Can you build a used car valuation product from Autotrader (or any other online classifieds) data?

Hmm .. Interesting that you say your sector isn't being addressed. Wonder why ... But if so, then that's an opportunity.

I've spent the past 4 years specializing in probably THE hardest market for making accurate appraisals: the domain name industry. For me as an engineer, that has meant crunching numbers. So I'm intimately familiar with the issues involved in making inferences about value from partial list-price and sale-price data.

In fact, I spend much of each day compiling raw data and building database-driven tools to organize and analyze it.

So I don't know the auto industry at all, but I know a thing or 2 about figuring out the value of assets in obscure areas.

Certainly a model can be constructed for extrapolating value from list prices. It only remains to be determined how wide the margin of error would be and what kind of latent bias might exist.

If you'd like my help, just ask.

Yes, I can help you build a used car valuation product using data from sources like Autotrader, Kelley Blue Book (KBB), Cars.com, CarGurus, etc.

Here’s how we can approach it:

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Goal:

Create a web-based or API-based product that provides estimated value for used cars based on real-time market data.

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Core Features:
1. User Input:
• Make, model, year
• Mileage
• Trim, condition
• Zip code or location
2. Data Collection (Scraping/API):
• Scrape real-time listings from Autotrader or other sites (unless APIs are available)
• Clean and aggregate data
3. Valuation Engine:
• Use regression or machine learning models to estimate value
• Optionally compare to official sources (KBB, NADA, Edmunds)
4. Interface:
• Simple web UI (Bubble, Webflow, or code-based)
• Or an API for integration into other tools
5. Optional Enhancements:
• Price trends over time
• Sellability score
• Alerts for deals on similar cars

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Tech Stack (Options):
• Frontend/UI: Bubble, React, Next.js
• Backend/API: Python (FastAPI), Node.js
• Scraping: BeautifulSoup, Selenium, Playwright (or Scrapy)
• ML/Valuation: Scikit-learn or OpenAI fine-tuned model
• Database: PostgreSQL, Firebase, or MongoDB

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Important Notes:
• Legal/Compliance: Web scraping from sites like Autotrader can violate terms of service. It’s better to use official APIs if available (e.g., KBB API, Carfax API).
• Rate Limits: If scraping, you’ll need rotating proxies and anti-bot strategies.

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Would you like to:
1. Build a quick MVP using scraping?
2. Use legitimate API access (if you have or want to apply for it)?
3. Build just the valuation engine and UI without scraping?

Let me know your preferred direction and budget/tech preference, and I’ll map out a full plan or even build you a starter prototype.