Booking Pro Host Scraper

Last updated: January 25, 2026

🎯 Why use this scraper?

Extract professional host contact information from Booking.com hotels in minutes with automated bulk processing. Perfect for B2B lead generation, property management companies, and real estate professionals who need to contact hotel owners and property managers directly.

✅ What you get

Complete professional host data including:

• Contact Information: Email addresses, phone numbers, company legal names
• Business Details: Registration numbers, trade register names, business addresses
• Hotel Information: Hotel names, addresses, locations, prices
• Geographic Data: Coordinates, distances from city center

🚀 Key Features

📊 Complete Data Extraction

• ✅ Extract from search results URL (automatic hotel discovery)
• ✅ Extract from single hotel URL
• ✅ Extract from multiple hotel URLs (bulk processing)
• ✅ Professional host contact details (email, phone, company name)
• ✅ Business registration information
• ✅ Hotel location and pricing data
• ✅ Automatic HTML fallback if API fails

⚡ Performance Optimizations (Latest Update: January 25, 2026)

• ✅ Parallel processing with automatic concurrency optimization based on available memory (up to 200 workers with 128GB)
• ✅ Resource blocking (images, CSS, fonts) for 50% faster page loads
• ✅ Optimized timeouts: Navigation (12-15s), request handlers (15s) - 40% faster than before
• ✅ Reduced wait times: Inter-request delays (25-50ms), post-navigation waits (500-750ms) - 50% faster
• ✅ Aggressive retry logic: Faster retries (300ms initial, 1.15x multiplier) for quicker error recovery
• ✅ Efficient GraphQL API usage with automatic HTML fallback
• ✅ Memory-aware scaling: Automatically adjusts concurrency based on available RAM (4GB → 20 workers, 8GB → 30 workers, 16GB → 50 workers, 32GB → 100 workers)

💼 Use Cases and Client Benefits

🏢 For Real Estate Agencies

The Problem: Finding property owners and managers requires manual research, visiting each hotel page, and extracting contact information one by one. This process is time-consuming and doesn't scale.

The Solution: Automatically extract professional host contact information from hundreds of hotels in a single run. Get emails, phone numbers, and business details ready for outreach.

Client Benefits:

• 📊 Time saved: Extract 100 hotels in minutes instead of hours
• 💰 Cost reduction: No need for manual data entry or research teams
• 🎯 Better targeting: Focus on professional hosts with business information
• 📈 Scalability: Process entire cities or regions automatically

ROI: Save 10+ hours per week on lead research, enabling teams to focus on sales and relationship building.

🏨 For Property Management Companies

The Problem: Identifying potential clients (hotel owners) requires extensive research and manual contact extraction from multiple sources.

The Solution: Extract professional host information from Booking.com search results, getting all contact details in a structured format ready for CRM import.

Client Benefits:

• 🚀 Faster prospecting: Generate lead lists in minutes
• 📋 Structured data: Ready for CRM import (CSV format)
• 🎯 Quality leads: Focus on professional hosts with business information
• 💼 B2B focus: Filter for companies with registration numbers

ROI: Generate 100+ qualified leads per day instead of 10-20 manually.

📊 For Market Research Companies

The Problem: Analyzing the hotel market requires collecting contact information from hundreds of properties, which is tedious and error-prone.

The Solution: Bulk extract professional host data from entire regions or cities, enabling comprehensive market analysis and competitive intelligence.

Client Benefits:

• 📈 Complete coverage: Extract all hotels from search results
• 🔍 Market insights: Analyze hotel distribution and pricing
• 📊 Data quality: Structured, normalized data ready for analysis
• 🌍 Geographic data: Coordinates and distances for mapping

ROI: Complete market research in days instead of weeks.

📈 Concrete Results: Before vs. After

Before (without the scraper)

• ⏱️ Time required: 5-10 minutes per hotel (manual extraction)
• ❌ Errors: Manual copy-paste mistakes, missing data
• 📉 Scale limitation: 10-20 hotels per day maximum
• 💰 Cost: High labor costs for manual research

After (with the scraper - Optimized January 2026)

• ⚡ Time saved: 100+ hotels in 3-4 minutes (previously 10-20 minutes)
• ✅ Accuracy: Automated extraction eliminates human errors
• 📈 Scale: Process hundreds of hotels per run
• 💰 Cost reduction: 90%+ reduction in manual research time + 30-40% cost savings from performance optimizations

Time saved: 95% reduction
Speed: 100+ hotels in 3-4 minutes (60% faster than before)
Quality improvement: Structured, normalized data with no manual errors
Cost efficiency: 30-40% reduction in compute costs thanks to optimized timeouts and delays

💰 Costs and Optimization

💵 Actual Cost Breakdown (Optimized - January 2026)

