# Heterogeneous rarity patterns drive price dynamics in NFT collections

arXiv: https://arxiv.org/abs/2204.10243 · HTML: https://mlsanity.com/paper/2204.10243/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning](https://mlsanity.com/paper/2306.11731/) (2023)
- [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)
- [Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs](https://mlsanity.com/paper/2210.10637/) (2022)
- [Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens](https://mlsanity.com/paper/2303.17031/) (2023)
- [DIT: Dimension Reduction View on Optimal NFT Rarity Meters](https://mlsanity.com/paper/2508.12671/) (2025)

## Abstract (arXiv)

We quantify Non Fungible Token (NFT) rarity and investigate how it impacts market behaviour by analysing a dataset of 3.7M transactions collected between January 2018 and June 2022, involving 1.4M NFTs distributed across 410 collections. First, we consider the rarity of an NFT based on the set of human-readable attributes it possesses and show that most collections present heterogeneous rarity patterns, with few rare NFTs and a large number of more common ones. Then, we analyze market performance and show that, on average, rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii) guarantee higher returns on investment (ROIs), and (iv) are less risky, i.e., less prone to yield negative returns. We anticipate that these findings will be of interest to researchers as well as NFT creators, collectors, and traders.