We extract property details, room configurations, occupancy status, pricing tiers, and amenity lists from Stanza Living. 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 stanzaliving.com. All fields typed and schema-versioned.
"property_id": "SL-BLR-042", "name": "Stanza Living Frankfurt House", "city": "Bengaluru", "micro_market": "Electronic City", "gender_type": "Co-ed", "property_type": "Coliving", "base_price": 12500.0, "rating": 4.2
| # | property_id | name | city | micro_market | gender_type | property_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Room Inventory & Pricing objects from stanzaliving.com. All fields typed and schema-versioned.
"room_id": "RM-8492", "property_id": "SL-BLR-042", "occupancy_type": "Double Sharing", "price_per_month": 14000.0, "deposit_amount": 14000.0, "availability_status": "Available", "available_from": "2024-08-01"
| # | room_id | property_id | occupancy_type | price_per_month | deposit_amount | lock_in_period |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities & Services objects from stanzaliving.com. All fields typed and schema-versioned.
"property_id": "SL-BLR-042", "food_included": true, "laundry_included": true, "wifi_speed": "100 Mbps", "gym_available": false, "security_type": "Biometric & CCTV", "power_backup": "100%"
| # | property_id | food_included | laundry_included | wifi_speed | gym_available | security_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Location & Mapping objects from stanzaliving.com. All fields typed and schema-versioned.
"property_id": "SL-BLR-042", "latitude": 12.8399, "longitude": 77.677, "address": "Phase 1, Electronic City, Bengaluru", "nearest_landmark": "Wipro Gate 1", "distance_to_transit": "1.2 km", "transit_type": "Metro Station"
| # | property_id | latitude | longitude | address | nearest_landmark | distance_to_university |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from stanzaliving.com. All fields typed and schema-versioned.
"review_id": "REV-99214", "property_id": "SL-BLR-042", "rating_overall": 4.5, "rating_food": 3.8, "rating_hygiene": 4.8, "verified_resident": true, "review_date": "2024-05-12"
| # | review_id | property_id | reviewer_name | rating_overall | rating_food | rating_hygiene |
|---|---|---|---|---|---|---|
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Our Stanza Living scraper handles the platform's dynamic single page application architecture. We extract property details, inventory metadata, and spatial coordinates with session management and anti-bot circumvention built in.
Extract name, city, micro-market, gender restrictions, base pricing, and total bed counts for every listed property.
Capture pricing and availability for single, double, and triple occupancy configurations within each property.
Track price fluctuations, deposit requirements, and lock-in periods across all active inventory.
Extract latitude, longitude, exact addresses, and proximity to major universities or tech parks.
Map included services like food menus, laundry limits, WiFi speeds, and housekeeping schedules per property.
Aggregate overall ratings and sub-category scores for food, hygiene, and security across properties.
Extract data across Bengaluru, Delhi, Pune, Hyderabad, and all other operational Stanza Living markets.
Run one-off bulk exports or configure continuous pipelines at weekly or daily cadences.
Bypass rate limits and CAPTCHAs using residential proxy pools and full JavaScript rendering.
Brief in. Clean data out.
Provide target cities, micro-markets, or specific property URLs. We design the extraction schema together.
We configure Playwright crawlers, proxy rotation, session management, and rate-limit handling for stanzaliving.com.
Schema validation, null-rate checks, price-outlier detection, and coordinate mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate platforms invest in scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Stanza Living limits high-frequency requests from data centre IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to blend in with normal user traffic.
Stanza Living relies heavily on React and dynamic API calls. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering to capture pricing data that headless HTTP clients miss entirely.
PropTech sites change their DOM structure frequently. Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and API interception, ensuring layout changes do not break your data feed.
For large property catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops. SLA uptime is contractual.
Coliving operators and student housing providers monitor Stanza Living pricing and deposit structures to optimise their own yields.
Real estate analysts track bed counts, occupancy rates, and geographical expansion to map the student housing market.
Investors correlate micro-market pricing with local real estate costs to calculate potential yields on coliving investments.
Universities and educational consultants analyze accommodation availability and pricing near major campuses.
Planners use student density data and property locations to assess transit requirements in education hubs.
Private equity firms track property growth, review sentiment, and geographic footprint to evaluate PropTech companies.
"Stanza Living holds the most granular pricing and inventory data for student housing in India, but extracting it requires bypassing strict rate limits."
Most teams underestimate the compute required to track dynamic occupancy and pricing across thousands of coliving properties. DataFlirt handles the proxy rotation, JavaScript execution, and schema maintenance. Your engineers get clean data, not maintenance tickets.
Everything supported by our stanzaliving.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 and ECS. 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 stanzaliving.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Stanza Living is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated property, pricing, and amenity data. We do not extract personal data, circumvent authentication walls, or violate GDPR/DPDP. Clients should review Terms of Service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate limit spikes in real time and trigger pool rotation automatically.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per property and room variant for pricing, deposit amounts, and availability from the date your pipeline starts.
Yes. The pipeline can iterate through all active cities and micro-markets listed on the platform, capturing the entire national footprint.
Our smallest packages start at a defined city list with weekly delivery. For full national catalogues or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 50 properties as part of the pre-engagement scoping process, so you can validate schema fit, field completeness, 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 all major cities, we scope, build, and operate the pipeline. Tell us what you need.
