  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.
