We extract vehicle listings, price adjustments, dealer inventories, and technical specifications from automobile.it. 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 Vehicle Listings objects from automobile.it. All fields typed and schema-versioned.
"listing_id": "31459281", "make": "Fiat", "model": "Panda", "price": 12500.0, "mileage_km": 45000, "registration_year": 2021, "fuel_type": "Hybrid", "seller_type": "Dealer"
| # | listing_id | make | model | version | price | mileage_km |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technical Specs objects from automobile.it. All fields typed and schema-versioned.
"listing_id": "31459281", "body_type": "City car", "doors": 5, "emission_class": "Euro 6d-TEMP", "co2_emissions_g_km": 89, "engine_capacity_cc": 999, "drivetrain": "Front", "empty_weight_kg": 1055
| # | listing_id | body_type | doors | seats | emission_class | co2_emissions_g_km |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dealer Data objects from automobile.it. All fields typed and schema-versioned.
"dealer_id": "D-84729", "dealer_name": "Auto Roma Nord SRL", "city": "Rome", "region": "Lazio", "inventory_count": 142, "dealer_rating": 4.7, "joined_date": "2015-03-12"
| # | dealer_id | dealer_name | address | city | region | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & History objects from automobile.it. All fields typed and schema-versioned.
"listing_id": "31459281", "current_price": 12500.0, "original_price": 13200.0, "discount_pct": 5.3, "valuation_rating": "Great Price", "financing_available": true, "monthly_installment": 185.0, "vat_deductible": false
| # | listing_id | current_price | original_price | discount_pct | price_history_array | valuation_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Features & Equipment objects from automobile.it. All fields typed and schema-versioned.
"listing_id": "31459281", "exterior_colour": "White", "interior_material": "Cloth", "alloy_wheels": true, "parking_sensors": "Rear", "climate_control": "Manual", "bluetooth": true
| # | listing_id | exterior_colour | interior_colour | interior_material | alloy_wheels | navigation_system |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Automobile.it scraper handles every layer of the platform: individual listings, dynamic pricing, dealer inventories, and technical specifications, with JavaScript rendering and anti-bot circumvention built in.
Make, model, version, mileage, registration date, fuel type, and transmission extracted accurately from every vehicle page.
Capture current price, original listing price, dealer discounts, and monthly financing installments timestamped per crawl.
Extract dealer name, location, contact details, inventory size, and aggregate ratings across the entire platform.
Parse detailed engine metrics, drivetrain layouts, weight, dimensions, and performance statistics for market analysis.
Map vehicle availability and pricing variations across different Italian regions, provinces, and specific postal codes.
Extract high-resolution image URLs and gallery metadata for computer vision models and visual cataloguing.
Structure unstructured feature lists into boolean fields for alloy wheels, navigation, parking sensors, and climate control.
Track Euro emission classes and CO2 output metrics to monitor compliance with regional urban access restrictions.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide target makes, models, regions, or dealer IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for automobile.it.
Schema validation, null-rate checks, price-outlier detection, and sample records before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Classified sites invest heavily in scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Automobile.it employs bot detection based on request patterns and IP reputation. Our crawlers use Italian residential ISP proxies with realistic browser fingerprints and randomised request timing trained on real user behaviour.
Vehicle detail pages and dealer inventories rely on JavaScript for rendering image galleries and contact reveals. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
DOM structures on classified sites change frequently. Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and JSON-LD extraction, ensuring layout changes do not break your pipeline.
For large vehicle catalogues, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute cost and downstream processing load. You receive a clean changelog.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, inventory drops, and schema drift, responding before you notice. SLA uptime is contractual.
Dealerships and valuation algorithms consume real-time pricing data to adjust their own inventory prices against market averages.
Automotive OEMs and competing dealer networks track competitor stock levels, days on market, and discount strategies.
Analysts track the transition to EV and hybrid vehicles by monitoring fuel type ratios and emission class distribution across regions.
Insurance companies and warranty providers identify high-volume dealerships to target with B2B partnership offerings.
Machine learning teams use structured vehicle specifications and image URLs to train computer vision models for damage detection and vehicle recognition.
Rental and leasing companies analyse residual values of specific makes and models based on secondary market pricing trends.
"Automobile.it holds the pulse of the Italian automotive market, but extracting structured pricing and inventory data requires dedicated infrastructure."
Most teams underestimate the investment required: reliable scraping requires Italian residential proxies, full JavaScript rendering, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our automobile.it 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 localised to Italy. 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 is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About automobile.it scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from classified sites is generally permissible under applicable law, provided it targets public, non-authenticated vehicle and dealer data. DataFlirt does 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 use Italian residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains, and we monitor for blocking rate spikes in real time to trigger pool rotation automatically.
Full catalogue refreshes at daily cadence complete within a defined execution window. For targeted subsets, such as specific dealer inventories or high-value models, we can configure higher frequency runs.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per listing for price adjustments and days on market from the date your pipeline starts.
Our smallest packages start at a defined query set or dealer list with weekly delivery. For full-site extraction or custom schema requirements, we price based on volume and delivery frequency.
Yes, we extract publicly listed dealership names, addresses, phone numbers, and aggregate ratings available on the dealer profile pages.
Absolutely. We provide a sample run of up to 500 vehicle listings as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off vehicle catalogue dump or a continuous price-monitoring feed across 300K listings, we scope, build, and operate the pipeline. Tell us what you need.
