Every music catalog loses value over time. The question is not whether earnings decline after release, but how fast, how far, and where they stabilise. Getting these answers right is the difference between a well-priced acquisition and an expensive mistake.
The royalty decay curve is the single most important analytical tool for understanding how a catalog's earnings change over time. It describes the rate at which royalty income decreases from its post-release peak, and it is the foundation of every serious catalog valuation model.
This guide covers how to define, calculate, and apply decay curves to real catalog data -- including the concept of Dollar Age, the mechanics of stabilisation, and the data quality requirements that most models fail to meet.
What Is a Royalty Decay Curve?
A royalty decay curve is a time-series representation of how a track or catalog's earnings change over successive periods after release. It typically follows a predictable pattern:
- Release spike. Earnings peak in the first 6 to 18 months, driven by playlist placement, marketing spend, and algorithmic promotion.
- Steep decay. Earnings drop significantly in years two through four as promotional support fades and listener attention shifts to newer releases.
- Stabilisation. Earnings flatten to a base level driven by passive consumption -- library adds, lean-back playlists, and catalog-driven discovery.
The shape of this curve varies by genre, artist profile, release strategy, and platform mix. But the general pattern holds across virtually all commercial music.
| Year post-release | Typical earnings index | Phase | Investor interpretation |
|---|---|---|---|
| Year 1 | 100 | Release spike | Peak earnings; not a reliable baseline for projections |
| Year 2 | 55-70 | Steep decay | Rapid decline; expected and modeled |
| Year 3 | 35-50 | Steep decay | Continued decline; decay rate measurable |
| Year 4 | 25-40 | Transition | Approaching stabilisation in strong catalogs |
| Year 5 | 20-35 | Transition | Stabilisation visible if data is clean |
| Year 7 | 15-25 | Stabilisation | Base-level earnings; reliable for long-term modeling |
| Year 10+ | 10-20 | Deep catalog | Minimal further decay; evergreen floor |
Indicative decay curve for a mainstream streaming-driven release. Actual values vary significantly by genre and artist.
These figures are illustrative. The critical point is that the decay curve is not linear -- it follows a power-law or exponential decline, and the rate of change itself changes over time.
Why Earnings Decline After Release
Understanding the mechanics behind decay is essential for modeling it accurately. There are four primary drivers.
Algorithmic relevance windows
Streaming platforms prioritise new releases in editorial and algorithmic playlists. A track's visibility on Spotify's Release Radar, New Music Friday, or algorithmic mixes is highest in the first few weeks. Once the track exits these windows, its discovery rate drops sharply. This is the single largest driver of early decay.
Pro-rata payout dilution
Streaming royalties are calculated on a pro-rata basis -- each stream's value depends on the total number of streams on the platform in that period. As total platform streams grow (typically 15 to 25 percent annually), the per-stream rate for any individual track declines even if its stream count remains flat. This creates a structural headwind that compounds over time.
Sync and performance volatility
Synchronisation income (from film, TV, and advertising placements) is lumpy and unpredictable. A single sync placement can create a significant earnings spike in one quarter, followed by a return to baseline. Performance royalties from radio and live events are similarly event-driven. Neither source produces the smooth, predictable decline that streaming does, which makes them harder to model but important to separate.
Physical and download obsolescence
For catalogs with meaningful physical or download revenue, these sources are in secular decline. The shift to streaming means that any earnings from CD sales, vinyl, or iTunes downloads will trend toward zero over a 5 to 10 year horizon. This adds an additional layer of decay on top of the streaming curve.
How to Calculate a Decay Rate
The decay rate is the percentage decline in earnings from one period to the next. It can be calculated at various levels of granularity.
Three-year decay rate
Decay Rate = 1 - (Year 3 Earnings / Year 1 Earnings) ^ (1 / 2)
This gives the annualised compound decay rate over the three-year window. For example, if Year 1 earnings are 100 and Year 3 earnings are 49, the annualised decay rate is 1 - (49/100)^(1/2) = 30 percent.
Period-level vs. annual
Decay rates can be calculated on a monthly, quarterly, or annual basis. Annual rates are most common for catalog-level analysis, but quarterly rates provide more granularity for identifying inflection points. Monthly rates are useful for individual tracks but are noisy at the catalog level.
The choice of period matters because it affects how seasonal patterns are captured. Streaming earnings have consistent seasonal patterns -- Q4 is typically strong due to holiday listening, while Q1 often shows a dip. Using annual periods smooths these effects; using quarterly periods requires seasonal adjustment.
Source-adjusted decay rates
A critical refinement is to calculate separate decay rates for each revenue source. Streaming, sync, performance, and mechanical royalties each follow different decay profiles. Blending them into a single rate masks the underlying dynamics.
For example, a catalog might show a blended annual decay rate of 12 percent. But when separated, streaming might be declining at 8 percent while sync income dropped 40 percent due to a one-off placement in the base year. Modeling these as a single rate would understate streaming stability and overstate overall risk.
The Role of Dollar Age in Decay Curve Analysis
Dollar Age is a weighted-average metric that captures how "old" a catalog's earnings are, weighted by the revenue contribution of each track. It is more informative than simple catalog age because it accounts for the fact that a catalog's earnings are often concentrated in a small number of tracks.
