Use Cases
Six ways teams use the hotel data, with the columns each one relies on. Each is a starting point: tell us yours and we will say which fields and countries fit.
- Sales prospecting for hotel suppliers
- OTAs, bedbanks and travel tech
- Revenue management and rate intelligence
- Real estate, investors and hotel development
- Mapping, GIS and geofencing
- AI travel assistants and search
Sales prospecting for hotel suppliers
Companies that sell to hotels need to know which hotels exist in their territory, how big and how upscale they are, who they belong to and how to reach them. Linen and amenity suppliers, food and beverage distributors, property management and channel-manager vendors, and revenue-management consultancies all start from the same list.
Filter by country and city, then by hotel_class and rating to match your product to the right segment. Use brand and chain_from_website to separate independents from chain hotels, since they buy differently. review_count is a rough proxy for size and activity, and building_area_m2 gives the footprint of the building. Contact fields give the route in: phone, website and, in the full dataset, publicly listed email, WhatsApp and social profiles.
Monthly releases keep the list current: new hotels appear with their first_seen_month, closed ones drop out of active segments. The guide How to build a hotel prospect list in Excel walks through the filters.
OTAs, bedbanks and travel tech: content mapping and supply gaps
Online travel agencies, bedbanks and travel-technology firms hold hotel inventory from several suppliers, each with its own IDs and spellings. Mapping those records to one reference, and seeing which hotels are missing, is a constant job.
Send your hotel list and we match it to our places: each row comes back with our stable ld_id, coordinates, name_score, city_score and a match_tier, so high-confidence matches can be accepted automatically and the rest reviewed. Because ld_id does not change between releases, later monthly files join on it directly.
To find supply gaps, compare your matched hotels with all hotels in a city or country. booking_sites_latest shows which sites already sell each hotel, and the detail columns (amenities, check_in_time, hotel_class) fill missing content. See hotel list matching.
Revenue management and rate intelligence
Revenue managers and rate-intelligence teams want to know what a hotel charges, where, and how that compares with the hotel down the road. The price columns capture each hotel's nightly offer on every booking site that lists it, every month.
hotel_rates_by_site gives the offer per site, lowest_offer_latest the lowest, and direct_offer_latest the price on the hotel's own website. direct_vs_lowest_ota_pct shows rate parity at a glance: in the sample, 63% of hotels with a direct offer were priced above the lowest OTA. rate_history_by_month and the yearly averages give the trend, and nearby_lodging_* defines a competitive set from the neighbourhood.
Rates are indicative nightly offers captured for the search dates at the time of capture, not contracted rates. The guide Hotel direct vs OTA prices explains how to read the gap.
Real estate, investors and hotel development
Investors, developers and real-estate analysts look at where hotels are opening and closing, and at how a neighbourhood is changing around a site. Because every release since January 2025 is compared with the previous one, the data records those events month by month.
first_seen_month, closed_month and lifecycle_stage describe each hotel's life, and status_history shows how its status moved. Around each hotel, nearby_openings_*, nearby_closures_* and neighbourhood_momentum_12m summarise what is opening and closing within 3 km, and possible_replacements flags places that may have taken over a closed site. building_area_m2 and places_inside_building describe the building itself.
A place missing from one release is treated as a gap, not a closure, so the closure counts are conservative. See Track hotel openings and closures.
Mapping, GIS and geofencing
GIS analysts, mobility and location-analytics teams need more than a point: they need the outline of the building. Each hotel is matched to its building footprint from OpenStreetMap or Overture where one exists, and delivered as a WKT MULTIPOLYGON in WGS84 in building_polygon_wkt.
A footprint makes a tight geofence for footfall attribution: a device inside the polygon is at the hotel, not at the café next door. building_match_grade (A to E) and polygon_source tell you how much to trust each match; grades A and B suit automated use. In the sample, 154 hotels have a grade-A footprint. The neighbourhood counts (count_<group>_3km) add context for location scoring.
The polygons load straight into GeoPandas, PostGIS and QGIS: see Load hotel polygons (WKT).
AI travel assistants and search
AI travel assistants, chat products and travel search tools answer questions such as "Which four-star hotels near the old town have parking and a late check-out?" Answers like that need checked, structured facts rather than text scraped on the fly.
The data gives each hotel's hotel_class, rating, amenities and amenities_unavailable, check_in_time and check_out_time, languages, coordinates and the nearest services within 3 km. facts_change_timeline and last_confirmed_month show how recently a fact was seen, so an assistant can prefer current information and say when it is not sure.
The monthly change files make it possible to refresh a retrieval index incrementally instead of rebuilding it. See formats and delivery.
Get the data
Download the free Excel sample to see every column with real rows, or tell us the countries and fields you need for counts and a quote. We reply within 24 hours.