We extract global building directories, desk availability, pricing tiers, amenities, and meeting room specs from WeWork. 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 Buildings & Locations objects from wework.com. All fields typed and schema-versioned.
"building_id": "WW-NYC-100", "name": "100 Broadway", "city": "New York", "address": "100 Broadway, NY 10005", "country": "USA", "open_hours": "24/7"
| # | building_id | name | address | city | country | coordinates |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Memberships objects from wework.com. All fields typed and schema-versioned.
"building_id": "WW-NYC-100", "membership_type": "Hot Desk", "price_monthly": 399.0, "currency": "USD", "min_term": 1, "availability_status": "Available"
| # | building_id | membership_type | price_monthly | currency | min_term | deposit_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Private Offices objects from wework.com. All fields typed and schema-versioned.
"office_id": "OFC-4A", "capacity_persons": 4, "price_monthly": 2400.0, "currency": "USD", "window_view": true, "floor_number": 4
| # | office_id | building_id | capacity_persons | square_footage | price_monthly | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Amenities objects from wework.com. All fields typed and schema-versioned.
"building_id": "WW-NYC-100", "amenity_name": "Espresso Bar", "category": "Food & Drink", "is_free": true, "booking_required": false, "available_hours": "8 AM - 4 PM"
| # | building_id | amenity_name | category | description | is_free | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Meeting Rooms objects from wework.com. All fields typed and schema-versioned.
"room_id": "MR-102", "room_name": "Brainstorm Room", "capacity": 8, "price_per_hour": 40.0, "currency": "USD", "whiteboard": true
| # | room_id | building_id | room_name | capacity | price_per_hour | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our WeWork scraper handles dynamic map rendering, IP-based pricing geo-fences, and complex hierarchical building directories — delivering clean workspace data without the infrastructure overhead.
Extract all 800+ locations across cities and countries. Capture addresses, coordinates, and building status in a single run.
Monitor hot desk, dedicated desk, and private office rates. Track price changes and promotional discounts over time.
Track which locations are sold out versus accepting new members to gauge local commercial real estate demand.
Capture building-specific perks like espresso bars, bike storage, wellness rooms, and event spaces for competitive analysis.
Extract hourly rates, seating capacity, and AV equipment details for all bookable meeting rooms within a building.
Pull nearest subway stations, parking details, and wheelchair accessibility information for every location.
Extract native pricing data across GBP, USD, EUR, INR, and other local currencies using region-specific proxies.
Collect high-resolution gallery links for building interiors, floor plans, and specific office configurations.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide target cities, countries, or specific building URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for wework.com.
Schema validation, null-rate checks, price-outlier detection, and sample locations before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
WeWork relies on dynamic React frontends and IP-based geo-fencing. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
WeWork's location map and dynamic pricing widgets require full JavaScript execution. We run full Playwright browser sessions to intercept background XHR requests and hydrate the DOM, capturing data that headless HTTP clients miss entirely.
WeWork alters pricing and currency based on IP location. We route requests through region-specific residential proxies to ensure you receive accurate, localised pricing rather than default USD rates.
DOM structures for amenities and floor plans change frequently. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and XHR response parsing — so a layout change doesn't break your data pipeline.
For global location monitoring, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs when desk availability or prices change — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in pricing fields, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Track flexible workspace supply, pricing trends, and occupancy signals across major global metropolitan areas.
Rival coworking operators monitor WeWork's pricing tiers, amenity offerings, and promotional discounts to adjust their own positioning.
Enterprise teams track private office availability across global hubs to plan remote workforce deployments and satellite offices.
Aggregators use location and amenity data to enrich their own platforms and build comprehensive workspace directories.
Analyse the correlation between transit hubs, parking availability, and premium coworking spaces for urban development models.
PE firms track footprint expansion or contraction, building closures, and pricing adjustments to evaluate the flexible office sector.
"WeWork's global footprint represents the most accurate leading indicator for flexible office demand and commercial real estate pricing trends."
Extracting this data requires navigating dynamic React frontends, IP-based pricing geo-fences, and complex hierarchical building structures. DataFlirt handles the JavaScript rendering and proxy routing so your team can focus entirely on real estate analysis.
Everything supported by our wework.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 wework.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available building and pricing data from WeWork is generally permissible under applicable law. DataFlirt targets only public, non-authenticated workspace and location data. We do not extract personal data or circumvent authentication walls.
We use Playwright to execute JavaScript, intercept background XHR requests, and render dynamic map components. This ensures we capture all location coordinates and building metadata accurately.
Yes. We route requests through country-specific residential proxies, ensuring WeWork serves the native pricing and currency for each location rather than defaulting to USD.
We can run pipelines daily or hourly depending on your requirements. Real-time streaming pipelines achieve low latency for price and availability signals on a defined set of buildings.
Yes, including capacity, hourly rates, whiteboard availability, and AV equipment lists for all public-facing meeting rooms within a specific building.
Our smallest packages start at a defined city list or country footprint with weekly delivery. For full global directory extraction, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off global directory dump or a continuous price-monitoring feed across 800+ locations — we scope, build, and operate the pipeline. Tell us what you need.
