🏨 Booking.com Room-Level Price Intelligence

Track every room type, every price, every day — up to 365 days into the future

Extract complete room-level pricing data from any Booking.com hotel including all room types, bed configurations, meal plans, occupancy limits, discount detection, and sold-out tracking.

Built for: Revenue managers, hoteliers, competitive intelligence teams, and travel analysts who need complete market visibility.

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⚡ Key Features

✅ All Room Types — Extract pricing for every room category, not just the cheapest
✅ 365-Day Range — Scrape up to a full year of pricing in a single run
✅ Discount Tracking — Detect original vs. discounted prices automatically
✅ Sold-Out Intelligence — Know exactly which dates have high demand
✅ Bed & Occupancy Data — Get detailed room configurations
✅ Meal Plans — Track breakfast inclusion, half-board, all-inclusive
✅ Availability Alerts — "Only 2 rooms left!" indicators
✅ Refund Policies — Refundable vs. non-refundable room rates
✅ Quality Checks — Built-in price validation and data quality scoring
✅ Multi-Hotel Support — Scrape up to 10 hotels in one run
✅ Global Coverage — Works with any Booking.com property worldwide
✅ Reliable Extraction — Engineered for consistent, long-term use

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📥 Input

Field	Type	Required	Default	Description
hotelUrls	array	✅	—	Booking.com hotel URLs (1-10 hotels per run)
daysAhead	integer	❌	30	Number of days to scrape (1–365)
startDate	string	❌	Today	Start date (YYYY-MM-DD format)
currency	string	❌	"USD"	Currency code (USD, EUR, MAD, GBP, etc.)
adults	integer	❌	2	Number of adult guests
children	integer	❌	0	Number of children
rooms	integer	❌	1	Number of rooms
includeSoldOut	boolean	❌	true	Include sold-out dates in results

📝 Example Input

{
  "hotelUrls": [
    "https://www.booking.com/hotel/ma/riad-dar-anika.fr.html",
    "https://www.booking.com/hotel/gb/the-savoy.html"
  ],
  "daysAhead": 30,
  "startDate": "2026-02-13",
  "currency": "USD",
  "adults": 2,
  "children": 0,
  "rooms": 1,
  "includeSoldOut": true
}

💡 Tip: You only need the base hotel URL. Parameters like ?checkin=... are automatically stripped.

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📤 Output

Each record represents a single room type for a specific date. Get your data in JSON, CSV, or Excel format.

🔑 Output Fields

Field	Type	Description
hotel_url	string	Booking.com hotel URL
hotel_name	string	Hotel name
star_rating	integer	Hotel star rating (0-5)
check_in_date	date	Check-in date (YYYY-MM-DD)
check_out_date	date	Check-out date (YYYY-MM-DD)
room_id	integer	Booking.com internal room ID
room_name	string	Room type name
bed_type	string	Bed configuration
max_occupancy	integer	Maximum guests per room
price	number	Final price per night
original_price	number	Original price (if discounted)
currency	string	Currency code
is_discounted	boolean	Discount applied?
price_suspicious	boolean	Unusual price detected?
rooms_left	integer	Rooms available at this price
is_sold_out	boolean	Completely sold out?
meal_plan	string	Breakfast, half-board, etc.
is_refundable	boolean	Refundable booking?
search_adults	integer	Adults in search
search_children	integer	Children in search
search_rooms	integer	Rooms in search
scrape_timestamp	datetime	When data was extracted
status	string	available, sold_out, or error
data_quality_score	float	Quality score (0-1)
data_quality_issues	array	Detected issues (if any)

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📊 Example Output (JSON)

