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Non-fungible tokens (NFTs): A review of pricing determinants, applications and opportunities

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Kräussl, Roman, Tugnetti, Alessandro·2024·Journal of Economic Surveys
DOI 10.1111/joes.12597
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Abstract

This paper provides a review of the development of the non‐fungible tokens (NFTs) market, with a particular focus on its pricing determinants, its current applications, and its future opportunities. We investigate the current state of the NFT markets and highlight the perception and expectations of investors toward these products. We summarize and compare the financial and econometric models that have been used in the literature for the pricing of non‐fungible tokens with a special focus on their predictive performance. We design a framework that can help to understand the price formation of NFTs. We further aim to shed light on the value‐creating determinants of NFTs in order to better understand investors’ behavior on the blockchain.

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  • In the review by [35], based on eight papers, hedonic regression models dominate, adopted in five of eight papers, while repeat sales and vector autoregressive models are each employed in one paper.

    “Their analysis, based on eight papers, shows a heavy dominance of hedonic regression models over the others, as that class of models is adopted in five out of eight papers, with repeated sales regressions and vector autoregressive models being employed just in one paper each.”

    From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [35]

    Section 3.2 says hedonic regression is the most widely used model, supporting the claim of its dominance. But it says half of the studies analyzed use hedonic modeling, and the discussion following Table 2 refers to nine papers—not eight. The cited passages do not establish the claimed one-paper counts for repeat-sales and vector autoregressive models. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • A recent review of pricing determinants for NFTs classified methods into five classes: hedonic regression models, repeat sales regressions, vector autoregressive models, machine learning, and wavelet models.

    “A recent review of pricing determinants for NFTs has been presented by [35], who focused on the methods to identify determinants and suggested a classification based on the following five classes: hedonic regression models, repeat sales regressions, vector autoregressive models, machine learning, and wavelet models.”

    From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [35]

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Research works printed but not linked: 29

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Authors
Kräussl, R., & Tugnetti, A.
Affiliations
  • Hoover Institution (US)
  • University of Luxembourg (LU)
Corresponding author
Kräussl, Roman
ORCID
Venue
Journal of Economic Surveys
Publisher
Wiley
Published
2023-11-08
Volume
38
Issue
2
Pages
555-574
ISSN
  • 0950-0804 (ISSN-L)
  • 1467-6419
DOI
10.1111/joes.12597
OpenAlex
W4388487801
Cited by
96
References
53
Citations by year
2026: 20 · 2025: 34 · 2024: 23 · 2023: 17 · 2022: 2
FWCI
18.77
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Journal of Economic Surveys (publishedVersion)
  • SSRN Electronic Journal (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Econstor (Econstor) (submittedVersion)
  • City Research Online (City University London) (submittedVersion)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
  • Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
  • Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
Keywords
non-fungible tokens, NFT pricing, price formation, blockchain, investor behavior, value creation, NFT market, pricing models, predictive performance, digital assets

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