We extract property listings, historical pricing, IPTU tax data, condominium fees, and neighbourhood metadata from QuintoAndar. 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 Rental Listings objects from quintoandar.com.br. All fields typed and schema-versioned.
"property_id": "89341234", "url": "https://www.quintoandar.com.br/imovel/89341234", "property_type": "Apartment", "area_m2": 65, "bedrooms": 2, "bathrooms": 1, "rent_price": 2500.0, "total_price": 3100.0
| # | property_id | url | property_type | area_m2 | bedrooms | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sale Listings objects from quintoandar.com.br. All fields typed and schema-versioned.
"property_id": "89341235", "property_type": "House", "area_m2": 120, "sale_price": 650000.0, "condominium_fee": 0.0, "iptu_fee": 120.0, "price_per_m2": 5416.66, "status": "available"
| # | property_id | url | property_type | area_m2 | bedrooms | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Features objects from quintoandar.com.br. All fields typed and schema-versioned.
"property_id": "89341234", "has_elevator": true, "has_pool": false, "has_balcony": true, "accepts_pets": true, "is_furnished": false, "proximity_metro": "500m"
| # | property_id | has_elevator | has_pool | has_gym | has_balcony | accepts_pets |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financial Breakdown objects from quintoandar.com.br. All fields typed and schema-versioned.
"property_id": "89341234", "base_rent": 2500.0, "condominium_fee": 450.0, "iptu_fee": 110.0, "fire_insurance": 30.0, "service_fee": 10.0, "total_monthly_cost": 3100.0
| # | property_id | base_rent | sale_price | condominium_fee | iptu_fee | fire_insurance |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location & Map Data objects from quintoandar.com.br. All fields typed and schema-versioned.
"property_id": "89341234", "latitude": -23.5505, "longitude": -46.6333, "street_name": "Rua Augusta", "neighbourhood": "Consolacao", "city": "Sao Paulo", "state": "SP"
| # | property_id | latitude | longitude | street_name | neighbourhood | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our QuintoAndar scraper handles the complexities of map-based pagination, GraphQL endpoints, and dynamic Next.js payloads to extract highly structured property records.
Extract area, bedrooms, bathrooms, parking spaces, floor level, and building amenities for every apartment and house.
Parse the exact financial structure of every listing: base rent, sale price, IPTU tax, condominium fees, and fire insurance.
We use precise bounding box iteration to extract properties from specific neighbourhoods, bypassing standard pagination limits.
Track price drops and increases over time. We log timestamped changes for rent and sale prices across the catalogue.
Extract specific listing policies including pet acceptance, furnishing status, and guarantor requirements.
Capture listed distances to metro stations, bus terminals, and major urban infrastructure.
Extract high-resolution image URLs, floor plan links, and 3D virtual tour endpoints for spatial analysis.
Monitor publication dates and delisting events to calculate exact days on market for specific property types.
Run daily or weekly pipelines that only emit modified records, reducing downstream processing load and storage costs.
Brief in. Clean data out.
Provide target cities, neighbourhoods, or bounding box coordinates. We establish the target schema and frequency.
We configure GraphQL interception, proxy rotation with Brazilian residential IPs, and map iteration logic.
Schema validation, null-rate checks on IPTU fields, and coordinate verification before full deployment.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on the agreed cadence.
Modern proptech platforms rely on complex frontend frameworks and aggressive rate limiting. Here is how we maintain data flow.
Rather than parsing brittle HTML, our crawlers intercept the underlying GraphQL network requests. This provides access to structured JSON data including exact coordinates, unrounded financial figures, and backend property IDs.
QuintoAndar caps search results at a hard limit. We bypass this by recursively dividing map bounding boxes into smaller grids until the result count falls below the pagination threshold, ensuring 100% geographic coverage.
Requests originating outside South America are frequently blocked or served cached data. We route all traffic through high-reputation Brazilian residential ISP proxies to ensure consistent access and accurate regional pricing.
For individual property pages, we extract the hydrated Next.js state directly from the DOM script tags. This guarantees we capture all nested metadata without executing heavy frontend JavaScript on every page.
Listings frequently mix total costs with base costs. Our pipeline separates and normalises base rent, IPTU, condominium fees, and insurance into distinct, typed columns for immediate analytical use.
Data science teams use historical pricing and property features to train Automated Valuation Models (AVMs) for the Brazilian market.
Investors track rent-to-sale price ratios across specific neighbourhoods to identify high-yield investment opportunities.
Other proptech platforms monitor QuintoAndar inventory volume and pricing strategies to adjust their own market positioning.
Researchers map rental price inflation against public transport expansion to track gentrification and urban mobility trends.
Real Estate Investment Trusts (FIIs) analyse macro liquidity and days-on-market metrics before acquiring residential portfolios.
Property developers analyse the premium placed on specific amenities (e.g., balconies, parking) to optimise new construction blueprints.
"QuintoAndar holds the most accurate pulse on Brazilian urban real estate pricing. Extracting this requires navigating complex map-based APIs and heavily protected endpoints."
Most teams underestimate the complexity of scraping modern proptech platforms. Reliable QuintoAndar extraction requires Brazilian residential proxies, GraphQL payload interception, and precise bounding box iteration to bypass pagination limits. DataFlirt handles the infrastructure so your analysts can focus on yield modelling.
Everything supported by our quintoandar.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 and deduplication. We bypass standard HTML parsing by intercepting QuintoAndar's internal GraphQL requests, yielding cleaner data at higher throughput.
We maintain dedicated pools of Brazilian residential ISP proxies. This prevents regional blocks and ensures the platform returns accurate, localised pricing and availability data.
Pipelines run on AWS Lambda and ECS. Airflow handles map-grid scheduling and dependency management. All state and historical price logs are stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About quintoandar.com.br scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible. DataFlirt targets only public, non-authenticated property, pricing, and metadata. We do not extract personal data of landlords or tenants, nor do we bypass authentication walls to access private contracts. Clients should consult legal counsel for their specific use cases.
QuintoAndar limits search results to a fixed number per query. We solve this using a recursive bounding box algorithm. The pipeline divides large geographic areas into smaller coordinate grids until the result count for each grid falls safely below the pagination threshold.
Yes. We maintain a hash index of last-seen values per property ID. If a rent or sale price changes, we log the new value alongside a timestamp, allowing you to build a complete time-series of price adjustments.
Yes. We can configure the pipeline to target specific cities, discrete neighbourhoods, or custom polygon coordinates provided by your team.
For targeted neighbourhood monitoring, pipelines can run at sub-hourly frequencies. Full city-wide catalogue refreshes typically run on a daily cadence, completing within a 4-hour window depending on the target region size.
Our minimum engagement typically starts at a defined geographic scope (e.g., specific zones in Sao Paulo or Rio de Janeiro) with daily delivery. Contact us with your target regions for a precise quote.
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 Brazil - we scope, build, and operate the pipeline. Tell us what you need.
