How to Choose Airbnb Competitors: The Complete Criteria

What a Comp Set Actually Is (and Why Most Hosts Build the Wrong One)

A comp set, short for competitive set, is the small group of listings a guest is actually choosing between when they book yours instead of theirs. It is not every Airbnb within city limits. It is not whatever comes up first when you search your own neighborhood on a random Tuesday. It is the handful of properties, usually five to ten, that overlap so closely with yours on location, size, and quality that a guest genuinely weighs one against the other before clicking “reserve.” If the terminology here is new, our Airbnb revenue management glossary covers comp set, RevPAR, and the rest of the vocabulary this article assumes.

Most hosts skip straight past this definition and build a comp set out of convenience. They pull whatever the Airbnb map shows nearby, or whatever their pricing software defaulted to on setup day, and never touch it again. The result is a benchmark that looks like data but behaves like noise: a three-bedroom lake house with a private dock sitting in the same comp set as a two-bedroom apartment six blocks from the highway, both feeding into one blended average that describes neither property.

The fix is not more data. It is stricter filtering. A comp set of six listings that genuinely match yours will produce a more useful benchmark than thirty listings pulled from a loose radius search.

The Five Criteria That Actually Decide Who Belongs in Your Comp Set

Every credible comp set methodology, from AirDNA’s own comp set tooling to independent operator frameworks, converges on the same five filters. Apply them in order, because each one narrows the field before the next one gets applied.

1. Micro-location, not neighborhood

Guests do not compare your listing against the whole city. They compare it against what shows up in the same search radius, usually a one to two mile window in a dense urban market, tighter in a small beach or mountain town where “close to the water” or “walk to Main Street” changes the product entirely. A listing four miles away in a different micro-market is not a competitor even if it shares your bedroom count and your price. Draw the radius first, before you look at anything else.

2. Matching capacity: bedrooms, bathrooms, and guest count

A three-bedroom property should never sit in the same comp set as a two-bedroom, even if the square footage happens to land close. Bedroom count is the single strongest driver of both price tier and the traveler segment a listing attracts. A one-bedroom competes against other one-bedrooms. A four-bedroom family house competes against other four-bedroom family houses. Bathroom count and maximum guest capacity are the second filter inside that band, since a three-bedroom with one bathroom and a three-bedroom with three bathrooms are not really the same product for a group of eight.

3. Price tier

Within a matched location and capacity band, listings still split into tiers. A budget-tier three-bedroom with dated furniture and a luxury-tier three-bedroom with a private pool are technically the same size in the same neighborhood, and they are still not competing for the same guest. A workable rule is to keep candidates within roughly 25 to 30 percent of your own average rate before you even look at amenities. Anything further outside that band belongs in a different tier’s comp set, not yours.

4. Amenity tier

Pool versus no pool, hot tub versus no hot tub, dedicated workspace versus none: in a warm-weather market a pool alone can move a listing into a different tier entirely, regardless of what the price tag says today. Match your comp set to your actual amenity profile, not your aspirational one. If you do not have a pool, comparing yourself against pool properties will make your occupancy look artificially weak against a benchmark you were never going to hit.

5. Listing quality and guest rating

A five-year-old listing with 400 reviews and a 4.95 rating and a brand-new listing with six reviews are not selling the same level of guest trust, even in an identical unit next door. Filter for a similar review volume and rating band where you can, and treat a mismatch here as a reason to weight a comp lower in your analysis rather than drop it outright, since trust signals shift over time in ways location and bedroom count do not.

How Many Competitors You Actually Need

Five to ten listings is the number that shows up consistently across operator frameworks and data platforms alike, and there is a real reason for the range rather than a single fixed count. Fewer than five and a single outlier, one severely underpriced distressed seller or one luxury outlier, skews your median hard enough to be dangerous. More than ten and you start diluting the set with marginal matches just to hit a round number, which reintroduces the exact noise problem stricter filtering was supposed to solve.

If you manage a portfolio, the 10 to 15 range makes sense at the market level, but each individual property inside that portfolio still needs its own tight five to ten comp set for actual pricing decisions. A portfolio-wide average is a planning tool, not a nightly rate input.

Where to Pull the Data From

Manual research works for a single property: search Airbnb directly for your dates and area, filter by bedroom count, and note who keeps showing up on page one across multiple date searches. That repetition matters, since a listing that appears once might just have gotten lucky with the algorithm that day, while one that appears across five separate date searches is a genuine, consistently visible competitor.

For anything beyond one or two properties, that manual process stops scaling. AirDNA’s comp set tooling builds candidate lists from geographic proximity and bedroom, bathroom, and guest count similarity automatically, and assigns each candidate a match score so you can see at a glance how close a fit it actually is, rather than guessing. That kind of scored, filterable comp data is exactly what a live pricing configuration needs behind it. Our own platform at app.revenuenaire.com builds comp set logic into the pricing engine itself, so the rate a client sees on their calendar is already anchored to a comp set that respects location, capacity, and tier, instead of a citywide average dressed up as a benchmark.

