Cleanliness is next to income: a machine-learning cleanliness measure and Airbnb outcomes during COVID-19 (Journal of Regional Science, 2022)

When COVID-19 arrived, travel stopped and the short-term rental market was hit immediately. The pandemic also changed what guests cared about: a clean unit went from a nicety to a health concern. This paper, with Sean Wilkoff in the Journal of Regional Science (2022), uses Airbnb data from Austin, Texas, to measure both the shock and the premium that cleanliness earned during it.

Measuring cleanliness from reviews

Cleanliness is not a field in a listing; it is a judgment guests form and write about. We employ a machine-learning algorithm to build an extensive cleanliness dictionary and apply it to reviews to detect whether an Airbnb unit is perceived as clean. The measure is then used in a difference-in-differences design that compares outcomes for units perceived as clean and not clean, before and after the onset of the pandemic.

Results

• The shock. Available listings declined by 25 percent once the pandemic hit. Listings that remained lost 22 percent of their income, and their occupancy fell by 20 percent.
• The cleanliness premium. Properties perceived as clean increased their income by 17.5 percent and their occupancy by 16.5 percent, which mitigated a large part of the negative shock.
• Quantity, not price. Rental prices for clean listings did not increase after COVID-19. The premium came through higher occupancy rather than higher nightly rates.

The paper also examines how the supply of Airbnb units interacts with the long-term rental market during a market decline.

Why it matters

The result is a clean example of soft information at work. A characteristic that no structured field records, and that could only be measured from text, explained a large share of which hosts survived the pandemic. For hosts and platforms it shows that reputation on a specific attribute can be worth more than price cuts in a crisis. For researchers it is an early application of the text-measurement approach that runs through my work on listing remarks. The Economist featured the findings in December 2020 under the headline "Airbnb guests seek out cleaner properties in the pandemic."