🚗 European Used Car Price Scraper

Context & Problem

As a data analyst working with B2B used car transaction data, I realized we lacked visibility into the B2C side of the market. Understanding consumer-facing price trends across European countries could bridge that gap — and help explain price dynamics in wholesale auctions.

Solution & Action

To address this, I built a Python-based web scraper targeting AutoScout24, one of the largest online marketplaces for used cars in Europe. The script collects structured data including:

• Make, model, fuel type, transmission
• First registration, mileage, and price
• Geo-location (country, city, ZIP)
• Scraping timestamp

It loops through listings from 🇧🇪 Belgium, 🇳🇱 Netherlands, 🇩🇪 Germany, 🇫🇷 France, and 🇮🇹 Italy — collecting up to 50 pages per country, and appends new results to a local CSV file (used_car_prices.csv), maintaining a historical data trail.

Result

This tool enables continuous monitoring of B2C pricing trends. By aligning them with B2B auction price data, we can:

• Identify pricing deltas between market segments
• Detect seasonal or geographic trends
• Support pricing strategy, inventory decisions, and market forecasting

✅ Built with requests, BeautifulSoup, and pandas
✅ Ready for scheduled automation via task schedulers (e.g. Windows Task Scheduler or cron)

📁 Sample Output Columns

📂 View on GitHub

View the Python script on GitHub: 🔗 used_car_price.py on GitHub