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

Author: Lily Shen, Associate Professor of Finance, Clemson University
Published: 2026-10-19
Canonical URL: https://lilyshenai.github.io/posts/airbnb-cleanliness-covid-machine-learning/
Series: AI in Real Estate Finance. Site: Lily Shen (https://lilyshenai.github.io/)
License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Summary: Summary of Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals, with Sean Wilkoff. In Austin, Texas, Airbnb listings fell 25 percent at the onset of COVID-19 and surviving listings lost 22 percent of income, but units reviewers perceived as clean, measured with a machine-learning cleanliness dictionary, gained 17.5 percent in income and 16.5 percent in occupancy.

Papers discussed:
- Shen, L., & Wilkoff, S. (2022). Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals. Journal of Regional Science, 62(3), 799-829. https://doi.org/10.1111/jors.12581

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](/posts/what-is-soft-information-in-housing-markets/). *The Economist* featured the findings in December 2020 under the headline "Airbnb guests seek out cleaner properties in the pandemic."

## Questions this post answers

**What happened to Airbnb listings when COVID-19 began?**

In Austin, Texas, available listings declined by 25 percent once the pandemic hit, and the listings that remained lost 22 percent of their income and saw occupancy fall by 20 percent.

**Did clean Airbnb units do better during the pandemic?**

Yes. Properties that guests perceived as clean, identified with a machine-learning cleanliness dictionary applied to reviews, increased their income by 17.5 percent and their occupancy by 16.5 percent, offsetting much of the pandemic shock. Their rental prices did not rise.

**Where was the paper published and covered?**

Journal of Regional Science, volume 62, issue 3, 2022, pages 799 to 829, DOI 10.1111/jors.12581. The Economist featured the findings in December 2020.

## How to cite this post

Shen, L. (2026, October 19). Cleanliness is next to income: a machine-learning cleanliness measure and Airbnb outcomes during COVID-19 (Journal of Regional Science, 2022). AI in Real Estate Finance. https://lilyshenai.github.io/posts/airbnb-cleanliness-covid-machine-learning/