Building Effective Marketplaces

What does it take for a marketplace to succeed?

Much has been published along these lines already, but in our own efforts to scale Royalty Exchange (a marketplace for music royalties), we discovered a foundational principle that seems to have been overlooked.

That principle is standardization.

Standardization

A market can’t function without a relatively simple set of standards used to understand and compare assets. Where would stock market analysts be without the framework of GAAP accounting and all the ratios built upon it? EPS, P/E ratio, P/B ratio, etc.? Could there even be a stock market without this type of standardization?

Standardization is created by emphasizing common attributes and de-emphasizing differences across a class of assets. Standardization allows assets to be understood, grouped, and priced at scale. And this is required to make an asset class broadly investable.

Standardization is a prerequisite of effective markets. And standardization is achieved by stripping away everything not critical in understanding the assets at scale.

Standardization is a prerequisite of effective markets. And standardization is achieved by stripping away everything not critical in understanding the assets at scale.

Our platform allows artists (mostly songwriters) and investors to transact with one another for mutual benefit at scale. These two groups often share a love of music, but otherwise, they couldn’t be more different. And so standardization is key to making it easier for these groups to transact with one another.

But how do you apply standardization to music royalties? The first step is to differentiate between music and the royalty income stream it produces.

Music is art. Music royalty streams are an asset.

Our most useful insight emerged upon the realization that music royalties, as an asset class, can be understood, grouped, and priced with the right data. And critically, that data doesn’t include the number of Grammy nominations or Billboard rankings.

So, yes, there are big differences between the music of the British rock band Dire Straits and the rapper Eminem, for instance. And yet, when you focus on the royalty streams produced by these (and other) catalogs, it’s clear that they have a lot, quantitatively, in common.

In these quantifiable commonalities, one can find the seed for the standardization that fuels effective markets.

A Market of Unique Assets

To illustrate the power of standardization and quantifiable commonalities, let’s look at a market most never think about: the market for cows.

On any given day hundreds of millions of dollars in value is transacted accounting for hundreds of thousands of cows. Market participants aren’t trading actual cows, they’re trading Live Cattle Futures contracts on the Chicago Mercantile Exchange (CME).

One would think (quite correctly) that there’s a lot of variation from one cow to another and yet the market has effectively organized itself around a handful of quantitative commonalities.

In the Live Cattle market participants focus on weight and delivery date. Live Cattle Futures are priced in pounds and traded in 40,000-lb lots called contracts.

As of this writing, a pound of beef delivered in April 2019 is worth about $1.27/pound. And we’re not talking about a pound of ground beef. We’re talking a pound of cow ready for slaughter — hooves and all.

Weight and delivery date seem awfully rudimentary in today’s world of big data. And yet the focus on quantitative commonality among cows works. And this lesson applies to music royalties too.

There’s even more variation from one song or artist to another. But there is enough commonality to provide the quantitative framework for grouping and pricing their royalty income streams at scale.

Where Cattle Futures have Weight and Delivery date, Music Royalties can be understood and valued with these metrics:

• How long songs in a catalog have been earning royalties. [Dollar Age]
• How much the catalog earned in the last 12 months. [LTM]

There are, of course, other data points of value. But, in our analysis, Dollar Age and LTM multiple are the two most critical factors and provide a framework for successful royalty investing.

Standardization and Scale

Let’s see how standardization turned an online curiosity into a thriving multi-billion dollar marketplace.

Prosper.com is America’s first peer-to-peer lending marketplace with $14 billion in funded loans so far. Borrowers request personal loans on Prosper and investors (individual or institutional) can fund anywhere from $2,000 to $35,000 per loan request.

The Prosper Marketplace gives investors access to fixed income assets and borrowers access to capital at the most compelling rates possible.

They’ve come a long way since their launch in 2006. In fact, from launch through to about 2010, Prosper matched borrower and lender through auctions.

Each individual loan request took the form of personalized auction listings where borrowers attempted to make the case that they were creditworthy and, more generally, playing on the sympathy of investors.

Each Prosper loan auction was as unique as the human trying to borrow. The novel approach drew some intrepid individual investors, but the lack of standardization kept serious capital on the sidelines.

Ultimately, Prosper came to the conclusion that showcasing the uniqueness of individual borrowers made participation unnecessarily difficult for borrowers and confusing for investors. So they developed underwriting standards which allowed for understanding and organization of loans at scale. Investors deployed ever-increasing amounts of capital on the platform. And things got easier for borrowers too as loan requests could be priced instantly and fulfilled quickly.

Today, Prosper appears to originate more loans in a single week than they did over the entire three years that individualized auctions dominated the platform.

The lesson is clear. Whether we’re talking cows, personal loans, or music royalties, assets need standardization to become broadly investible. And, standardization comes from focusing on quantitative similarities rather than qualitative differences.