We extract property listings, agent directories, transaction histories, and market dynamics from Compass. 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 Listings objects from compass.com. All fields typed and schema-versioned.
"listing_id": "123456789", "address": "15 Central Park West, Apt 14D", "neighbourhood": "Upper West Side", "price": 4500000, "bedrooms": 3, "bathrooms": 3.5, "property_type": "Condo", "status": "Active", "compass_exclusive": true
| # | listing_id | address | neighbourhood | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Agent Profiles objects from compass.com. All fields typed and schema-versioned.
"agent_id": "987654321", "name": "Jane Doe", "title": "Principal Agent", "team_name": "The Doe Team", "office_location": "New York, NY", "active_listings_count": 12, "past_sales_count": 84, "languages_spoken": "['English', 'Spanish']"
| # | agent_id | name | title | team_name | phone | |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Transaction History objects from compass.com. All fields typed and schema-versioned.
"property_id": "123456789", "event_date": "2023-11-14", "event_type": "Sold", "price": 4350000, "price_per_sqft": 2150, "source": "ACRIS", "buyer_agent_id": "987654321", "price_change_pct": -3.33
| # | property_id | event_date | event_type | price | price_per_sqft | source |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Building Data objects from compass.com. All fields typed and schema-versioned.
"building_id": "B-4567", "name": "The Century", "total_units": 426, "year_built": 1931, "active_sales": 5, "active_rentals": 2, "pet_policy": "Pets Allowed", "amenities": "['Doorman', 'Elevator', 'Gym']"
| # | building_id | name | address | total_units | year_built | amenities |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Open Houses objects from compass.com. All fields typed and schema-versioned.
"property_id": "123456789", "address": "15 Central Park West, Apt 14D", "date": "2023-12-03", "start_time": "13:00", "end_time": "15:00", "tour_type": "In-Person", "rsvp_required": false, "agent_id": "987654321"
| # | property_id | address | price | date | start_time | end_time |
|---|---|---|---|---|---|---|
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Our Compass scraper handles storefront listings, dynamic pricing, building directories, and agent intelligence. We bypass Next.js hydration and anti-bot systems automatically.
Address, price, beds, baths, square footage, amenities, HOA fees, and description text scraped at the individual listing level.
Extract agent names, contact details, team affiliations, active inventory, and historical sales volume across all operating regions.
Capture price drops, delistings, and final sale prices timestamped per event to track market velocity and asset depreciation.
Extract building-level metadata including total units, year built, amenities, pet policies, and aggregated active inventory.
Track upcoming open houses, virtual tour links, and RSVP requirements mapped to specific agents and properties.
Extract high-resolution image URLs, 3D tour links, and floorplan assets for automated property valuation models.
Capture latitude, longitude, neighbourhood boundaries, and assigned school districts for precise mapping integrations.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Scrape inventory across all Compass markets including NYC, Los Angeles, Miami, Chicago, and San Francisco from a unified schema.
Brief in. Clean data out.
Provide zip codes, neighbourhoods, agent IDs, or building URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for compass.com.
Schema validation, null-rate checks, price-outlier detection, and sample exports before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Compass invests in scraping detection and dynamic rendering. Here is how we stay resilient and deliver clean data.
Compass uses advanced bot detection based on TLS fingerprints and IP reputation. Our crawlers use US residential ISP proxies with realistic browser fingerprints and full cookie session management.
Compass is a React-heavy application. We extract the raw JSON state directly from the Next.js hydration payload, bypassing fragile DOM parsing and capturing complete property metadata instantly.
Real estate platforms update their layouts frequently. Our selector strategy uses multiple fallback chains per field, ensuring a frontend redesign does not break your data pipeline.
For large market monitoring, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, inventory drops, and schema drift. SLA uptime is contractual, not aspirational.
Institutional investors track price reductions, days on market, and inventory levels to identify distressed assets and buying opportunities.
Brokerages monitor competitor agent performance, sales volume, and active listings to identify top producers for recruitment.
Real estate analysts aggregate neighborhood-level pricing and transaction velocity to build predictive market models.
PropTech companies ingest raw listing data and transaction histories to train automated valuation models (AVMs).
Service providers extract agent contact details and new listing events to target real estate professionals with relevant offerings.
Startups sync active inventory and building directories to populate their own consumer-facing real estate applications.
"Compass holds the most accurate luxury real estate inventory and agent transaction records, but extracting it requires bypassing aggressive rate limits and dynamic Next.js rendering."
Most teams underestimate the investment required: reliable Compass scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our compass.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 US 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 compass.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings and agent profiles is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract personal client data or circumvent authentication walls. Clients should review Compass ToS and consult legal counsel for specific use cases.
Instead of relying purely on fragile DOM parsing, our crawlers intercept and parse the Next.js hydration state (JSON) embedded in the page source. This allows us to extract complete, structured property and agent metadata instantly and reliably.
Yes. We extract the full historical ledger available on the listing page, including past sales, price reductions, and delistings, complete with dates and associated agents.
Pipeline cadences are configurable. We can run daily full-market refreshes or hourly delta updates for specific high-value zip codes to ensure you capture new inventory and price changes immediately.
Yes. We extract public phone numbers, email addresses, office locations, and social media links present on the agent profile pages and listing directories.
Absolutely. We provide a sample run of up to 500 listings or agent profiles as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off market dump or a continuous inventory feed across multiple cities, we scope, build, and operate the pipeline. Tell us what you need.
