptions as a result of the COVID-19 pandemic are unable to refinance their loans to take advantage of historically low market rates. In this article, we analyze the effects of a streamlined refinance (“refi”) program for government-insured loans that would allow borrowers to refinance without needing to document employment or income. In addition, we consider a cash-out component that would allow borrowers to extract some of the substantial housing equity that many have accumulated in recent years.
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.
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 loan-processing times and largely replace human-based assessment 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-to-income ratios than previous underwriting guidelines would have typically accepted. In this way, technology brought about an exogenous change in lending standards, which helped raise the homeownership rate and encourage the conversion of rental properties to owner-occupied ones, but did not have large effects on housing prices. Technological innovation in mortgage underwriting may have allowed the 2000s housing boom to grow, however, because it enhanced the ability of both borrowers and lenders to act on optimistic beliefs about future house-price growth.