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www.nature.com/scientificreports/ Scientific Reports | Vol:.(1234567890) million dollars; and the Auction Winner Picks Name, an NFT with music video and dance track, sold at $1.33 million dollars. The profitability of NFTs has motivated celebrities to create their own NFTs, with collectibles of NBA and famous football players getting sold for hundreds of thousands dollars12. 3 Research on NFTs is still limited, and focuses mostly on technical aspects, such as copyright regulations ; components, protocols, standards, and desired properties13; new blockchain-based protocols to trace physical goods14; and the implications that NFTs have on the art world15,16, in particular as they allow to share secondary sale royalties with the artist. Empirical studies aiming at characterizing properties of the market have focused on a limited number of NFT collections, such as, CryptoKitties17,18, Cryptopunks, and Axie19, or on a single NFT market, such as, Decentraland19,20 or SuperRare21,22. These analyses revealed that the digital abundance of NFTs in digital games has led to a substantial decrease of their value17, and that, even if NFT prices are driven by the prices of cryptocurrencies19, the NFT market could be prone to speculation18,20. Further, it was shown that NFTs valued by experts are more successful21, and that, based on 16,000 NFTs sold on the SuperRare market, the structure of the the NFT co-ownership network is highly centralized, and small-world-like22,23. In this paper, we provide a first comprehensive quantitative overview of the NFT market. To this end, we ana- lyse a large dataset including 6.1 million trades of 4.7 million NFTs in 160 cryptocurrencies, primarly Ethereum and WAX, and covering the period between June 23, 2017 and April 27, 2021. We start by analysing the overall statistical properties of the NFT market and its evolution over time. Then, we study the network of interactions between NFT traders, and the network of NFT assets. NFTs are further clustered based on their visual features. Finally, we present the results of regression and classification models predicting the occurrence of NFT second- ary sales and their price. We break down our analysis by NFT categories, which are classified by manual inspection, with references to the classification proposed by NonFungible Corporation24, a specialized company that track NFTs sales, and OpenSea25, one of the largest NFT marketplace. However, the exact classification of different categories in which NFTs are used is outside of the scope of the present paper. For example, Art objects can be in some cases classified as Collectibles, while some Game objects may present sophisticated aesthetic and cultural properties that may qualify them as Art. Results The NFT market. Items exchanged on the NFT market are organized in collections, sets of NFTs that, in most cases, share some common features. Collections can be widely different in nature, from sets of collectible cards, to selections of art masterpieces, to virtual spaces in online games. Most collections can be categorised in six categories: Art, Collectible, Games, Metaverse, Other, and Utility (see also “SI”). We show the top 5 collections in terms of number of unique assets (n) for each category (see Fig. 1a). Following an initial rapid growth in late 2017, when CryptoKitties collection gained worldwide popularity, the size of the NFT market has remained substantially stable until mid 2020, with an average of ∼ 60 000 US dollars traded daily (see Fig. 1b). Starting from July 2020, the market has experienced a dramatic growth, with the total volume exchanged daily surpassing ∼ 10 million US dollars in March 2021, thus becoming 150 times larger than it was 8 months earlier. We measured to what extent different NFTs categories contribute to the size of the whole NFT market. Until the end of 2018, the market was fully dominated by the Art category, and in particular by the CryptoKitties col- lection. From January 2019, other categories started gaining popularity, both in terms of total volume exchanged (see Fig. 1b,c) and number of transactions (see Fig. 1d). Overall, in the period between January 2019 and July 2020, ∼ 90% of the total volume exchanged on NFT was shared by the Art, Games, and Metaverse categories, contributing 18%, 33%, and 39% respectively. Starting from mid July 2020, the market volume has been largely dominated by NFTs categorized as Art, which, since then, have contributed ∼ 71% of the total transaction volume, followed by Collectible assets accounting for12%. Importantly, however, the market composition is quite different when considering the number of transactions. Since July 2020, the most exchanged NFTs belong to the categories Games and Collectible, which account for 44% and 38% of transactions. Instead, only 10% of transactions are related to NFTs categorized as Art. Overall, we observe that the share of volume spent in Art has been growing since 2020, while its share of transactions has been decreasing (Fig. 1d). The discrepancy between volume and transactions reveals that prices of items categorized as Art are higher, on average, compared to other categories. We dig further into these differences by looking at the distribution of NFT prices across categories (see Fig. 2a), which we find to be broadly distributed. We observe that the average sale price of NFTs is lower than 15 dollars for 75% of the assets, and larger than 1594 dollars, for 1% of the assets. Considering individual categories, NFTs categorized as Art, Metaverse, and Utility reached higher prices compared to other categories, with the top 1% of assets having average sale price higher than 6290, 9485, and 12,756 dollars respectively. Note that these categories are different in sizes, so 1% of assets corresponds to 8593, 472, and 78 NFTs in the Art, Metaverse, and Utility categories, respectively. The highest prices so far were reached by assets categorized as Art, with 4 NFTs that were sold for more than 1 million dollars. To assess the market activity, we measured how often individual assets are traded. Here, we refer to the first time an asset is sold as the asset’s primary sale, and to all other sales as secondary sales. All assets considered in this study had a primary sale, but only ∼ 20% of them had a secondary sale (see “SI”). We observe that the tail of the distribution of number of sales s per asset, for s ≥ 10, is well characterized by a power-law function P(s) ∼ sβ, with β = −1.4, estimated following26 (see Fig. 2b). When looking at different categories, the distribu- tion of number of sales is affected by cut-off values. For example, the maximum number of sales for assets in the Utility category is 12, while an asset in the Games category is sold more than a thousand times, and an asset in the Art category more than five thousands times. Note that only 0.07% of all assets are sold more than 10 times. Also, the size of collections n is well described by a power-law function P(n) ∼ nα, with α = −1.5 (see Fig. 2c), (2021) 11:20902 | https://doi.org/10.1038/s41598-021-00053-8 2PDF Image | Mapping the NFT revolution
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