[
  {
    "hotel_url": "https://www.booking.com/hotel/ma/riad-dar-anika.fr.html",
    "hotel_name": "Riad Dar Anika",
    "star_rating": 4,
    "check_in_date": "2026-02-13",
    "check_out_date": "2026-02-14",
    "room_id": 36027001,
    "room_name": "Deluxe Double Room",
    "bed_type": "1 large double bed",
    "max_occupancy": 2,
    "price": 850,
    "original_price": 1000,
    "currency": "MAD",
    "is_discounted": true,
    "price_suspicious": false,
    "rooms_left": 2,
    "is_sold_out": false,
    "meal_plan": "Breakfast included",
    "is_refundable": true,
    "search_adults": 2,
    "search_children": 0,
    "search_rooms": 1,
    "scrape_timestamp": "2026-02-13T10:45:58Z",
    "status": "available",
    "data_quality_score": 1.0,
    "data_quality_issues": []
  },
  {
    "hotel_url": "https://www.booking.com/hotel/ma/riad-dar-anika.fr.html",
    "hotel_name": "Riad Dar Anika",
    "star_rating": 4,
    "check_in_date": "2026-02-14",
    "check_out_date": "2026-02-15",
    "room_id": 36027001,
    "room_name": "SOLD OUT",
    "bed_type": null,
    "max_occupancy": null,
    "price": null,
    "original_price": null,
    "currency": "MAD",
    "is_discounted": false,
    "price_suspicious": false,
    "rooms_left": 0,
    "is_sold_out": true,
    "meal_plan": null,
    "is_refundable": false,
    "search_adults": 2,
    "search_children": 0,
    "search_rooms": 1,
    "scrape_timestamp": "2026-02-13T10:46:02Z",
    "status": "sold_out",
    "data_quality_score": 1.0,
    "data_quality_issues": []
  }
]

---

💡 Understanding Sold-Out Detection

Sold-out dates are valuable competitive intelligence:

{
  "check_in_date": "2026-03-22",
  "is_sold_out": true,
  "room_name": "SOLD OUT",
  "price": null,
  "status": "sold_out"
}

This tells you which dates have peak demand in your market — critical data for revenue optimization.

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🔗 Integrations

Connect this Actor to your workflow:

• Apify Scheduler → Automate daily price monitoring
• Webhooks → Real-time notifications on price changes
• API Access → Integrate into your applications
• Google Sheets → Direct export for analysis
• Zapier / Make → No-code automation
• CSV / Excel / JSON → Export in any format

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💡 Best Practices

✅ Start with 7-30 days to test, then scale to 365
✅ Schedule daily runs — prices change constantly
✅ Monitor up to 10 hotels per run for optimal performance
✅ Use quality indicators — check price_suspicious and data_quality_score
✅ Track sold-out patterns — understand demand cycles

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🚀 Use Cases

Revenue Management
Monitor competitor pricing strategies and adjust your rates dynamically

Competitive Intelligence
Track market positioning, discount patterns, and demand indicators

Market Analysis
Identify seasonal trends, peak dates, and pricing opportunities

Dynamic Pricing
Feed real-time market data into pricing algorithms

Travel Platforms
Power booking engines with comprehensive room-level data

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❓ FAQ

Q: Does this work with hotels in any country?
A: Yes! The Actor works with any Booking.com property worldwide.

Q: What currencies are supported?
A: All currencies available on Booking.com (USD, EUR, MAD, GBP, SAR, AED, JPY, and more).

Q: Why track sold-out dates?
A: Sold-out detection reveals high-demand periods — essential for understanding your market and optimizing pricing strategies.

Q: How often should I run this?
A: For active revenue management, daily runs are recommended since prices and availability change frequently.

Q: Can I scrape multiple hotels at once?
A: Yes, up to 10 hotels per run for optimal reliability.

Q: Is the data reliable?
A: The Actor includes built-in quality checks and is engineered for consistent, long-term extraction.

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📝 Changelog

v1.0.0 (February 2026)
Initial release featuring room-level extraction, discount tracking, sold-out detection, bed configuration data, meal plan details, and data quality scoring.

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💬 Support

Have questions or feature requests?
Open an issue on the Issues tab or reach out via the Actor discussion page.

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🎯 Get Started

Ready to unlock complete pricing intelligence for your market?
Run Actor Now →

Built with precision for revenue managers and competitive intelligence professionals.

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