Raven Molloy’s research while affiliated with Board of Governors of the Federal Reserve System and other places

Of all major industries, construction is the only one to have registered negative average productivity growth since 1987. One might suspect measurement error to have biased growth downward since the deflators for this sector, which are used to translate nominal construction spending into the real quantity of structures, have risen much faster than those for other sectors. We find evidence of an upward bias in these deflators related to unobserved improvements in structure quality, but the magnitude is not large enough to alter the view that construction-sector productivity growth has been weak. We also find only small contributions from other potential sources of measurement error. We conclude that productivity growth may well have been quite low in construction, even if it has not been as low as implied by official statistics.

We examine the effects of housing supply constraints on housing affordability, which we measure directly using quality-adjusted rent as well as indirectly using structure sizes, lot sizes and household location choices. Empirically, we find that housing supply constraints have only modest effects on rents and housing consumption despite their larger effects on city growth and the price to purchase homes. Calibration of a dynamic, spatial equilibrium model shows that supply constraints increase price-rent ratios because investors expect future rents to increase more with expected demand growth. Because rent is what matters for affordability, supply constraints have reduced affordability less than is commonly understood despite their sizable effects on the purchase prices of homes.

We construct a new measure of aggregate housing wealth for the U.S. based on (1) home-value estimates derived from machine learning algorithms applied to detailed information on property characteristics and recent transaction prices, and (2) Census housing unit counts. According to our new measure, the timing and amplitude of the recent house-price cycle differs materially but plausibly from commonly-used measures, which are based on survey data or repeat-sales price indexes. Thus, our methodology generates estimates that should be of considerable value to researchers and policymakers interested in the dynamics of aggregate housing wealth.

We examine how housing supply constraints affect housing affordability, which we define as the quality-adjusted price of housing services. In our dynamic model, supply constraints increase the price of housing services by only half has much as the purchase price of a home, since the purchase price responds to expected future increases in rent as well as contemporaneous rent increases. Households respond to changes in the price of housing services by altering their housing consumption and location choices, but only by a small amount. We evaluate these predictions using common measures of housing supply constraints and data from US metropolitan areas from 1980 to 2016. We find sizeable effects of supply constraints on house prices, but modest-to-negligible effects on rent, lot size, structure consumption, location choice within metropolitan areas, sorting across metropolitan areas, and housing expenditures. We conclude that housing supply constraints distort housing affor dability, and therefore housing consumption and location decisions, by less than their estimated effects on house prices suggest.

We construct a new measure of mortgage credit availability using a technique developed for production frontier estimation. The resulting “loan frontier” describes the maximum amount obtainable by a borrower of given characteristics. We estimate this frontier using mortgage originations data from 2001 to 2014. We find a substantial expansion of mortgage credit for all borrowers during the housing boom, not only for low‐score or low‐income borrowers. The subsequent contraction in credit was most pronounced for low‐score borrowers. Using variation in the frontier across metropolitan areas over time, we show that borrowing constraints played an important role in the recent housing cycle.

In this note, we assess whether AVM estimates or owner valuations are better at approximating the market value of a home.

We construct a new measure of aggregate U.S. housing wealth based on Zillow’s Automated Valuation Model (AVM). AVMs offer advantages over other methods because they are based on recent market transaction prices, utilize large datasets which include property characteristics and local geographic variables, and are updated frequently with little lag. However, using Zillow’s AVM to measure aggregate housing wealth requires overcoming several challenges related to the representativeness of the Zillow sample. We propose methods that address these challenges and generate a new estimate of aggregate U.S. housing wealth from 2001 to 2016. This new measure provides insights into some of the disadvantages of other approaches to measuring housing wealth. Specifically, with respect to the owner valuations typically used in survey data, it appears that homeowners were slow to recognize the drop in housing wealth during the financial crisis and that their estimates of this drop were unrealistically small. At the same time, repeat-sales price indexes appear to overstate the extent of the drop in value between 2006 and 2011 and overstate the recovery thereafter.

We construct a new measure of mortgage credit availability that describes the maximum amount obtainable by a borrower of given characteristics. We estimate this "loan frontier" using mortgage originations data from 2001 to 2014 and show that it reflects a binding borrowing constraint. Our estimates reveal that the expansion of mortgage credit during the housing boom was substantial for all borrowers, not only for low-score or low-income borrowers. The contraction was most pronounced for low-score borrowers. Using variation in the frontier across metropolitan areas over time, we show that borrowing constraints played an important role in the recent housing cycle.

In 2012, several large firms began purchasing single-family homes, creating large portfolios of rental property, and securitizing these investments in capital markets. We present the first systematic evidence on this new investor activity in order to shed light on the factors that have supported its emergence. Three key factors were the ample supply of property for sale, tight mortgage financing and a decrease in acquisition and managerial costs brought about by technological advances. In addition, we show that buy-to-rent investors appear to have supported house prices in the neighborhoods where they concentrated.