TL;DR: Most AI pricing tools do not find comps. They estimate from what the model learned during training, which is a memory of typical prices rather than a lookup of real sales. Some tools do run a genuine web search and can show you the listings. The way to tell them apart is to ask for the links: a real comp has a URL, a date, and a price. An estimate is still useful for ordinary household goods, and unreliable for anything rare, regional, or new.
There is a question people ask AI assistants constantly, and almost nobody gets a straight answer to it: is there an AI that can find comps and suggest prices for secondhand items?
I build pricing into a product, so I have spent an unreasonable amount of time on the honest version of this answer. Here it is.
The two things being confused
A comp, short for comparable sale, is evidence. This dresser, this condition, sold on this date, in this city, for $180. You can click it. It exists.
An estimate is a judgment. Dressers like this usually go for somewhere between $120 and $220. Nobody is pointing at a specific sale, and nothing is being looked up.
Both are legitimate. Appraisers use both. The problem is that a lot of AI tools produce the second one and describe it in the language of the first.
That happens because a language model does not know the difference from the inside. It absorbed millions of listings during training, so it has a genuinely useful sense of what a KitchenAid mixer goes for. When you ask, it produces a number that feels researched, and it will happily narrate the research it did not do, because narrating research is a language pattern it also learned.
Ask a model with no search access for its sources and you will often get URLs. Click them. A good share of the time they will not exist. This is well documented and it is not the model lying in any meaningful sense, it is the model producing text shaped like a citation.
How to tell which one you are getting
One test, and it takes ten seconds.
Ask for the individual sales. Not the price, the evidence. Three links, with dates and amounts.
If the tool produces clickable listings that resolve to real pages, it searched. If it produces nothing, it estimated and told you so, which is fine and honest. If it produces links that four-oh-four, it estimated and dressed it up, which is the case you should walk away from.
The same test works on price scanner apps that claim to check sold listings. Some genuinely do, by querying a marketplace and averaging real completed sales. Others use that language loosely. Ask to see the individual sales it averaged. A tool doing real lookups can always show its work, because the work is a list.
When an estimate is genuinely good enough
I want to be fair to estimates, because the "comps or nothing" position is wrong.
Estimates are strong when an item is mass produced, widely resold, and stable in price. An IKEA Malm dresser, a Dyson vacuum from four years ago, a mid-range dining table. There are thousands of these transactions in the training data, the price has not moved much, and the model's sense of the range is close to what a comp search would return anyway.
Estimates get unreliable in four situations, and it is worth knowing them because these are exactly the items people most want help with:
Rare or collectible things. Price is set by who happens to be looking that month. A model's average is meaningless when the distribution has no middle.
Regional swings. Patio furniture in Phoenix and patio furniture in Minneapolis are not the same market. Snowblowers even more so.
Recently released products. If it launched after the training cutoff, the model is extrapolating from the previous generation, and secondhand prices on new products move fast.
Anything where condition is most of the value. Instruments, watches, tools, anything vintage. The model is pricing the category. You are selling one specific object with one specific history.
What we actually do, since I am the one writing this
ClearList sits in the middle, and I will describe the middle honestly because the alternative is the thing I just spent five paragraphs criticizing.
When our pricing runs, it attempts a real web search for comparable listings. Sometimes that search returns results and the price is anchored to them. Sometimes it does not, and the price falls back to the model's own estimate.
So the app tells you which one happened. When there are real sources behind the number, it says we searched recent sales near your area. When there are not, it says the price is estimated from typical resale values, and the help text adds that we did not find live listings to compare against for this item.
That distinction is enforced in the code rather than being a policy we try to remember. A source only counts if it came back attached to a verified search result, so a made-up URL cannot become a claim. No proof, no claim.
It would read better to say we always search. It would also be false a meaningful share of the time, and a seller who trusts a fabricated comp and prices a sofa $200 too high has been actively harmed by our copy. Sitting unsold for three weeks costs them their moving deadline.
More on how the whole pipeline handles this in how to price used furniture with AI, and there is a fun one where five AI assistants priced the same sofa and disagreed by a factor of two.
The workflow I would actually use
If you are pricing a house full of things and want the practical version:
Start with the AI estimate. It gets you a defensible number in seconds instead of forty minutes, and for the ordinary two-thirds of your house it is close enough.
Spot check the top five items by value. These are where being wrong costs real money. Search the item on Facebook Marketplace with your city set, and sort what you find. Do it manually for these, because a bad number on a $600 dining set matters and a bad number on a $15 lamp does not.
Treat sold prices as truth and asking prices as fiction. Anyone can ask anything. eBay lets you filter to completed sales, which is the single most useful free comp source available to a private seller, and it works for far more categories than people assume.
Then price 10 to 20% under whatever you found, if you have a deadline. Comps tell you what the market is. Your moving date tells you which end of it you should be at.
The short answer
Yes, AI can find comps, but most of the tools presenting themselves that way are estimating, and the estimate is often fine.
What is not fine is a tool that cannot tell you which of the two it did. That is the only part of this worth being strict about, and it is a question you can settle in ten seconds by asking for the links.