Valuation frameworks for crypto assets
Real-world asset networks evaluated through AUM, fee take rate, regulatory risk, asset backing, and distribution reach.
The model set is selected for the economic behavior of this asset class.
Static class view of valuation gap and evidence quality.
RWA Bubble Map
Static methodology preview. Live data pipeline not yet connected.
Each model states what it measures and where its confidence breaks down.
Relates value to tokenized asset base or assets under management.
Core inputs
AUM does not automatically accrue to tokenholders.
Evaluates the fee rate captured from managed or tokenized assets.
Core inputs
Fee rights and token rights must be separated.
Applies a revenue multiple to network or protocol revenue.
Core inputs
Revenue comparability varies materially by asset class.
Applies explicit discounts for policy, licensing, or structure risk.
Core inputs
Risk discount is qualitative unless source trails are strong.
Assesses treasury or asset backing where claims are sourceable.
Core inputs
Token claims may not match asset ownership.
Method confidence reflects evidence quality, source availability, and token-level capture clarity.
A local assumption shell for testing scenario sensitivity without live data.
Scenario sensitivity
Static methodology preview. Changes here recompute a local scenario number only.
Planned source families for turning this framework into a data-backed dashboard.