What makes a good Airbnb comp?

Bedrooms, bathrooms, and guest capacity are the hard ones. A guest searching for a place that sleeps six will never see your four-sleeper, so a six-sleeper isn't competing with you regardless of how similar it looks in photos. Capacity in particular acts as a filter in guest search, which makes it a harder boundary than it appears.

Then proximity — but proximity is where most comp sets quietly go wrong, and it deserves its own section.

A one-mile circle drawn around your property is a convenient approximation and a poor model of how guests choose. A mile in one direction might be the same walkable neighborhood; a mile in another might cross a highway, a river, or a boundary into somewhere with a completely different reason to visit.

What matters is whether a guest planning a specific trip would consider both properties for that trip. Two listings equally far from you can belong in and out of your comp set for exactly that reason. If you're assembling comps by hand, sanity-check the map rather than trusting the distance number — and note that distances reported by any tool are approximate anyway, since listing platforms deliberately obscure a host's exact location.

This one silently corrupts more comp sets than anything else on this list. A listing with a two-week or thirty-day minimum stay is running a fundamentally different business: it's priced for a corporate relocation or a snowbird, not a weekend, and its nightly rate reflects a bulk discount applied to a long block.

Average that into a nightly comp set and you drag your market rate downward for reasons that have nothing to do with your market. Worse, it looks fine — the listing is genuinely nearby, genuinely the right size, and genuinely on the same platform. Nothing flags it except the minimum-stay field.

HostScore excludes stays with two-week-plus minimums from its rate math for exactly this reason, and tells you how many were set aside so you know how much of your local inventory is actually long-stay. If you're building a comp set manually, make minimum stay a column you actually look at.

Short-term rental rates swing hard by season, day of week, and local events. A comp's rate for one specific Saturday tells you about that Saturday. Pricing off it tells you about nothing at all.

This is why serious comp data uses trailing averages — typically trailing twelve months — which smooth seasonality into a stable baseline. The tradeoff is that an annual average lags a market that's currently moving, which is worth knowing when you read one. A useful pairing is a long baseline for stability plus a shorter recent window to check whether the market is currently running above or below its own history.

This is the trap that produces the most convincing wrong answers. If you compute "listings with a hot tub charge X% more" across a small comp set, and only two of them have a hot tub, you haven't measured a hot tub premium. You've measured those two specific listings — including everything else about them.

We ran into a sharp version of this in our own product. An early version analyzed only five comps, and every styling premium it reported was effectively "which two of the five cheapest listings happen to have this feature." Two unrelated features produced identical numbers because they landed on the same two listings. The fix wasn't better math — it was more comps.

So: require a minimum number of listings on each side of any comparison before you believe it, and treat the count as part of the finding rather than a footnote. A premium backed by three listings and one backed by thirty are not the same claim.

Watch for hotel and aparthotel inventory listed on the same platforms — it competes for some of the same guests but prices on completely different economics. Shared rooms and single private rooms shouldn't sit in an entire-place comp set. And listings that haven't been booked or updated in a long stretch tell you about a dead listing rather than a live market.

A reasonable comp set for a typical property lands somewhere in the low dozens. Far fewer and your averages are fragile; far more and you've almost certainly loosened your filters enough to include listings that aren't really competing with you.