We extract location details, real-time pricing, meeting room availability, and amenity lists across Regus, Spaces, and HQ. Delivered as clean JSON, CSV, or Parquet to your warehouse 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 Location Data objects from iwg.com. All fields typed and schema-versioned.
"location_id": "LOC-8492", "brand": "Spaces", "name": "Spaces - London, Oxford Street", "city": "London", "postal_code": "W1D 1BS", "country": "UK", "latitude": 51.5162, "longitude": -0.1354, "total_desks": 450
| # | location_id | brand | name | address_line_1 | city | postal_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Private Offices objects from iwg.com. All fields typed and schema-versioned.
"office_id": "OFF-104", "location_id": "LOC-8492", "capacity_persons": 4, "price_per_month": 2400.0, "currency": "GBP", "minimum_term_months": 6, "window_view": true, "floor_number": 3
| # | office_id | location_id | capacity_persons | price_per_month | currency | minimum_term_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Coworking Plans objects from iwg.com. All fields typed and schema-versioned.
"plan_id": "COW-DED-01", "location_id": "LOC-8492", "access_type": "Dedicated Desk", "price_per_month": 450.0, "currency": "GBP", "days_included": 30, "access_24_7": true
| # | plan_id | location_id | access_type | price_per_month | currency | days_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Meeting Rooms objects from iwg.com. All fields typed and schema-versioned.
"room_id": "MR-04", "location_id": "LOC-8492", "room_name": "Boardroom A", "capacity": 12, "price_per_hour": 75.0, "currency": "GBP", "av_equipment": true, "catering_available": true
| # | room_id | location_id | room_name | capacity | price_per_hour | price_per_day |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Virtual Offices objects from iwg.com. All fields typed and schema-versioned.
"package_id": "VO-PREM", "location_id": "LOC-8492", "package_name": "Virtual Office Plus", "price_per_month": 120.0, "currency": "GBP", "mail_handling": true, "business_address": true
| # | package_id | location_id | package_name | price_per_month | currency | mail_handling |
|---|---|---|---|---|---|---|
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Our IWG pipeline handles the complexity of map-based search interfaces, multi-brand normalisation, and dynamic availability pricing — delivering clean commercial real estate data.
Extract and normalise schema across Regus, Spaces, HQ, and Signature locations into a single unified dataset.
Track price variations based on contract length, start dates, and capacity constraints for private offices and coworking desks.
Capture exact latitude, longitude, and map-cluster data to map out competitor density and location strategy.
Scrape hourly and daily rates for meeting rooms, including capacity details and included AV amenities.
Extract structured arrays of location amenities: parking, gym access, showers, cafe facilities, and accessibility features.
Track pricing tiers for business addresses, mail handling, and phone answering services across all global locations.
Monitor new location openings, closures, and capacity changes with daily or weekly differential updates.
Extract local pricing and currency codes for locations across 120+ countries without geo-blocking issues.
Extract total building capacity and available desk counts where surfaced by the booking engine.
Brief in. Clean data out.
Provide target cities, countries, or specific IWG brands. We design the extraction schema together.
We configure API interceptors and Playwright crawlers to handle IWG's map interfaces and booking flows.
Schema validation, location deduplication, and currency formatting checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or via Webhook on agreed cadence.
Extracting data from IWG requires handling heavily nested XHR responses and dynamic state. Here is how we maintain pipeline stability.
IWG surfaces location data via complex, token-secured map APIs. We intercept these backend XHR requests directly, bypassing the heavy DOM rendering to extract clean JSON payloads containing full location metadata and pricing tiers.
Pricing for private offices requires maintaining session state through the booking funnel. Our crawlers manage cookies and session tokens to simulate user flows, ensuring we capture accurate rates for specific contract lengths and start dates.
Regus and Spaces run on different frontend architectures with varying data structures. We map these disparate sources into a single, normalised schema, so your downstream analytics teams query one consistent format.
IWG alters pricing and availability based on the visitor's IP location. We route requests through residential proxies matching the target country to ensure we extract accurate local currency pricing and regional inventory.
Some buildings host both Regus and Spaces facilities. Our pipeline uses geospatial clustering and address normalisation to flag co-located inventory, preventing double-counting in your market saturation models.
Rival coworking operators track Regus and Spaces pricing across specific postcodes to optimise their own desk rates.
Property funds analyse IWG location density and pricing trends to gauge sub-market commercial demand and yield potential.
Workspace booking platforms ingest IWG inventory and availability to populate their own search engines and directories.
Enterprise travel managers map IWG locations against employee distributions to negotiate corporate access rates.
Urban planners and retail analysts use coworking density as a proxy for footfall and professional demographic shifts.
PropTech ML teams train dynamic pricing models using historical IWG rate fluctuations against local capacity constraints.
"IWG controls the largest global footprint of flexible workspace — but tracking their dynamic pricing across 4,000 locations requires intercepting complex map APIs."
Scraping IWG requires handling heavily nested JSON responses from their map interfaces, normalising data across Regus and Spaces brands, and managing session state for accurate availability pricing. DataFlirt maintains the infrastructure so you receive clean inventory records without managing the extraction complexity.
Everything supported by our iwg.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 and deduplication. Playwright handles JavaScript execution and session management for complex booking funnels.
Custom middleware intercepts undocumented backend APIs powering the IWG map interfaces, extracting clean JSON payloads before DOM rendering.
Pipelines run on AWS Lambda and ECS. 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 iwg.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available inventory and pricing data from IWG websites is generally permissible. DataFlirt extracts only public, non-authenticated location and rate information. We do not bypass authentication walls to extract private member data or proprietary corporate rates.
Our pipeline supports the entire IWG portfolio, including Regus, Spaces, HQ, and Signature. We normalise the data from these different brand sites into a single, unified schema.
Yes. Every pipeline run produces timestamped records. By running daily or weekly extractions, you build a historical time-series dataset of pricing fluctuations and capacity changes per location.
Instead of interacting with the map UI using browser automation, we intercept the underlying XHR requests that populate the map. This approach is faster, more reliable, and yields structured geospatial data directly from IWG's backend.
Yes. We extract meeting room capacity, hourly rates, half-day rates, and full-day rates, along with listed amenities like AV equipment and catering options.
Engagements typically start with a defined geographic scope (e.g., all locations in North America and Europe) with weekly delivery. We price based on the volume of locations tracked and the frequency of extraction.
Yes. We provide a sample run covering up to 100 locations across multiple brands during the scoping phase, allowing your engineering team to validate the schema and data fidelity before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off global footprint export or continuous price monitoring across 4,000 locations — we scope, build, and operate the pipeline. Tell us your requirements.
