What "Comp Set" Actually Means in an STR Revenue Projection

What a comp set is trying to do

You can't know what a specific property will earn before you own it — no one can. A comp set solves that problem by assuming that similar properties in a similar location, operated in a similar way, will perform similarly. Instead of guessing at your subject property directly, the model looks at how a cluster of comparable listings actually performed and uses that as a stand-in.

The quality of that stand-in depends entirely on how "similar" is defined. A comp set built from properties with the same bedroom count, similar square footage, comparable amenities (pool, hot tub, view), and a tight geographic radius will track your property's likely performance much more closely than one built loosely from anything within a wide radius that happens to be a short-term rental.

This same basic idea shows up everywhere in real estate — a sales-comparison appraisal works the same way, using comparable sales instead of comparable bookings. What makes an STR comp set a bit more demanding is that short-term rental performance is sensitive to more variables than a home's sale price is. A long-term rental comp mostly needs to match on size, condition, and location. An STR comp also needs to account for amenities, proximity to specific attractions, listing quality, and how the property is marketed — all of which can move performance meaningfully even between two otherwise similar properties.

The variables that define a comp set

• Geographic radius: how far from the subject property a listing can be and still count as a comp — tighter radius generally means more relevant comps, but a smaller pool to draw from.
• Bedroom and occupancy count: comps are typically filtered to match how many guests the property can sleep, since this drives both ADR and demand.
• Amenity match: a pool, hot tub, waterfront access, or notable view can meaningfully separate a property from otherwise-similar neighbors, so amenity-aware comp sets filter for this.
• Property type: a detached cabin, a condo unit, and a townhome in the same town can have very different demand curves even at the same price point.
• Time period: comps are pulled from a specific historical window, and how recent that window is affects how relevant it still is.

There's an inherent tension between these filters that's worth understanding on its own terms. Tighten every filter as much as possible and you might end up with only one or two genuinely matching comps — a very relevant but very thin data set. Loosen the filters and you get a bigger, more statistically stable sample, but one that includes properties that aren't quite as similar to yours. Neither extreme is automatically correct; the right balance depends on how dense the local short-term rental market actually is.

Why this matters more than the final revenue number itself

Two projections can show the exact same headline revenue figure and deserve very different levels of trust, depending entirely on the comp set underneath. A number built from a dozen tightly matched comps within a short radius is a fundamentally different piece of evidence than the same number built from a handful of loosely matched listings spread across a much larger area.

This is also why it's worth asking about the comp set even when a projection's number matches your own expectations. A number that happens to look right for the wrong reasons — built from a mismatched comp set that coincidentally lands near a plausible figure — isn't actually reliable, even though it feels confirmatory. The comp set is the part of the process that determines whether a projection is reasoning correctly, independent of whether you happen to like the answer it produces.

It's worth building the habit of asking for the comp set details as a standard step, not an exceptional one reserved for numbers that seem off. Treating it as routine due diligence — the same way you'd routinely check a home inspection report rather than only when something looks visibly wrong — keeps you from only scrutinizing the projections that already make you suspicious, which is exactly the set of projections you're least likely to be misled by in the first place.