Understanding Your AI Price Estimate

Every item you analyze on LegacyLoop receives a detailed pricing breakdown powered by AI and real market data. Here is what each part means and how to use it.

Local Pickup Price: This is what your item is likely to sell for to someone in your area. It factors in your zip code's market demand. High-demand areas like Portland, Boston, or New York see higher local prices, while rural areas may see a discount of 10-30 percent compared to the national average.

Ship Nationwide Price: This is the price you can expect when selling to buyers anywhere in the country via shipping. It removes the local demand factor and reflects the full national market. Shipping nationwide almost always gets you a higher price, though you need to subtract shipping costs and our 10 percent platform fee.

Best Market: LegacyLoop identifies the metro area where your item would fetch the highest price. For example, mid-century furniture sells best in Brooklyn and San Francisco, while vintage tools do well in the Midwest.

Confidence Score: Every estimate includes a confidence percentage from 1 to 100. Scores above 75 mean the AI found strong comparable sales data. Scores below 50 mean the item is unusual and you may want additional analysis. Confidence is based on how many similar items have recently sold at known prices.

MegaBot Consensus: For 5 credits, you can run MegaBot — our multi-AI system. It sends your item to four separate AI models (OpenAI, Claude, Gemini, and Grok) simultaneously. Each model independently estimates the value. MegaBot then compares all four results and produces a consensus price. If all four agree within a tight range, you can be very confident in the estimate. If they disagree, MegaBot flags the item for further review.

Market Comps: When available, the system pulls recent sold listings from eBay and other marketplaces to anchor the AI estimate in real transaction data. These comparable sales are shown on your item page so you can verify the pricing yourself.

Tip: If the confidence score is low, add more photos showing labels, markings, or condition details. The AI improves dramatically with better visual information.