Music royalty income generally follows a predictable pattern: a track typically earns the most in its early years, then declines as listener attention shifts to newer releases. Over time, the decline tends to slow and earnings settle into a long, stable tail. The rate and shape of this decline, the music royalty decay curve, is one of the core inputs to any music catalog DCF model.
Getting the decay curve right matters because it determines every projected cash flow between the earnings baseline and the terminal value. If the curve is too steep, the model undervalues the catalog. If it is too flat, it overstates future income.
The shape of the decay curve matters as much as the rate. A track that decays quickly in its first three years but stabilizes by year five has a very different NPV profile than one that declines steadily at the same average rate over the same period.
How Track Age Drives Music Royalty Decay
The most fundamental driver of decay is track age: how many years have passed since release. This is where any decay analysis should begin.
A newly released track typically experiences steep decline in its first two to three years as it exits promotional playlists and algorithmic new-release boosts. After that initial period, the rate of decline generally slows. For commercially successful tracks, earnings often stabilize at a level that can persist for decades, driven by catalog playlists, lean-back listening, and sustained cultural relevance.
This pattern is not linear. Modeling it as a fixed annual percentage (e.g., “earnings decline 5% per year”) misrepresents the shape. A fixed rate overstates decline for older tracks and understates it for newer ones. The actual pattern is age-dependent: steep early, gradually flattening. The decay function must capture this.
A catalog’s aggregate decay characteristics are determined in large part by its age composition. Two catalogs with identical current-year earnings but different age profiles will have different DCF values. The older catalog’s tracks have already passed through their period of steepest decline. The younger catalog’s earnings are still falling toward their tail level, and more of its value depends on the stabilization assumption being correct.
Dollar Age, the earnings-weighted average age of a catalog, is a useful shorthand for where a catalog sits on the lifecycle. A lower Dollar Age means more recent releases and more near-term decay risk. A higher Dollar Age means more mature, stable earnings.
Building Accurate Royalty Decay Models: Why Data Depth Matters
The precision of a decay model depends directly on the depth and quality of the data behind it. At a minimum, decay analysis requires track-level earnings by period, confirmed release dates, and at least three years of history (five or more is significantly better). With only aggregate catalog-level earnings, you can calculate a headline decay rate. With granular, clean data, you can build models that reflect the actual variation within the catalog.
This matters because a catalog is not a single asset. It is a portfolio of tracks at different points on their individual curves. Modeling at the catalog level averages away the differences. Modeling at the track or cohort level captures them, producing tighter, more accurate projections.
Decay Rates by Sales Type
Different sales types decay at different rates and patterns. This is the first and most important dimension to split on after age.
Streaming generally follows the classic age-dependent decay pattern. Initial decline is driven by the track exiting promotional and new-release playlists. Long-term stabilization is driven by catalog playlist inclusion, algorithmic discovery, and ongoing listener demand. The tail can be remarkably stable for tracks that enter the cultural canon.
Sync does not decay in the traditional sense. Sync income is driven by licensing activity, which is episodic and unpredictable. A catalog track that has not generated sync income in three years could produce a significant fee tomorrow if a placement materializes. Modeling sync with a smooth decay curve is incorrect. It should be treated separately as a volatile, non-decaying income stream.
Performance royalties (PRO income) generally decay more slowly than streaming for established catalog tracks. Radio play and public performance tend to be stickier, partly because programming is more conservative and partly because rates are set by collective management organizations rather than per-play market dynamics.
Mechanical income (non-streaming), covering downloads and physical sales, is in structural secular decline at the market level. The decay curve for this sales type should incorporate a terminal year assumption after which income reaches zero, unless the catalog has material exposure to markets where physical remains relevant (notably Japan).
Applying a single blended decay curve across all sales types averages together a growing category (streaming), a volatile one (sync), a stable one (performance), and a declining one (mechanical). The result is a projection that is wrong for every category individually.
Decay by Rights Type, Genre, and Territory
With sufficient data depth, decay can be modeled along additional dimensions beyond age and sales type.
