How Can You Collect Hotel Pricing and Availability Data for Smarter Revenue Management?

Introduction

In the competitive world of travel and hospitality, data-driven strategies are essential. Whether you're a market analyst, travel tech startup, or revenue manager, the ability to Collect hotel pricing and availability data across online travel agencies (OTAs) gives you an edge in understanding pricing dynamics, demand signals, and customer sentiment. This blog explores the process and value of scraping hotel data for a selected hotel chain operating in four or more cities, utilizing platforms such as Booking.com, MakeMyTrip, Agoda, and EaseMyTrip. The importance of Hotel pricing data scraping lies in its potential to reveal real-time market movement. Travelers make booking decisions based on fluctuating prices, weekend demand spikes, room types, and user ratings. Having visibility into these shifts not only helps hotels optimize their pricing strategies but also empowers OTA comparison tools, price aggregators, and travel management companies with critical data.

With increasing consumer demand for dynamic pricing, promotional discounts, and last-minute deals, Real-time hotel availability tracking has become crucial for any player in the travel ecosystem. Let's explore how to build a practical one-time dataset for a selected hotel chain with high presence across Indian cities, focusing on key booking metrics.

Objective for Hotel Data Collection

Our goal is to collect hotel pricing and availability data for a selected hotel chain that has operations in at least four cities. For each hotel-city-date combination, the following data points are targeted:

• Check-in Date
• Room Type
• Base Price (before tax)
• Tax Amount (if available)
• Final Price (including tax)
• Sold-Out Status or Rooms Left (as an occupancy signal)
• Customer Ratings & Review Counts (if accessible)

The dataset will cover a 14-day range, including weekdays and weekends, and will be collected once for now. The final output will be a structured CSV or Google Sheet, formatted to have one row per room per date per platform. The total volume is expected to be around 800–1000 rows, given the inclusion of 4 platforms × 4 cities × 3–5 hotels × 14 dates.

Platforms Selected for Data Collection

We will focus on four major OTAs that dominate hotel bookings in India:

• Booking.com
• MakeMyTrip
• Agoda
• EaseMyTrip

Each platform has distinct structures for displaying room types, pricing details, and availability signals. Capturing structured and consistent data across these will provide a well-rounded view of OTA pricing behavior.

Key Data Points for Each Hotel-City-Date Combination

Here’s a breakdown of the fields to extract:

Field	Description
Hotel Name	As listed on the OTA
City	Targeted location
Platform	Booking.com, MakeMyTrip, Agoda, or EaseMyTrip
Check-in Date	Daily over the 14-day window
Room Type	Standard, Deluxe, Suite, etc.
Base Price	Price before taxes
Taxes	Tax value (if shown separately)
Final Price	Price after tax
Sold-Out or Rooms Left	Occupancy signal (e.g., “Only 2 left!” or “Sold out”)
Rating	User rating, if accessible
Number of Reviews	Total review count per hotel

Such granular records across platforms support Real-time hotel pricing data analytics for any downstream application.

City and Hotel Chain Selection Strategy

To ensure consistency and coverage, pick a hotel chain with at least one property in the following metro or Tier-1 cities:

• Mumbai
• Delhi
• Bengaluru
• Hyderabad

For each city, select 3 to 5 hotels within the same chain, ensuring a mix of business and leisure-oriented locations. This ensures uniformity when comparing room types and pricing logic across platforms.

Platform-Specific Scraping Strategy

1. Booking.com

This platform is rich in dynamic content. To collect hotel pricing data from Booking.com, your scraper needs to:

• Parse the hotel’s daily availability and room listings.
• Extract taxes, base price, and total after charges.
• Detect sold-out rooms and limited inventory messages.
• Use Selenium or Puppeteer to render JavaScript-heavy pages.

2. MakeMyTrip

Known for promotional banners and segmented room offerings, real-time hotel availability scraping from MakeMyTrip requires:

• Navigating pop-ups and location selectors.
• Extracting room names, base rate, tax details, and final price.
• Capturing user ratings and “Only few left” messages.
• Handling location-based variations in hotel listings.

3. Agoda

Agoda offers layered pricing options like member-only deals and multi-night discounts. To extract hotel room prices and availability from Agoda, scrapers should:

• Handle dynamic modules like loyalty badges and coupon banners.
• Separate member-exclusive pricing from public rates.
• Track tax-inclusive vs exclusive representations.
• Pay close attention to mobile vs desktop views, which can differ.

4. EaseMyTrip

Lighter in design compared to others, it’s easier to extract from. The best way to scrape hotel listings from EaseMyTrip includes:

• Parsing clearly structured HTML layouts.
• Extracting ratings and user counts (if shown).
• Tracking last-room availability messages.
• Scraping prices consistently across weekdays and weekends.

