NFTScan can help you screen NFT collections by comparing floor prices, recent sale prices, sales counts, and trading volume across time periods. It cannot tell you that a collection is truly undervalued: those figures are signals to investigate, not proof of fair value, liquidity, or future demand.
What NFTScan can—and cannot—tell you
NFTScan describes itself as a multi-chain NFT explorer and data platform, with collection analytics, rankings, trading information, and NFT API products. Its homepage is the starting point for exploring supported data; available chains and interface features can change.
On its Ethereum ranking page, NFTScan lists collection market cap, volume, sales, floor price, and percentage changes in volume over one and seven days. Its trading view includes sales, volume, floor price, average price, seven-day average price, and seven-day volume, with selectable periods. These views are useful for screening and comparison, not as an appraisal or a buy signal. See the Ethereum ranking and trading analytics pages.
Understand NFTScan’s market-value formula
NFTScan’s FAQ states: “NFT Collection market value = Average price of the last 50 trades * number of items.” This is the platform’s stated ranking formula, not a universal valuation standard. It combines a recent-trade average with collection size; it does not establish what buyers would pay for every item or whether a collection’s activity will continue. The definition appears in NFTScan’s FAQ.
Read portfolio values as estimates
NFTScan says it estimates portfolio NFT value by summing the value of each NFT and using the collection floor price as the item’s price. A floor price is the lowest listed price, not a promise that an NFT can be sold for that amount. The FAQ explains the platform’s method, but an estimate based on listings should not be treated as guaranteed proceeds.
How to screen collections with NFTScan
- Choose a ranking or trading view and a relevant period. Start with the collection ranking or trading analytics, then compare multiple time windows. A brief burst can look compelling in isolation; checking a longer period helps show whether activity persists. NFTScan’s trading view provides sales and volume alongside price metrics and allows period selection.
- Compare the floor with completed sales. Treat the floor as a listing signal. Compare it with average sale prices and check whether sales are continuing. A low floor may reflect a single distressed listing, while the displayed price says nothing by itself about the number of buyers ready to transact at that level.
- Check whether sales and volume are consistent. A spike in either metric is a reason to investigate, not proof of independent buyer demand. NFT trading can be difficult to interpret because pseudonymous addresses may obscure whether activity reflects distinct buyers. Research has examined suspected wash trading and the possibility that one participant controls multiple addresses; see the paper on suspicious NFT trading.
- Inspect the collection and individual NFTs. Review available collection and address analytics, supply, ownership patterns, metadata, activity, and item traits or rarity. Check the individual asset rather than assuming collection-level averages describe it. Which fields are available or complete can vary by collection and chain.
- Compare candidates on the same axes. Use a consistent period and examine recent sales activity, volume consistency, floor versus average sale price, collection size or supply, holder concentration where visible, item traits, chain, and marketplace context. The goal is to identify meaningful differences, not to manufacture a single “undervaluation score.”
- Write down what would change your mind. State a specific, testable reason to investigate further—for example, sales continuing across periods while the floor remains below recent average sale prices. Also state what would weaken that case, such as activity concentrated in one short burst or a floor with little evidence of completed sales nearby. This makes the hypothesis easier to revisit instead of mistaking a screen for a verdict.
Why a low floor or high volume does not prove a bargain
NFT markets can be volatile and trades may be sparse. The floor records the lowest listing, not an executable sale price for the whole collection. Volume reports transaction activity, but the figure alone does not show whether trades represent independent demand. A market-value estimate based on recent trades and item count likewise reflects NFTScan’s stated formula; it is not an authoritative appraisal.
There is also no universal shortcut from an NFT’s traits or appearance to its value. Research into factors associated with NFT prices found that text and image features used to explain price variation within collections did not generalize to unseen collections. That is a caution against applying one collection’s apparent pricing pattern to another, not a claim that traits never matter. See the study on NFT price factors and generalization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to frame the decision
Use NFTScan to generate candidates and gather comparable signals. Before acting, ask whether recent sales support the apparent price gap, whether activity persists across more than one period, and whether you understand the specific NFT and marketplace involved. If the evidence is thin or contradictory, the responsible conclusion is that the collection needs more investigation—not that it is undervalued.
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NFTs are speculative assets, and no metric shown here predicts appreciation or guarantees that you can sell at a displayed price. If you decide to buy and self-custody an NFT, follow wallet-security practices and verify the transfer details. Ledger’s documentation explains transferring Ethereum NFTs to a Ledger account and offers NFT security guidance; a hardware wallet is an optional security measure, not a tool for assessing value.
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