We extract property details, room availability, dynamic pricing, and IHG One Rewards point values. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Property Details objects from ihg.com. All fields typed and schema-versioned.
"hotel_id": "LONHA", "brand_name": "Holiday Inn", "hotel_name": "Holiday Inn London - Kensington High St.", "city": "London", "country": "United Kingdom", "star_rating": 4.0, "total_rooms": 706, "check_in_time": "15:00"
| # | hotel_id | brand_name | hotel_name | address_line | city | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Room Pricing objects from ihg.com. All fields typed and schema-versioned.
"hotel_id": "LONHA", "room_name": "Standard Room", "check_in_date": "2024-11-15", "check_out_date": "2024-11-16", "base_price": 145.0, "total_price": 174.0, "currency": "GBP", "points_price": 28000
| # | hotel_id | room_id | room_name | check_in_date | check_out_date | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Availability objects from ihg.com. All fields typed and schema-versioned.
"hotel_id": "LONHA", "date": "2024-11-15", "is_available": true, "remaining_inventory": 4, "minimum_stay_required": 1, "max_occupancy": 2, "bed_type": "Queen", "scraped_at": "2024-05-12T08:14:22Z"
| # | hotel_id | date | room_id | is_available | remaining_inventory | minimum_stay_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from ihg.com. All fields typed and schema-versioned.
"hotel_id": "LONHA", "rating_overall": 4.5, "rating_cleanliness": 4.8, "rating_service": 4.2, "review_text": "Excellent location near the tube station.", "travel_type": "Business", "date_stayed": "2024-04-10"
| # | hotel_id | review_id | rating_overall | rating_cleanliness | rating_service | rating_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Amenities & Policies objects from ihg.com. All fields typed and schema-versioned.
"hotel_id": "LONHA", "parking_available": true, "parking_fee": 35.0, "pet_friendly": false, "gym_available": true, "wifi_included": true, "resort_fee": 0.0
| # | hotel_id | parking_available | parking_fee | pet_friendly | pet_fee | pool_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our IHG scraper handles every layer of the platform: property catalogues, dynamic pricing matrices, availability calendars, and reward point values - with JavaScript rendering and anti-bot circumvention built in.
Extract data across all 19 IHG brands including Six Senses, InterContinental, Kimpton, Crowne Plaza, and Holiday Inn.
Capture base rates, taxes, total prices, and rate types (flexible vs non-refundable) across multiple dates and occupancy configurations.
Monitor room availability, remaining inventory warnings, and minimum stay requirements for revenue management analysis.
Extract dynamic point values required for reward nights alongside cash prices to calculate point valuations.
Route requests through specific country proxies to uncover regional pricing disparities and localised offers.
Map complex room descriptions, bed configurations, and views into structured, queryable fields.
Extract exact penalty dates and non-refundable clauses tied to specific rate codes.
Aggregate guest ratings across cleanliness, service, and location metrics to track property performance.
Run continuous pipelines at hourly or daily cadences to capture intra-day yield management adjustments.
Brief in. Clean data out.
Provide target cities, hotel IDs, or brand filters. We design the extraction schema and date ranges together.
We configure Playwright crawlers, proxy rotation, session management, and anti-bot handling for ihg.com.
Schema validation, null-rate checks, price-outlier detection, and timezone standardisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hotel chains invest heavily in rate scraping detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
IHG uses advanced edge protection to block automated rate scraping. Our crawlers use residential ISP proxies with realistic browser fingerprints, passing TLS and JS challenges natively.
IHG search results and pricing matrices rely heavily on client-side rendering. We run full Playwright browser sessions to execute JavaScript, wait for API hydration, and extract the final rendered DOM.
Extracting 90-day pricing curves requires complex date iteration. Our pipeline automates calendar interactions, capturing daily rates and availability without triggering rate limits.
Prices change based on the searcher's IP. We route requests through specific proxy nodes to capture accurate point-of-sale pricing for your target markets.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing pricing nodes, and schema drift - and respond before you notice.
Competing hoteliers monitor IHG pricing, length-of-stay discounts, and availability to optimise their own daily rates.
Agencies and aggregators compare ihg.com direct prices against OTA listings to detect parity violations.
Travel analysts track IHG One Rewards point requirements against cash prices to calculate point valuations across regions.
Investors track new property openings, room counts, and brand distribution to evaluate market penetration.
Procurement teams ingest bulk pricing data to negotiate corporate rates based on actual market averages.
Hedge funds use forward-looking availability and pricing data as leading indicators for hospitality sector performance.
"Hotel pricing is the most dynamic dataset in travel. Without automated infrastructure, capturing accurate 90-day pricing curves across thousands of properties is impossible."
Most teams underestimate the investment required: reliable hospitality scraping requires residential proxies, full JavaScript rendering, date iteration logic, and constant monitoring of anti-bot systems. DataFlirt absorbs that complexity so your engineers can focus on yield analysis - not infrastructure.
Everything supported by our ihg.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ihg.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from ihg.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property details, pricing, and availability data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review IHG terms and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for block rate spikes in real time and trigger pool rotation automatically.
Yes. Our pipeline can extract both the cash rate and the required points value for reward nights, allowing you to track point valuation fluctuations across properties and dates.
We typically configure pipelines to extract 30, 60, or 90-day forward-looking pricing curves. Longer horizons are possible depending on your required update frequency and compute budget.
Yes. We extract the base rate, estimated taxes, additional resort fees, and the final total price as displayed on the final booking step.
Our smallest packages start at a defined property list (typically 500-2,000 properties) with daily delivery of 30-day pricing curves. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 50 properties across a 14-day date range as part of the pre-engagement scoping process - so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous price-monitoring feed across 5,000 hotels - we scope, build, and operate the pipeline. Tell us what you need.
