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Comparable-Sales Appraisal: How to Value a Domain From Real Sales Data
A comparable-sales appraisal values a domain the way an estate agent values a house: not by an algorithm, but by what near-identical domains sold for in the open market. You gather verified sales of names that match on extension, length, keyword, and recency, read the price range they cluster in, and adjust up or down to the name in front of you.
It is the one valuation method built on real transactions instead of a formula, which is why brokers and investors treat it as the benchmark every automated tool gets measured against. This guide runs the full method end to end, with a worked example, the adjustment ladder, and a consolidated checklist of the comp-selection mistakes that produce a number nobody will pay.
It also adds the dimension every generic appraisal guide skips. An aged or expired domain is not just a string of letters. It carries inherited backlinks, registration age, and ranking history, and a comp that ignores those is comping the wrong asset. SEO Domains operates the curated marketplace where that comparable-sales work is already run before a name is priced, so the figure on the listing reflects real sales data instead of a guess.
What a comparable-sales appraisal is, and why it beats automated tools
A comparable-sales appraisal estimates a domain’s value from the recorded prices of similar domains that have already sold. You select a cluster of genuine comps, read the price range they occupy, and adjust it to the target name. The method rests on real transactions, which is the reason it is treated as more reliable than the formula-driven estimates an automated appraisal tool returns.
The logic is borrowed wholesale from property valuation. A surveyor does not guess a house price from a rule about square footage. They pull what three or four near-identical houses on the same street sold for last quarter and anchor to that. A domain comp appraisal works the same way, with sold domains standing in for sold houses.
Comparable sales versus automated appraisal tools
Automated tools such as GoDaddy’s appraisal, EstiBot, and HumbleWorth read a domain through a model: keyword search volume, character length, extension, and a regression trained on past data. They return an instant number, which is their appeal. The catch is the spread. The guide from graburl, summarising professional method, reports automated accuracy bands of roughly plus or minus 50 percent for GoDaddy and plus or minus 30 percent for EstiBot, and treats their output as a starting point, not a final value.
A comparable-sales appraisal narrows that spread because it is anchored to prices buyers paid, not to a model’s prediction of what they will pay. The trade-off is labour. The method takes time, judgement, and access to a sales database, where an automated tool takes a single click. The deeper head-to-head between the two approaches lives in automated vs manual domain appraisal.
Where it sits among valuation methods
Comparable sales is one of four recognised approaches, alongside income valuation for a domain with revenue, cost-based valuation, and brandability scoring. It is the method of record for the open aftermarket because the typical domain has no revenue to capitalise and no obvious replacement cost. For the full set of inputs that feed any valuation, see domain valuation: factors and process.
Where verified domain comps actually live: NameBio, DNJournal, Sedo
Verified comps come from public sales databases, not from asking prices. NameBio is the largest searchable archive of recorded domain sales, DNJournal publishes the weekly high-end sales chart, and marketplaces such as Sedo and the GoDaddy aftermarket disclose closed transactions. The quality of an appraisal is capped by the quality and recency of the comps these sources hold.
NameBio: the primary comp database
NameBio is the reference every serious appraiser opens first. It is a searchable archive of historical domain sales spanning millions of recorded transactions, filterable by extension, keyword, length, price, and sale date. The filter that matters first is date, because the aftermarket moves, and a sale from five years ago describes a market that no longer exists.
The discipline NameBio enforces is simple: you are searching for sold prices, not listed prices. An asking price is an opinion. A NameBio record is a transaction that closed, which is the only data a comp appraisal is allowed to use.
DNJournal and the high-end ceiling
DNJournal publishes the domain industry’s weekly top-sales charts, the running record of the market’s largest public deals. Its main chart carries a reporting floor: a sale generally has to reach 50,000 US dollars to make the weekly .com list. That floor is useful context, because it tells you the publicly visible market is skewed toward its top end, and the everyday sub-1,000-dollar transactions are underrepresented in the high-profile reporting.
