We extract property listings, RERA IDs, builder portfolios, locality pricing, and floor plans from Commonfloor. 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 commonfloor.com. All fields typed and schema-versioned.
"property_id": "CF-10928374", "price": 12500000.0, "bhk": 3, "super_built_up_area": 1650, "locality": "Whitefield", "city": "Bangalore", "listing_type": "Sale", "posted_by": "Broker"
| # | property_id | title | property_type | listing_type | price | price_per_sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Projects & Societies objects from commonfloor.com. All fields typed and schema-versioned.
"project_id": "PRJ-99281", "project_name": "Prestige Shantiniketan", "builder_name": "Prestige Group", "rera_id": "PRM/KA/RERA/1251/446/PR/171014/000123", "project_status": "Ready to Move", "total_units": 3002, "city": "Bangalore"
| # | project_id | project_name | builder_name | rera_id | project_status | possession_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Builder Profiles objects from commonfloor.com. All fields typed and schema-versioned.
"builder_name": "Sobha Limited", "established_year": 1995, "total_projects": 168, "ongoing_projects": 34, "operating_cities": "['Bangalore', 'Pune', 'Chennai', 'Gurgaon']", "url": "https://www.commonfloor.com/sobha-limited-builder"
| # | builder_id | builder_name | logo_url | description | established_year | total_projects |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Locality Insights objects from commonfloor.com. All fields typed and schema-versioned.
"locality_name": "Koramangala", "city": "Bangalore", "avg_price_per_sqft": 14500.0, "price_yoy_growth": 8.5, "livability_score": 9.2, "rental_yield": 3.8
| # | locality_id | locality_name | city | avg_price_per_sqft | price_yoy_growth | rental_yield |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Floor Plans objects from commonfloor.com. All fields typed and schema-versioned.
"project_id": "PRJ-99281", "bhk_type": "3 BHK", "super_built_up_area": 1820, "price_range": "1.8 Cr - 2.1 Cr", "image_url": "https://is1-3.housingcdn.com/floor_plans.jpg", "is_3d_view": false
| # | plan_id | project_id | bhk_type | super_built_up_area | carpet_area | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Commonfloor scraper handles the complete real estate taxonomy: listings, projects, builder profiles, and locality metrics, with full JavaScript rendering for map-based interfaces built in.
Title, price, area, BHK configuration, furnishing status, facing direction, and amenity lists scraped at the individual listing level.
Capture project status, possession dates, total units, tower counts, and verified RERA registration IDs for compliance tracking.
Extract builder history, total completed projects, ongoing developments, and operating cities to evaluate developer footprint.
Track average price per square foot, YoY growth, livability scores, and rental yield metrics across thousands of micro-markets.
Identify the listing source to filter out broker duplicates and target direct owner properties for lead generation.
Extract high-resolution floor plan images, 3D views, and project brochures linked to specific BHK configurations.
Extract latitude and longitude data embedded in map views for precise spatial analysis and distance calculations.
Monitor how long properties remain on the market by tracking initial post dates against current active status.
Run one-off bulk city exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide target cities, localities, or builder names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for commonfloor.com.
Schema validation, null-rate checks, price-outlier detection, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate portals employ strict rate limits and complex map-based pagination. Here is how we maintain reliable extraction pipelines.
Property portals strictly limit requests per IP to prevent competitor scraping. Our crawlers use Indian residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain uninterrupted access.
Commonfloor relies on dynamic map-based pagination and lazy-loaded property clusters. We run full Playwright browser sessions with JavaScript execution to trigger map movements and capture listings that headless HTTP clients miss entirely.
Property detail pages frequently change layout based on property type or builder tier. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
For large city-wide catalogues, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
While we cannot bypass OTP walls for direct contact details, we programmatically interact with click-to-reveal elements to capture broker agency names and unmasked secondary contact points where publicly available.
Real estate aggregators ingest competitor inventory to benchmark coverage, pricing, and time-on-market metrics across major Indian cities.
Institutional investors correlate capital values with rental rates by locality to identify high-yield micro-markets for residential acquisition.
Real estate agencies monitor direct owner listings to acquire new mandates and track competing broker activity within their designated territories.
Fintech and mortgage lenders use historical price-per-square-foot data to train automated valuation models (AVMs) for loan underwriting.
Developers track competing project launches, possession timelines, and amenity offerings to position their own upcoming residential projects.
Researchers map project density, infrastructure proximity, and livability scores to study urban sprawl and housing affordability trends.
"Commonfloor holds the ground truth for Indian real estate inventory, but extracting clean, structured property data requires bypassing strict rate limits and complex map-based pagination."
Most data teams underestimate the investment required: reliable real estate scraping requires residential proxies, full JavaScript rendering for dynamic map loads, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our commonfloor.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 interactions, and lazy loads. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda for burst scaling and ECS for sustained extraction. Airflow handles scheduling and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About commonfloor.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Commonfloor is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated property, project, and locality data. We do not extract personal data behind OTP walls or violate user privacy. Clients should review platform terms of service and consult legal counsel for specific use cases.
We use full Playwright browser sessions to interact with the map interface programmatically. Our crawlers simulate pan and zoom events to trigger backend API calls, ensuring we capture all property clusters within a given bounding box.
Yes, we extract the RERA registration number for any project where it is publicly displayed on the project detail page. This allows you to cross-reference listings with official state RERA databases.
Full city catalogue refreshes at daily cadence complete within a 4-8 hour window depending on inventory size. For targeted micro-markets, we can configure sub-hourly pipelines to track new listings as they go live.
Yes. We extract the 'posted by' metadata field for every listing, allowing you to segment the dataset into direct owner properties, broker listings, and builder primary sales.
We extract the direct URLs for all floor plan images, master plans, and brochure PDFs. If required, we can also download these binary assets and sync them directly to your S3 bucket alongside the structured metadata.
Our smallest packages start at a defined city or locality list with weekly delivery. For pan-India extraction or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off city inventory dump or a continuous price-monitoring feed across top Indian metros, we scope, build, and operate the pipeline. Tell us what you need.
