Will you capture the embedded rent?
Every rent-controlled acquisition is a bet on timing. The embedded rent — the difference between in-place and market rent — is real, but will it unlock within your hold period, or does it belong to the next buyer? Traditional models can't answer that question. But the tenant behavior data buried in your rent rolls can.
Who this is for
Built for teams making acquisition decisions in rent-controlled markets
This analysis works where the data exists and the scale makes it meaningful.
Roles we work with
- Chief Investment Officers setting acquisition criteria who want confidence that timing assumptions reflect how tenants actually behave.
- Acquisitions Teams evaluating rent-controlled multifamily who want to move beyond uniform turnover assumptions.
- VPs of Acquisitions & Asset Management responsible for portfolio performance over multi-year hold periods.
Where this applies
- Rent-controlled multifamily portfolios with active acquisition pipelines
- Portfolios large enough that behavioral patterns are statistically meaningful (typically 500+ units)
- Competitive markets where an information advantage translates directly to better bids
- Organizations that have experienced the gap between modeled and actual NOI growth
Scale matters. The more units you have across similar markets and building types, the clearer your behavioral patterns become and the bigger your informational advantage. Your data does what industry averages can't.
Where traditional models fall short
Uniform turnover treats all tenants the same. They aren't.
Standard acquisition models apply a single turnover rate across all units. Clean math and a tidy model, but it quietly assumes a tenant paying 2% below market behaves the same as one paying 20% below. They don't.
The deep-discount tenant is saving thousands per year by staying put. Why would they ever give that up? The near-market tenant has no financial penalty for leaving. If conditions shift or a competitor offers a deal, they're gone.
This means two buildings with identical embedded rent today can have completely different unlock timelines that depend entirely on how that rent is distributed across tenants. The half-life concept captures this: how long until 50% of the embedded upside actually materializes through natural turnover?
Example: two buildings, same embedded rent
Building A: many small discounts
Unlocks on timeTenants clustered 5–10% below market. Each has a modest discount to protect. Enough to extend tenure, but not enough to lock them in for a decade.
Building B: few large discounts
Belongs to next buyerHalf the units are at or above market. High value, but high churn. The other half 15–25% below market causing tenants not move for years. Same total initial embedded rent, but much slower to unlock.
But your model sees them as identical.
Interactive tool
Calculate the half-life for any rent roll
Upload a rent roll CSV, set your market growth and guideline assumptions, and see the projected embedded rent half-life unit by unit, with projected turnover dates for each tenancy.
| Unit | Type | In-Place Rent | Market Rent | Lease Start | Curve |
|---|---|---|---|---|---|
| 101 | 1-Bed | $1,523 | $1,800 | 2021-03-01 | 1-Bedrooms ⌄ |
| 102 | 2-Bed | $1,878 | $2,100 | 2023-01-15 | 2+ Bedrooms ⌄ |
| 103 | 1-Bed | $1,650 | $1,900 | 2019-07-01 | 1-Bedrooms ⌄ |
| 104 | 2-Bed | $1,720 | $2,050 | 2022-10-01 | 2+ Bedrooms ⌄ |
| 105 | 1-Bed | $1,590 | $1,800 | 2020-05-15 | 1-Bedrooms ⌄ |
| 106 | 2-Bed | $2,010 | $2,100 | 2024-02-01 | 2+ Bedrooms ⌄ |
| ··· 84 more units |
How we help
Your existing portfolio is a data advantage.
Your historical lease data shows exactly how tenants in your buildings behave at different rent positions. We help you derive Turnover Curves from that data composed of how long tenants stay given their current discount to market rent and calibrated by market and building type.
Apply those curves to any acquisition target to calculate its embedded rent half-life with real precision. You'll know whether the upside is likely to materialize in your hold period before you bid.
If you own 500+ units across similar markets, your portfolio tells you what's likely to happen in buildings you don't yet own. Competitors bidding on the same asset are guessing. You're not.
What we deliver
- Turnover curves calibrated to your markets and unit types
- Half-life analysis for acquisition targets
- Bid-level confidence on embedded rent timing
- Ongoing refinement as your portfolio data grows
The bidding advantage
Win deals you should win. Walk away from ones you shouldn't.
When competitors are all applying the same 20% turnover assumption, the edge goes to whoever understands the rent roll. Your curves let you bid aggressively when timing works and pass when it doesn't.
How it works
From pilot to portfolio capability.
We don't ask you to commit until we've proven the value exists in your data. We'll tell you honestly if it isn't there.
Discovery conversation
We discuss your portfolio, acquisition cadence, and where you've felt the gap between modeled and actual NOI growth. Honest assessment of fit without a sales pitch.
45 minutes · FreeFree pilot engagement
We work from data on 4–6 sample properties to analyze tenant behavior at different rent positions. If the signal is there, we show you what the analysis reveals about a live target or recent acquisition and what it means for your bidding approach.
2–3 months · FreeAcquisition tooling
Once value is proven, we deploy half-life analysis tools for your acquisitions team. Every deal gets evaluated against your actual turnover data, not industry averages.
Quoted based on portfolio sizeReady to find out?
Start with a 45-minute conversation.
We'll look at your acquisition process and give you an honest answer about whether your portfolio data is strong enough to generate a real edge. No cost, no commitment.