Real World Assets token secondary market : a real(T) case analysis
Jean-Baptiste Pleynet
Nathaniel Pleynet
Introduction
This is the second article in our ongoing series exploring RealT’s secondary market (YAM). In Part 1, we looked at historical token prices and trading volumes. This time, we shift the focus to what truly drives investor decisions: yield.
Price alone tells only part of the story. RealTokens represent income‑producing properties, so understanding the returns they generate — expressed as annualized yields — is essential for evaluating whether the market is efficient or overpriced. Here, we dive into YAM’s historical data to see how yields have evolved over time and what they reveal about the market’s behavior.
Why we quote in yield, not dollars
YAM trades settle in USD‑pegged stablecoins, so each token has a headline price. But raw price is misleading: one RealToken might have a face value of $50, another $200, yet both represent very different slices of underlying real estate. What investors really care about is how much income each slice throws off.
That’s why every YAM listing shows an expected net yield alongside the ask price. We calculate it as:
Both the rent figure and the valuation are frozen at the block where the trade executes, so the yield updates in real time as either variable moves.
Viewing the market through this lens lets us compare Detroit duplex tokens and Cleveland triplex tokens on equal footing. In the sections that follow, we treat every historical trade as a yield transaction and analyze how those yields have behaved over time and across cities.
Time‑series of implied yields
Re‑plotting every YAM trade as a net‑yield transaction transforms the price scatter from Part 1 into the chart below:
The takeaway is immediate: most trades cluster around an 11 % annual yield. For perspective, industry data show that single‑family rentals across the U.S. typically trade at 4–10 % cap rates, with Detroit itself averaging roughly 10 %. In other words, RealTokens offer returns at the upper end — if not above — conventional real‑estate ranges.
Higher yield, of course, usually signals higher perceived risk. That premium likely reflects several factors:
RealT’s focus on low‑cost Midwestern housing with historically volatile rents.
The experimental nature of tokenised ownership and blockchain settlement.
Startup risk: RealT the company is still young, so investors demand a cushion.
Whether that premium is justified is up for debate; the next sections test how stable these yields have been over time and across cities.
Bias assessment
Before we drill further into the data, we flag two potential sources of bias and how we handled them.
Token‑type bias. RealT issues two broad categories of tokens:
Equity tokens (fractional property ownership with proportional rent sharing) — the focus of our study.
Debt/loan tokens (fixed‑income notes secured by real estate).
Loan tokens follow a very different price dynamic, so we excluded them entirely. In practice, they account for a minority of all YAM trades during the sample period.
Survivorship bias. Tokens tied to properties that have been sold or delisted drop out of the dataset. We have not yet quantified the effect.
With those caveats in mind, the remaining dataset should capture the behaviour of the active, rent‑producing segment of the YAM market.
Trend smoothing
To separate noise from signal, we plot the 14‑day rolling average of net yield for all equity‑type trades. Unlike the raw price chart in Part 1 — which mixed $50 and $200 tokens — yield lets us aggregate every property on a single scale.
The smoothed series hugs an 10.5–12.5 % corridor for most of the 18‑month window. Such tight clustering suggests a market that is both liquid and broadly efficient: when a token’s price drifts, counter‑orders quickly pull the implied yield back toward equilibrium.
Next week we’ll test just how resilient that equilibrium is by examining the July 2025 stress episode, when trading volume and bid–ask spreads spiked.
City-level yield differences
Zooming in by location shows a clear hierarchy:
Chicago tokens cluster just under 12 %,
Detroit hovers around 12 %,
Toledo tops the table at roughly 14 %.
In other words, the market assigns a ~200 bps premium to Toledo over Chicago. That spread likely reflects differing perceptions of tenant stability, property condition, and local economic risk — all topics we’ll unpack in a later article. For now, the takeaway is simple: location still matters, even on-chain.
Conclusion
Stepping back, today’s yield‑first lens gives us three takeaways:
Market centre‑of‑gravity ≈ 11–12 %. Whether you smooth the data or slice it by city, most RealTokens gravitate toward a double‑digit net yield — comfortably above conventional U.S. single‑family cap‑rates.
Surprising stability. The 14‑day rolling series wobbles less than one percentage point for eighteen straight months, suggesting that liquidity providers on YAM are quick to close any pricing gaps.
Location still prices risk. Chicago trades at the low end, Detroit in the middle, and Toledo offers a hefty premium — evidence that investors do weigh city‑specific fundamentals even when everything settles through the same smart‑contract.
That’s the “cruise‑speed” picture. In the next instalment we’ll fast‑forward to July 2025, when an external shock jolted volume, widened spreads, and briefly knocked yields off their axis. It’s the perfect stress‑test for YAM’s price‑discovery engine — stay tuned to see how the market coped.
Disclaimer
We strive to ensure that our work remains objective and grounded in data. In the interest of full transparency, we disclose that we hold investments in RealT assets and, more broadly, recognize the company’s approach as a noteworthy and innovative contribution to the real world asset sector.
Preliminary results are shared with the RealT team one week prior to publication, solely for the purpose of identifying potential bugs or inaccuracies in the data presented. The RealT team does not participate in the analysis, nor do they influence the methodology or conclusions in any way.
This study is conducted independently and is entirely self-funded.
This research draws partly on the free, open-source analytics suite maintained by the RealToken Community.