Tools and Frameworks to Use

To streamline extraction across platforms, consider the following tools:

• Selenium / Playwright: For JavaScript-rendered content and user simulation.
• Scrapy: For structured crawling and scalable scraping logic.
• BeautifulSoup: For post-render HTML parsing.
• Headless Browsers: For efficient scraping without loading full browser UIs.
• Proxies + Rotating IPs: To avoid getting blocked on high-volume requests.

These fall under professional OTA data scraping tools, designed to handle complex page structures, variable content loads, and rate-limiting algorithms.

Structuring the Output

The output dataset will be prepared as:

• One row = one hotel + room type + date + platform
• Columns will include: city, hotel, room type, price before tax, tax amount, final price, rating, number of reviews, and availability notes.
• Export formats: CSV, Excel, or Google Sheets, depending on end-user preference.

The structured format ensures the data can easily be analyzed using pivot tables, price trend graphs, or imported into analytics dashboards.

Sample Row from Final Dataset

City	Hotel Name	Platform	Date	Room Type	Base Price	Tax	Final Price	Availability Status	Rating	Reviews
Mumbai	Hotel XYZ	Booking.com	2025-08-10	Deluxe	₹4,000	₹500	₹4,500	Only 2 rooms left	8.2	123

Benefits of This One-Time Hotel Data Collection

Collecting hotel pricing and availability data across OTAs not only supports competitive analysis but also enables deep insights into consumer behavior, platform pricing logic, and booking trends. Below are five expanded use cases that illustrate the power of structured hotel data scraping.

• Benchmark Competitor Pricing: Understand how different OTAs price identical hotel rooms by analyzing base rates, added fees, and promotional discounts. This helps hotel chains adjust their rate strategies across platforms and cities, gaining a real-time view of their competitive positioning in the market.
• Evaluate Occupancy Signals: Extracting phrases like “Only 1 room left” or “Sold out” reveals high-demand dates and popular room types. These occupancy signals can help hoteliers anticipate peak periods, optimize availability, and strategically manage room allocation for higher margins and efficiency.
• Analyze Weekend vs Weekday Patterns: Compare daily pricing over a two-week span to uncover patterns between weekdays and weekends. Business hotels often show dips during weekends, while leisure destinations surge. These insights help forecast demand and tailor pricing based on traveler type and timing.
• Spot Tax Disclosure Differences: Tax components can vary across platforms—some show inclusive prices, others add fees during checkout. Analyzing how taxes and fees are presented lets hotels better understand final consumer costs and detect inconsistencies in pricing transparency across booking channels.
• Track Review Sentiment: By capturing ratings and the number of reviews, you gain quick insight into customer satisfaction and perception trends. Even without full review text, average scores and volume provide critical signals for brand trust, experience quality, and platform loyalty comparison.

Next Steps: Automate or Expand Scope?

While this blog focuses on a one-time collection, the same framework can be adapted for:

• Automated daily scraping for real-time rate monitoring.
• Expansion to more cities or additional hotel chains.
• Including more platforms like Goibibo, Trivago, or Expedia.
• Building dashboards for internal hotel pricing teams or travel aggregators.

As competition increases in hospitality, structured and timely pricing data becomes a non-negotiable asset.

How Travel Scrape Can Help You?

• Multi-Platform Coverage: We extract hotel pricing and availability data from top OTAs like Booking.com, MakeMyTrip, Agoda, and EaseMyTrip, ensuring comprehensive and consistent data collection across platforms.
• Dynamic Content Handling: Using advanced tools like Selenium and Playwright, we scrape JavaScript-rendered content such as real-time prices, availability, and pop-up occupancy signals that typical scrapers miss.
• Structured Data Output: Our scrapers organize raw data into clean, structured formats like CSV or Google Sheets with one row per room per date per platform for easy analysis.
• Geo-Targeted and Date-Specific Scraping: We accurately collect hotel data across cities and check-in dates, capturing variables like weekend vs weekday pricing, taxes, and remaining room counts.
• Review and Rating Extraction: Where accessible, we also scrape hotel ratings and review counts to provide a fuller picture of each listing's popularity and customer sentiment.

Conclusion

Building a one-time dataset to collect OTA hotel pricing and availability provides critical intelligence into how different platforms represent and price the same hotel. With a well-designed scraper, businesses can analyze room type differences, availability signals, and pricing variances across dates, cities, and brands.

This type of project empowers revenue managers, travel startups, and pricing analysts with the kind of information they previously only hoped to access through third-party APIs or manual monitoring. Our team offers advanced Hotel Data Scraping Services tailored for travel industry needs—whether for one-time research or long-term data feeds.

Combining this scraped information with internal performance metrics unlocks new layers of Hotel pricing intelligence , enabling faster decisions, better positioning, and improved margins. If you're looking to extract aggregated hotel prices across OTAs for strategic insight or operational efficiency, now is the time to leverage smart scraping infrastructure.