Comp quality
What makes a good sold comp
A good comp is a sold listing that matches your item on five points: the exact product and generation, the condition band, completeness, size or variant, and recency. Miss one and the median of the set is precise and wrong.
Two listings can share a brand, a model name and a photograph and still be different products with different markets. Comp selection is where pricing is won or lost.
What makes a sold listing a good comp?
- Identity. The same product, not a similar one. Generations, model years, regional variants and reissues share names and do not share prices.
- Condition. Same band. New with tags, new without, excellent, used, and for-parts are separate markets, not points on one scale.
- Completeness. Box, papers, accessories, cables, dust bag, original packaging. On many items this is a large fraction of the value.
- Size or variant. Common sizes and unusual ones frequently sell at different prices for the same garment or shoe.
- Recency. Prices move. A sale from two years ago is history rather than a comp, and in fast-moving categories even a few months matters.
Which sold comps look right but aren't?
| Looks like | What it is | Effect on your price |
|---|---|---|
| Same model name | A different generation or model year | Can be wrong by a multiple, in either direction |
| Same item | A bundle or lot sale | Inflates, because the buyer paid for several things |
| A sale | A best-offer accepted well below the listed price | Depends on which figure you read. Read what was paid |
| Same condition | Described as excellent, photographed as rough | Inflates your expectation for a worse item |
| Same product | A different regional version | Different demand, different market, often different voltage or sizing |
| A real sale | A relisted item that never shipped | Rare, but it happens, and it tends to sit at the top of the range |
Why open the highest and lowest comps first?
Open the highest and lowest comps in your set before trusting either. Thirty seconds each. You are looking for the explanation, and there usually is one: a lot of six, a rare colorway, a damaged item sold for parts, an exceptional set of photographs.
Opening the extreme comps does more for pricing accuracy than any amount of arithmetic, because it turns an outlier from noise into information.
Why are my sold comps so different from each other?
A wide spread is itself a finding. It usually means one of three things:
- Your set is mixed. Two different products, or two condition bands, have been collected together. Split them and price against the right half.
- Presentation is driving price. In categories where photographs and description carry a lot of weight, the spread reflects seller effort rather than the item. That is an opportunity if you photograph well.
- The market is thin. Few buyers, sporadic sales, and price depends heavily on who is looking that week. Handle it differently.
Why does identifying the item come first?
Wrong identification poisons a comp set: if you have the wrong item, every later step is wasted precision. Model numbers, tags, serial plates, and the details printed on the object beat visual similarity every time.
Time spent identifying the item exactly is worth more than time spent averaging prices for the wrong one.
What makes a good sold comp?
Why are my sold comps so different from each other?
Should I include bundle sales as comps?
How recent do sold comps need to be?
Disclosure What's It Sold For comes from the team that builds FlowLister, an AI tool that turns your photos into a complete eBay listing and prices it from sold listings, with the sales behind the number shown. It's AI and it misses things, which is why every listing waits for your OK before it goes up on eBay. Read this site as a guide from a tool maker with a stake in the topic, not an independent review.
Keep reading
How to see sold items on eBay, and read what you find
The Sold filter on the website and app, the 90-day limit, Product Research, and what the list hides.
How to price a used item from what it sold for
Build a comp set, trim the outliers, pick a number you can defend.