We extract residential and commercial listings, price trends, locality intelligence, and agent profiles from Magicbricks. 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 magicbricks.com. All fields typed and schema-versioned.
"property_id": "MB-4829104", "title": "3 BHK Flat for Sale in Whitefield", "property_type": "Apartment", "price": 14500000, "carpet_area": 1450, "status": "Ready to Move", "furnishing": "Semi-Furnished", "posted_by": "Agent"
| # | property_id | title | property_type | price | carpet_area | status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Trends objects from magicbricks.com. All fields typed and schema-versioned.
"property_id": "MB-4829104", "current_price": 14500000, "price_per_sqft": 10000, "propworth_value": 14200000, "locality_avg_price": 9850, "yoy_growth": 8.4, "scraped_at": "2026-05-12T09:14:00Z"
| # | property_id | current_price | price_per_sqft | rent_estimate | propworth_value | price_history |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Locality Intelligence objects from magicbricks.com. All fields typed and schema-versioned.
"locality_id": "LOC-8492", "name": "Whitefield", "city": "Bengaluru", "pin_code": "560066", "overall_rating": 4.2, "connectivity_rating": 4.0, "safety_rating": 4.3, "lifestyle_rating": 4.5
| # | locality_id | name | city | pin_code | overall_rating | connectivity_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Agent Profiles objects from magicbricks.com. All fields typed and schema-versioned.
"agent_id": "AGT-39281", "name": "Rahul Sharma", "company_name": "Prime Realty Services", "operating_cities": "['Bengaluru', 'Mysuru']", "properties_listed": 45, "experience_years": 8, "rera_certified": true, "profile_url": "https://magicbricks.com/profile/AGT-39281"
| # | agent_id | name | company_name | operating_cities | properties_listed | experience_years |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for New Projects objects from magicbricks.com. All fields typed and schema-versioned.
"project_id": "PRJ-9921", "name": "Prestige Shantiniketan", "builder": "Prestige Group", "possession_status": "Completed", "total_units": 3002, "project_area": "105 Acres", "available_bhk": "['2 BHK', '3 BHK', '4 BHK']", "price_range": "1.2 Cr - 3.5 Cr"
| # | project_id | name | builder | possession_status | launch_date | rera_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Our Magicbricks scraper handles every layer of the platform: property listings, dynamic price trends, PropWorth estimates, and locality reviews - with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, price, carpet area, furnishing, facing, floor level, and society details - scraped at property level with high precision.
Extract automated valuation models and historical price graphs per locality to track real estate appreciation.
Capture resident feedback, safety scores, lifestyle ratings, and connectivity metrics across thousands of micro-markets.
Extract RERA details, total units, possession dates, launch timelines, and project area metrics.
Track operating areas, active listings, experience levels, and RERA certification status for brokers.
Separate schemas for office spaces, retail shops, residential apartments, and independent houses.
Extract latitude and longitude coordinates alongside nearby landmark distances for spatial analysis.
Capture high-resolution image links and layout diagrams for property galleries and valuation models.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide city names, locality URLs, property types, or agent IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for magicbricks.com.
Schema validation, null-rate checks, price-outlier detection, and sample localities before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Magicbricks employs aggressive rate limiting and bot detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Magicbricks limits requests heavily by IP. Our crawlers use India-based residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Property detail pages and locality maps rely on heavy JavaScript execution. We run full Playwright browser sessions to trigger lazy-loads and hydrate dynamic pricing widgets.
Magicbricks frequently alters its DOM structure across different property types. Our selector strategy uses fallback chains so layout changes do not break your data pipeline.
For large city catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs - capturing price drops and status changes efficiently.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops - SLA uptime is contractual.
Train AVM models using historical asking prices, PropWorth data, and locality trends.
Identify high-yield rental localities and undervalued properties across emerging micro-markets.
Brokerages track rival agent listings, market share, and inventory duration.
Analyse city-wise inventory overhang, possession delays, and new project launch volumes.
Match property listing data with home loan offerings or interior design services.
Map commercial real estate availability and residential density indicators for store planning.
"Magicbricks holds the definitive pulse on Indian real estate pricing - but extracting that intelligence across 150 cities requires serious infrastructure."
Most teams underestimate the investment required: reliable Magicbricks scraping requires India-based residential proxies, full JavaScript rendering for map data, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers focus on analysis, not infrastructure.
Everything supported by our magicbricks.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, cookie sessions, and interaction flows. 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 (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 magicbricks.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Magicbricks is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated property, pricing, and locality data. We do not extract personal data behind OTP walls or violate data protection regulations. Clients should review Magicbricks ToS and consult legal counsel for specific use cases.
We use India-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
Yes. We support extraction across Tier 1, Tier 2, and Tier 3 cities listed on Magicbricks, normalising the location hierarchy (City > Locality > Sub-locality) into a clean schema.
Full city catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size. We track listing addition dates and status changes to maintain accurate active inventory counts.
Yes. We capture PropWorth valuation estimates and historical price trends per locality, providing time-series data for quantitative analysis.
Our smallest packages start at a defined city or locality list with weekly delivery. For pan-India coverage or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 properties or 50 locality pages 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 city export or a continuous price-monitoring feed across 500K listings - we scope, build, and operate the pipeline. Tell us what you need.
