We extract property listings, new project details, RERA compliance records, builder profiles, and locality price trends from Squareyards. 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 squareyards.com. All fields typed and schema-versioned.
"property_id": "SQY-892341", "title": "3 BHK Flat for Sale in Whitefield", "price": 12500000.0, "area_sqft": 1540, "bhk": 3, "furnishing_status": "Semi-Furnished", "floor_number": 4
| # | property_id | title | property_type | price | area_sqft | bhk |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for New Projects objects from squareyards.com. All fields typed and schema-versioned.
"project_id": "PRJ-9012", "project_name": "Prestige Shantiniketan", "builder_name": "Prestige Group", "rera_id": "PRM/KA/RERA/1251/446/PR/170915/000281", "status": "Ready To Move", "possession_date": "2021-12-01", "min_price": 8500000.0
| # | project_id | project_name | builder_name | rera_id | status | possession_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Locality Insights objects from squareyards.com. All fields typed and schema-versioned.
"locality_name": "Whitefield", "city": "Bengaluru", "avg_price_per_sqft": 8200.0, "price_trend_yoy": 12.4, "rental_yield": 4.2, "livability_score": 8.5, "review_rating": 4.1
| # | locality_id | locality_name | city | avg_price_per_sqft | price_trend_yoy | rental_yield |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Builder Profiles objects from squareyards.com. All fields typed and schema-versioned.
"builder_name": "Godrej Properties", "operating_since": 1990, "total_projects": 142, "ongoing_projects": 45, "completed_projects": 97, "average_rating": 4.3, "cities_present": "['Mumbai', 'Bengaluru', 'Pune', 'NCR']"
| # | builder_id | builder_name | operating_since | total_projects | ongoing_projects | completed_projects |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent Data objects from squareyards.com. All fields typed and schema-versioned.
"agent_id": "AGT-5512", "agent_name": "Rahul Sharma", "agency_name": "Prime Realty", "rera_registration": "A51900001722", "properties_listed": 48, "rating": 4.6, "experience_years": 8
| # | agent_id | agent_name | agency_name | rera_registration | properties_listed | localities_served |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Squareyards scraper handles complex property datasets: nested project hierarchies, dynamic pricing charts, RERA compliance records, and map-based locality data with JavaScript rendering and CAPTCHA handling built in.
Extract BHK, area, price, furnishing, facing, and floor details for residential and commercial listings.
Map parent projects to individual tower and unit configurations with possession timelines.
Capture RERA registration numbers and verification status for projects and agents.
Extract historical pricing data, YOY growth metrics, and rental yields per neighbourhood.
Track builder portfolios, project completion rates, and historical delivery timelines.
Scrape data across top Indian metros (NCR, Mumbai, Bengaluru) and UAE markets (Dubai, Abu Dhabi).
Extract URLs for high-resolution property images, floor plans, and master plan PDFs.
Normalise unstructured amenity lists (gym, pool, security) into structured boolean fields.
Capture active listing counts, service areas, and RERA IDs for individual brokers.
Track price drops, status changes (e.g., Under Construction to Ready to Move), and delisted properties.
Brief in. Clean data out.
Provide target cities, localities, property types, or builder names. We design the schema together.
We configure Scrapy/Playwright crawlers, manage sessions, and handle map-based pagination limits.
Schema validation, null-rate checks on critical fields like price and area, and outlier detection.
JSON / CSV / Parquet pushed to S3, BigQuery, or Snowflake on your schedule.
Property portals aggressively protect their inventory data. We manage the infrastructure required to extract accurate listings at scale.
Squareyards limits standard list pagination. We interact with map APIs and adjust bounding boxes to ensure total coverage of dense localities without missing inventory.
Historical price trends require JavaScript execution. We use Playwright to hydrate chart widgets and extract raw time-series data directly from the DOM.
We route requests through residential ISP proxies in India and the UAE to match target geography, preventing geo-blocking and aggressive rate limits.
Real estate data is hierarchical. We map individual unit listings back to their parent project and builder profiles, ensuring relational integrity in the final dataset.
For property monitoring, we maintain a hash index of listings. Subsequent runs only push price adjustments or status changes, saving compute and storage costs.
Data science teams ingest historical price trends and transaction data to train automated valuation models (AVMs).
Real estate developers track competitor project launches, pricing strategies, and inventory absorption rates.
Agencies identify high-yield localities and track top-performing brokers for recruitment and market expansion.
Institutional investors analyse rental yields, capital appreciation, and infrastructure scores to identify emerging micro-markets.
B2B service providers target new project launches for interior design, material supply, and facility management contracts.
Consultancies aggregate city-level inventory data to publish quarterly real estate market reports.
"Property data is notoriously fragmented. Extracting clean, structured project hierarchies and price trends from portals like Squareyards is a massive data engineering challenge."
Building a reliable real estate scraper means handling infinite map scrolls, nested project-to-unit relationships, and aggressive bot mitigation. DataFlirt manages the proxy rotation, JavaScript hydration, and schema maintenance so your analysts can focus on market trends rather than broken selectors.
Everything supported by our squareyards.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 concurrency and queue management. Playwright executes JavaScript to render dynamic property maps and pricing charts.
Requests are routed through residential IPs matching the target market (India or UAE) to bypass geo-fencing and rate limiting.
Extracted data is normalised into relational structures, mapping individual properties to projects, builders, and localities before delivery.
Data delivered to where your team already works — no new tooling required.
About squareyards.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property listings and project data is generally permissible. DataFlirt extracts only public, non-authenticated data. We do not bypass OTP walls to extract private owner phone numbers.
Squareyards limits standard list pagination. We programmatically adjust map bounding boxes to extract listings across dense micro-markets, ensuring zero data loss.
Yes. We hydrate the JavaScript pricing charts on locality and project pages to extract the underlying time-series data for YOY and QOQ analysis.
Yes. Our pipeline fully supports Squareyards UAE listings, including Dubai and Abu Dhabi markets, using localised residential proxies.
Our schema extracts the full hierarchy. We capture the parent project, builder details, tower configurations, and link all individual listings back to this parent record.
Pipelines can be configured for daily or weekly refreshes depending on your requirements. Change detection ensures we only update records where prices or statuses have changed.
Yes. We capture RERA registration numbers and verification statuses for both new projects and registered brokers.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop dealing with broken selectors and map pagination. Tell us the cities and property types you need, and we will deliver clean, structured data directly to your warehouse.
