# What Determines the Price of NFTs?

arXiv: https://arxiv.org/abs/2310.01815 · HTML: https://mlsanity.com/paper/2310.01815/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [TweetBoost: Influence of Social Media on NFT Valuation](https://mlsanity.com/paper/2201.08373/) (2022)
- [Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction](https://mlsanity.com/paper/2302.01676/) (2023)
- [Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem](https://mlsanity.com/paper/2110.12545/) (2021)
- [The impact of NFT profile pictures within social network communities](https://mlsanity.com/paper/2206.06443/) (2022)

## Abstract (arXiv)

In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology. NFTs serve as the foundational framework that has revolutionized the market for digital art, enabling artists to showcase and monetize their creations in unprecedented ways. NFTs combine metadata stored on the blockchain with off-chain data, such as images, to create a novel form of digital ownership. It is not fully understood how these factors come together to determine NFT prices. In this study, we analyze both on-chain and off-chain data of NFT collections trading on OpenSea to understand what influences NFT pricing. Our results show that while text and image data of the NFTs can be used to explain price variations within collections, the extracted features do not generalize to new, unseen collections. Furthermore, we find that an NFT collection's trading volume often relates to its online presence, like social media followers and website traffic.