Signals That Should Disqualify a Listing From Your Comp Set

Filtering in the right listings is half the job. Filtering out the wrong ones, even when they technically match on paper, is the other half.

• Stale calendars. A listing that has not adjusted its rate in three or four months is not actively competing for bookings the way a managed listing is. Its price is a historical artifact, not a live market signal.
• Ghost or dormant listings. Properties with no recent bookings, no recent reviews, and long blocked stretches on the calendar are not converting guests and should not anchor your sense of what the market will bear.
• Multi-unit buildings dressed as single properties. A listing inside a 40-unit short-term rental building with a shared front desk and daily housekeeping is functionally closer to a hotel than to an independent Airbnb, and its pricing power reflects that scale.
• New listings still in their seasoning window. A property in its first two to four weeks is often deliberately underpriced to build review velocity, a launch tactic we cover in detail in our new listing pricing strategy guide. That temporary discount is not the market rate, and treating it as one will pull your whole comp set down.
• Listings with a wildly different booking policy. A seven-night minimum-stay property and a two-night minimum-stay property in the same building are not selling to the same guest, even at an identical nightly rate.

How Much Weight to Give Superhost Status and Reviews

Superhost is a quarterly, account-wide badge, not a per-listing score, and it is awarded automatically to hosts who clear four thresholds over a trailing twelve months: a 4.8 or higher overall rating, a 90 percent or higher response rate, a cancellation rate under 1 percent, and at least ten completed stays. Because guests can filter search results to show Superhosts only, a badge holder in a competitive market captures a slice of demand that a non-badge listing never even gets shown to. That makes Superhost status worth noting in your comp set, but not worth treating as a hard filter on its own.

The more useful signal sits one level under the badge: the underlying rating and review count. A 4.8-plus rating with a real review volume, not two perfect reviews from a listing’s first month, indicates a comparable trust level. Weight a comp down, rather than dropping it, when its review profile diverges sharply from yours, since a five-star listing with 200 reviews and a five-star listing with four reviews are not converting the same way even at an identical price point. We go deeper on exactly how rating and review volume translate into pricing power in our piece on Airbnb rating and pricing power.

When a Hotel Actually Belongs in Your Comp Set

Hotels are the comp set question hosts get wrong most often, in both directions. Some hosts dismiss hotel data entirely because “hotels are a different product.” Others benchmark blindly against every hotel within driving distance because the data happens to be easy to pull. Neither instinct holds up. A hotel room sells a bed and daily housekeeping. A short-term rental sells a bed plus a kitchen, extra bedrooms, and privacy, and those extras matter enormously for some trips and not at all for others. We built a full substitutability framework for exactly this question in our piece on whether Airbnb hosts should consider hotels competitors, and the short version is that hotel compression only belongs in your comp set when your listing scores well on trip length, party size, and location type against that framework. Otherwise the hotel occupancy data you are looking at is noise, not signal.

A Worked Example: Building a Comp Set From Scratch

Say you operate a two-bedroom downtown condo. You draw a 1.2 mile radius, filter to two-bedroom units within 25 percent of your rate, and land on eight genuine matches after dropping two stale listings and one that turned out to be inside a 60-unit building with front-desk service. Here is what the comp set and your own numbers show over a trailing twelve-month window, presented as an illustrative model rather than a live client figure.

The RevPAR gap of $13.76 a night is the number that matters, not the ADR number on its own. Your rate is higher than the comp median, but occupancy is falling far enough behind to cost more than the higher rate earns back. Model what happens if you move toward the comp median: at an ADR of $190 with occupancy recovering to a still-conservative 64 percent, RevPAR becomes $121.60, an improvement of $8.20 a night over your current position. Across 365 available nights that is roughly $2,993 in additional annual revenue, without touching a single other lever on the listing. The arithmetic, not the instinct to defend a higher headline rate, is what should decide the move.

How Often to Refresh It

A comp set is not a one-time setup task. Run a full audit quarterly: pull your current comp list, open each listing, confirm it is still active, still similar in size and amenities, and still charging a rate that reflects live demand rather than an abandoned calendar. Drop anything inactive for 60 days or more and replace it with a fresh candidate from the same radius and tier. Between quarterly audits, a lighter weekly spot check, just glancing at whether your top comps have moved their rates for the coming month, keeps you from pricing against a benchmark that quietly went stale between deep reviews. New supply enters a sub-market faster than most hosts expect, and a comp set built in January can be meaningfully out of date by summer in a fast-growing area.

Frequently Asked Questions

Conclusion

A comp set is only as useful as the discipline behind it. Draw the radius tight, match on bedroom count before anything else, filter out stale and dormant listings, and treat Superhost status and reviews as a weighting factor rather than a hard cutoff. Five to ten genuine matches will tell you more than thirty loose ones ever will, and the arithmetic in the worked example above is the same arithmetic that should decide every pricing move from here. Get in touch if you would like us to build and maintain that comp set for you.