Andra Ghent, Professor of Finance at the University of Utah’s David Eccles School of Business, discusses the current state of housing affordability in an era of high interest rates.
Using 391 high-skilled firm entries in the U.S. from 1990–2010, we estimate the effects of the firm entry on incumbent residents’ consumption, finances, and mobility. We compare outcomes for residents living close to the entry location with those living far away while controlling for their proximity to potential high-skilled firm entry sites.
We study the microstructure of the U.S. housing market using a novel data set comprising housing search and bargaining behavior for millions of interactions between sellers and buyers. We first establish a number of stylized facts, the most prominent being a nearly 50--50 split between houses that sold below final listing price and those that sold above final listing price. Second, we compare observed behavior with predictions from a large theoretical housing literature.
We document what fraction of the housing stock in US cities is affordable to different family types. Rather than looking at what fraction of their income people actually pay in rent in each city, which reflects a mix of households’ ability to pay and supply conditions, we look at the extent to which the housing stock is affordable using discrete housing expenditure share cutoffs and the distribution of rents in the American Community Survey from each city.
Using a convolutional neural networks approach to process the images, this study reviews Airbnb listings in two cities and derives a descriptive model of image technical features, content, and other property attributes (e.g., price, textual information, characteristics) to predict demand at the property level.
In financial markets, forward contracts reflect market perception of future price dynamics. Nontransparent markets, like commercial real estate investments, lack such tools. We use a panel of NYC office leases between 2005 and 2016 to estimate a dynamic term structure of forward lease rates (rental revenues), which reflects changing expectations by tenants and landlords about future rental contract conditions.
We model leverage cycles in the natural laboratory of a mature asset class, namely US Commercial Real Estate. In this setting we can observe entrepreneurs' asset values as well as debt balance and thus model capital-market yields, as conditioned by market-wide leverage, which indicates debt availability. Using a VAR framework, we examine variance decompositions and impulse-response functions. We show that leverage constitutes the primary driver of innovations in capital-market yields and vice versa. We further find evidence for flight to quality as well as knock-on effects that affect low-leverage entrepreneurs in the market.
This paper studies the investment decisions and price impact of non-resident foreigners in the Paris housing market, employing unique micro-level transaction data over the period 1992–2016. We find that these “out-of-country” buyers generally purchase relatively small but high-quality properties in desirable neighborhoods and in areas with high ratios of compatriots.
We examine the link between endowment investment performance and the expertise of university board members. Harnessing detailed information on 11,019 members for 579 universities, we find that expertise in alternatives and larger professional networks are associated with higher allocations to alternatives and better investment results.
Flight to safety (FTS) affects the markets for risky assets such as stocks, corporate bonds, and commodities. Yet, little is known about the effects on commercial real estate. Our findings benefit investors by providing estimates of the short-term return and liquidity response of REITs to FTS episodes, and by documenting long-term effects on REIT revenues and real asset values.
We use changes in real estate prices to study the sensitivity of CEO compensation to luck and to responses to luck. Pay for luck can be optimal when CEOs are expected to react to luck.