The short version. Across 42,443 established US Airbnb listings we analyzed from Inside Airbnb's June 2026 data, cleanliness ratings track closely with performance. Listings rated 4.9 to 5.0 on cleanliness are booked a median of 144 nights per year and earn a median of about 37,500 dollars, versus 60 nights and roughly 13,000 dollars for listings rated below 4.5. That is 2.4 times the occupancy and 2.9 times the revenue. Cleanliness is also the single strongest driver of a listing's overall star rating, and one of the two categories listings score lowest on. This is a correlation, not proof of cause: cleaner listings and busier listings tend to be the same well-run listings.
Source: RapidEye analysis of Inside Airbnb data, retrieved July 2026 (June 2026 scrapes). n = 42,443 listings with 20+ reviews across six US markets.
The core finding: cleanliness climbs with bookings
Search "how many photos should my Airbnb have" or "does cleanliness affect Airbnb bookings" and you get the same thing every time: confident advice built on nothing. "Cleaner listings get more bookings." "Guests punish messy homes." No sample sizes, no numbers, no dataset. So we built the study that should already exist.
Using Inside Airbnb's open dataset, we grouped every established listing by its guest cleanliness rating and compared each band's estimated occupancy and revenue. The relationship is not subtle. Median estimated occupancy rises monotonically at every step, from 60 nights a year for the least-clean listings to 144 nights for the cleanest.
Median nights booked per year, by cleanliness rating
Inside Airbnb estimated occupancy (last 365 days), n = 42,443
Occupancy is Inside Airbnb's model estimate (50% review rate, ~3-night average stay, capped), not a figure Airbnb reports. Bars show the median listing in each band.
Revenue follows the same staircase, and steepens it, because busier listings also tend to price higher. The median top-cleanliness listing brings in nearly three times what the median sub-4.5 listing does.
Median estimated annual revenue, by cleanliness rating
Inside Airbnb estimated revenue (last 365 days), USD
Median revenue per band. Revenue is a model estimate derived from occupancy and quoted price.
Why a 4.7 is worse than it looks
Airbnb cleanliness scores are heavily compressed at the top. In our sample the median is 4.86 and the 25th percentile is 4.74. Put differently: a listing sitting at what feels like a comfortable 4.7 is actually below three-quarters of its competitors. Nearly 41 percent of listings score 4.9 or higher, so the visible range operators compete inside is razor-thin.
Share of listings, by cleanliness band
Distribution across 42,443 established listings
Bars scaled to the largest band. Median score 4.86; 10th percentile 4.57.
This compression is the whole game. When four in ten listings are already at 4.9-plus, the gap between "clean enough" and "genuinely spotless" is measured in hundredths of a star, and, as the occupancy staircase shows, that thin margin separates a listing booked 60 nights a year from one booked 144. For a professional operator running turnovers at scale, holding the top band is not a vanity metric. It is the difference between an average calendar and a full one.
Cleanliness is where listings lose stars
Here is the part operators underrate. Cleanliness is not just correlated with bookings, it is the single most influential input into the overall star rating guests see. Its Spearman correlation with the overall rating is 0.82, higher than accuracy, check-in, communication, location, or value. When cleanliness moves, the headline rating moves with it.
And cleanliness is one of the two categories guests are hardest on. Airbnb asks guests to rate whether the home was free of health hazards like mold and pests, and free of "extensive dust, pet dander, dirty dishes," according to Airbnb's guest rating guidance. Across the six subscores, cleanliness has the second-lowest average and is the single lowest-scoring category for a listing 27.8 percent of the time, second only to value.
How often each category is a listing's lowest subscore
Share of listings where this is the single weakest of six categories
n = 63,773 listings with all six subscores. Value and location are partly fixed by pricing and geography; cleanliness is the one an operator directly controls every single turnover.
Value and location are largely outside daily control, set by the market and the map. Cleanliness is the exception: it is the category an operator resets from zero on every turnover, and the one that moves the overall rating most. That combination is why cleanliness is the highest-leverage number on the scorecard. It is also why verifying what actually happened at each turnover matters more than trusting a checklist got ticked.
It varies a lot by market
Cleanliness scores are not uniform across the country. Dense urban markets with high turnover volume tend to run lower and messier; leisure and mountain markets run cleaner. New York City has the largest share of cleanliness problems in our sample, with 11.6 percent of listings below 4.5, while Asheville has almost none.
| Market | Listings (n) | Median cleanliness | Below 4.5 | Median nights/yr | Median revenue |
|---|---|---|---|---|---|
| New York City, NY | 9,050 | 4.80 | 11.6% | 102 | $21,120 |
| Broward County, FL | 7,530 | 4.83 | 7.4% | 114 | $28,266 |
| Los Angeles, CA | 15,628 | 4.86 | 5.8% | 126 | $32,499 |
| Nashville, TN | 5,732 | 4.88 | 3.4% | 144 | $41,565 |
| Denver, CO | 2,541 | 4.92 | 1.5% | 168 | $34,671 |
| Asheville, NC | 1,962 | 4.95 | 0.9% | 114 | $27,000 |
Superhosts, unsurprisingly, cluster at the clean end: their median cleanliness score is 4.90, versus 4.77 for regular hosts. It is one of the more consistent gaps in the data, and it lines up with what the Superhost program rewards operationally.
