How to find Airbnb comps (and why search results mislead you)

A comp — a comparable listing — is a property genuinely competing for the same booking as yours. That means the same rough bedroom, bathroom, and guest-capacity configuration, close enough that a guest choosing between the two would consider it the same trip.

"Airbnbs in my city" is not a comp set. Neither is "the average nightly rate in my ZIP code." A downtown studio and a four-bedroom house twenty minutes away are not competing, and averaging them together produces a number that describes neither. Getting the comp set right matters more than any analysis you run on it afterward — a precise calculation over the wrong listings is just precisely wrong.

The obvious approach is to search Airbnb for your dates and area, filter to your size, and look at what comes up. It's free and it takes five minutes. It also has a selection-bias problem that's easy to miss and hard to correct for.

Airbnb search shows you listings that are available. A listing that's booked solid for the dates you searched doesn't appear at all. So the set you're looking at is systematically weighted toward the listings that haven't sold — which frequently means the ones priced too high, or the ones guests are skipping. The best-performing listings in your market are the ones most likely to be invisible in your own research.

Second problem: what you see is an asking price, not an achieved price. A listing displaying $400/night that books three nights a month is not a $400/night listing in any sense that matters to your revenue. Search results can't distinguish the two, and neither can you from the outside.

This method is still useful for one thing: looking at how competing listings present themselves — their titles, their lead photos, their descriptions. Just don't price off it.

Airbnb shows related listings on a listing page. It's tempting to treat that as a ready-made comp set, but it's built for a different purpose: helping a guest who didn't like this listing find another one they might book. It optimizes for guest conversion, not for competitive benchmarking.

In practice that means it will happily show you properties of a different size, or in a different price bracket, because those are still plausible alternatives for the guest. Useful for understanding what a guest sees next. Not a measurement tool.

Providers like AirROI, AirDNA, and similar services aggregate public short-term-rental performance data and will tell you things search results structurally cannot: what listings actually got booked at, occupancy rates, historical averages rather than today's asking price, and amenity-level detail across a whole set at once.

This is the honest answer for anyone doing real pricing work. The tradeoffs are cost (usually per-call or subscription) and effort — you still have to define your own comp set, pull the data, and do something with it. You're buying better inputs, not an answer.

The fourth option is to skip the assembly step: give a tool your address and configuration and have it return the comp set already matched, with achieved rates and occupancy attached.

That's what HostScore does — it pulls real comparable listings for your specific address and bed/bath/guest counts, filters out the ones that would corrupt the math (monthly and long-stay rentals, which aren't comparable to a nightly rate), and reports trailing-twelve-month averages rather than whatever today's asking price happens to be. It then goes a step further and analyzes your listing photos against your top comps' photos, which is the part no spreadsheet of comps can do for you.

Whichever route you take, the fields worth capturing are: nightly rate (achieved, not asking), occupancy, the two of those multiplied together as RevPAR, bedroom/bathroom/guest counts, minimum stay, cleaning fee, and the amenity list. Rate without occupancy is the single most common way to draw a confident wrong conclusion.

Then there's the part that isn't in any data feed: what the listing actually looks like. Two listings with identical configurations, amenities, and locations routinely earn very differently, and the gap usually shows up in the photos rather than the spreadsheet.