Infinite but Rare: Valuation and Pricing in Marketplaces for Blockchain-Based Nonfungible Tokens

45 Pages Posted: 2 Dec 2020 Last revised: 20 Oct 2021

See all articles by Pavel Kireyev

Pavel Kireyev

INSEAD

Ruiqi Lin

affiliation not provided to SSRN

Date Written: October 20, 2021

Abstract

Blockchain technologies have enabled the creation of decentralized applications which let users own and transact scarce digital assets called nonfungible tokens or NFTs. Although still in its infancy, the industry has generated over $2.5bn in transaction volume and attracted interest from organizations such as the NBA, several football (soccer) clubs, major brands, and gaming companies to create platforms for trading digital collectibles. A major question faced by NFT platforms is how to help participants value the digital items. We introduce a novel dataset and study how traditional approaches to valuation may exhibit significant biases in this market. We find that while buyers value NFTs much like we would expect them to value physical collectibles, sellers have a tendency to price sub-optimally, which causes traditional hedonic regression approaches to generate inaccurate valuations. We develop a valuation approach based on a structural model of the selling mechanism used in a popular NFT market to highlight these biases and develop a proof-of-concept decision support tool to help participants make more informed decisions.

Keywords: Digital Items, Collectibles, Nonfungible Tokens, Pricing, Valuation, Marketplaces, Blockchain, Structural Model

Suggested Citation

Kireyev, Pavel and Lin, Ruiqi, Infinite but Rare: Valuation and Pricing in Marketplaces for Blockchain-Based Nonfungible Tokens (October 20, 2021). INSEAD Working Paper No. 2021/60/MKT, Available at SSRN: https://ssrn.com/abstract=3737514 or http://dx.doi.org/10.2139/ssrn.3737514

Pavel Kireyev (Contact Author)

INSEAD ( email )

Boulevard de Constance
77305 Fontainebleau Cedex
France

HOME PAGE: http://www.pavelkireyev.com

Ruiqi Lin

affiliation not provided to SSRN

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