Consider two catalogs, each generating $500,000 in last-twelve-month (LTM) earnings. One is dominated by tracks released in the past two years -- songs still riding algorithmic momentum and playlist placement. The other is anchored by recordings from a decade ago that have settled into a stable, predictable earning pattern. The LTM figure is identical. The risk profiles are entirely different.
The first catalog is likely somewhere near the top of its decay curve. The tracks that drive its earnings today will, statistically, earn significantly less in three to five years. The second catalog has already passed through its steepest decline. Its earnings have stabilised. The probability that next year looks like this year is materially higher.
This is the distinction that Dollar Age captures.
What Dollar Age Measures
Dollar Age is an earnings-weighted average of the age of every track in a catalog. Unlike a simple average age -- which treats a track earning $100 per year the same as one earning $100,000 -- Dollar Age weights each track's age by its contribution to total earnings. The result is a single number that tells you where the money is concentrated on the age spectrum.
The concept is rooted in a straightforward observation: older tracks that still generate meaningful income have demonstrated durability. They have survived the initial post-release decay, weathered changes in platform algorithms, and retained listener engagement over time. This is a form of the Lindy Effect -- the idea that the longer something has survived, the longer it is likely to continue surviving. A track that has earned steadily for ten years is a more reliable predictor of future income than a track released six months ago, even if the younger track currently earns more.
Dollar Age quantifies this durability at the portfolio level. A high Dollar Age means the catalog's earnings are concentrated in older, proven tracks. A low Dollar Age means earnings are concentrated in newer releases that have not yet demonstrated long-term staying power.
How to Calculate Dollar Age
The formula is straightforward:
Dollar Age
Dollar Age = Sum (Track LTM earnings x Track age in years) / Total catalog LTM earnings
Each track's age is multiplied by its LTM earnings to produce a weighted value. The sum of all weighted values is divided by the total catalog LTM earnings. The result is expressed in years.
Worked Example
Consider a small catalog of six tracks:
| Track | Release year | Track age (yrs) | LTM earnings (USD) | Weighted value |
|---|---|---|---|---|
| Track A | 2012 | 13 | $12,000 | $156,000 |
| Track B | 2015 | 10 | $8,000 | $80,000 |
| Track C | 2018 | 7 | $6,500 | $45,500 |
| Track D | 2020 | 5 | $9,000 | $45,000 |
| Track E | 2022 | 3 | $5,500 | $16,500 |
| Track F | 2023 | 2 | $2,000 | $4,000 |
Weighted value = Track age x LTM earnings
Total LTM earnings: $43,000
Sum of weighted values: $347,000
Dollar Age: $347,000 / $43,000 = 8.07 years
This catalog has a Dollar Age of approximately 8 years, indicating that its earnings are concentrated in tracks that have been generating income for nearly a decade. This positions it firmly in the stable tail of the typical decay curve.
First Collection Date vs. Release Date
A practical consideration: should you use the track's release date or the date of first royalty collection? In theory, the release date is the correct anchor, as it marks when the track entered the market. In practice, the first collection date is often more reliable and more consistently available across data sources.
For most catalogs, the difference between these two dates is small -- typically one to two quarters. For older catalogs or catalogs with complex distribution histories, the gap can be larger. The important thing is to be consistent: use the same date type for all tracks in the calculation, and document which one you chose.
What Dollar Age Tells You
Position on the Decay Curve
Music royalties follow a well-documented decay pattern. Earnings are highest in the first year after release, decline steeply through years two and three, and then gradually flatten into a long tail that can persist for decades. Dollar Age tells you where a catalog's earning centre of gravity sits on this curve.
Earnings concentrated in very recent releases. High probability of significant near-term decline as tracks pass through peak decay. Models should apply aggressive decay assumptions.
Catalog is transitioning from steep decline to more moderate decay. Some tracks have stabilised, others are still declining. Mixed risk profile requires track-level analysis.
Earnings are increasingly anchored in tracks that have passed through major decay. Risk of sharp decline is lower, but the catalog has not yet fully demonstrated tail stability.
Earnings centre of gravity is in the long tail. Tracks have demonstrated sustained earning power. Decay risk is low; primary risks shift to platform and structural changes.
Earnings dominated by tracks with a decade or more of proven income. Highest confidence in forward projections. These catalogs typically command premium multiples.
Risk-Adjusted Multiple Framing
Dollar Age can inform how you think about acquisition multiples. A catalog with a Dollar Age of 2 years and a 15x LTM multiple carries significantly more risk than one with a Dollar Age of 10 years at the same multiple. The younger catalog's earnings are more likely to decline, meaning the effective multiple -- based on what the catalog will actually earn over the hold period -- may be much higher than 15x.
Conversely, a catalog with a high Dollar Age may justify a higher headline multiple because the earnings are more defensible. The present value of a stable, long-duration cash flow stream is higher than the present value of a declining one, even if the starting point is the same.
Some investors use Dollar Age as an input to their discount rate: lower Dollar Age implies higher uncertainty, which warrants a higher discount rate. Others use it as a qualitative filter: catalogs below a certain Dollar Age threshold require additional due diligence before advancing to formal valuation.