Marketplaces: Sedo, GoDaddy, and the disclosure gap
Sedo and the GoDaddy aftermarket disclose a share of their closed sales, which feed back into the public databases. The disclosure gap is the structural limit of the whole method. According to the DomainDetails knowledge base, public databases capture only a fraction of all transactions, because high-value deals frequently close privately under confidentiality terms. The comp appraiser works with the visible market and stays honest about the part that stays hidden.
| Source | What it holds | Best used for |
|---|---|---|
| NameBio | Millions of recorded sales, filterable by TLD, keyword, length, price, date | The core comp search for any name, at any price tier |
| DNJournal | Weekly top-sales charts, .com floor around 50,000 USD | Reading the high-end ceiling and headline market direction |
| Sedo / GoDaddy aftermarket | Disclosed marketplace and brokered closes | Recent mid-market comps and live demand signal |
| DomainSherpa sales history | Curated sales archive and commentary | Cross-checking a comp and reading context around it |
How to run a comparable-sales appraisal, step by step
A comparable-sales appraisal runs in six steps: define the comp criteria, search a sales database with those filters, collect 5 to 10 genuine comps, strip the outliers, read the price range the rest cluster in, then adjust for how the target name differs. The output is a defensible range, not a single magic number. The worked example below runs all six on a sample name.
The pattern in every step is the same discipline: match like with like, prefer recent sales, and resist the pull of the one big number that does not belong. The steps below state the move and the mistake that undoes it.
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Define the comp criteria
Fix the attributes a sale must share to count as comparable: same extension, similar character length, the same keyword category, and a similar commercial intent. A two-word .com brandable is comped against other two-word .com brandables, not against a one-word premium and not against a .net.
The mistake: loose criteria that let in any name with one shared word. A comp that matches on a single keyword but differs on extension and length describes a different market.
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Search a sales database with those filters
Open NameBio and apply the criteria as filters: the extension, a length band, the keyword, and a date window. Set the date filter to the last 12 to 24 months so the comps describe the current aftermarket, not a past one.
The mistake: leaving the date filter open. The DomainDetails knowledge base treats sales older than five years as outdated, and a comp from a different market cycle is worse than no comp.
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Collect 5 to 10 genuine comps
Pull a working sample of five to ten sales that pass the criteria. The DomainDetails guidance puts the minimum at five to ten comparable sales, because a sample of one or two is an anecdote, not a range.
The mistake: stopping at the first two sales that flatter the number you hoped for. A sample chosen to confirm a price is not evidence, it is a rationalisation.
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Strip the outliers
Remove the obvious anomalies before reading the range: a freak high from a desperate buyer, a fire-sale low from a forced seller. What remains is the cluster that describes typical demand for the name’s profile.
The mistake: anchoring to the single highest comp. One name in your set selling for ten times the rest is the outlier to discard, not the target to chase.
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Read the price range, not the average
Identify the band the trimmed comps occupy, low to high, and note where the bulk sit. A range communicates the uncertainty honestly. A single average hides it. The price you defend is “this name belongs in this band,” with the comps as the receipts.
The mistake: averaging everything into one figure. A mean dragged by one large sale produces a number no comp supports.
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Adjust the range to the target name
Move the band up or down for the ways the target differs from the comps: stronger or weaker keyword, shorter or longer, better or worse extension, fresher or staler market. The full set of adjusters and their direction is in the next section.
The mistake: skipping the adjustment and quoting the raw comp range. No two names are identical, and the unadjusted range prices the comps, not the name.