Service	Usage	Cost
Actor compute	3-6 min for 100 hotels (previously 10-20 min)	~$0.05-0.12 (previously ~$0.10-0.20)
Dataset writes	100 items	~$0.01
Proxy (optional)	Residential recommended	~$0.05-0.10
Total		~$0.11-0.23 per 100 hotels (30-40% savings)

Performance improvements:

• ⚡ 60% faster execution (3-4 min vs 10-20 min for 100 hotels)
• 💰 30-40% cost reduction on compute units
• 🚀 Optimized timeouts and delays reduce waiting time by 50%

💡 Cost Optimization Tips

1. Memory allocation: The scraper automatically optimizes concurrency based on available RAM:
   • 4GB: 20 workers max (~$0.08-0.10 per 100 hotels)
   • 8GB: 30 workers max (~$0.11-0.15 per 100 hotels) - Recommended for most use cases
   • 16GB: 50 workers max (~$0.15-0.20 per 100 hotels) - For faster processing
   • 32GB+: 100+ workers max (~$0.20-0.30 per 100 hotels) - For maximum speed
2. Use datacenter proxies for testing (cheaper, but may be blocked)
3. Use residential proxies for production (more reliable, slightly more expensive)
4. Adjust maxConcurrency: The scraper auto-adjusts based on memory, but you can set a lower limit
5. Set maxHotels: Limit extraction for testing, use 0 (unlimited) for production

Latest optimizations (January 2026) automatically reduce costs by 30-40% through:

• Reduced timeouts (12-15s instead of 20-30s)
• Faster retry logic (300ms initial delay instead of 500ms)
• Optimized wait times (25-50ms between requests instead of 50-100ms)

📋 Complete Data Fields Extracted

Hotel Information (9 fields)

Field Name	Description	Example
hotel_url	URL of the hotel page	https://www.booking.com/hotel/fr/fesch.fr.html
hotel_name	Name of the hotel	Hotel Fesch
hotel_address	Street address	50-52 Rue Cardinal Fesch
hotel_city	City name	Ajaccio
hotel_latitude	Geographic latitude	41.9267
hotel_longitude	Geographic longitude	8.7369
hotel_distance_center_km	Distance from city center	0.5
hotel_price	Price for stay	120.50
hotel_price_currency	Currency code	EUR

Professional Host Contact Information (9 fields)

Field Name	Description	Example
hotel_company_name	Legal company name	Hotel Fesch SARL
hotel_email	Email address	contact@hotelfesch.fr
hotel_phone	Phone number	+33 4 95 21 45 00
hotel_registration_number	Business registration	12345678901234
hotel_trade_register	Trade register name	RCS Ajaccio
hotel_contact_address	Business address	50-52 Rue Cardinal Fesch
hotel_contact_city	Contact city	Ajaccio
hotel_contact_postal_code	Postal code	20000
hotel_contact_country	Country code	FR

Total: 18 fields per pro-host record

💡 How to Use the Data

1. B2B Lead Generation

• Import CSV into your CRM (Salesforce, HubSpot, etc.)
• Filter for hotels with email addresses
• Create email campaigns targeting professional hosts
• Use phone numbers for cold calling campaigns

2. Market Analysis

• Analyze hotel distribution by city/region
• Study pricing patterns
• Identify professional vs. individual hosts
• Geographic mapping using coordinates

3. Competitive Intelligence

• Track professional hosts in your market
• Monitor new hotel openings
• Analyze business registration patterns
• Identify property management companies

4. Partnership Opportunities

• Identify potential partners (property management companies)
• Contact hotel owners for collaboration
• Build supplier networks
• Create referral programs

📖 Input Configuration

Basic Configuration

{
  "urls": [
    "https://www.booking.com/searchresults.fr.html?ss=Orléans&checkin=2026-01-23&checkout=2026-01-24"
  ],
  "maxHotels": 10,
  "maxConcurrency": 5,
  "filterProHostsOnly": true,
  "deduplicateResults": true
}

Parameter Reference

Parameter	Type	Default	Description
urls	array	-	Required. List of Booking.com URLs (search results or hotel pages). The scraper automatically detects the type.
maxHotels	integer	10	Maximum hotels to extract from search results. Set to 0 for unlimited (extract all).
maxConcurrency	integer	5	Number of hotels to process in parallel. Recommended: 5-10 for testing, 20-30 for production.
filterProHostsOnly	boolean	true	Only keep hotels with email addresses (professional hosts).
deduplicateResults	boolean	true	Remove duplicate pro-hosts based on email address.
useApifyProxy	boolean	false	Enable Apify proxy for scraping. Recommended for production.
apifyProxyGroup	string	RESIDENTIAL	Proxy type: RESIDENTIAL (recommended) or DATACENTER (cheaper).

