Unit 2 · Assessing Your Market · Lesson 2.2
Creating market dashboards and comp sets
A market dashboard shows you what every listing around yours is doing; a comp set narrows that view to the listings a guest would genuinely book instead of yours. This lesson walks through building both: setting an address and radius that match how guests actually travel, filtering the market down to your category, and hand-vetting every candidate from the guest’s perspective until 12 to 15 reliable comparables remain.
The full lesson text below is an edited transcript of the video, published 2026-08-24. The complete course is free at the playbook.
Start with a market dashboard
In PriceLabs, a market dashboard is built from two inputs: an address and a radius. Take a four-bedroom home on Orcas Island, in Washington State’s San Juan Islands, with a 20 kilometre radius as the starting point. The temptation is to accept whatever circle those inputs produce. The better move is to ask how guests actually reach the place before settling the boundary.
The San Juans are an island group: San Juan Island itself, plus Orcas, Lopez, Shaw and a few smaller islands, reached by ferry from Anacortes, by seaplane, or by private boat. However a guest travels, each island is about equally easy to get to, so a guest weighing a home on Orcas is realistically weighing homes on every island. The competition is the island group as a whole, and the dashboard boundary should say so: the circle can be repositioned and resized until every main island sits inside it. Drawn that way, it captured 537 listings. Not a deep market, but an honest map of the competition.
Three properties of market dashboards are worth knowing before you create one. They are address-based, and the centre is fixed the moment the dashboard is generated, so position it deliberately; it does not have to sit on your own address. The radius is the one input you can change afterwards, as often as you like, out to 50 kilometres. And the data refreshes daily, which is what turns the dashboard from a one-off report into an ongoing instrument. Dashboards are a paid add-on, with a small monthly cost that scales with the number of listings the circle captures, and a new one takes only a few minutes to generate.
Narrow to your category
A fresh dashboard shows the entire market, in this case everything from private rooms and studios up to 10-bedroom houses, with separate Airbnb and Vrbo views of the data. PriceLabs itself suggests that unless you operate in a Vrbo-driven market, the Airbnb dataset is generally the more comprehensive of the two, which makes it the sensible default.
The whole market is rarely the view you need. Every market has its higher end and its lower end, the luxurious penthouse fetching top dollar and the corner shack barely standing and fetching a few hundred, and a four-bedroom home competes with neither extreme, nor with studios and one-bedroom apartments. Filtered to four-bedroom listings, the Orcas Island dashboard dropped from 537 listings to 41 active ones: the pool the comp set will be drawn from.
The map view is where the boundary logic gets tested. Seen on the map, the question is whether guests really treat the islands as one destination, and by ferry, seaplane or boat they do, so every listing stayed in. For markets where a specific pocket matters more, a lasso tool draws a free-hand shape on the map and narrows the data to the listings inside it. Use it when your competition is a neighbourhood; leave it alone when your competition is the whole map.
Adopt the guest perspective
Before any filtering starts, fix the mindset. A comp set is not a revenue comparison. Yes, part of the point is seeing what comparable homes charge, but money is an outcome, not a selection criterion. One competitor runs rental arbitrage, another is a weekend warrior who does not need the income, a third simply sells whatever their listing happens to sell. None of that is visible to a guest, and none of it should decide who makes your set.
The question behind every inclusion is this: if a potential guest clicked on that listing while considering yours, might they book it instead? That framing does some narrowing on its own. A four-bedroom home appeals to a small family, or a couple of families holidaying together, rather than a romantic couple, so the guests doing the comparing are already a specific crowd. From here, every judgement runs through their eyes, not yours.
Filter the field
The comp-set builder lists every candidate in a table: listing ID and link, bedrooms, star rating, review count, price, estimated occupancy, estimated revenue, estimated active nights, minimum stay requirements, and the level of dynamic pricing behind each listing. Column filters can add whatever amenities matter to your situation: pets allowed, beachfront, hot tub, pool. Selecting a listing adds it to the set; the work is deciding which ones deserve the tick.
The example home has neither a pool nor a hot tub, so listings that have them were filtered out: a home without a hot tub should compare itself with its equals. Filters like these deserve a light touch when candidates are scarce, a hot tub lifts price points more than it changes lengths of stay or booking windows, but with enough listings they sharpen the set. A star rating above 4.5 and more than 10 reviews kept the field to serious, proven listings.
Price and estimated revenue were deliberately ignored: guest perspective, not money. Listings showing zero occupancy or zero active nights became candidates for exclusion, not because they are bad homes but because there is not enough data behind them to be representative, often the sign of a newish listing. The dynamic pricing column was left alone too; whether a competitor is connected to pricing software, holds a few seasonal rates, or sells every night at one flat price is nice to know, not a selection criterion. Those few filters cut the 41 four-bedroom listings to roughly 25.
Vet every candidate by hand
Filters produce a short list; they do not build the set. The last step is opening every remaining listing and assessing it the way a guest would: the photos, the indoor and outdoor spaces, the bedrooms and their furnishing, the style, the view. Read the listing copy and the reviews, and check the fine print a guest weighs without saying so: house rules, cancellation policy, anything that would make a stay feel stricter or looser than yours. This is slow, and it is the part that makes the comp set worth having.
Personal taste has to be set aside during the walk-through. A candidate with cramped photography and a style you would never choose can still be a home your guest would happily book, and it belongs in the set. A game room you do not have is a reason to include a listing, not to exclude it: it is exactly the alternative your guest will be weighing. Some candidates stay maybes, like one isolated home with little life around it and a home office parked in the kitchen, comparable on paper but a noticeably different guest experience. And your own listing stays out: you already know what you are doing, and the set exists to watch the external competition, not yourself.
Size matters at the end. Somewhere around 12 to 15 listings is the point where the set starts producing reliable data; the example set was saved at 15. If honest vetting leaves you short of that, loosen the amenity filters before you loosen the guest-perspective standard.
Put the set to work
A saved comp set becomes a filter across the entire market dashboard. Apply it, and every chart and table narrows from the whole market to just the listings you chose, with the dashboard showing which filter is active, the radius in force and when the data last refreshed, so it is always clear which slice of the market you are reading. Analysing your true competitors rather than the market average is the payoff for all the vetting.
The dashboard itself is organised into seven sections: KPIs, the listing map and comp sets, a market summary, prices and occupancy trends, length of stay and booking window trends, amenities, and policies and fees. Each rewards a proper walkthrough of its own. The habit to build now is simpler: whenever you open the dashboard, apply the comp set filter first, so that everything you read describes the competition your guests are actually comparing you against.