Hoodly is a market map for choosing where to open a cafe, restaurant, or retail business. It overlays foot traffic, transit, competition, business churn, neighbourhood demographics, the development pipeline, and zoning context on an interactive map — city by city — and sells a per-address PDF site-intelligence report for a specific corner.
Independent operators and small teams — someone about to sign their first or second commercial lease. It is built for people deciding with their own money, not for corporate site-selection departments with a GIS team.
A broker's report tells you what's for lease. Hoodly tells you what happens at an address you already care about — who your competitors are, how many people walk past, whether the block has churned through tenants, and whether the neighbourhood is under- or over-supplied. The two answer different questions.
A 5-page PDF for one specific address: a 0–100 composite score with a plain verdict, a six-axis weighted breakdown (flow, transit, demand, cluster, momentum, stability), the five nearest competing venues with names, ratings and distances, the category mix within 400 m, development applications within 500 m, business churn for the surrounding block, the neighbourhood demographic profile against the city census baseline, and a written analyst interpretation.
Between $5 and $15 CAD, depending on the city's data grade. Toronto, Montréal, and Vancouver are $15; most other cities are $9; smaller markets are $5. The exact price for your city is shown before checkout.
About 60 seconds from payment to the report landing in your email.
No. A report is decision support, not a recommendation. It summarises public data signals and does not replace independent legal, zoning, financial, or feasibility review before you sign a lease. See the Terms of Use.
Yes. If the report looks off — wrong corner, stale data, missing context — email frank@feepfoop.com and we'll re-run it for the same corner, or a nearby one if you mis-pinned, free of charge. Details in the Refund Policy.
14 Canadian cities: Toronto, Montréal, Vancouver, Calgary, Ottawa–Gatineau, Edmonton, Winnipeg, Québec City, Hamilton, Halifax, Kitchener–Waterloo–Cambridge, Kelowna, Victoria, and Fredericton. The full list with data grades is on the cities page.
It reflects how complete a city's underlying data is. A-grade cities have every score axis on primary data; B-grade cities have measured foot traffic and named competitors but may lack a full churn record; C-grade cities are scouting-grade. A lower grade means fewer measured axes — the report says exactly which — not a cheaper copy of the same picture.
Municipal open data per city (business licences, food-safety inspections, development applications, pedestrian counts, business improvement areas, zoning, heritage register), Statistics Canada Census, local transit GTFS feeds, OpenStreetMap, and a curated establishment database deduplicated with PostGIS.
From measured municipal pedestrian counts where they exist, and from a statistical model where no real count sits within walking distance. The report always labels which kind a figure is; modelled estimates carry a confidence band.
The public map aggregates venue data to spatial cells and never shows it below a three-venue privacy floor. Individual venue names and addresses appear only in the paid report, for the specific addresses it analyses.
Six axes are weighted — flow 25%, transit 15%, demand 18%, cluster 12%, momentum 12%, stability 18% — plus bonuses for cafe-eligible zoning and business-improvement-area membership. Where a city has no dataset for an axis, that axis is excluded and the remaining weights renormalise, so a thinner city is scored honestly rather than zero-filled.
Yes. The analyst narrative is generated by a large language model from the computed signals, and is not reviewed by a person before delivery. Where the narrative and the structured figures disagree, the structured figures govern.
Survival data runs against intuition: a new venue with no other food business nearby survives less often than one with real peers. A strip of venues creates a destination and generates trips no single unit could generate alone. The isolated corner has to generate every visit on its own.