What is it about?
Why can two similar NFTs sell for very different prices? We study how the factors that shape NFT value change as an NFT moves through the market. Using 48,319 transactions from the Bored Ape Yacht Club collection between 2021 and 2025, we examine both the visual characteristics of individual NFTs and the trading histories of the digital wallets that buy and sell them. We find that an NFT's traits are especially important when it is first sold. In later transactions, however, information about the wallets involved and their previous activity becomes increasingly important in explaining differences in prices. We also find that the history of previous owners can matter more than the characteristics of the current buyer or seller. For example, previous sellers' wallet values and spending on transaction fees are associated with substantial differences in subsequent price growth. Finally, some unusual combinations of wallet characteristics and transaction activity are associated with very large price premiums in small groups of transactions. These patterns are consistent with reputation signaling or coordinated trading rather than fully independent market activity, although the small number of transactions involved means these results should be interpreted cautiously. Overall, our findings suggest that NFT value is not determined only by what a digital asset looks like. As it is traded, its ownership history and the reputation and behavior of the wallets connected to it can become an important part of its market value.
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Photo by Markus Spiske on Unsplash
Why is it important?
NFT markets provide an unusual setting in which ownership histories and transactions are publicly visible, yet understanding why prices change remains difficult. Our study shows that the sources of value can evolve over an asset's lifetime: visual traits matter strongly at the beginning, while wallet history and trading behavior become more important in secondary markets. This matters for researchers studying digital markets, but it also has practical implications for NFT marketplaces, market participants, and regulators. Looking only at an NFT's characteristics may miss important information contained in its transaction history. Wallet-level patterns may help marketplaces better understand reputation effects, identify transactions that warrant additional scrutiny, and design more transparent market-monitoring systems. More broadly, the findings show how blockchain-based markets can transform provenance—the history of who owned and traded an asset—into an economically meaningful part of the asset itself.
Perspectives
We started with a simple question: if the underlying NFT does not change, why can its price change so dramatically as it moves from one wallet to another? Our results suggest that the answer lies partly in the history that accumulates around the asset. At its first sale, buyers largely respond to what the NFT is—its visible traits and rarity. As trading continues, however, the market also begins to respond to where the NFT has been, who has held it, and how those wallets have behaved. One of the most interesting findings is that earlier wallet activity can remain associated with later prices even after the NFT has changed hands. This suggests that blockchain provenance can function as more than a record of ownership: it can also become a market signal. At the same time, some of the largest premiums we observe occur in relatively small and unusual groups of transactions. We therefore view these results as evidence of patterns that deserve further investigation rather than proof of manipulation or coordinated behavior. Understanding these patterns may become increasingly important as digital-asset marketplaces develop better tools for pricing, transparency, and market oversight.
Qirui Liu
Emory University
Read the Original
This page is a summary of: From Pixels to Provenance: Empirical Study of How NFT Valuation Evolves from Traits to Reputation, ACM Transactions on Management Information Systems, September 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3820902.
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