# Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs

arXiv: https://arxiv.org/abs/2210.10637 · HTML: https://mlsanity.com/paper/2210.10637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Generative Language Modeling for Automated Theorem Proving](https://mlsanity.com/paper/2009.03393/) (2020)
- [FNet: Mixing Tokens with Fourier Transforms](https://mlsanity.com/paper/2105.03824/) (2021)
- [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)
- [Heterogeneous rarity patterns drive price dynamics in NFT collections](https://mlsanity.com/paper/2204.10243/) (2022)
- [NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks](https://mlsanity.com/paper/2204.12932/) (2022)
- [Prediction of Domain Values: High throughput screening of domain names using Support Vector Machines](https://mlsanity.com/paper/1707.00906/) (2017)
- [Ethereum Name Service: the Good, the Bad, and the Ugly](https://mlsanity.com/paper/2104.05185/) (2021)

## Abstract (arXiv)

Existing works on valuing digital assets on the Internet typically focus on a single asset class. To promote the development of automated valuation techniques, preferably those that are generally applicable to multiple asset classes, we construct DASH, the first Digital Asset Sales History dataset that features multiple digital asset classes spanning from classical to blockchain-based ones. Consisting of 280K transactions of domain names (DASH_DN), email addresses (DASH_EA), and non-fungible token (NFT)-based identifiers (DASH_NFT), such as Ethereum Name Service names, DASH advances the field in several aspects: the subsets DASH_DN, DASH_EA, and DASH_NFT are the largest freely accessible domain name transaction dataset, the only publicly available email address transaction dataset, and the first NFT transaction dataset that focuses on identifiers, respectively. We build strong conventional feature-based models as the baselines for DASH. We next explore deep learning models based on fine-tuning pre-trained language models, which have not yet been explored for digital asset valuation in the previous literature. We find that the vanilla fine-tuned model already performs reasonably well, outperforming all but the best-performing baselines. We further propose improvements to make the model more aware of the time sensitivity of transactions and the popularity of assets. Experimental results show that our improved model consistently outperforms all the other models across all asset classes on DASH.