Hyatt Hotel Scraper

Get structured, up-to-date data on Hyatt hotels — availability, room rates, cancellation policies, and property details — pulled from Booking.com and Hyatt's official site. Built for hotel price comparison, travel research, rate monitoring, and hospitality analytics.

This Hyatt hotel scraper covers the full Hyatt portfolio: Park Hyatt, Grand Hyatt, Hyatt Regency, Hyatt Place, Hyatt House, Hyatt Centric, Andaz, Alila, Thompson Hotels, Dream Hotels, and more. It never mixes in hotels from other chains.

What you can scrape

• Hyatt hotel availability for specific dates
• Room types, bed types, and maximum occupancy
• Booking.com hotel prices and rate plans
• Official Hyatt hotel prices
• Hotel details: address, city, country, coordinates, star rating, review score
• Amenities and images
• Cancellation policies and refundability
• Meal plans (breakfast included, etc.)
• Date-specific hotel pricing for any check-in/check-out combination

How it works

Search by:

• Hotel name — target one specific Hyatt property, e.g. Grand Hyatt New York
• Area / city — discover Hyatt-family hotels in a destination, e.g. New York
• ZIP / postal code — discover Hyatt-family hotels near a postal code, e.g. 10019

Choose your data source:

• booking_com — live rates and availability from Booking.com
• official — data from Hyatt's own website
• both — both sources, clearly labeled per record

Every record includes source, source_url, and observed_at so you always know where the data came from and when it was collected.

Set check-in/check-out dates, number of adults, children, and rooms, and a preferred currency. Results are returned as clean, normalized JSON — no raw HTML, no duplicate records.

Input example

{
  "hotel": "Grand Hyatt New York",
  "source": "both",
  "check_in": "2026-10-15",
  "check_out": "2026-10-17",
  "adults": 2,
  "children": 0,
  "rooms": 1,
  "currency": "USD",
  "limit": 5
}

Or discover hotels in an area:

{
  "area": "Chicago",
  "source": "booking_com",
  "check_in": "2026-11-01",
  "check_out": "2026-11-03",
  "adults": 2,
  "limit": 10
}

Output example

{
  "hotel_id": "777",
  "source": "booking_com",
  "source_url": "https://www.booking.com/hotel/us/nyc-hyattregency.html",
  "observed_at": "2026-08-22T00:00:00.000Z",
  "hotel_name": "Hyatt Regency Times Square",
  "brand": "Hyatt",
  "check_in": "2026-10-15",
  "check_out": "2026-10-17",
  "length_of_stay": 2,
  "adults": 2,
  "children": 0,
  "rooms": 1,
  "room_type": "Twin Room",
  "bed_type": "twin",
  "price": 500,
  "total_price": 570,
  "currency": "USD",
  "availability": true,
  "city": "New York",
  "country": "US",
  "star_rating": 4,
  "review_score": 8.6,
  "review_count": 3000
}

Use cases

• Hotel price comparison across Hyatt properties
• Hotel market research and competitive pricing analysis
• Travel data pipelines and rate monitoring
• Hospitality analytics and demand forecasting
• Hotel availability monitoring for revenue management

Notes

• Fields that aren't available for a given hotel or source are returned as null — nothing is fabricated.
• When source is both, Booking.com and official-site data are always kept in separate, clearly labeled records rather than merged.
• If one source is temporarily unavailable, the Actor continues with the other rather than failing the whole run.