Ask ten domain investors what a name is worth and you will get ten numbers. Ask what it sold for and there is only one. That gap, between an estimated value and the price a buyer actually paid, is where most domain portfolios make or lose money.
To see how wide the gap really is, we took 200 domains at random from the NetMark comparable-sales database, appraised each one with a simple formula, and then checked the results against two data-driven methods built on hundreds of thousands of recorded sales. The results say a lot about how domain prices are actually set.
How we tested
We drew 200 sales at random from NetMark’s comparable-sales database: every name sold for at least $100 between January 2023 and October 2026, across all extensions and sales venues. Each name was then valued three ways.
- A formula appraisal (the Math model). It scores the name on visible traits such as extension, length, hyphens, numbers and dictionary words, then maps the score to a price range. This is how most instant online appraisal tools work.
- Comparable sales. NetMark’s comps engine looks for recent sales of similar names in the same extension and category. The name’s own sale was hidden from it, so it had to price the name blind.
- The NetMark Intelligence model. A statistical model trained on about 479,000 recorded sales. It was asked for a price as of the day before each sale, using only data available then, and for the right market: end-user retail or investor auction.
We compared each estimate with the real sale price. Because domain prices span from $100 to millions, we measured the error as a ratio, not in dollars: an estimate of $2,000 for a $1,000 sale is “2x high”.
What the numbers showed
The formula was the least reliable way to price a name: it came within 2x of the real price only 32% of the time. The median sale in the sample was $3,217.
| Method | Names priced | Within 2x of sale price | Typical estimate vs. price |
|---|---|---|---|
| Formula (Math model) | 200 | 32% | 35% of the price |
| Comparable sales | 107 | 50% | 77% of the price |
| NetMark Intelligence | 200 | 70% | 113% of the price |
Three findings stand out.
- The formula prices names in a few fixed boxes. It gave 103 of the 200 names the same $1,000–$3,000 range. Those 103 names actually sold for anything from $105 to $600,000. The real price fell inside the formula’s range only 27% of the time.
- Extensions outside .com break the formula. For the 75 non-.com sales it was never within 2x. It valued stocks.si at $24; it sold for $25,000. The two data-driven methods handled these names about as well as .com.
- Comparable sales are only as good as the comparables. The comps engine found no trustworthy match for 88 of the 125 .com names and declined to guess. When it did match, half its estimates were within 2x.
The chart shows the most important pattern. Every method overvalued the cheapest sales and undervalued the most expensive ones. The data models were close in the middle of the market, where most sales happen: NetMark Intelligence came within 2x on 97% of the $2,500–$5,000 sales.
Why value and price drift apart
A domain does not have one value. It has a likely price in a given market, on a given day, to a given buyer. Four forces explain most of the gap we measured.
- The market decides the price. The same kind of name sells for very different amounts depending on where it sells. In our sample, expired-domain auctions closed at a median of $305, while end-user sales on marketplaces like Sedo and Afternic had a median of $3,550. The formula gave the expired names a typical estimate of $1,732, five times what they fetched.
- The top of the market is driven by single buyers. socrates.com sold for $600,000 and liquid.org for $300,000. No model priced either above $80,000. A six-figure sale usually means one buyer needed that exact name, and no pattern in past sales can predict that buyer.
- Surface traits miss meaning. A formula sees that win.ai and gats.io are short names on popular extensions. It cannot see that one is a common word with obvious buyers. Models built on real sales pick up demand for words, categories and extensions that a points system never captures.
- Every estimate leans toward the average. Models learn from the bulk of the market, which sits between $1,000 and $5,000. Pulled toward that middle, they overvalue the rare cheap sale and undervalue the rare expensive one. That is the pattern in the chart.
What it means for buyers and sellers
Treat any single appraisal as a starting point, not a price tag. The practical lessons from these 200 sales:
- Ignore formula appraisals for pricing decisions. They are fine for sorting a list, but in this test they were within 2x of the real price less than a third of the time.
- Price by market, not by name alone. Decide first whether you are selling to an end user or to investors. The same name can be worth ten times more on a marketplace listing than at an expired auction.
- Check more than one data source. Recent comparable sales and a sales-trained model together cover more names than either alone. When they disagree badly, the name deserves a closer look.
- Expect the extremes to surprise you. Below $1,000, models are often too optimistic. Above $20,000, they are almost always too low. Great names are worth testing with a higher asking price, because the buyer who needs them is not in anyone’s model.
The bottom line: a domain’s value is an estimate of where the market is likely to settle. Its sale price is where one buyer and one seller actually met. Good data narrows the gap between the two, but it never closes it. That gap is where careful pricing pays off.
