Booking.com Hotel Listings (Scrapeable)

Booking.com Hotel Listings (Scrapeable) is the retail shelf of one of the world's largest accommodation marketplaces, flattened into typed rows. Public destination and city pages render structured lodging cards - the Amsterdam destination page alone verifies 1,212 properties - and each card carries the fields a rate shopper actually reads: property name, district, review score out of 10 with its verdict label, review count, the lowest nightly price for the chosen dates, distance from a named landmark, an occasional official star rating, and amenity snippets.

Around the cards sit destination-level facts most catalogs throw away. Filter facets span star ratings 1 through 5 and review-score buckets - Wonderful 9+, Very Good 8+, Good 7+, Pleasant 6+. Category tiles count the market outright: 648 Amsterdam properties advertise views, 181 a balcony, 33 a spa and 32 a pool, and a destination summary prices the star classes at around $211 for 3-star and around $559 for 5-star nights.

Within Datadory's catalog of 1,744 datasets across 159 viable industries, this slice holds sixteen records averaging 7.69 on our quality rubric against a 7.81 catalog-wide mark; this one rates 6/10 - credited for verified per-property economics, discounted because classification fields populate unevenly. Get a sample of this dataset scoped to the destinations you care about.

Rows from the Amsterdam destination page, exactly as delivered:

# Amsterdam destination page, first-card tier (verified 2026-08-21)
property_name : Tribe Amsterdam City
district      : Amsterdam            review_score : 9.0-9.3 range observed
review_count  : 6001

# nightly-rate floor and ceiling on the same card grid (1 night, 2 adults)
property_name : Outside Inn           price_from : $83.81
property_name : Monet Garden Hotel     price_from : $233.77

# destination-level aggregates riding beside the cards
destination_stat : properties with views        value : 648
destination_stat : 3-star average nightly rate  value : around $211

Read what the grid already says. Tribe Amsterdam City's 9-range score stands on 6,001 verified reviews, so nobody mistakes one delighted guest for a consensus. The rate spread on the same card grid runs from $83.81 at Outside Inn to $233.77 at Monet Garden Hotel, both quoted on the identical basis - one night, two adults - which makes the comparison structural rather than accidental. And the aggregates prove the destination is more than the sum of its cards: 648 properties claiming views is a supply-mix fact no single listing can state.

Eight columns carry every property row, all definitions verified against live destination pages during the August 2026 research pass rather than inferred from column names. Identity arrives twice - the display property name and its district - so reports read like a human wrote them while joins still resolve cleanly. Reputation comes as a pair: the review score with its verdict label, and the review count that states the sample size behind it. Commerce arrives as price from, always tied to the stay dates and occupancy requested. Context fills the rest: distance from landmark in the destination's native units, the star rating where an official classification exists, and amenities as card-face snippets.

The guest quote, the destination facet tallies and the star-class price averages live on cards or destination blocks rather than on every row, so they fold under additional fields on request below instead of padding the table with blanks.

• Geography - global by construction. Destination and city pages organize lodging inventory across virtually every country, one destination at a time, so coverage scales with the city list you name rather than with a prebuilt country extract.
• Temporal - every row prices a specific stay window: the check-in and check-out you specify, which is what makes two rows comparable at all. Review scores and counts travel as collected during the August 2026 pass.
• Granularity - one row per property per destination view, keyed by name and district, with a second layer of destination-level aggregates: facet counts per filter and star-class averages. Against the slice's 7.69 average, this record's 6/10 reflects honest gaps in classification fields, not thin geography.

API, files, or your warehouse. Daily, weekly, or hourly.

You pick the channel and the cadence; the eight-column dictionary above travels unchanged through all three. Rows land flattened and typed - name, district, score, count, price, distance, star class, amenities - so a rate-benchmark table joins against your portfolio, RevPAR or review-corpus tables on the first attempt instead of after a parsing project. Cadence changes are a settings conversation, not a re-integration, and a sample cut to your named destinations and stay windows comes first either way.

• Market researchers and consultants read destination cards as market structure: rate floors and review tiers by district show where a city's value concentrates and where an asking price outruns its reputation.
• Competitive intelligence and product teams benchmark rival properties off the same grid - whose rate moved, whose verdict tier slipped - without waiting for a quarterly deck to say it.
• Investors and quants ground-truth the operating assets behind lodging REIT holdings with observable street-level pricing before trusting a filing's narrative.
• Data scientists and ML engineers train nightly-rate and demand models on labeled rows - score, count, star class, amenity flags and landmark distance make a feature set, price makes the target.
• Developers and builders wire rate-watch alerts and mapping dashboards onto one stable column skeleton that never changes shape between destinations.

Market researchers and consultants hold this record highest in the hotel & resort REITs pack: it is the slice's only per-property rate-and-verdict snapshot keyed to named landmarks and districts (market researchers). Competitive intelligence and product teams run rival-property watchlists off the same card grid (competitive intelligence and product teams). Investors and quants pair street-level pricing with the filings-grade fundamentals elsewhere in the slice (investors and quants). Data scientists and ML engineers inherit a flat, typed property panel with a ready target column (data scientists), developers and builders get a schema stable enough to alert on (developers and builders), and journalists, academics and students get claims - a named hotel, a price, a score, a count - a reader can re-check (journalists and academics).

The Booking.com Connectivity API Suite works the same marketplace from the transactional side - partner-facing property, rates and reviews services - while this record stays observational: snapshots for analysis rather than plumbing for bookings. The Amadeus Hotel Search & Booking APIs cover the same rate-shopping job through a second distribution network, useful as a cross-check on whose price the traveler actually sees. For reputation depth, the 515K Hotel Reviews in Europe corpus supplies the full review text this record compresses into a score and a count, and the Hugging Face hotel-search record adds booking-demand flags such as cancellation outcomes. To close the loop back to capital markets, the Yahoo Finance quote pages for hotel REITs price the owners of the buildings these cards rent by the night. All of them sit ranked in the hotel resort reits data hub beside the best hotel-resort-reits datasets list.