We extract active residential listings, commercial properties, agent directories, pricing histories, and neighbourhood analytics from kw.com. 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 kw.com. All fields typed and schema-versioned.
"listing_id": "KW-738291", "mls_number": "TX-882910", "price": 450000, "beds": 4, "baths": 3.5, "square_feet": 2850, "status": "Active", "city": "Austin"
| # | listing_id | mls_number | property_type | status | price | beds |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Agent Profiles objects from kw.com. All fields typed and schema-versioned.
"agent_id": "A-59281", "full_name": "Sarah Jenkins", "office_name": "KW Austin Southwest", "phone_number": "+1-512-555-0198", "active_listings_count": 14, "specialties": "['Luxury', 'Relocation']", "languages_spoken": "['English', 'Spanish']"
| # | agent_id | full_name | license_number | office_id | office_name | phone_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & History objects from kw.com. All fields typed and schema-versioned.
"listing_id": "KW-738291", "current_price": 450000, "original_price": 475000, "price_per_sqft": 157.89, "days_on_market": 42, "tax_amount": 6200, "hoa_fee": 150
| # | listing_id | current_price | original_price | price_per_sqft | days_on_market | price_history |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Open Houses objects from kw.com. All fields typed and schema-versioned.
"listing_id": "KW-738291", "open_house_id": "OH-9921", "date": "2026-06-14", "start_time": "13:00:00", "end_time": "16:00:00", "virtual_event": false, "agent_name": "Sarah Jenkins"
| # | listing_id | open_house_id | start_time | end_time | date | event_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Office & Brokerage objects from kw.com. All fields typed and schema-versioned.
"office_id": "O-9182", "office_name": "KW Austin Southwest", "city": "Austin", "state": "TX", "agent_count": 342, "active_listings": 1205, "phone_number": "+1-512-555-0000"
| # | office_id | office_name | broker_name | address | city | state |
|---|---|---|---|---|---|---|
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Our KW scraper handles every layer of the platform: property listings, dynamic pricing, map-based search results, and agent directories. Built with JavaScript rendering, session management, and anti-bot circumvention.
Title, beds, baths, square footage, description, images, virtual tours, and every metadata field Keller Williams surfaces.
Extract KW associates, bios, contact details, active listings, and specialisations across all market centres.
Monitor active, pending, sold, and off-market status changes on a daily or hourly basis.
Track list price changes, original price, and days on market to identify pricing trends.
Capture local property taxes, HOA fees, and assessment histories attached to residential listings.
Extract dates, times, and agent details for upcoming open houses across target zip codes.
Extract KW Commercial listings, zoning information, cap rates, and lease terms.
Map KW franchises, operating principals, and roster sizes across different states and regions.
Extract data across US, Canada, and KW Worldwide regions using a unified extraction schema.
Standardise addresses, zip codes, and coordinate data for immediate integration into mapping tools.
Brief in. Clean data out.
Provide target zip codes, states, or agent criteria. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for kw.com.
Schema validation, null-rate checks, and geospatial anomaly detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate platforms invest heavily in scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Keller Williams uses anti-scraping firewalls to block data centre IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
KW's map search is heavily JavaScript-rendered and relies on dynamic API calls based on bounding boxes. We run full Playwright browser sessions to trigger map movements and capture the underlying JSON payloads.
Real estate DOM structures change frequently based on property type. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
For large MLS markets, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute cost and downstream processing load for status and price updates.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops, responding before you notice.
Identify underpriced properties, calculate price per square foot, and assess rental yield potential across specific neighbourhoods.
Brokerages track top-performing KW agents, sales volumes, and active listings for targeted recruiting campaigns.
Analyse days on market, price reductions, and inventory levels across specific zip codes to predict market shifts.
Enrich internal real estate portals with active listings, open house dates, and agent contact information.
Identify new listings rapidly to target buyers with pre-approval offers and mortgage products.
Feed automated valuation models (AVMs) with historical pricing, tax assessments, and comparable property data.
"Keller Williams holds one of the most comprehensive agent and property datasets in North America, but mapping it requires infrastructure built for dynamic map-based interfaces."
Extracting real estate data at scale requires bypassing sophisticated anti-bot firewalls, rendering complex map clusters, and normalising inconsistent MLS feeds. DataFlirt handles the proxy rotation, JavaScript execution, and schema maintenance so your data science teams can focus on valuation models and market analysis.
Everything supported by our kw.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, map API interception, and interaction flows.
We maintain pools of residential ISP proxies across North America. Rotation happens per-request with sticky sessions where required to bypass WAF rules.
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 kw.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from kw.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property listings and public agent profiles. We do not extract private consumer data, circumvent authentication walls, or scrape confidential MLS remarks.
We use Playwright to execute the JavaScript necessary to load the map interfaces, intercepting the underlying API calls that return the JSON payloads for property clusters within specific bounding boxes.
Yes. We extract public office phone numbers, public email addresses, social media links, and website URLs listed on the public KW agent directory.
Depending on your requirements, pipelines can be configured for daily full-market refreshes or sub-daily streaming for specific target zip codes to capture new listings and price changes rapidly.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per listing, allowing us to track original price, current price, and calculate days on market.
Our smallest packages start at a defined city or state level with weekly delivery. For national coverage or custom schema requirements, we price based on volume and delivery frequency.
Yes. We support extraction of KW Commercial listings, including specific commercial fields like zoning, cap rates, building class, and lease terms.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off agent directory export or continuous market monitoring across multiple states, we scope, build, and operate the pipeline. Tell us what you need.
