The task
The buyback company needed an objective estimate of the market value of used cars. That required a large, clean database of listings with specifications and prices — as a training and reference dataset for the valuation system.
What we did and what data we collected
We set up classified listings collection for car sales across several platforms: make, model, generation and trim, year, mileage, condition, region, price and date. The data was mapped to unified dictionaries and cleaned to remove anomalies, producing a dataset ready for analytics and model training. Collection ran regularly to keep the market picture current.
Challenges and how we solved them
First, duplicates and reposts. One car is listed on several platforms and more than once. We deduplicated listings — otherwise popular models would skew the statistics.
Second, inconsistent specifications. Make, model, generation and trim are written differently everywhere. We mapped them to unified dictionaries.
Third, junk prices and fake listings (bait pricing, resellers). We filtered out the outliers so valuation would rest on real deals rather than anomalies. Some platforms restrict automated collection — we worked carefully, at a sensible frequency and with proxies.
The result
The client received a regularly updated, cleaned database of listings with comparable specifications — a reliable foundation for the buyback valuation system.
Services in this case study: Classified Ads Scraping · Data for AI/ML · Marketplace Price Monitoring
Useful reading: Data normalization · Scraping dynamic sites · Web scraping proxies