We extract buy and rent listings, price trends, project approvals, RERA details, amenities, and broker profiles from housing.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 housing.com. All fields typed and schema-versioned.
"property_id": "P1849201", "price": 15000000, "bhk_count": 3, "area_sqft": 1850, "city": "Bengaluru", "locality": "Whitefield", "furnishing_status": "Semi-Furnished", "construction_status": "Ready to Move"
| # | property_id | title | property_type | listing_type | price | area_sqft |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for New Projects & RERA objects from housing.com. All fields typed and schema-versioned.
"project_id": "PRJ9921", "rera_id": "PRM/KA/RERA/1251/446/PR/190809/002771", "developer_name": "Prestige Group", "total_units": 450, "project_status": "Under Construction", "minimum_price": 12000000, "possession_date": "2027-12-01"
| # | project_id | project_name | developer_name | rera_id | rera_status | launch_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Price Trends objects from housing.com. All fields typed and schema-versioned.
"locality_name": "Koramangala", "avg_price_per_sqft": 12500, "price_appreciation_yoy": 8.4, "transit_score": 9.2, "rent_yield_pct": 4.1, "city": "Bengaluru"
| # | locality_id | locality_name | city | avg_price_per_sqft | price_appreciation_yoy | rent_yield_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Broker Data objects from housing.com. All fields typed and schema-versioned.
"broker_id": "BRK4421", "broker_name": "Rahul Sharma", "agency_name": "Prime Real Estate", "rating": 4.6, "properties_listed": 142, "verified_status": true, "experience_years": 8
| # | broker_id | broker_name | agency_name | experience_years | properties_listed | localities_served |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities objects from housing.com. All fields typed and schema-versioned.
"property_id": "P1849201", "security_24x7": true, "power_backup": true, "club_house": true, "metro_distance_km": 1.2, "hospital_distance_km": 2.5, "swimming_pool": true
| # | property_id | swimming_pool | gym | security_24x7 | power_backup | club_house |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our housing.com scraper handles every layer of the platform: property listings, developer projects, price trends, locality insights, and broker intelligence. Built with JavaScript rendering, session management, and anti-bot circumvention.
Title, configuration, area, price, furnishing status, facing, floor details, and every metadata field housing.com surfaces. Scraped at listing level with image and floor plan URLs.
Extract RERA registration numbers, project status, launch dates, possession timelines, and developer details for under-construction properties.
Capture locality price appreciation, rental yields, and historical per-square-foot pricing trends across major Indian cities.
Extract broker profiles, verified status, operating localities, total listings, and user ratings to build agent intelligence databases.
Extract transit scores, lifestyle ratings, top projects, and demand-supply indices for specific neighbourhoods.
Capture property-level amenities and distance to critical infrastructure like schools, hospitals, and metro stations.
Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad, Chennai, and 40 other tier-1 and tier-2 cities. All from a unified schema.
Our crawlers interact with map clusters and bounding boxes to extract listings that are hidden behind infinite scroll and map boundaries.
Run one-off bulk exports or configure continuous pipelines at daily or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide city names, locality URLs, developer names, or broker IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and map interaction logic for housing.com.
Schema validation, null-rate checks, price-outlier detection, and coordinate mapping before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate platforms invest heavily in scraping detection and use complex map-based interfaces. Here is how we stay resilient.
Housing.com relies heavily on React and Next.js. We extract structured JSON payloads directly from the application state, bypassing brittle DOM parsing and capturing hidden metadata.
Real estate platforms hide listings behind map clusters. We use Playwright to manipulate bounding boxes and zoom levels, ensuring complete capture of dense localities without missing properties.
Housing.com uses Cloudflare and behavioural analysis. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Housing.com changes its DOM structure frequently. Our selector strategy uses multiple fallback chains per field, so a layout change does not break your data pipeline overnight.
For large property catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost, storage bloat, and downstream processing load.
Automated valuation models train on historical price trends, amenity data, and locality scores to predict property values.
Real estate agencies map top-performing brokers and identify underserved localities to target for expansion.
Builders monitor competitor project launches, possession timelines, and pricing tiers to optimise their own project positioning.
Institutional investors track rental yields, price appreciation, and infrastructure proximity to identify high-ROI micro-markets.
City planners and researchers correlate housing density and price trends with transit infrastructure development.
Property portals and classifieds monitor housing.com inventory depth, new listings velocity, and broker participation.
"Housing.com holds the most granular locality and pricing data in the Indian real estate market, but extracting it requires navigating complex map-based pagination and dynamic React payloads."
Most teams underestimate the investment required: reliable housing.com scraping requires residential proxies, full JavaScript rendering for map clusters, daily selector maintenance, and anomaly monitoring for price outliers. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our housing.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 Next.js payload extraction.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request with sticky sessions where required to prevent Cloudflare blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About housing.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from housing.com is generally permissible under Indian law. DataFlirt targets only public, non-authenticated property listings, price trends, and broker data. We do not extract personal data behind OTP walls or violate user privacy.
Housing.com clusters properties on a map view. We use Playwright to programmatically adjust zoom levels and pan across bounding boxes, triggering the underlying API calls to expose all listings in a given locality.
We support all cities available on housing.com, including tier-1 markets like Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad, and Chennai, as well as tier-2 and tier-3 cities.
Full city catalogue refreshes at weekly or daily cadences complete within a 12-24 hour window depending on size. Real-time pipelines can monitor specific projects or localities with sub-hourly latency.
Yes. We extract RERA registration numbers, developer details, launch dates, possession timelines, and compliance status for all listed under-construction projects.
Absolutely. We provide a sample run of up to 1,000 property listings or 5 localities as part of the pre-engagement scoping process, so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous price-monitoring feed across 50 cities, we scope, build, and operate the pipeline. Tell us what you need.