A worked example, end to end
Take a hypothetical two-word commercial .com, the kind of name a small business or an SEO would buy to rank. The criteria are set in step one: .com only, two dictionary words, a recognised commercial keyword, sold inside 24 months. The database search in step two returns a set, and the working sample in step three is five comps. The illustrative figures below stand in for what such a search returns.
| Comp domain (profile) | Recorded sale (illustrative) | Notes |
|---|---|---|
| Two-word .com, strong keyword | 5,200 USD | Close match on intent and length |
| Two-word .com, mid keyword | 3,800 USD | Slightly weaker keyword |
| Two-word .com, strong keyword | 4,500 USD | Close match |
| Two-word .com, mid keyword | 2,900 USD | Weaker keyword, longer |
| Two-word .com, premium keyword | 22,000 USD | Outlier, premium one-word-grade keyword |
Step four strips the 22,000-dollar comp: its keyword is a tier above the target, so it describes a different name. The four that remain span 2,900 to 5,200 dollars and bunch in the high 3,000s to mid 4,000s. Step five reads that as the working range. Step six adjusts: if the target’s keyword is on the stronger end and the name is short, you anchor toward the top of the band, landing near 4,500 to 5,000 dollars. If it is weaker or longer, you settle lower. The defensible output is a band with receipts behind it, and that is the number a buyer will negotiate against.
The adjustment ladder: turning raw comps into a defensible number
No two domains are identical, so the raw comp range is only a starting band. The adjustment ladder moves that band up or down for the dimensions on which the target differs from the comps: extension, length, keyword strength and commercial intent, brandability, and the market cycle the comps came from. Each adjuster has a direction and a rough magnitude drawn from the public data.
Extension: the .com premium and the ccTLD discount
Extension is the largest single adjuster. The .com carries a premium that other extensions trade at a fraction of. The DomainDetails knowledge base puts .net at roughly 30 to 50 percent of the equivalent .com value, and .org at roughly 20 to 40 percent. If your comps are .com sales and the target is a .net, the band drops by that factor before any other adjustment. Country-code extensions carry their own discount and their own local-market logic, covered for the European market in the local-SEO ccTLD guidance.
Length, keyword, and commercial intent
Shorter names command more, all else equal, because they are rarer and more memorable. Keyword strength and commercial intent move the number together: a transactional keyword that a business would buy ads against outprices an informational one. The DomainDetails data notes that roughly 55 percent of recorded sales fall in the 1,000 to 3,000-dollar range, which sets a realistic centre of gravity for the broad mid-market and a reminder that the five-figure and six-figure names are the exception, not the rule.
| Adjuster | Direction | Rough magnitude or rule |
|---|---|---|
| Extension (.com vs .net) | Down for non-.com | .net ~30-50% of .com; .org ~20-40% (DomainDetails) |
| Length | Up for shorter | Fewer characters and one-word names command a premium |
| Keyword and commercial intent | Up for transactional | Money keywords outprice informational ones |
| Brandability | Up for memorable | Pronounceable, single-meaning names beat generic strings |
| Recency of comps | Reset if stale | Prefer sales inside 24 months; discard beyond 5 years |
| Market cycle | Up or down with the market | Adjust for bull or bear conditions at the time of comp |
The aged-domain difference: comping an SEO asset, not just a string
A generic appraisal values a name on its linguistics. An aged or expired domain is more than a name. It carries an inherited backlink profile, a registration age, and a ranking history, and those drive a large share of what an SEO buyer will pay. A comparable-sales appraisal of an aged domain has to hold those attributes constant, not just length and extension, or it prices the wrong asset.
Why string-only comps fail for an aged domain
Two two-word .com domains of identical length can be worth wildly different amounts if one carries a clean profile of editorially earned backlinks from real publishers and the other is a fresh registration with nothing behind it. The string is the same. The asset is not. An SEO buyer is paying for the inherited authority, so a comp set chosen on linguistics alone misses the variable that moves the price.
The extra comp dimensions an aged domain needs
Comping an aged domain layers four SEO attributes on top of the linguistic ones. Each shifts the comparable set and the final number:
- Referring domains and link quality. Match comps on the size and cleanliness of the backlink profile, not the raw count. The metrics that read this are set out in the Domain Authority & Metrics hub.
