Real World Assets token secondary market : a real(T) case analysis
Jean-Baptiste Pleynet
Nathaniel Pleynet
Last week we dissected YAM’s July–August 2025 stress episode. This week we’re back at “cruise speed” to ask a simple microstructure question: do better-priced (higher-yield) offers fill faster?
We measure time-to-execution (TTE) as the elapsed time from an offer’s last update (new listing or price change) to the block where it executes.
Intuition says a more attractive quoted net yield should clear more quickly. However, YAM includes built-in frictions — most notably RealT’s per-property whitelisting, which allows only pre-approved addresses to trade — and that gatekeeping can delay fills regardless of price attractiveness.
Below, we quantify the TTE distribution, relate it to quoted yield in normal conditions, and then compare those patterns to the stress window.
Time-to-execution (TTE). We define TTE as the elapsed time from an offer’s last update (new listing or price change) to the first on-chain execution of that offer. Timestamps use block time and are reported in UTC.
Scope. As in Part 1 , we analyze executed equity-type trades only (no debt tokens; cancelled/expired offers are excluded). The “cruise-speed” window is 1 Jan 2024–30 Jun 2025; the stress window is Jul–Aug 2025 when noted.
Presentation. We show the TTE distribution (ECDF/histogram) and summarize results in intuitive buckets: <1 h, 1–6 h, 6–24 h, 1–3 d, 3–7 d, >7 d. Yields are net yields as defined in Part 2.
Limitation. Because we focus on executed offers, the analysis is conditional on execution (right-censoring of never-filled offers). We revisit this in a future survival-analysis note.
We relate the quoted net yield at execution to the offer’s age, defined as the time since its last update (new listing or price change).
Most fills occur quickly — a pattern that shows up even more clearly in the distribution charts below.
The cumulative view confirms the headline numbers: 50 % of offers execute within ~23 hours, and 75 % within ~6 days of their last update.
Do better deals clear faster? Yes — but the effect is modest. The boxplots below group fills by time-to-execution (TTE) and show the quoted net yield at execution in each bucket.
The pattern slopes downward: older offers tend to clear at lower yields. Concretely:
For offers that fill < 1 hour, the middle 50% of yields sits roughly between 11% and 12.8%.
For offers that linger > 7 days, the middle 50% compresses to about 10%–12%.
Outliers exist at both ends (occasionally near 0% or above 30%), but they are rare and this chart doesn’t weight by traded size. Many of these extremes coincide with valuation timing gaps, single-ticket mispricings, or unusual rent adjustments.
Caveat: Results are conditional on execution and subject to RealT’s per-property whitelisting, which can delay fills for reasons unrelated to price attractiveness.
In the July–August 2025 stress episode, volumes spiked and the market repriced yields upward. A natural question is whether time-to-execution (TTE) lengthened — signalling strained liquidity — or shortened as buyers chased perceived bargains.
Result: fills got faster. The share of offers executed within 1 hour rose noticeably relative to the cruise-speed baseline.
The yield–speed relationship also steepened: the fastest fills carried the highest yields. During the stress window, offers executed < 1 hour cleared around a ~12.6% median net yield, whereas offers that lingered > 7 days compressed to < 12%.
The cumulative view makes the acceleration explicit: 50% of fills occurred within ~5 hours, and 75% within ~61 hours of the last price update.
On possible interpretation, in line with the findings of article 3 , is that this pattern is consistent with demand-led liquidity: buyers competed to snap up discounted listings from panic sellers, pulling execution times forward and rewarding speed with higher yields.
This review is intentionally narrow. Several factors that likely influence time-to-execution (TTE) are left out and merit deeper work:
Time patterns. Day-of-week, time-of-day (RealT claim to have investors in 150 countries, so as many different time zone), and seasonal effects (trading activity and attention cycles).
Location. City-level risk and investor base differences (Chicago vs Detroit vs Toledo).
Order size & float. Ticket size and circulating token supply per property (larger floats may clear faster).
Whitelist density. Number of approved addresses for a given property (more eligible buyers → shorter TTE).
Valuation recency. Time since last property revaluation (staler values can create temporary mispricing). This effect will be analyze deeper in the coming articles.
Price signal. Deviation of quoted net yield from the market’s recent average (the “bargain” gap).
Network frictions. Occasional blockchain/settlement issues that delay otherwise attractive orders.
A fuller treatment would model the hazard of execution (survival analysis/logit) with these covariates and fixed effects. That’s beyond today’s scope, but it’s a natural next step for the series.
Three takeaways from today’s “speed” lens:
Better deals clear faster — modestly. Offers that execute < 1 hour carry higher net yields than those that linger for a week or more, but the slope isn’t dramatic and whitelisting can delay fills regardless of price.
At cruise speed, YAM is quick: about 50 % of fills occur within ~23 hours of the last price update and 75 % within ~6 days.
Stress accelerated the clock. In July–August 2025, the share of < 1 hour fills rose and the yield–speed gradient steepened, consistent with buyers competing for discounted listings. Whether those purchases were ultimately “smart” will show up in subsequent cash flows and revaluations.
What’s next: we’ll stay with the time theme but switch the lens to valuation recency — how time since the last property valuation relates to pricing and quoted yields on YAM.
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.
