Paris commercial premises from open data: shopfront history, street turnover, sourced figures.
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Context is the product.
Most commercial-property tools describe the unit - floor area, rent, photos - and leave you to infer the rest. But, a shopfront is only one part of the answer. Compass reads the street around it: what was there, what is changing, and what shapes its daily life nearby.
Open the map β Β· no account needed
Compass sells neither coverage nor granularity β it sells interpretation. The point is how clearly we turn a raw figure into a decision you can make today.
And an interpretation is worth only what you can check. The tools in this market promise answers grounded in audited data β that names who verified. Compass promises figures re-derivable from a cited public source β that names who can verify: you. An internal audit is a promise. A cited source is a test anyone can rerun, including against us.
A 3/6/9 lease is a nine-year bet, made today on one visit, a hunch about passing trade, and whatever the landlord says.
You are standing in front of an empty shopfront. The agent says the street is lively.
Compass answers on six axes at once.
| What was here | florist 2017 β florist 2020 β gone by 2023 |
| How the street behaves | half the units turned over in three years β and the empty ones refilled |
| What it costs | median fonds 160 000 β¬, 220 000 β¬ for a cafΓ© |
| What is moving now | an insolvency filed here is public months before any listing |
| What is around it | schools, healthcare, food, parks and transit counted within 800 m, aggregated into walkability |
| What the environment is worth | air quality measured (Copernicus), natural and technological risks within 1 km, noise modelled from major roads at 500 m |
Each of those arrives with its source, its date and its confidence level.
Note the wording, because the product keeps it too: air quality is measured, noise is modelled. Noise and footfall are proxies, labelled as such on screen and here.
Has a kitchen ever been here? If a restaurant occupied the unit, the extraction, grease trap and power are probably already in. If not, creating them runs into tens of thousands and needs the building's agreement. The most expensive question this data answers β before you travel.
Is this a graveyard or a good street? Rue d'Argout turned over half its shops in three years β but it also filled its three empty units. Churning upward, not dying. And turnover only means something against its own trade: of the cafΓ©s trading in 2017, 77% were still trading six years later around Les Halles, 56% in the quartier du Mail. Same city, same trade, a different bet.
What did people actually pay? 25 496 goodwill sales published with their price since 2015. For the 5 934 tied to a single shopfront, the median Paris fonds changes hands at 160 000 β¬ β and the trade decides almost everything.
| Food shop | CafΓ© / restaurant | Clothing | Personal services |
|---|---|---|---|
| 250 000 β¬ | 220 000 β¬ | 86 000 β¬ | 50 000 β¬ |
Every figure arrives with its source, its licence, its date β and how sure it is.
Four levels, never a percentage. A confidence score out of 100 would be exactly the kind of unverifiable number this product refuses. The level is computed from columns that already exist, and every row carries the reason that produced it.
Today's composition across the corpus β this is the quality metric, and improving means moving these four numbers leftward:
| established | corroborated | probable | undetermined |
|---|---|---|---|
| 51.4% | 5.9% | 36.7% | 6.0% |
That 36.7% is structural, not laziness: BODACC names an address, BDCom names a unit, and 69% of units share their street number. No public data will say which of eight shopfronts was sold.
A gate runs the whole corpus against 41 invariants, 24 frozen baselines and 8 hand-verified chronologies before anything ships β recounted 5 September 2026 with grep -c '^-- @invariant ' eval/invariants.sql; this line said 37, counted 2 September, and four have landed since. Most of them check what the functions return; one checks what they are β a function exposing an observed column must be SECURITY DEFINER, because row-level security silently turns a withheld row into "never surveyed".
If a number cannot be re-derived from a cited public source, it is not shown.
No landlord declarations, no scraped listings, no proprietary estimate, no score invented to fill a gap. Missing data is displayed as missing β n/a, never 0.
If two units on the same street get the same verdict, Compass has said nothing.
The useful granularity is the street segment, sometimes the side of the pavement. An indicator that does not vary at that scale describes general context; it does not settle a decision.
The entrepreneur β the shopkeeper, restaurateur, craftsperson or franchisee who decides where to open. Once or twice in a working life, committing to nine years.
An agent β an LLM asking the same question through an MCP server. Same scoring core, same traceability requirement, different output: JSON and a chain of thought instead of a map.
Not brokers. A broker qualifies dozens of locations a month and needs portfolios, bulk comparison, national coverage. Every one of those makes the product heavier for someone studying a single address in depth. Serving both serves neither β a broker who wants Compass gets what the agent gets: the API.
Every refusal buys something back. The trade is the point.
| It refuses | What that buys |
|---|---|
| A listings portal | Position upstream of the listing. Compass shows what is coming free β ceased trading, goodwill sold, court-wound-up, censused empty β not what everyone already sees |
| A rent estimate | Honesty about the one number everybody wants. No open dataset of actual commercial rents exists in France |
| A revenue forecast | Credibility. Inventing it would poison every other number on the page |
| A single score out of 100 | A bakery wants footfall, a yoga studio wants quiet, a wine merchant wants median income. One score averages away what pulls against itself |
| National coverage | Depth. The sources that carry the value are local |
| An account to explore | Nothing to get past before finding out whether the tool is any use |
What rent will I pay? No open observatory of commercial rents exists in France. Local rent observatories cover private housing; INSEE's ILC is a revision index, not a level. Street-level commercial values are sold by private vendors β which is the proof they are not open. Goodwill sale prices carry an indirect signal and nothing more.
How many people walk past this door? Paris has no permanent pedestrian sensor: the city's multimodal counters cover bikes, scooters, motorcycles, cars, lorries and buses β not pedestrians.
Vendors do sell the number, and "proprietary and expensive" is the weak objection β worth being precise about what it actually is. It comes from a panel of mobile handsets: SDKs embedded in third-party apps report GPS coordinates in the background, and the sample is then extrapolated by weighting it against known demographics. The vendors say so themselves β "We don't see everyone. We see a sample." Peer-reviewed work on a comparable panel measured a mean sampling rate of 7.5%, swinging between 4.5% and 14.5%, with low-income and less-educated populations under-represented and the urban/rural bias reversing sign mid-series.1
So the figure is modelled, never measured β and the buyer cannot see the panel size on their own street, cannot replay the weighting, and is not told when a recalibration moves last year's number. The objection is not honesty, it is verifiability. Compass measures presence and rhythm from open sources instead, and labels it a proxy on screen.
1 Li Z., Ning H., Jing F., Lessani M. N. (2024). Understanding the bias of mobile location data across spatial scales and over time. PLOS ONE 19(1):e0294430.
What do people here spend, and on what? Card transaction data: proprietary. No workaround.
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