CRE brokerages and site-selection consultancies use catalog.xmap.ai to build trade-area studies and co-tenancy analyses for retail and franchise clients, at a fraction of legacy footfall-data pricing.
Rank candidate sites on trade-area density, co-tenancy, and traffic signals.
See who's within a mile of a candidate site, at what rating and review volume.
See exactly which brands share a mall or shopping center before recommending a lease.
Validate a retail asset's surrounding footprint before an acquisition recommendation.
catalog.xmap.ai is a self-serve database of 140M+ real business locations across 40+ countries. Every record is an actual, physically-located business (not an estimate) with up to 36 structured fields: name, full address, latitude/longitude, category (three levels deep), brand and chain affiliation, hours of operation, phone and website, star rating and review count, open/closed status, and more.
You filter the catalog by category, brand, or geography in a live web interface, watch the matching record count update as you narrow the search, and export the results as a clean CSV file, ready to open in a spreadsheet, load into GIS software, or join against your own data.
The dataset refreshes every 30 days, so an export reflects recent openings, closures, and changes rather than a static snapshot from years ago.
Composite score across three candidate sites.
| Field | Type | Why it matters here |
|---|---|---|
| latitude / longitude | float | Drives every trade-area radius and drive-time map |
| traffic_score / dwell_time | numeric | Ranks candidate sites by relative footfall |
| venue_hierarchy | string | Separates anchor tenants from inline shops for co-tenancy analysis |
| open_status | string | Flags recent openings and closures for market-momentum context |
A site-selection consultancy exports competitor density, traffic score, and co-tenancy for three candidate sites, ranks them in a single spreadsheet, and hands the client a defensible recommendation the same week.