How the check works
1. Who lives around the site
We find the address with OpenStreetMap, then count residents by age and sex in a circle of the radius you pick. The counts are WorldPop's estimates (University of Southampton) for 2020 on a 100 m grid, available for most countries. We count the same thing in a circle three times wider, so every figure can be compared with the area around the site.
The form suggests a radius for your kind of spot: 2 km on a residential street, 3 km for a shopping centre, and 1 km on a high street, in an office district, a tourist area or near a campus, where the residents within a short walk are the steady trade. These are rules of thumb at about 5 km/h on foot and 25 km/h by car in town, so the walking and driving times are rough. You can always change the radius.
The groups whose share of residents differs most from the wider circle are named in one sentence, with the difference in their share rounded to 5%, because these are estimates.
2. Who your concept speaks to
Your concept gets a pull score from 0 to 3 for each of seven age groups. AI reads your description to set them. Any groups you pick yourself always count as your core.
3. The fit level
We add up the share of residents your concept speaks to, weighted by those pull scores, inside your circle and in the wider one.
| Good match | That share is at least as high here as in the area around it, and at least 25% of residents. |
|---|---|
| Partial match | Anything in between. |
| Mismatch warning | That share is under 80% of the area's, or under 15% of residents. |
We also say how common your core groups are, here and around, as "1 in 4 residents", or "4 in 10" once it passes 3 in 10. That is quicker to picture than a percentage. It counts your core groups only, so it can differ from the share above.
The full report also counts the residents your concept speaks to. Every group aged 15 and over counts by its pull score: a core group in full, a good fit two-thirds and some pull one-third.
The fit level describes the residents. It is not a prediction, and a red result does not mean a place will fail: plenty of shops thrive on people who don't live nearby.
When the match isn't good, we run the same check for ten common kinds of place (coffee shop, bakery, pizza and so on), using the usual regulars of each, and list up to three that match these residents better. It is a rough guide that gives a mismatch somewhere to go, not a reading of your concept. One tap checks any of them properly.
4. What's nearby
We count the food and drink places OpenStreetMap lists in your circle and in the wider one, and the ones like your concept (for a pho shop, Vietnamese and noodle places). The preview names the nearest one; the full report compares them per 10,000 residents with the surrounding area and names the others, so you can go and see them. When fewer than 3 places like yours are in your circle, one opening or closing swings that comparison, so the report says to lean on the wider circle's count instead.
Within 1 km (or your circle, if it is smaller) we also count what brings people who don't live there: stations, offices, schools, universities and colleges, hospitals, supermarkets and malls, hotels and attractions. A station, a college, a hospital, 30 or more offices, 5 or more hotels or 3 or more attractions raise the rating in step 5 by one level. The full report names the nearest of each and says who it brings past and when.
The map is read at the moment you check, and the date is shown. OpenStreetMap is built by volunteers and misses some places, so treat every count as a floor.
5. Where real customers can differ
Resident data counts where people live, not who walks past. Shopping centres, office districts and tourist areas draw most of their trade from outside, so we rate the gap between residents and real customers as Low, Medium or High for your kind of spot, one level higher when step 4 finds places that bring people in. The full report includes a 3-day street test so you can count who actually walks past: the foot-traffic count the big tools buy, done by you.
6. Spots nearby where more of your people live (full report)
We run the same resident check at six points around the site, each 1.5 times your radius away (north, north-east, south-east, south, south-west and north-west), with the same size of circle. Points with at least 10% more residents your concept speaks to (see step 3) than your site are listed, up to three, with the suburb name, a map link and a link that runs the free check there with your concept. Points with very few residents (water, parks, farmland) are skipped. It is a screening aid: rent, foot traffic and competition at those points are not checked.
7. The money checks
- Break-even (free with the preview): fixed costs ÷ what each customer leaves after food cost ÷ open days.
- Rent as a share of cautious sales: cautious sales are 70% of your own sales guess, or break-even sales if you don't give one. 8% or under is comfortable; over 11% is red. For reference, the US full-service median is 5.7% (National Restaurant Association, 2024). The report also turns it into days: how many of each month's trading days go to rent.
- Slow start: what you lose each month trading at 40% of break-even sales, and how long your cash set aside lasts.
- Regulars needed: how many regulars break-even takes, as a share of the residents your concept speaks to (see step 3). The 3% and 8% lines are our own rule of thumb, not research.
What the check cannot see
- Diners, shoppers, workers and visitors who live elsewhere.
- Household type, income and ethnicity. The global data only has age and sex.
- Change since 2020 in fast-growing areas.
- Places OpenStreetMap hasn't listed yet, and how busy any of them really is.
- Visibility, parking and the hundred other things you will only learn by standing on the street.
That is why the report gives you fit and gap, not a verdict. You decide.