We extract flight routes, hotel availability, dynamic pricing, and price prediction signals from Hopper. 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 Flight Itineraries objects from hopper.com. All fields typed and schema-versioned.
"origin": "JFK", "destination": "LHR", "price": 450.5, "currency": "USD", "airline": "Delta", "flight_number": "DL15"
| # | route_id | origin | destination | departure_date | return_date | airline |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Price Predictions objects from hopper.com. All fields typed and schema-versioned.
"current_price": 450.5, "prediction_status": "wait", "expected_price_drop": 45.0, "buy_recommendation": false, "confidence_score": 0.88, "check_date": "2026-05-12"
| # | route_id | check_date | current_price | prediction_status | expected_price_drop | buy_recommendation |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotel Inventory objects from hopper.com. All fields typed and schema-versioned.
"hotel_id": "H-9821", "name": "The Standard", "nightly_rate": 215.0, "currency": "USD", "room_type": "King Bed", "star_rating": 4.5
| # | hotel_id | name | location_coordinates | star_rating | check_in | check_out |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Car Rentals objects from hopper.com. All fields typed and schema-versioned.
"provider": "Hertz", "vehicle_type": "Midsize SUV", "daily_rate": 54.2, "currency": "USD", "transmission": "Automatic", "location_code": "LAX"
| # | rental_id | location_code | pickup_date | dropoff_date | vehicle_type | provider |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Short-Term Rentals objects from hopper.com. All fields typed and schema-versioned.
"property_id": "STR-4421", "title": "Downtown Loft", "nightly_rate": 145.0, "cleaning_fee": 50.0, "max_guests": 4, "bedrooms": 1
| # | property_id | title | host_name | location_neighbourhood | nightly_rate | cleaning_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Hopper scraper handles the complexities of mobile-first API endpoints, dynamic pricing, and strict rate limits. We extract clean, structured JSON directly from the source.
Extract direct and connecting flights, airlines, durations, and pricing across dates.
Capture Hopper proprietary buy or wait recommendations and confidence scores.
Scrape dynamic room rates, availability, and promotional discounts across global properties.
Track daily rates, vehicle classes, and provider availability at major airport codes.
Extract host details, property metadata, nightly rates, and hidden fees.
Scrape matrix pricing for variable day windows to map entire demand curves.
Bypass web presentation layers to extract structured JSON directly from Hopper mobile endpoints.
Use residential proxies to capture region-specific pricing and localised inventory.
Execute minute-level extraction for volatile routes during peak booking windows.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide origin and destination pairs, hotel IDs, or dates. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and API interception for hopper.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hopper invests heavily in API security and rate limiting. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Hopper prioritises its mobile app. We reverse-engineer mobile API endpoints and replicate client telemetry to extract structured data before it hits the presentation layer.
Cloudflare and Akamai protect Hopper endpoints. We use residential ISP proxies with realistic TLS fingerprints and automated token refresh cycles.
Airlines and hotels change prices based on IP and session history. Our crawlers use clean, stateless sessions for every request to ensure normalised baseline pricing.
Hopper updates its API payload structures frequently. We use JSON path fallbacks and schema validation to prevent pipeline breakage.
Aggressive polling triggers IP bans. We distribute requests across a global IP pool with randomised delays to maintain throughput without triggering blocks.
Online travel agencies monitor Hopper pricing and prediction signals to adjust their own margins.
Airlines and hotels track how their inventory is priced and presented on third-party aggregators.
Hedge funds and analysts aggregate booking volumes and price trends to forecast travel demand.
Travel booking platforms detect price anomalies and delayed cache updates to secure lower wholesale rates.
Data science teams use historical flight prices and Hopper predictions to train their own forecasting models.
Tour operators combine extracted flight and hotel data to build custom vacation packages.
"Hopper holds the most predictive travel pricing data in the market. Accessing it programmatically requires bypassing aggressive mobile-first anti-bot systems."
Extracting data from Hopper requires more than simple HTTP requests. Their infrastructure relies heavily on mobile API endpoints, dynamic token generation, and strict rate limits. DataFlirt manages the reverse-engineering, proxy rotation, and session handling so you receive clean pricing data without the operational overhead.
Everything supported by our hopper.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.
We intercept and replicate Hopper mobile app traffic using mitmproxy, bypassing web limitations to access raw JSON payloads.
Pools of residential ISP proxies across global regions. Rotation happens per-request to capture localised pricing without triggering rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State is stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About hopper.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and route data is generally permissible. We do not extract personal user data or bypass authentication walls.
We reverse-engineer the API calls, replicate the necessary headers, and manage the token generation to extract data directly from the backend.
Yes. We capture the buy or wait recommendation, expected price drop, and confidence score for any tracked route.
Yes. We can configure the extraction to request pricing in USD, EUR, GBP, INR, or other supported currencies.
For high-priority routes, we can poll at 5-minute intervals. Full market scans typically run on a 12 to 24 hour cadence.
Yes. Provide a list of Hopper hotel IDs or coordinates, and we will track availability and rates for those specific properties.
Our smallest packages start at a defined route or property list with daily delivery. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off route dump or a continuous price-monitoring feed across 50,000 flights, we scope, build, and operate the pipeline. Tell us what you need.
