Scraping Expedia & Booking.com for Dynamic Hotel Rates

Introduction

The travel and hospitality industry thrives on dynamic pricing. Platforms like Expedia and Booking.com constantly adjust hotel rates based on demand, competition, local events, and user behavior. Monitoring these real-time rate changes is crucial for OTAs, travel aggregators, hotel chains, and market analysts. iWeb Data Scraping was approached by a leading travel insights firm to extract real-time hotel prices from Expedia and Booking.com. The goal was to provide a comparative rate intelligence dashboard to support pricing strategies and improve decision-making for hotel partners.

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objectives

Objectives:

  • Scrape real-time hotel room rates for 300+ properties across major U.S. and EU cities.
  • Track rate fluctuations across date ranges, room types, and user sessions.
  • Compare pricing between Expedia and Booking.com hourly.
  • Deliver structured datasets for use in dashboards and pricing models.

    • Challenges:

  • Both platforms personalize content based on session history, IP location, and device type.
  • Dynamic HTML and JavaScript rendering required headless browsing.
  • Room rates differed by number of guests, taxes, and cancellation policy.
    • the-challenges
    iWeb Data Scraping’s Strategy

    iWeb Data Scraping’s Strategy:

    1. Targeted Scraping Architecture:

    • Used Playwright and Puppeteer for headless browser automation.
    • Deployed proxy pools and rotating user agents to mimic real user behavior.
    • Scheduled data collection every 2 hours across 10 major cities (e.g., NYC, Paris, Berlin, LA, London).

    2. Rate Element Extraction:

    • Parsed rate breakdowns including:
    • Base price
    • Taxes & fees
    • Total per night
    • Refundability & perks (breakfast included, loyalty points, etc.)

    3. Data Normalization and Hotel Mapping:

    • Created a unified hotel identifier by matching:
    • Hotel name
    • Address & coordinates
    • Room type standardization (Deluxe, Suite, Twin Room, etc.)

    Sample Output Table:

    Platform Hotel Name City Room Type Base Rate Total Price Refundable Breakfast Date Timestamp
    Expedia Grand Plaza New York King Room $189 $214.20 Yes Yes 2025-06-15 2025-06-01 08:00 AM
    Booking.com Grand Plaza New York King Room $199 $225.00 No Yes 2025-06-15 2025-06-01 08:00 AM
    Expedia Urban Stay Hotel London Twin Room £125 £137.50 Yes No 2025-06-20 2025-06-01 08:00 AM

    Results:

    Tracked Over 50,000 Price Points Weekly

    • Across 300+ hotels in 10 cities, refreshed every 2 hours.

    Delivered 94% Hotel Matching Accuracy

    • Ensured consistency in comparison despite different naming on platforms.

    Exposed Rate Parity Gaps > 8% on Average

    • Enabled client to alert hotel partners of pricing discrepancies.

    Improved Client’s Dynamic Pricing Models

    • Data was integrated into AI models to improve booking recommendations.
    result
    Dashboards Delivered

    Dashboards Delivered:

    • Real-Time Rate Comparison Grid (Expedia vs Booking)
    • City-Wise Rate Volatility Heatmaps
    • Hotel-Level Price History Tracker
    • Export Formats: JSON, CSV, REST API Feed

    Technology Stack:

    • Languages: Python, Node.js
    • Tools: Puppeteer, Playwright, BeautifulSoup
    • Databases: MongoDB, PostgreSQL
    • Delivery Mechanism: AWS S3 + REST API + Email Report
    • Visualization: Looker Studio, Tableau, Power BI (client side)
    Technology Stack
    Why-iWeb-Data-Scraping

    Why iWeb Data Scraping?

    • Real-time OTA rate monitoring
    • Multi-region hotel mapping with geo-standardization
    • Rate transparency for better OTA strategy
    • Used by pricing, revenue management, and distribution teams

    Client Testimonial

    “With iWeb’s scraping engine, we see hotel rate shifts across platforms in real time. It’s critical data for our OTA clients and revenue partners."

    — Director, Travel Analytics Firm USA

    Next Steps:

    • Expand coverage to Hotels.com, Agoda, and Google Hotels.
    • Integrate reviews and ratings for sentiment-linked pricing analysis.
    • Add mobile vs desktop rate variation benchmarking.
    Next Steps:

    Conclusion

    With dynamic rate scraping from iWeb Data Scraping, travel firms and hotel partners gain the real-time intelligence needed to stay competitive across OTAs. In a market where price changes by the hour, visibility into Expedia and Booking.com ensures smarter pricing, greater profitability, and optimized distribution strategies.

    Let’s Talk About Product

    What's Next?

    We start by signing a Non-Disclosure Agreement (NDA) to protect your ideas.

    Our team will analyze your needs to understand what you want.

    You'll get a clear and detailed project outline showing how we'll work together.

    We'll take care of the project, allowing you to focus on growing your business.