What Dollar Age Does Not Tell You
Dollar Age is a measure of earnings-weighted maturity. It is not a comprehensive risk metric. Several important risk factors are not captured:
- Source concentration: A catalog with a high Dollar Age but 90% streaming income is exposed to platform risk regardless of track maturity
- Sync dependency: Sync income is lumpy and unpredictable; a high Dollar Age does not make sync revenue stable
- Geographic concentration: Earnings concentrated in a single territory carry currency and regulatory risk that Dollar Age does not reflect
- Artist concentration: If the catalog is dominated by a single artist, reputational risk can override maturity effects
Dollar Age at the Portfolio Level
For funds and institutional investors managing multiple catalogs, Dollar Age becomes a portfolio-level risk metric.
Portfolio-Level Calculation
The same formula applies at the portfolio level. Each catalog's Dollar Age is weighted by its LTM contribution to the total portfolio:
Portfolio Dollar Age = Sum (Catalog LTM earnings x Catalog Dollar Age) / Total portfolio LTM earnings
This produces a single number that describes the earnings-weighted maturity of the entire portfolio. A portfolio with a Dollar Age of 8 is, in aggregate, positioned in the stable tail. A portfolio with a Dollar Age of 3 is concentrated in recent releases with high decay risk.
Cohort Analysis
Beyond the aggregate number, breaking Dollar Age into cohorts reveals portfolio composition dynamics. Group tracks (or catalogs) into age bands -- 0-2 years, 2-5 years, 5-10 years, 10+ years -- and calculate each cohort's share of total LTM earnings. This shows not just the average position but the distribution of risk across the maturity spectrum.
A portfolio with a Dollar Age of 6 could be composed entirely of tracks aged 5-7 years (concentrated, moderate risk) or it could be a barbell of very young and very old tracks (dispersed, mixed risk). The cohort analysis distinguishes between these scenarios.
Rebalancing Signals
Changes in Dollar Age over time provide a signal about portfolio trajectory. If Dollar Age is declining, it means newer acquisitions are disproportionately weighting the portfolio toward younger, higher-risk tracks. If Dollar Age is increasing, it means the portfolio is aging -- which may indicate stability but could also indicate insufficient new acquisition activity.
Neither direction is inherently good or bad. The signal is in whether the trajectory aligns with the fund's investment thesis and risk targets.
The Data Requirements for a Reliable Dollar Age
Dollar Age is only as good as the data feeding it. Four data requirements must be met for the metric to be meaningful.
Per-track LTM earnings: Dollar Age requires earnings at the track level, not the catalog level. If earnings are reported only in aggregate, you cannot weight by individual track performance.
ISRC resolution: Every earnings line must be attributed to a specific recording. Unresolved earnings -- line items that cannot be matched to an ISRC -- must either be resolved or excluded. If more than 10% of earnings are unresolved, Dollar Age becomes unreliable.
Release date accuracy: The age of each track must be known. Errors in release dates directly distort the calculation. A track incorrectly dated as 2010 instead of 2020 would contribute ten extra weighted years.
Multi-period history: While Dollar Age can technically be calculated from a single period, it is most useful when tracked over time. At least four quarters of data allow you to observe whether Dollar Age is stable, increasing, or decreasing -- which tells you about the portfolio's trajectory, not just its current position.
| Data requirement | Impact on Dollar Age if missing | Direction of error |
|---|---|---|
| Per-track LTM earnings | Cannot calculate; must use catalog-level approximation | Unknown -- depends on catalog composition |
| ISRC resolution over 90% | Unresolved earnings excluded, biasing result | Unpredictable; depends on which tracks are unresolved |
| Accurate release dates | Track ages incorrect; weighted calculation distorted | Older errors inflate Dollar Age; newer errors deflate it |
| 4+ quarters of history | Point-in-time only; no trend analysis possible | No directional error, but reduced analytical value |
Dollar Age vs. Simple Average Age
It is worth addressing why Dollar Age exists when you could simply calculate the average age of tracks in a catalog. There are two fundamental reasons:
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Simple average age treats all tracks equally. A catalog of 500 tracks where 490 are from 2022 and 10 are from 1995 has a simple average age of roughly 3 years. But if those 10 older tracks generate 80% of the income, the catalog's earnings profile is far more mature than the average age suggests. Dollar Age would correctly reflect this by weighting toward the older, higher-earning tracks.
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Simple average age is distorted by long-tail filler. Many catalogs contain hundreds of tracks that generate negligible income. These tracks pull the average age in whatever direction they cluster, even though they contribute nothing meaningful to the earnings picture. Dollar Age ignores them automatically because their earnings weight is near zero.
In short, simple average age tells you about the catalog's discography. Dollar Age tells you about the catalog's income. For valuation and risk purposes, income is what matters.
Conclusion
Dollar Age is a single number that captures a complex reality: where a catalog's earnings sit on the decay curve. It distinguishes between catalogs that look similar on an LTM basis but carry fundamentally different levels of forward risk. It works at the track level, the catalog level, and the portfolio level, making it useful for analysts, fund managers, and institutional investors alike.
The metric is not a replacement for full valuation analysis. It does not capture source concentration, sync volatility, or geographic risk. But as a first-pass filter for earnings stability -- and as an input to discount rate and multiple decisions -- it is one of the most efficient tools available.
Calculate it early. Track it over time. And always pair it with the data-quality checks that make it reliable.