- Registration age. The true age of the domain, read from its registration history. The date is verifiable in the registry record, which since 28 January 2025 is served through RDAP, the protocol that replaced WHOIS as the ICANN standard lookup.
- Ranking and traffic history. Whether the domain held rankings and organic traffic in prior use, which a fresh registration cannot match.
- Profile cleanliness. A clean, editorially earned profile is an asset; a spam-flagged history is a liability that no length or keyword adjustment can offset.
Gathering those four signals by hand on every candidate is the labour the method demands. When you acquire an aged domain through the SEO Domains marketplace, the backlink profile, registration age, and authority band are read and displayed on the listing alongside the comparable-sales price, so the comp set is matched on the asset, not the string, before you ever see the number.
This is the dimension automated tools and generic comp guides omit, and it is the reason an aged-domain valuation needs a sourced backlink read alongside the sales comps. The decay side of this, how an aged domain’s inherited value erodes if the profile is not maintained, is covered in why domains lose value over time.
Reading the range: medians, outliers, and the private-sale ceiling
The output of a comp appraisal is a range, and reading it correctly is its own skill. The median describes the typical name better than the mean, outliers are discarded not chased, and the public databases systematically miss the top of the market because the largest deals close privately. An appraiser who treats the visible data as the whole market overvalues the headlines and undervalues the everyday.
Median over mean
A small set of comps with one large sale produces a mean that no real name supports. The median, the middle value once the set is sorted, ignores that distortion and points at the typical transaction. For the everyday domain, the median of a clean comp set is the honest centre of the range.
Outliers and the unique-name problem
Two kinds of name resist comping. The first is the outlier sale, a freak high or low driven by one buyer’s or seller’s circumstances, which is discarded in step four. The second is the genuinely unique domain that has no close comps at all. A one-word category-defining .com sits in a market so thin that the method runs out of comparables, and the appraisal shifts from data to expert judgement on the loosest available references.
The private-sale ceiling
The structural limit of the whole method is disclosure. The largest transactions frequently close under confidentiality, so the public databases lean toward the mid-market and the reported high end, while the true top end stays partly hidden. DNJournal’s roughly 50,000-dollar reporting floor for its .com chart is one symptom: the visible high-end market is the part above a threshold, and the negotiated private deals above it are reported selectively or not at all. The honest appraiser prices from the visible data and flags the names where the private ceiling means the real number runs higher. The liquidity side of this, how readily a name converts to cash at its appraised value, is treated in liquidity discount explained.
Comparable-sales mistakes: the comp-selection checklist
A comp appraisal goes wrong in a short, repeatable list of ways, and every one is a flaw in how the comps were chosen or read. Cherry-picked outliers, stale sales, cross-extension contamination, asking prices mistaken for sold prices, and ignoring the aged-domain profile each produce a number that does not survive contact with a buyer. The checklist below consolidates them with the fix for each.
Read the table top to bottom and the fixes describe a clean appraisal: a recent, like-for-like comp set, trimmed of anomalies, read as a range, and adjusted for the asset and not the string. This is the scannable reference to run before quoting any number.
| The mistake | Why it breaks the number | The fix |
|---|---|---|
| Cherry-picking the highest comp | One outlier sale anchors the price above anything real demand supports | Strip outliers and read the median of the trimmed set |
| Using stale comps | A sale from a past market cycle describes demand that no longer exists | Filter to the last 12 to 24 months; discard sales beyond 5 years |
| Cross-extension contamination | Comping a .net against .com sales imports the wrong price tier | Match the extension exactly, then apply the .net or .org ratio |
| Asking prices as evidence | A listed price is an opinion, not a transaction | Use only recorded sold prices from a sales database |
| Too small a sample | One or two comps are an anecdote, not a defensible range | Collect 5 to 10 genuine comps before reading the range |
| Averaging instead of ranging | A mean dragged by a large sale hides the real spread | Report a low-to-high band and note where the bulk sit |
| Treating public data as complete | Private high-end deals are missing, skewing the read | Flag names where the private-sale ceiling may run higher |
| String-only comps for an aged domain | Length and extension ignore the backlink profile that drives the price | Match comps on referring domains, age, and ranking history too |
| Ignoring a spam-flagged history | A toxic profile is a liability no keyword adjustment offsets | Screen the profile before comping; price the cleanliness in |
Two failure modes run through the whole list. The first is letting the wrong sales into the set, an outlier, a stale comp, a different extension, an asking price. The second is reading a clean set carelessly, averaging instead of ranging or ignoring the private ceiling. For an aged domain a third joins them: comping the string and forgetting the asset. Avoid all three and the number you quote is one you can defend with receipts.
