We extract location metadata, private office pricing, coworking desk rates, and virtual office availability from Regus. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Location Metadata objects from regus.com. All fields typed and schema-versioned.
"center_id": "1849", "name": "London, Broadgate Tower", "city": "London", "postal_code": "EC2A 2EW", "latitude": 51.5217, "longitude": -0.0799, "amenities_list": "['Lounge Area', 'Vending machines', 'Elevator', 'Showers', 'Bicycle Storage']", "scraped_at": "2026-05-12T08:14:00Z"
| # | center_id | name | address_line_1 | address_line_2 | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Private Offices objects from regus.com. All fields typed and schema-versioned.
"center_id": "1849", "office_type": "Private Office", "capacity_persons": 4, "term_length_months": 12, "price_per_month": 2450.0, "currency": "GBP", "availability_status": "Available Now", "window_view": true
| # | center_id | office_type | capacity_persons | term_length_months | price_per_month | price_per_person |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Coworking Desks objects from regus.com. All fields typed and schema-versioned.
"center_id": "1849", "desk_type": "Dedicated Desk", "access_type": "24/7 Access", "price_per_month": 420.0, "currency": "GBP", "term_length_months": 6, "locker_included": true, "availability_status": "Limited Availability"
| # | center_id | desk_type | access_type | days_per_month | price_per_month | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Virtual Offices objects from regus.com. All fields typed and schema-versioned.
"center_id": "1849", "package_name": "Virtual Office Plus", "includes_address": true, "includes_phone_answering": true, "lounge_access_days": 5, "price_per_month": 185.0, "currency": "GBP", "term_length_months": 12
| # | center_id | package_name | includes_address | includes_mail_handling | includes_phone_answering | lounge_access_days |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Meeting Rooms objects from regus.com. All fields typed and schema-versioned.
"center_id": "1849", "room_name": "Boardroom A", "capacity_persons": 12, "price_per_hour": 75.0, "price_per_day": 500.0, "currency": "GBP", "video_conferencing": true, "whiteboard_included": true
| # | center_id | room_name | room_type | capacity_persons | price_per_hour | price_per_half_day |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Regus scraper navigates map-based search interfaces, intercepts backend pricing APIs, and normalises complex contract-term pricing models across global locations.
Extract metadata for thousands of Regus centers worldwide, including precise coordinates, operating hours, and contact details.
Capture pricing variations based on contract term lengths (1 month, 6 months, 12 months) and capacity requirements.
Separate pricing and availability data for private offices, dedicated desks, hot desking, and virtual office packages.
Extract and normalise center amenities like showers, parking, gym access, and on-site cafes across all listings.
Extract local currency pricing and standardise numeric formats regardless of regional display differences.
Scrape hourly, half-day, and full-day rates for meeting rooms and boardrooms based on capacity parameters.
Bypass frontend rendering by intercepting backend geographic APIs to extract complete location datasets efficiently.
Run scheduled pipelines that only output delta records when pricing or availability changes at a specific location.
Extract high-resolution image URLs for building exteriors, reception areas, and specific office configurations.
Brief in. Clean data out.
Specify target cities, countries, or specific center IDs. Define the exact workspace types and term lengths you need tracked.
We configure API interceptors and Playwright sessions to extract dynamic pricing data across regional Regus subdomains.
We validate currency parsing, normalise amenity lists, and ensure capacity tiers match expected schema formats.
Clean JSON, CSV, or Parquet delivered to your S3 bucket or data warehouse on your required schedule.
Extracting accurate pricing from Regus requires navigating session-based state and map-driven navigation. Here is how we build resilient pipelines.
Regus uses map-based bounding boxes to load location data dynamically. We bypass the frontend DOM entirely, reverse-engineering the internal API calls to iterate through geographic grids and extract complete location lists without browser overhead.
Office pricing on Regus changes based on selected start dates, capacity, and contract duration. Our crawlers maintain stateful sessions, injecting specific parameters to extract the full matrix of pricing options rather than just the default display price.
Regus redirects users and alters currency displays based on IP location. We route requests through region-specific residential proxies to ensure we capture accurate local market pricing in native currencies.
High-volume requests to pricing endpoints trigger rate limits. We distribute requests across thousands of IPs, managing headers and request timing to blend in with standard user traffic patterns.
Amenities and office descriptions vary wildly between regions. We apply post-extraction normalisation to map hundreds of unique amenity strings into a clean, queryable boolean structure.
Real estate platforms aggregate flexible workspace pricing to benchmark local market rates and track supply density.
Coworking operators monitor Regus desk and office rates in overlapping postcodes to optimise their own pricing strategies.
Enterprise real estate teams model the cost of distributed workforce hubs versus traditional long-term commercial leases.
Private equity firms analyze Regus location growth, pricing power, and footprint contraction to evaluate commercial real estate trends.
Workspace booking platforms ingest Regus location data and amenities to enrich their own marketplace listings.
Analysts use flexible office pricing and availability metrics as leading indicators for regional business activity and startup growth.
"Regus operates the largest global footprint of flexible workspaces, making it the definitive baseline for commercial real estate pricing intelligence."
Extracting global workspace pricing requires navigating map-based search APIs, handling session-dependent rate calculations, and normalising hundreds of currency formats. DataFlirt manages the extraction infrastructure so your analysts can focus on yield management and market density models.
Everything supported by our regus.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 bypass heavy frontend rendering by directly targeting the internal APIs Regus uses to populate map data, reducing extraction time by 80%.
Our residential proxy network routes requests through local IPs, ensuring Regus returns the correct regional pricing and currency data.
Airflow manages complex dependency chains, ensuring location metadata is extracted first before triggering deep-dive pricing matrix calculations.
Data delivered to where your team already works — no new tooling required.
About regus.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and location data is generally permissible. DataFlirt extracts only public information available to standard web visitors without requiring authentication. We do not bypass login screens or extract personal user data. Clients should consult their legal counsel regarding their specific use case.
Our crawlers simulate user interactions to select different contract lengths (e.g., 1 month vs 12 months) and capacities. We extract the resulting price changes and structure them into a comprehensive pricing matrix for each location.
Yes. We can scope the pipeline to target specific geographic bounding boxes, postal codes, or country subdomains to limit data volume and focus on your specific markets of interest.
For targeted location lists, we can run daily or weekly refreshes. For the entire global Regus catalogue, we typically recommend a weekly or bi-weekly cadence to balance infrastructure costs with data freshness.
Yes. We capture pricing and inclusions for all virtual office tiers, as well as hourly and daily rates for meeting rooms based on their stated capacity.
We maintain a mapping dictionary that translates regional variations of amenities (e.g., 'Car Park' vs 'Parking') into a unified set of boolean fields in the final dataset.
Yes. We provide a sample extraction of up to 50 locations across multiple regions to validate our schema structure and data accuracy against your requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually checking office rates. We build and maintain the infrastructure to deliver structured Regus pricing directly to your warehouse. Tell us your target markets.
