🇮🇹 Italy Car Insurance Prices — RC Auto by Province & Region

The only ready-to-use dataset of Italian motor-insurance (RC Auto) prices broken down by province, region and macro-area — straight from the official IVASS regulator.

Get the average premium actually paid for car, motorcycle and moped insurance in every one of Italy's 107 provinces, with year-over-year change, price percentiles and contract volumes — refreshed every quarter. No quote forms, no personal data, no guesswork: just clean, structured, analysis-ready numbers.

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💡 Why this actor

Italian RC Auto pricing is famously territorial — a driver in Naples can pay €270+ more than the same driver in Aosta. Until now, that territorial price map lived inside PDF reports and multi-tab Excel files published by IVASS (the Italian insurance authority). This actor turns it into a single tidy dataset you can drop into a spreadsheet, BI tool, database or model in seconds.

• ✅ Official source — IVASS IPER survey (the same data ISTAT uses for the national CPI)
• ✅ Full territorial coverage — all 107 provinces + 20 regions + 5 macro-areas + national
• ✅ 3 vehicle types — cars (autovetture), motorcycles (motocicli), mopeds (ciclomotori)
• ✅ Rich fields — average premium, YoY %, price percentiles (5th–99th), contract counts, coefficient of variation
• ✅ Time series — pull the latest quarter or the entire history back to 2021
• ✅ Zero PII — reads published aggregate statistics only

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🎯 Who uses it & how

Use case	What you do with the data
Insurance brokers & agents	Benchmark your quotes against the true local market average; show clients how their province compares.
Insurtech & comparison sites	Power "average price in your area" widgets, lead-gen calculators and pricing pages with authoritative numbers.
Pricing & actuarial teams	Feed territorial baselines and percentiles into pricing models and competitiveness analyses.
Market researchers & analysts	Map regional price gaps, track YoY inflation in motor insurance, build reports and dashboards.
Data scientists / ML	Use province-level premiums + percentiles + contract volumes as features or training data.
Journalists & consumer associations	Source verifiable, citable figures on how much Italians pay for car insurance by area.
Fintech & lead generation	Geo-segment campaigns by price level (target high-premium provinces with savings offers).

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📦 What you get (output)

The actor outputs ~356 rows per quarter, organised on four levels via the level field.

provincia — the core: 107 provinces × 3 vehicle types

{
  "level": "provincia",
  "provincia": "Napoli",
  "sigla": "NA",
  "regione": "Campania",
  "macroarea": "Sud",
  "vehicleType": "autovetture",
  "premioMedio": 604.7,
  "variazioneAnnua": 0.6,
  "numContratti": 71190,
  "cv": 47.6,
  "p5": 277, "p10": 333.9, "p25": 435.5, "p50": 558.5, "p75": 723.9, "p95": 1147.8, "p99": 1731.4,
  "period": "2025-Q4",
  "periodType": "trimestre",
  "source": "IPER",
  "publicationDate": "2026-04-20",
  "sourceFile": "https://www.ivass.it/.../tavole_IV_trimestre.xlsx"
}

Field	Meaning
provincia / sigla	Province name and plate code (e.g. Napoli / NA)
regione / macroarea	Region and macro-area (Nord-Ovest, Nord-Est, Centro, Sud, Isole)
vehicleType	autovetture (cars), motocicli (motorcycles), ciclomotori (mopeds)
premioMedio	Average premium actually paid, in €
variazioneAnnua	Year-over-year change, %
numContratti	Number of contracts in the sample (sample size / weight)
cv	Coefficient of variation, % (price dispersion)
p5–p99	Price percentiles, € (5th / 10th / 25th / 50th-median / 75th / 95th / 99th)
period	Reference quarter, e.g. 2025-Q4

regione, macroarea, nazionale — ready aggregates

• regione — official IVASS contract-weighted regional average (cars).
• macroarea — Nord-Ovest / Nord-Est / Centro / Sud / Isole, contract-weighted from province data (all 3 vehicle types), with premioMin / premioMax.
• nazionale — national contract-weighted average per vehicle type. (Sanity check: cars ≈ €432–437, matching the official IVASS headline.)

Aggregates are weighted by numContratti, reproducing the IVASS methodology — not a naive province mean.

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⚙️ Input — quick start

The defaults already give you the latest quarter, all vehicle types, all territorial levels. Just hit Start.

{
  "source": "iper",
  "mode": "latest",
  "maxPublications": 1,
  "vehicleTypes": ["autovetture", "motocicli", "ciclomotori"]
}

Want the full history (every quarter since 2021)?

{ "source": "iper", "mode": "all_available" }

Want a specific span of years?

{ "source": "iper", "mode": "year_range", "yearFrom": 2023, "yearTo": 2025 }

Option	What it does
mode	latest (most recent period) · year_range · all_available (since 2021)
maxPublications	How many recent periods to pull in latest mode
vehicleTypes	Pick cars / motorcycles / mopeds
includeProvinceData	Per-province rows (on by default)
includeRegionalAggregates	Official regional averages
includeMacroareaAggregates	Nord/Centro/Sud/Isole averages
includeNationalAggregate	National average
proxyConfiguration	Italian proxy recommended (default)

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📤 Exporting

From the run's Output tab, export to Excel, CSV, JSON, or via API — or connect it to Google Sheets / your database with Apify integrations. Schedule it quarterly to keep a fresh price feed automatically.

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📑 Source & terms

Data originates from IVASS (Istituto per la Vigilanza sulle Assicurazioni), Indagine IPER — the official survey of effective RC Auto prices, published openly on ivass.it.

This actor automates the retrieval and structuring of public statistical data; it does not bypass authentication and submits no personal data. Any onward use or redistribution of the retrieved data is the user's responsibility — always cite the source ("Fonte: IVASS — Indagine IPER"), and for commercial reuse check IVASS's reuse terms / request authorisation where required.

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🛠️ Tech & local dev

Node.js 20 · Apify SDK 3 · Crawlee (CheerioCrawler) · SheetJS · cheerio · got-scraping.

npm install
npm run test:parser   # validates parsing + aggregation

Questions or a custom breakdown (extra fields, other IVASS tables, weekly schedule)? Open an issue or contact the author.