Quadratic Regression Models for Profile Picture NFT Valuation
In this study, we propose a valuation methodology for Non-Fungible Tokens (NFTs), focusing on the profile picture (PFP) NFT category represented by the Bored Ape Yacht Club (BAYC). To identify the attributes that influence the value of individual BAYC NFTs, we develop a hedonic pricing model that us...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11058936/ |
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| author | Geun-Cheol Lee Hoon-Young Koo Heejung Lee |
| author_facet | Geun-Cheol Lee Hoon-Young Koo Heejung Lee |
| author_sort | Geun-Cheol Lee |
| collection | DOAJ |
| description | In this study, we propose a valuation methodology for Non-Fungible Tokens (NFTs), focusing on the profile picture (PFP) NFT category represented by the Bored Ape Yacht Club (BAYC). To identify the attributes that influence the value of individual BAYC NFTs, we develop a hedonic pricing model that uses the NFT’s value as the dependent variable and its properties as independent variables. We apply Term Frequency-Inverse Document Frequency (TF-IDF) to quantify attributes of NFTs. Three hedonic models—linear, quadratic, and full quadratic—are proposed. For the full quadratic model, we introduce a systematic procedure to select first-order, squared, and interaction terms in the model. To evaluate the performance of the proposed models, we carried out comparative computational experiments. We collected actual BAYC transaction data and split it into a training set (70%) and a validation set (30%). For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. The machine learning models perform well on the training set, however, this was largely due to overfitting. In contrast, the proposed hedonic models maintained consistent performance with minimal degradation from the training to the validation set. Among them, the full quadratic model demonstrates the highest explanatory power on the validation set in terms of adjusted <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and other evaluation metrics. |
| format | Article |
| id | doaj-art-fa6b394f60764625b51ea02647efa8d1 |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fa6b394f60764625b51ea02647efa8d12025-07-08T23:00:22ZengIEEEIEEE Access2169-35362025-01-011311402911403710.1109/ACCESS.2025.358422211058936Quadratic Regression Models for Profile Picture NFT ValuationGeun-Cheol Lee0https://orcid.org/0000-0002-8555-7064Hoon-Young Koo1https://orcid.org/0000-0001-7786-928XHeejung Lee2https://orcid.org/0000-0001-7548-9291College of Business, Konkuk University, Seoul, South KoreaSchool of Business, Chungnam National University, Daejeon, South KoreaSchool of Interdisciplinary Industrial Studies, Hanyang University, Seoul, South KoreaIn this study, we propose a valuation methodology for Non-Fungible Tokens (NFTs), focusing on the profile picture (PFP) NFT category represented by the Bored Ape Yacht Club (BAYC). To identify the attributes that influence the value of individual BAYC NFTs, we develop a hedonic pricing model that uses the NFT’s value as the dependent variable and its properties as independent variables. We apply Term Frequency-Inverse Document Frequency (TF-IDF) to quantify attributes of NFTs. Three hedonic models—linear, quadratic, and full quadratic—are proposed. For the full quadratic model, we introduce a systematic procedure to select first-order, squared, and interaction terms in the model. To evaluate the performance of the proposed models, we carried out comparative computational experiments. We collected actual BAYC transaction data and split it into a training set (70%) and a validation set (30%). For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. The machine learning models perform well on the training set, however, this was largely due to overfitting. In contrast, the proposed hedonic models maintained consistent performance with minimal degradation from the training to the validation set. Among them, the full quadratic model demonstrates the highest explanatory power on the validation set in terms of adjusted <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and other evaluation metrics.https://ieeexplore.ieee.org/document/11058936/Bored ape yacht club (BAYC)hedonic modelnon-fungible tokens (NFTs)profile picture (PFP) NFTsquadratic model |
| spellingShingle | Geun-Cheol Lee Hoon-Young Koo Heejung Lee Quadratic Regression Models for Profile Picture NFT Valuation IEEE Access Bored ape yacht club (BAYC) hedonic model non-fungible tokens (NFTs) profile picture (PFP) NFTs quadratic model |
| title | Quadratic Regression Models for Profile Picture NFT Valuation |
| title_full | Quadratic Regression Models for Profile Picture NFT Valuation |
| title_fullStr | Quadratic Regression Models for Profile Picture NFT Valuation |
| title_full_unstemmed | Quadratic Regression Models for Profile Picture NFT Valuation |
| title_short | Quadratic Regression Models for Profile Picture NFT Valuation |
| title_sort | quadratic regression models for profile picture nft valuation |
| topic | Bored ape yacht club (BAYC) hedonic model non-fungible tokens (NFTs) profile picture (PFP) NFTs quadratic model |
| url | https://ieeexplore.ieee.org/document/11058936/ |
| work_keys_str_mv | AT geuncheollee quadraticregressionmodelsforprofilepicturenftvaluation AT hoonyoungkoo quadraticregressionmodelsforprofilepicturenftvaluation AT heejunglee quadraticregressionmodelsforprofilepicturenftvaluation |