Tribune. Non-fungible tokens, or NFTs (“non-fungible tokens”), have been around for several years, but interest in them has grown rapidly in 2021 with a number of high-profile sales, including one by Christie’s, for $69.3 million (around 63.2 million euros), NFT Everydays. The First 5 000 Daysa digital artwork by American artist Beeple.
Despite concerns regarding the environmental impact of the underlying technology, NFT sales reached $2.5 billion in the first half of 2021, an all-time high.
But why do some NFTs sell for so much? This question is at the heart of a new study funded by the Turing Institute that we recently published in Scientific Reports – the first comprehensive and quantitative overview of the NFT market.
First, let’s review the basics.
An NFT is a unique unit of data that can be used to certify ownership of a digital asset. NFTs are created using blockchain technology – typically the Ethereum blockchain – and can represent any digital object, including photos, artwork, memes, music, digital collectibles, and even tweets.
500,000 buyers and sellers
There are many marketplaces for NFTs, where sellers offer either an auction or a buy-it-now option. The novelty of NFTs compared to “physical” works of art is that digital assets can in theory be reproduced and/or downloaded an unlimited number of times, therefore “owned” by anyone.
The idea behind NFTs is therefore to determine, among the many copies, which is the original, and to whom it belongs.
To map the evolution of the NFT market, we looked at a dataset comprising 4.7 million NFTs traded by over 500,000 buyers and sellers between June 23, 2017 and April 27, 2021. The articles included digital art as well as other collectibles such as digital maps and items used in video games (e.g. weapons and clothing).
The prices of the NFTs analyzed showed great variability, with only the top 1% of items trading for more than $1,500, with 75% of items selling for less than $15.
Three factors
In order to find what determines the sale price of an NFT, we developed a “machine learning” model taking into account three factors: first, the visual characteristics; then previous sales of related NFTs, and finally the popularity of buyers and sellers.
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