Methodology
We downloaded Inside Airbnb's detailed listings.csv files for six US markets and analyzed them locally. Every number on this page comes from that dataset. Nothing here is scraped from Airbnb directly, and nothing is modeled by us beyond simple grouping and medians.
Data source
Inside Airbnb open dataset, retrieved July 2026. Underlying scrapes dated June 14–30, 2026.
Markets (6)
Los Angeles, New York City, Nashville, Broward County FL, Denver, Asheville.
Sample
109,754 total listings scraped; 42,443 analyzed after exclusions.
Inclusion rule
Listings with 20+ reviews and a published cleanliness score, so ratings and estimates are stable.
Performance metrics
estimated_occupancy_l365d and estimated_revenue_l365d, Inside Airbnb model estimates.
Statistic reported
Median per band (robust to skew). Correlations are Spearman.
Two honesty notes matter for anyone citing this. First, correlation is not causation. We cannot say cleaning harder causes 84 more booked nights. Cleaner listings and busier listings tend to be the same professionally managed listings, and reverse causation is plausible, a listing that gets booked and reviewed constantly is under more pressure to stay spotless. Second, Inside Airbnb's occupancy estimate is itself derived from review volume (it assumes a 50 percent review rate and roughly a 3-night average stay, according to Inside Airbnb's data assumptions), so occupancy and review count share an underlying signal. We restricted the sample to listings with 20 or more reviews specifically to strip out the artifact where a brand-new listing scores a perfect 5.0 off two reviews. The cleanliness-to-performance staircase holds cleanly on that established cohort.
We chose these six markets for geographic and use-case diversity: two dense urban cores (NYC, LA), a leisure and events market (Nashville), a beach and vacation market (Broward), a mountain and business market (Denver), and a small high-tourism town (Asheville). This is a US snapshot, not a global claim, and the absolute occupancy numbers reflect Inside Airbnb's conservative model rather than booking data from Airbnb itself.
Cite this study
RapidEye (2026). The Airbnb Cleanliness Score Study: How Cleanliness Ratings Track With Occupancy and Revenue. Analysis of 42,443 Inside Airbnb listings across six US markets, June 2026 data. rapideyeinspections.com/blog/airbnb-cleanliness-study/
Quick FAQ
What is a good cleanliness score on Airbnb?
Across 42,443 established US listings we analyzed from Inside Airbnb data (June 2026), the median cleanliness score is 4.86 out of 5, and 40.5 percent of listings sit at 4.9 or higher. Because scores are compressed near the top, a 4.7 that looks near-perfect is actually around the 25th percentile. A competitive cleanliness score today is 4.9 or above; anything below 4.5 puts a listing in the bottom 6.5 percent.
Do cleaner Airbnb listings really get booked more?
In our data, yes, on average. Median estimated occupancy climbs from 60 nights per year for listings rated below 4.5 on cleanliness to 144 nights for listings rated 4.9 to 5.0, a 2.4x difference, and median estimated revenue rises from about 13,000 dollars to 37,500 dollars. The staircase is consistent across every band. This is a correlation, not proof of causation: well-run listings tend to be both cleaner and busier, and Inside Airbnb's occupancy figure is itself derived from review volume.
How is cleanliness scored on Airbnb?
Cleanliness is one of the six category subscores guests can leave after a stay, alongside accuracy, check-in, communication, location, and value. Airbnb tells guests the home should be free of health hazards like mold and pests, and free of extensive dust, pet dander, and dirty dishes. In our data cleanliness is the single most influential subscore in the overall star rating, with a Spearman correlation of 0.82.
Which review category do Airbnb listings score lowest on?
Cleanliness is one of the two weakest categories. Across the six subscores, cleanliness has the second-lowest average (4.78, just above value at 4.74) and is the single lowest-scoring category for a listing 27.8 percent of the time. Because cleanliness also moves the overall rating more than any other category, it is the most common place a listing loses stars.
What data is this study based on?
It uses Inside Airbnb's open dataset retrieved in July 2026, covering scrapes dated June 2026 for six US markets: Los Angeles, New York City, Nashville, Broward County Florida, Denver, and Asheville. From 109,754 total listings we analyzed the 42,443 established listings with at least 20 reviews and a published cleanliness score. Occupancy and revenue are Inside Airbnb model estimates, not figures reported by Airbnb.
Sources
- Inside Airbnb, "Get the Data" (detailed listings.csv files, June 2026 scrapes, retrieved July 2026)https://insideairbnb.com/get-the-data/
- Inside Airbnb, "Data Assumptions" (occupancy model: 50% review rate, average length of stay, occupancy cap)https://insideairbnb.com/data-assumptions/
- Airbnb Help Center, "How star ratings work" (the six review categories and cleanliness definition)https://www.airbnb.com/help/article/1257