We extract residential and commercial listings, pricing signals, spatial coordinates, and agency intelligence from Properati. 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 properati.com. All fields typed and schema-versioned.
"property_id": "PRP-849201", "title": "Departamento 2 Ambientes en Palermo", "property_type": "Apartment", "operation_type": "Sale", "price": 125000.0, "currency": "USD", "rooms": 2, "bathrooms": 1, "surface_total": 55, "surface_covered": 50
| # | property_id | title | property_type | operation_type | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Data objects from properati.com. All fields typed and schema-versioned.
"property_id": "PRP-849201", "address": "Av. Santa Fe 3200", "neighborhood": "Palermo", "city": "Buenos Aires", "state": "Capital Federal", "country": "Argentina", "latitude": -34.5882, "longitude": -58.4105
| # | property_id | address | neighborhood | city | state | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Valuations objects from properati.com. All fields typed and schema-versioned.
"property_id": "PRP-849201", "current_price": 125000.0, "original_price": 130000.0, "currency": "USD", "price_per_sqm": 2500.0, "maintenance_fees": 15000.0, "discount_pct": 3.8, "listed_date": "2025-08-14"
| # | property_id | current_price | original_price | currency | price_per_sqm | maintenance_fees |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Amenities & Features objects from properati.com. All fields typed and schema-versioned.
"property_id": "PRP-849201", "has_pool": true, "has_gym": false, "parking_spaces": 1, "security_24h": true, "balcony": true, "elevator": true, "pet_friendly": true, "year_built": 2015
| # | property_id | has_pool | has_gym | parking_spaces | security_24h | balcony |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agency & Broker Data objects from properati.com. All fields typed and schema-versioned.
"agency_id": "AGE-9921", "agency_name": "Remax Palermo", "agent_name": "Carlos Gomez", "contact_phone": "+541145551234", "whatsapp_number": "+5491145551234", "agency_url": "https://www.properati.com.ar/inmobiliarias/remax-palermo", "total_listings": 142
| # | agency_id | agency_name | agent_name | contact_phone | whatsapp_number | agency_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Properati scraper handles every layer of the platform: property details, map based spatial coordinates, agency intelligence, and pricing histories with JavaScript rendering and anti bot circumvention built in.
Title, description, surface area, rooms, bathrooms, and every metadata field Properati surfaces extracted at the listing level.
Capture current price, original listing price, maintenance fees, and calculate price per square metre timestamped per crawl.
Extract precise latitude and longitude coordinates, neighbourhood boundaries, and city normalisation for spatial analysis.
Structured extraction of property features including pools, gyms, parking spaces, security, and pet policies.
Agency name, agent contact details, WhatsApp numbers, and total active listings for competitor analysis.
Properati Argentina, Colombia, Peru, Ecuador, and Uruguay all consolidated into a unified schema.
Handle mixed currency listings (USD, ARS, COP, PEN) with native extraction to support automated FX conversion downstream.
Filter and extract specific segments including retail spaces, offices, warehouses, or standard residential properties.
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 cities, neighbourhoods, property types, or agency IDs. We design the extraction schema together.
We configure Scrapy crawlers, Playwright renderers, LATAM proxy rotation, and session management for Properati domains.
Schema validation, null rate checks, coordinate boundary verification, and sample listings before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Properati employs map based pagination and regional bot protections. Here is how we stay resilient.
Properati restricts access from non LATAM data centres. Our crawlers use residential ISP proxies located in Argentina, Colombia, and Peru with realistic browser fingerprints to bypass regional blocks.
Properati relies on dynamic map interfaces for high density areas. We run full Playwright browser sessions to execute JavaScript, trigger map cluster expansion, and extract hidden listings that standard HTTP clients miss.
Property portals change DOM structures frequently. Our selector strategy uses fallback chains for critical fields like price and surface area so a layout update does not break your data pipeline.
For city wide 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, missing coordinates, schema drift, and coverage drops.
Real estate technology firms use historical pricing and spatial data to train Automated Valuation Models for LATAM markets.
Institutional investors compare rental yields against sale prices across specific neighbourhoods to identify high return assets.
Analysts monitor time on market and price drop frequencies to gauge real estate liquidity and macroeconomic health.
Brokerages track competitor listing volumes, exclusive mandates, and pricing strategies to optimise their market positioning.
Urban planners and researchers map property density and price per square metre to evaluate city development trends.
Economists track real estate pricing in USD versus local currencies (ARS, COP) to monitor inflation and currency devaluation impacts.
"Properati holds the definitive spatial and pricing record for LATAM real estate — but extracting it requires navigating aggressive bot mitigation and complex map-based pagination."
Most teams underestimate the investment required: reliable Properati scraping requires localised LATAM proxies, full JavaScript rendering for map clusters, and daily schema maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our properati.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 and deduplication. Playwright handles map rendering, cluster expansion, and interaction flows for complex spatial pagination.
We maintain pools of residential ISP proxies across Argentina, Colombia, Peru, and Ecuador to bypass strict regional geofencing and rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About properati.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings from Properati is generally permissible under applicable law. DataFlirt targets only public, non authenticated property and agency data. We do not extract personal user data or circumvent authentication walls.
We use LATAM based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass regional blocks and rate limits.
We support Properati Argentina, Colombia, Peru, Ecuador, and Uruguay. All data is normalised into a unified schema regardless of the source domain.
Full city or neighbourhood refreshes at daily cadence complete within a 6 to 12 hour window. Historical snapshots are available from the day your pipeline is commissioned.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time series table per property ID for price changes, currency shifts, and listing status from the date your pipeline starts.
Our smallest packages start at a defined geographic bounding box or city with weekly delivery. For national catalogues, we price based on volume and delivery frequency.
Properati limits standard list pagination. We utilise Playwright to interact with the map interface, zooming and panning across precise coordinate grids to trigger cluster expansion and extract all underlying listings.
Absolutely. We provide a sample run of up to 500 properties for a specific neighbourhood 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 city export or a continuous price monitoring feed across LATAM we scope, build, and operate the pipeline. Tell us what you need.
