We extract property listings, pricing signals, rental yields, agency intelligence, and amenity data from Infocasas.uy. 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 Sale Listings objects from infocasas.com.uy. All fields typed and schema-versioned.
"property_id": "18947264", "title": "Apartamento 2 Dormitorios en Pocitos", "property_type": "Apartamento", "price": 215000.0, "currency": "USD", "bedrooms": 2, "total_area_sqm": 75.0, "neighbourhood": "Pocitos"
| # | property_id | url | title | property_type | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Rental Listings objects from infocasas.com.uy. All fields typed and schema-versioned.
"property_id": "19938271", "title": "Alquiler Casa 3 Dormitorios Carrasco", "monthly_rent": 85000.0, "common_expenses": 0.0, "currency": "UYU", "bedrooms": 3, "guarantees_accepted": "['Porto Seguro', 'SURA', 'ANDA']", "neighbourhood": "Carrasco"
| # | property_id | url | title | property_type | monthly_rent | common_expenses |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agency Data objects from infocasas.com.uy. All fields typed and schema-versioned.
"agency_id": "ag-4829", "name": "Kosak Inversiones Inmobiliarias", "active_listings_count": 412, "phone_number": "+598 2902 4111", "properties_for_sale": 310, "properties_for_rent": 102, "website": "kosak.com.uy"
| # | agency_id | name | profile_url | active_listings_count | address | phone_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for New Developments objects from infocasas.com.uy. All fields typed and schema-versioned.
"project_id": "proy-992", "name": "Nostrum Bay", "developer_name": "Altius Group", "status": "En Construcción", "delivery_date": "2026-12", "min_price": 115000.0, "currency": "USD"
| # | project_id | name | developer_name | status | delivery_date | min_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Amenities objects from infocasas.com.uy. All fields typed and schema-versioned.
"property_id": "18947264", "has_garage": true, "has_bbq": false, "has_balcony": true, "security_24h": true, "heating_type": "Losa Radiante", "year_built": 2018
| # | property_id | has_pool | has_garage | has_bbq | has_balcony | year_built |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Infocasas.uy pipeline handles property listings, dynamic map-based searches, and broker directories with built-in normalisation for Uruguayan real estate metrics.
Extract title, description, price, area, bedrooms, bathrooms, and all associated metadata for every property on the market.
Capture sale prices, monthly rental rates, and common expenses (gastos comunes) across UYU and USD currencies.
Extract precise latitude and longitude coordinates, neighbourhood classifications, and city data for spatial analysis.
Track off-plan and under-construction projects, including developer details, delivery timelines, and unit availability.
Collect agency names, contact numbers, active listing counts, and physical addresses to map the broker landscape.
Parse unstructured descriptions into boolean fields for garages, pools, 24-hour security, and heating types.
Capture high-resolution image URLs, floor plan links, and virtual tour endpoints for visual machine learning models.
Track daily price drops, new listings, and de-listed properties without re-scraping the entire historical catalogue.
Run extractions daily, weekly, or monthly depending on your market monitoring requirements.
Brief in. Clean data out.
Provide target cities, property types, or specific agencies. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for infocasas.com.uy.
Schema validation, null-rate checks, and currency normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Real estate platforms present unique technical hurdles. Here is how our infrastructure maintains reliable data flow.
Infocasas uses dynamic map loads and cursor-based pagination that standard HTTP clients fail to traverse. We use Playwright to execute JavaScript, interact with map boundaries, and reliably page through deep search results.
Property data in Uruguay mixes USD and UYU, while area metrics fluctuate between square metres and hectares. Our pipeline normalises these fields into consistent numeric formats for immediate database ingestion.
To prevent IP bans during high-volume extractions, we route traffic through LATAM-based residential proxies with request pacing modelled on human browsing behaviour.
Real estate portals frequently update their UI. We employ multi-layer fallback selectors using CSS, XPath, and JSON-LD structured data to ensure pipeline stability when visual layouts change.
We maintain a stateful index of all known property IDs. When a listing disappears from search results, it is flagged as inactive rather than deleted, allowing you to track time-on-market metrics.
Proptech companies use historical sale prices and property attributes to train machine learning appraisal models.
Real estate funds cross-reference sale prices with rental rates to calculate gross rental yields across different neighbourhoods.
Brokerages monitor competitor inventory, pricing strategies, and time-on-market metrics to optimise their own operations.
Urban planners and economists track housing supply, price per square metre trends, and new development density.
B2B service providers extract agency contact details and new project announcements to build targeted sales pipelines.
Property managers adjust rental asking prices based on real-time comparable listings in the immediate vicinity.
"Infocasas.uy holds the most comprehensive real estate inventory in Uruguay, but extracting structured property data requires purpose-built infrastructure."
Most engineering teams underestimate the complexity of scraping real estate portals. Dynamic map loads, paginated search results, and inconsistent broker formatting require continuous schema maintenance. DataFlirt handles the extraction pipeline so your team can focus on pricing models and market analysis.
Everything supported by our infocasas.com.uy 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 retry logic. Playwright executes JavaScript to interact with map elements and dynamic content. Combined via scrapy-playwright middleware.
We maintain pools of LATAM residential ISP proxies to avoid geographic blocking. Rotation happens per-request with sticky sessions where required.
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 infocasas.com.uy scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible. DataFlirt targets only public, non-authenticated property data. We do not extract personal user data or circumvent authentication walls. Clients should review platform terms of service and consult legal counsel for specific use cases.
We utilise headless browsers via Playwright to interact with map boundaries and trigger the underlying API requests, ensuring complete capture of properties within a specified geographic polygon.
Pipelines can be configured for daily or weekly runs. A full sweep of active listings on infocasas.com.uy typically completes within 4 to 8 hours depending on the required depth of extraction.
Yes. Every pipeline run produces timestamped snapshots. We maintain a stateful database of property IDs, allowing us to flag price drops, increases, and time-on-market metrics.
We extract all high-resolution image URLs associated with a listing. We do not download the binary image files to our servers by default, but we provide the direct links for your systems to ingest.
Our minimum engagement starts with a defined geographic scope or property type with weekly delivery. For full-country daily extractions, we price based on compute volume and delivery frequency.
Yes. We apply regex patterns and natural language parsing to unstructured description text to populate boolean fields for common amenities like garages, pools, and security.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous market monitoring feed across Uruguay, we scope, build, and operate the pipeline. Tell us what you need.
