Model notes
Methodology before output.
Catalog Underwriter starts with observable evidence, identifies what is missing, and exposes every material valuation assumption. The financial engine is deterministic.
- Methodology
- public-underwrite-1.0.0
- Engine
- dcf-1.0.0
01
What the model collects
MusicBrainz provides catalog evidence: canonical artist identity, release groups, dates, types, and genre metadata. Last.fm supplies cumulative scrobbles and listener counts as demand evidence. When configured, YouTube supplies cumulative views for conservatively matched official music videos, official audio, and artist-topic uploads.
02
What remains unavailable
Royalty statements, private ownership contracts, annual platform consumption, recoupment positions, negotiated distribution terms, and buyer bids are generally not public. The application labels those fields as unknown or user assumptions; it never treats missing information as zero and never annualizes cumulative observations silently.
03
Catalog normalization
The economic model distinguishes compositions, recordings, and commercial releases. The current live provider returns release groups and removes identical identifiers. The stored fixture applies a deeper development normalization; recording-level live normalization is intentionally marked incomplete.
04
Observed consumption
Last.fm observations support relative artist scale, track concentration, breadth, long-tail strength, and persistence. YouTube view counts add another cumulative demand signal. Both are explicitly labeled cumulative observations; neither is an annual stream count, royalty statement, or cash-flow estimate.
05
Modeled consumption
The PublicConsumptionEstimate layer is reserved for defensible current annual/run-rate observations or a disclosed modeled annualization. Cumulative Last.fm and YouTube totals remain separate and cannot populate annual audio or video ranges by themselves. When annualization evidence is missing, those ranges stay unavailable.
06
Economic assumptions and cash flow
RightsEconomics is a separate bridge from annual consumption to estimated annual rights revenue. It models audio streaming, YouTube, publishing, and other categories separately under editable low, base, and high assumptions. It makes no fixed Spotify payout claim. Without annual consumption and complete economic assumptions, automatic cash flow stays unavailable and the user may enter a manual normalized annual cash flow.
07
Valuation
The deterministic DCF projects annual cash flow for ten explicit years and discounts each year at the selected risk rate. A separate market approach multiplies normalized annual cash flow by an editable multiple whose starting range is derived from curated transactions with disclosed income multiples.
08
Reconciliation
The interface shows DCF value, market-multiple value, and a reconciled indicative range. The range spans the selected DCF result and the transaction-derived market band; it is not a mechanical average. Rights scope and evidence quality still require underwriting judgment.
09
Comparable transactions
The repository-managed dataset links to transaction reports and labels rights scope, confidence, comparability, and disclosed income-multiple basis. Benchmarks are validation data, never targets for fitting Last.fm to sale price. Missing prices and multiples remain undisclosed.
10
What the model cannot know
The evidence chain is kept explicit: observed consumption → modeled consumption → economic assumption → estimated cash flow → valuation. This tool cannot determine actual artist income, legal ownership, contractual restrictions, future cultural relevance, or a definitive market-clearing bid. It supports judgment; it does not replace diligence, legal advice, or verified financial statements.
Catalog Underwriter is an educational analytical tool. Estimates are not verified royalty statements, ownership interests, market offers, investment advice, or legal advice.