Dollar Age
Dollar Age = Sum of (Track Age * Track Revenue Share) for all tracks
A catalog with 500 tracks released over 20 years might have a simple average age of 10 years. But if 60 percent of its revenue comes from tracks released in the last 3 years, its Dollar Age might be only 4 years -- meaning the earnings profile is still in the steep decay phase.
Dollar Age is a direct input to decay curve positioning. It tells you where the catalog sits on its aggregate decay curve, which determines how much further decline to expect.
Majority of earnings are from recent releases still in steep decay. Expect significant further decline before stabilisation.
Earnings are transitioning from steep decay to stabilisation. Decay rate is measurable but declining.
Earnings are at or near stabilisation. Further decline is modest and predictable.
Earnings are fully stabilised. Remaining decay is minimal, driven primarily by pro-rata dilution and format shifts.
The Dollar Age framework is especially valuable when comparing catalogs. Two catalogs with the same LTM (last twelve months) earnings may have very different forward profiles if one has a Dollar Age of 2 years and the other has a Dollar Age of 8 years.
Modeling the Stabilisation Point
The stabilisation point is the moment when a catalog's decay rate flattens to a long-term sustainable level. Identifying this point accurately is one of the most consequential steps in catalog valuation.
Identifying stabilisation
Stabilisation is not a single moment but a transition zone. It is best identified by tracking the rate of change in the decay rate itself. When the annualised decay rate drops below 3 to 5 percent and remains there for at least four consecutive quarters, the catalog can be considered stabilised.
In practice, this means looking at the second derivative of the earnings curve. During steep decay, the absolute earnings drop is large but the rate of decline is also decreasing. Stabilisation occurs when both the absolute decline and the rate of decline converge to a low, steady level.
Danger of premature stabilisation
A common modeling error is to assume stabilisation has been reached based on a short period of flat earnings. This can happen when a sync placement or viral moment temporarily arrests the decline, creating the appearance of a floor that does not exist.
To guard against this, stabilisation should be confirmed over at least 12 to 18 months of data, and it should be validated at the source level. If streaming earnings are still declining at 10 percent annually but a sync placement has made the blended number look flat, the catalog has not stabilised.
Catalog-level vs. track-level
Stabilisation analysis can be performed at the catalog level or the track level. Catalog-level analysis is simpler but can mask divergent behaviour among tracks. A catalog might appear stabilised because a few evergreen tracks are offsetting continued decline in the rest of the portfolio.
Track-level analysis is more computationally intensive but provides a more accurate picture. It allows you to identify which tracks have genuinely stabilised and which are still decaying, which in turn improves the accuracy of forward projections.
Decay Curves Across Different Catalog Types
Not all catalogs decay the same way. The shape and rate of the decay curve depend heavily on the catalog's composition.
Hit-driven catalogs are characterised by a small number of high-performing tracks that generate the majority of revenue. These catalogs typically show steep initial decay as the hits move off playlists, followed by a relatively high stabilisation floor if the tracks achieve "evergreen" status. The risk is concentration -- if the top tracks decay faster than expected, the entire catalog underperforms.
Deep catalog portfolios consist of large numbers of tracks with relatively even revenue distribution. These catalogs tend to show gentler decay curves because no single track dominates the earnings profile. They stabilise earlier and at a more predictable level. The risk is that low per-track earnings make them sensitive to pro-rata dilution and platform economics.
Mixed catalogs combine recent releases with older catalog tracks. These are the most complex to model because the aggregate decay curve is a blend of tracks at different stages. Dollar Age is particularly important for these catalogs, as it captures the weighted position on the decay curve. The risk is that the recent releases mask the performance of the older material, or vice versa.
What Clean Data Looks Like for Decay Modeling
Decay curve analysis is only as good as the data it is built on. Most catalog data falls short of what is needed for reliable modeling.
| Data requirement | Why it matters | Common gap |
|---|---|---|
| Monthly or quarterly earnings by track | Enables track-level decay calculation and stabilisation analysis | Earnings reported at catalog level only, with no track breakdown |
| Source breakdown (streaming, sync, performance, mechanical) | Each source decays differently; blended rates are misleading | Sources lumped together or inconsistently categorised across periods |
| Consistent reporting periods | Period misalignment creates artificial volatility in decay calculations | Statements cover different date ranges, overlap, or have gaps |
| Currency normalisation | FX movements can mask or amplify true earnings trends | Earnings reported in mixed currencies without conversion metadata |
| Release dates for all tracks | Required to calculate track age and Dollar Age | Release dates missing or inaccurate for catalog tracks |
| At least 3 years of history | Minimum needed to estimate decay rate and identify stabilisation | Only 12-18 months of data available, insufficient for trend analysis |
Minimum data requirements for reliable royalty decay curve modeling.
Without these data elements, any decay model is built on assumptions rather than observations. The most common failure mode is not that the model is wrong in concept, but that the data feeding it is too incomplete or too aggregated to support the analysis.
Conclusion
The royalty decay curve is the foundational analytical tool for music catalog investment. It describes how earnings change over time, where they stabilise, and what drives the rate of decline. Combined with Dollar Age and source-level analysis, it provides the basis for every forward projection and every valuation.
Getting the decay curve right requires clean, granular data and a disciplined approach to source separation, period alignment, and stabilisation identification. The models themselves are not complex -- the challenge is in the data preparation and the analytical discipline to avoid common shortcuts.
For institutional investors and acquisition analysts, mastering decay curve analysis is not optional. It is the difference between a valuation model that reflects reality and one that reflects assumptions.