Input Modes

Mode 1: Search Results URL (Recommended for bulk extraction)

{
  "urls": [
    "https://www.booking.com/searchresults.fr.html?ss=Orléans&checkin=2026-01-23&checkout=2026-01-24"
  ],
  "maxHotels": 50
}

Mode 2: Single Hotel URL

{
  "urls": [
    "https://www.booking.com/hotel/fr/fesch.fr.html"
  ]
}

Mode 3: Multiple URLs (Mixed types)

{
  "urls": [
    "https://www.booking.com/searchresults.fr.html?ss=Orléans&checkin=2026-01-23&checkout=2026-01-24",
    "https://www.booking.com/hotel/fr/fesch.fr.html",
    "https://www.booking.com/hotel/fr/another-hotel.fr.html"
  ]
}

🚀 Installation and Usage

Local Installation

cd booking-pro-host-scraper
npm install
npm start

Apify Platform

1. Push to Apify: apify push
2. Configure input: Set searchUrl or hotelUrl/hotelUrls
3. Run: Start the actor
4. Download results: Get CSV file from Key-value store or Dataset

Example: Extract Pro-Hosts from Search Results

{
  "urls": [
    "https://www.booking.com/searchresults.fr.html?ss=Orléans&checkin=2026-01-23&checkout=2026-01-24"
  ],
  "maxHotels": 100,
  "maxConcurrency": 10,
  "useApifyProxy": true,
  "apifyProxyGroup": "RESIDENTIAL"
}

Example: Extract from Single Hotel

{
  "urls": [
    "https://www.booking.com/hotel/fr/fesch.fr.html"
  ],
  "maxConcurrency": 5
}

Example: Bulk Processing (Multiple URLs)

{
  "urls": [
    "https://www.booking.com/searchresults.fr.html?ss=Paris&checkin=2026-01-23&checkout=2026-01-24",
    "https://www.booking.com/hotel/fr/hotel1.fr.html",
    "https://www.booking.com/hotel/fr/hotel2.fr.html"
  ],
  "maxConcurrency": 10
}

📊 Output Format

Dataset Output

The scraper outputs data to the Apify Dataset with the following structure:

{
  "hotel_url": "https://www.booking.com/hotel/fr/fesch.fr.html",
  "hotel_name": "Hotel Fesch",
  "hotel_address": "50-52 Rue Cardinal Fesch",
  "hotel_city": "Ajaccio",
  "hotel_latitude": 41.9267,
  "hotel_longitude": 8.7369,
  "hotel_distance_center_km": "0.5",
  "hotel_price": 120.50,
  "hotel_price_currency": "EUR",
  "hotel_company_name": "Hotel Fesch SARL",
  "hotel_email": "contact@hotelfesch.fr",
  "hotel_phone": "+33 4 95 21 45 00",
  "hotel_registration_number": "12345678901234",
  "hotel_trade_register": "RCS Ajaccio",
  "hotel_contact_address": "50-52 Rue Cardinal Fesch",
  "hotel_contact_city": "Ajaccio",
  "hotel_contact_postal_code": "20000",
  "hotel_contact_country": "FR"
}

CSV Output

The scraper also generates a CSV file (OUTPUT.csv) in the Key-value store with semicolon (;) delimiter, ready for import into Excel, Google Sheets, or CRM systems.

⚠️ Important Notes

1. Contact Information Availability: Not all hotels have professional host contact information available. Some hotels may not have email addresses or phone numbers listed.

2. Data Quality: Contact information is extracted from publicly available data on Booking.com. Some fields may be empty if not provided by the hotel.

3. Rate Limiting: Use residential proxies for production to avoid blocks. Datacenter proxies may be blocked more frequently.

4. Legal Compliance: Ensure you comply with GDPR and local data protection regulations when using contact information for marketing purposes.

🔧 Troubleshooting

No Contact Information Extracted

• Check hotel type: Some hotels may not have professional host information
• Verify URL format: Ensure the hotel URL is correct
• Check logs: Review actor logs for extraction errors

Timeout Errors

• Reduce concurrency: Lower maxConcurrency value (or let the scraper auto-adjust based on memory)
• Timeouts are optimized: Navigation (12-15s), handlers (15s) - if you still get timeouts, check your connection
• Use residential proxies: More reliable than datacenter proxies
• Memory allocation: If you get frequent timeouts, try increasing memory allocation (8GB → 16GB) for better stability

GraphQL API Errors

• Automatic fallback: The scraper automatically falls back to HTML extraction if GraphQL fails
• Retry logic: Built-in retry mechanism handles temporary errors
• Check search URL: Ensure the search URL is valid and accessible

📞 Support

• Email: corentin@outreacher.fr
• LinkedIn: https://www.linkedin.com/in/robertcorentin/

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Last updated: January 25, 2026
Latest update: Performance optimizations - 60% faster execution, 30-40% cost reduction