Rights type is one of the most important. Master recordings are tied to a specific recorded performance; when that recording loses momentum, the master’s income declines. Publishing rights (musical compositions) can generate income from multiple recordings, sync licensing, public performance, and mechanical reproduction. This diversification of income channels tends to give publishing rights a shallower decay profile than masters for the same underlying song, and is one factor behind publishing catalogs historically commanding higher valuation multiples.
The same principle applies across other dimensions. Genre matters: pop hits tend to decay faster than evergreen genres like classic rock or jazz standards. Territory matters: a track may have stabilized in the US while still declining in Germany or growing in Brazil. Source matters: decay on Spotify may differ from decay on YouTube or Apple Music. Each additional segmentation can improve model accuracy, provided the underlying data is clean and deep enough to support it.
The practical constraint is data quality. Segmenting along multiple dimensions simultaneously requires large volumes of resolved, period-aligned, track-level earnings data. Without it, over-segmentation introduces noise rather than precision.
Beyond Statistical Models: Time Series Diffusion Embedding (TSDE)
Traditional decay modeling is a statistical exercise: fitting a curve to historical earnings data and extrapolating forward. This works well for catalogs with deep, clean histories, but it has inherent limitations. It assumes the future will broadly resemble the past, and it requires substantial data to produce stable estimates.
A complementary approach is forecasting through the Time Series Diffusion Embedding (TSDE) model, developed by Chapter Two in collaboration with STIM (the Swedish performing rights organization) and based on published research by Valentin Buchner, Chapter Two’s Chief Architect. TSDE uses demand-side signals, not just historical earnings, to estimate how a track’s consumption will evolve. Rather than relying solely on what a track has earned, it incorporates patterns in how music is consumed across platforms and populations.
Trained on data from more than 100,000 catalogs and over $2 billion in royalty records, TSDE is particularly valuable where historical data is limited, such as newer catalogs, recently onboarded tracks, or where the statistical model alone produces wide confidence intervals. Used alongside traditional methods, it provides an additional perspective that can either reinforce or challenge the statistical projection.
Common Decay Modeling Errors
Using a flat annual percentage decline misrepresents the curve shape. Decay generally slows as a track ages. A flat rate overstates decline for older tracks.
Calibrating on too short a history. A curve fitted to two years of data may capture a temporary trend (a viral moment, a playlist addition) rather than the structural pattern. Five or more years of history produces more reliable estimates.
Blending sales types. Applying streaming decay to sync income, or averaging a growing and declining sales type, produces systematic error.
What a Well-Specified Music Royalty Decay Model Looks Like
A reliable royalty decay model is age-dependent, with the rate of decline slowing as tracks mature. It is split by sales type at minimum, with streaming, sync, performance, and mechanical modeled separately. It is calibrated on clean data: period-aligned and currency-normalized. And it is applied at the right granularity for the data available, whether that is per-track, per-cohort, or per-sales-type.
The decay curve, together with the earnings baseline, produces the projected cash flow stream that the discount rate and terminal growth rate convert into present value.
Frequently Asked Questions
How fast do music royalties decline after release?
The steepest decline typically occurs in the first two to three years after release, as a track exits promotional playlists and algorithmic new-release boosts. After that, the rate of decline generally slows. For commercially successful catalog tracks, earnings can stabilize at a persistent tail level for decades. The exact rate depends on track age, sales type, genre, and territory.
Why can’t you use the same decay curve for every sales type?
Because different sales types follow completely different patterns. Streaming decays in a predictable age-dependent curve. Sync income is episodic and not time-dependent; it spikes on placement activity and is effectively zero in between. Performance royalties tend to be stickier than streaming. Mechanical income is in structural long-term decline. Applying one blended curve to all of these produces projections that are wrong for every category individually.
What is Dollar Age and why does it matter for decay modeling?
Dollar Age is the earnings-weighted average age of the tracks in a catalog. It indicates where a catalog sits on the lifecycle: a low Dollar Age means income is concentrated in recently released tracks still experiencing steep decline, while a high Dollar Age means income comes from mature tracks that have already stabilized. Dollar Age determines whether near-term decay risk is high or low, and affects how conservative the decay assumptions need to be.