We extract property listings, broker details, builder projects, and pricing trends from Realestateindia.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 Residential Properties objects from realestateindia.com. All fields typed and schema-versioned.
"property_id": "REI-847291A", "title": "3 BHK Flat for Sale in Whitefield", "property_type": "Apartment", "bhk": 3, "price": 12500000, "area_sqft": 1650, "city": "Bangalore", "locality": "Whitefield", "listed_by": "Broker"
| # | property_id | title | property_type | bhk | price | price_per_sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Commercial Properties objects from realestateindia.com. All fields typed and schema-versioned.
"property_id": "REI-C928174", "title": "Commercial Office Space for Rent", "property_type": "Office Space", "price": 45000, "area_sqft": 1200, "city": "Mumbai", "locality": "Andheri East", "furnishing_status": "Furnished", "parking_spaces": 2
| # | property_id | title | property_type | price | area_sqft | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Broker Profiles objects from realestateindia.com. All fields typed and schema-versioned.
"broker_id": "BRK-48291", "company_name": "Prime Realty Solutions", "contact_person": "Rahul Sharma", "operating_cities": "['Delhi', 'Gurgaon', 'Noida']", "total_properties": 142, "rera_registration": "RERA-HR-1928", "established_year": 2012
| # | broker_id | company_name | contact_person | operating_cities | total_properties | residential_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Builder Projects objects from realestateindia.com. All fields typed and schema-versioned.
"project_id": "PRJ-99281", "project_name": "Prestige Tranquility", "builder_name": "Prestige Group", "city": "Bangalore", "project_status": "Ready to Move", "total_units": 850, "price_range": "85L - 1.2Cr", "rera_id": "PRM/KA/RERA/1251/446/PR/170915/000281"
| # | project_id | project_name | builder_name | city | locality | project_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from realestateindia.com. All fields typed and schema-versioned.
"keyword": "flats for sale", "location": "Pune", "position": 3, "property_id": "REI-772910", "price": 7500000, "featured_badge": true, "dealer_type": "Builder", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | location | property_category | position | property_id | title |
|---|---|---|---|---|---|---|
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Our Realestateindia.com scraper captures deep property metadata, builder credentials, and broker portfolios with full JavaScript rendering and pagination handling.
Extract apartments, villas, plots, office spaces, and retail shops. Capture price, area, BHK configuration, and furnishing status.
Monitor new project launches, possession timelines, total units, RERA registration numbers, and configuration options.
Map the broker network. Extract agency names, operating cities, active listing counts, and RERA credentials.
Capture exact project locations, nearby landmarks, and specific amenities like clubhouses, power backup, and security features.
Monitor listing prices over time. Track price per square foot across different localities and property types.
Extract high-resolution image URLs, property videos, and floor plan documents attached to listings.
Track organic and featured placement for specific localities and property types to monitor broker visibility.
Run scheduled pipelines to detect new listings, price drops, and sold properties across targeted cities.
Bypass IP blocks and CAPTCHAs using residential proxy rotation and realistic browser fingerprinting.
Brief in. Clean data out.
Provide target cities, property types, or broker URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and pagination handling for realestateindia.com.
Schema validation, null-rate checks, and price anomaly detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Property portals use dynamic loading and aggressive bot protection. Here is how we maintain data flow.
Many property portals hide broker contact details or exact locations behind JavaScript events. We use Playwright to execute these scripts and extract the underlying data payloads without triggering bot defenses.
Search results often cap at a specific page limit. We bypass this by dynamically filtering searches by micro-localities, price brackets, and property types to extract the entire catalogue.
Commercial plots and residential apartments have entirely different DOM structures. Our parsers normalise these disparate layouts into a single, clean relational schema.
The same property is often listed by multiple brokers. We extract specific identifiers and metadata to help your downstream systems deduplicate identical physical properties.
To prevent geo-blocking and rate limiting, we route requests through Indian residential proxies, ensuring consistent access to localized search results and pricing.
Aggregate listings to build comprehensive property search engines and market intelligence dashboards.
Analyze rental yields, capital appreciation, and supply metrics across commercial and residential sectors.
Monitor competitor listings, track new project launches, and identify lead generation opportunities.
Study urban expansion, housing density, and infrastructure development correlations using property data.
Feed automated valuation models (AVMs) with recent listing prices, area specifications, and locality trends.
Track new project launches and construction statuses to target builders for material procurement contracts.
"Realestateindia contains critical market signals for the entire subcontinent, but extracting it requires bypassing aggressive rate limits and complex DOM structures."
Building a reliable property scraper requires managing residential IP pools, handling infinite scroll pagination, and normalising highly variable listing formats. DataFlirt manages the entire extraction lifecycle, delivering clean, structured property data directly to your warehouse so your team can focus on market analysis.
Everything supported by our realestateindia.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 and retry logic. Playwright executes JavaScript to reveal hidden contact details and dynamic property attributes.
We maintain pools of Indian residential ISP proxies. Rotation happens per-request to avoid IP bans and ensure consistent data access.
Pipelines run on AWS infrastructure. Airflow handles scheduling and dependency management, ensuring data is delivered precisely on time.
Data delivered to where your team already works — no new tooling required.
About realestateindia.com scraping, legality, and pipeline operations.
Ask us directly →We extract all publicly visible contact information. If numbers are hidden behind a 'click to view' JavaScript event, our Playwright renderers trigger the event to capture the data.
Search results often truncate after a certain number of pages. We programmatically segment searches by micro-localities, specific price brackets, and exact property types to ensure full catalogue extraction without hitting pagination limits.
Yes. We can run delta extractions on a defined set of property URLs daily or weekly, capturing price modifications and status changes (e.g., active to sold).
Yes, we capture RERA IDs for both builder projects and registered brokers wherever they are published on the listing pages.
For targeted city or locality pipelines, we can deliver daily updates. Full national catalogue refreshes typically run on a weekly or bi-weekly cadence due to the volume of listings.
Yes. We map unstructured amenity lists into boolean columns (e.g., has_pool, has_gym) and normalise property types to ensure clean insertion into your database schema.
20-minute scoping call. Pilot dataset within the week. Production within two. From targeted locality tracking to nationwide broker extraction, we build and manage the infrastructure. Tell us your data requirements.
