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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.

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