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This paper employs a ML-Hedonic approach to quantify the value of uniqueness, a type of "soft" information embedded in real estate advertisements. We first propose an unsupervised learning algorithm to quantify levels of semantic deviation ("uniqueness") in descriptions, the textual portions of real estate advertisements. We then estimated the impact of description uniqueness on real estate transaction outcomes using linear hedonic pricing models. The results indicate textual data disseminate information that numerical data cannot capture, and property descriptions effectively narrow the information gap between structured real estate data and the houses by conveying "soft" information about unique house features. A one standard deviation (0.08) increase in description uniqueness compared to neighboring properties leads to a 5.6% increase in property sale prices and a 2.3-day delay in the closing time, controlling for house characteristics, transaction circumstances, and agent unobservables. This paper provides theoretical and empirical insights on how to utilize the emerging Machine Learning tools in economic research.

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This paper exploits a natural experiment afforded by the fracking boom in Pennsylvania to shed light on the determinants of mortgage default. Looking only at mortgages originated before fracking became viable, and using the underlying geology as a supply shifter, we find that mortgages on homes exposed to shale drilling experience a significant reduction in default risk. This effect is more than four times greater for borrowers who are underwater on their loans. Additional evidence shows that fracking activity does not raise house prices, but significantly increases household income through higher royalty payments, wages, and salaries. Furthermore, we find that fracking directly leads to employment increases in the drilling/mining and construction sectors at the county level and reduces income from unemployment benefits at the ZIP-code level. Finally, in addition to reducing mortgage-default risk, we show that fracking lowers credit card delinquencies. These results are most consistent with the “double-trigger” theory of mortgage default, where underwater borrowers subject to an adverse income shock are much more likely to lose their homes to foreclosure. This paper was accepted by David Simchi-Levi, finance.

The short-term rental market provides a close to real-time signal of how events of regional and national importance can affect the demand for housing. We use Airbnb data from Austin, Texas to empirically investigate the impact of the onset of coronavirus disease 2019 (COVID-19) on the short-term rental market. Specifically, we employ a machine-learning algorithm to create an extensive cleanliness dictionary to detect whether an Airbnb unit is clean.We use a difference-in-difference specification to value the change in income related to reviewer perceived cleanliness during the COVID-19 pandemic. We find the following results: First, available listings declined by 25% once the pandemic hit and those that remained lost 22% of their income and had occupancy decrease by 20%. Second, properties that were perceived to be clean increased their income by 17.5% and their occupancy by 16.5%, mitigating the negative shock due to COVID-19. Third, rental prices for clean Airbnb listings did not increase after COVID-19. In addition, we study the interaction of Airbnb supply on the long-term rental market during a market decline.