We extract property listings, rent and sale prices, IPTU taxes, condo fees, and broker details from Vivareal.br. 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 Listings objects from vivareal.com.br. All fields typed and schema-versioned.
"property_id": "2589104432", "title": "Apartamento com 3 Quartos à Venda, 120m2", "listing_type": "SALE", "property_type": "APARTMENT", "area_m2": 120, "bedrooms": 3, "bathrooms": 2, "parking_spaces": 2, "status": "ACTIVE"
| # | property_id | title | listing_type | property_type | status | area_m2 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financials objects from vivareal.com.br. All fields typed and schema-versioned.
"property_id": "2589104432", "price_sale": 850000.0, "price_rent": "None", "iptu": 2400.0, "condo_fee": 950.0, "price_per_m2": 7083.33, "currency": "BRL", "last_updated": "2026-05-12T10:15:00Z"
| # | property_id | price_sale | price_rent | iptu | condo_fee | price_per_m2 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location objects from vivareal.com.br. All fields typed and schema-versioned.
"property_id": "2589104432", "street_name": "Rua Augusta", "neighborhood": "Consolação", "city": "São Paulo", "state": "SP", "zip_code": "01305-000", "latitude": -23.5558, "longitude": -46.6581
| # | property_id | street_name | street_number | neighborhood | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities objects from vivareal.com.br. All fields typed and schema-versioned.
"property_id": "2589104432", "has_pool": true, "has_gym": true, "has_elevator": true, "pet_friendly": true, "furnished": false, "security_24h": true, "balcony": true
| # | property_id | has_pool | has_gym | has_elevator | pet_friendly | furnished |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Broker & Agency objects from vivareal.com.br. All fields typed and schema-versioned.
"property_id": "2589104432", "broker_name": "João Silva Corretor", "creci": "SP-123456", "agency_name": "Imobiliária Paulista", "whatsapp_available": true, "phone_visible": false, "agency_url": "https://www.vivareal.com.br/imobiliaria/imobiliaria-paulista/"
| # | property_id | broker_id | broker_name | creci | agency_name | agency_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Vivareal scraper handles every layer of the platform: property listings, dynamic pricing, IPTU calculations, broker intelligence, and geolocation data. We manage JavaScript rendering, session management, and anti-bot circumvention.
Title, description, area, bedrooms, bathrooms, and parking spaces. Extracted precisely for both residential and commercial properties.
Capture sale price, rent price, IPTU, and condo fees. We normalise these values to calculate accurate price per square metre metrics.
Extract street names, neighborhoods, zip codes, and exact latitude/longitude coordinates from embedded map data.
Broker name, CRECI registration number, agency affiliation, and contact availability for every listing on the platform.
Monitor price drops and increases over time. We maintain a time-series log of all financial changes for active listings.
Structured extraction of building features: pools, gyms, elevators, 24-hour security, and pet-friendly policies.
High-resolution image URLs and floorplan links extracted directly from the property gallery.
Track visibility and ranking for specific neighborhoods or property types across default search parameters.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide target neighborhoods, cities, property types, or agency URLs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for vivareal.com.br.
Schema validation, null-rate checks, price-outlier detection, and geographic bounding box checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate portals invest heavily in scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Vivareal blocks data centre IPs aggressively. Our crawlers use Brazilian residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with local user traffic.
Critical data points like exact coordinates and broker contact buttons rely on JavaScript execution. We run full Playwright browser sessions to hydrate these components and capture data that headless HTTP clients miss entirely.
Property portals frequently A/B test their layouts. Our selector strategy uses multiple fallback chains per field, combining CSS selectors, XPath, and Next.js state data extraction to ensure a layout change does not break your pipeline.
For city-wide catalogues, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute cost, storage bloat, 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. We respond before you notice.
Proptech companies use historical pricing, IPTU, and condo fees to train machine learning models for accurate property valuations.
Real estate investment trusts (REITs) compare rent prices against sale prices to identify high-yield neighborhoods and mispriced assets.
Agencies monitor competitor listings, time-on-market metrics, and pricing strategies to optimise their own portfolio.
Analysts track development density, amenity distribution, and price-per-square-metre trends across different city zones.
Aggregators normalise Vivareal data to build comprehensive market dashboards for buyers and institutional investors.
Construction firms correlate listing volume and average time-on-market to forecast housing demand in developing neighborhoods.
"Vivareal holds the definitive dataset for Brazilian real estate, but extracting accurate IPTU, condo fees, and historical pricing requires navigating aggressive anti-bot measures."
Most teams underestimate the investment required: reliable Vivareal scraping requires Brazilian residential proxies, full JavaScript rendering for map data, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our vivareal.com.br 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 Brazilian 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 vivareal.com.br scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Vivareal is generally permissible for non-personal data. DataFlirt targets only public property listings, prices, and broker details. We do not extract private user data, circumvent authentication walls, or violate GDPR/LGPD. Clients should review Vivareal's Terms of Service and consult legal counsel for specific use cases.
We use Brazilian 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.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined set of neighborhoods. Full city-wide catalogue refreshes at daily cadence complete within a 6-12 hour window.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per property ID for sale price, rent price, IPTU, and condo fees from the date your pipeline starts.
Our smallest packages start at a defined neighborhood or city list (typically 10,000-50,000 listings) with weekly delivery. For larger state-wide catalogues, we price based on volume and delivery frequency.
We extract phone numbers and WhatsApp links only when they are publicly visible on the listing page without requiring a user login or lead submission form.
Absolutely. We provide a sample run of up to 1,000 listings or 50 search result pages as part of the pre-engagement scoping process, so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off neighborhood extract or a continuous price-monitoring feed across São Paulo, we scope, build, and operate the pipeline. Tell us what you need.