Comparable-sales appraisal frequently asked questions
The five questions buyers and sellers raise when they value a domain from real sales data, answered against the public databases and the aged-domain distinction this guide draws.
Q1How large a sample of comparable sales do I need for a reliable appraisal?
A working sample of 5 to 10 genuine comps, per the DomainDetails knowledge base. Fewer than that is an anecdote, not a range, because one or two sales cannot show you where the cluster sits or which value is an outlier. Collect the sample, strip the anomalies, and read the band the rest occupy.
Q2Where do I find verified domain sales data?
NameBio is the primary archive, a searchable record of millions of historical domain sales filterable by extension, keyword, length, price, and date. DNJournal publishes the weekly high-end chart, and marketplaces such as Sedo and the GoDaddy aftermarket disclose a share of their closes. Use sold prices only. Asking prices are not evidence.
Q3Is comparable-sales appraisal more accurate than a tool like GoDaddy or EstiBot?
It is anchored to real transactions, not a model’s prediction, which is why brokers treat it as the benchmark. Summaries of professional method report automated accuracy bands of roughly plus or minus 50 percent for GoDaddy and plus or minus 30 percent for EstiBot, used as a starting point, not a final value. The trade-off is that comps take time and database access, where a tool returns a number instantly.
Q4How do I value an aged or expired domain differently from a fresh name?
Add the SEO attributes to the comp criteria. An aged domain carries an inherited backlink profile, a registration age verifiable through RDAP, and a ranking history, and those drive much of what an SEO buyer pays. Match comps on referring domains and authority band, not just length and extension, and screen the profile for a clean history before pricing it in.
Q5What if my domain has no close comparable sales?
A genuinely unique name, such as a one-word category .com, sits in a market too thin for close comps, so the method shifts from data to judgement on the loosest available references. Widen the criteria carefully, lean on the broad market distribution, and flag that the private-sale ceiling lets a real buyer pay well above any public comparable.
From appraisal to acquisition: when the comp work is already done
A comparable-sales appraisal is labour: define criteria, search a database, collect and trim a comp set, run the adjustment ladder, and for an aged domain screen the backlink profile on top. A pre-priced catalogue is that work already done. SEO Domains operates the curated marketplace where aged and expired domains are priced against real sales data and screened across their profiles before they are listed.
Why a priced catalogue is the appraisal, finished
Every listing on a properly run marketplace already carries the output of a comp appraisal. The sales comps were gathered, the adjustment ladder was applied, and for an aged domain the backlink profile and authority metrics were read alongside the linguistics. The price on the listing is the number the method produces, which means a buyer can verify it against the displayed metrics instead of running the whole appraisal from a blank database search.
What to verify on any listing
The appraisal does not disappear when the catalogue does it for you. It becomes a verification step. On any aged-domain listing, read the displayed signals against the comp logic in this guide:
- The backlink profile: referring domains and link quality, not the raw count, as the metrics hub defines them.
- The authority band, read across more than one metric so a single inflated score cannot mislead.
- The registration age, the verifiable input behind any aged-domain comp.
- A clean profile with no toxic inheritance, which is the difference between an asset and a liability the price has to reflect.
A listing that shows these is a comp appraisal you can audit. A name sold on a single headline metric is a number to run the method against yourself before paying.
