Agoda Scraper for Hotel Price and Rate Data

Agoda Hotel Data: The Fields Behind a Property and a Rate

Scope is agreed in writing before anything runs. These are the fields an Agoda scraper can extract from public pages, grouped the way Agoda groups them.

• Query context. Numeric city id, check-in date, length of stay, rooms, adults, children and their ages, currency, language, guest origin and capture time. The form takes up to 9 rooms, 36 adults and 9 children, a check-in up to 365 days out and a stay of up to 90 nights, so the grid has hard edges.
• Property identity. Numeric property id, name, translated name, accommodation type, address, area, city, country and coordinates.
• Classification. Star rating and its provenance: gold stars declared by the property, pink stars Agoda derives from amenities, guest ratings and room size, and a verified badge where a government set it. Chain and brand, Agoda Preferred and Preferred Plus labels.
• Scores and reviews. Guest score on a 10-point scale, review count, sub-scores for Location, Cleanliness, Facilities, Room comfort, Service and Value for money, the spread across the 9 plus, 8 to 9, 7 to 8, 6 to 7 and below 6 bands, and public review text with dates.
• Rooms and offers. Room name, bedding, sleeps, benefits, extra bed and breakfast state, room photos, and the supplier behind each offer.
• Rate lines. Base rate excluding taxes and fees, the same rate including them, tax and fee amounts split out, per night against per stay, strike-through rate, discount percentage and promotion label.
• Charges beyond the rate. The included and not-included surcharge lists, extra bed charges and the tax breakdown.
• Terms. Cancellation wording with its deadlines, refundable state, breakfast and other benefits, and whether the offer is pay now, pay later or paid at the property on arrival.
• Availability. Room offers left, sold-out room types, the no-rooms state and the high-demand badge.
• Location and house rules. Check-in from and check-out until, distance to city centre and to the nearest airport, landmark distances, location rating and property photos.

Every public review carries a reviewer name and country. That is personal data and we leave it out by default.

Locales, Agoda Homes and the Edges of a Public Crawl

Agoda runs one inventory behind many storefronts. Its robots file points at a default sitemap index plus 48 locale variants, eight of them regional English builds, and the locale indexes are thinner: the default lists 261 files where the Japanese and Thai builds list a little over a hundred. Each file holds about 45,000 URLs and hotel pages fill 142 of them, roughly 6.35M crawlable property pages against the 6.59M the world page counted in September 2026. Currency and guest nationality are separate axes again: Agoda's partner documentation asks for the guest country in order to return accurate rates, and a rate row without its locale, currency and stated origin compares to nothing. The Asia-Pacific tilt shows in the fields, not just the map - pay-at-hotel is a browsable theme, properties carry UnionPay and Alipay flags, and Japanese stock comes through JTB.

Alternative stays need their own schema. Apartments, villas, hostels, homestays and guest houses sit under Agoda Homes, flagged as an entire home or apartment, sometimes managed by a private host, described by rooms, beds and common space instead of room types, and starred on a scale Agoda derives rather than one a chain declares. Day Use stays are a separate funnel, with check-in and check-out on the same date.

Some things stay out of reach, and we say so up front. Member, VIP and AgodaCash prices sit behind a sign-in; we do not log in and take nothing from behind it. Booking, payment and trip pages are closed in robots. Sections 1.3.4 and 1.3.5 of the terms of use reserve automated access, so an Agoda scraping run is scoped with that document open.