Are you choosing the optimal leasing strategy or just going on gut feel?
Every vacant unit in a rent-controlled building creates the same choice: discount the rent or offer free rent at market? Most leasing teams default to a rule of thumb. But the two incentives don't produce the same tenant behavior. Across hundreds of lease-ups, that hidden difference compounds into the gap between projected and actual NOI.
Who this is for
Built for asset management and leasing teams
making decisions at scale
This analysis works where the decision frequency is high enough that the pattern matters and the data exists to reveal it.
Roles we work with
- VPs of Asset Management responsible for portfolio-wide leasing strategy and NOI performance who want to move from rules of thumb to evidence-based guidance.
- Asset Managers who set leasing parameters for their buildings and want to give leasing teams a defensible framework rather than a gut-feel directive.
- Leasing Managers who make these calls daily and would benefit from real-time guidance calibrated to current market conditions.
Where this applies
- Rent-controlled multifamily portfolios with active ongoing vacancy
- 500+ units where behavioral patterns are statistically meaningful
- Markets where tenant behavior varies materially by incentive type
- Organizations that currently default to portfolio-wide rules of thumb ("always maximize in-place rent" or "always minimize vacancy")
Scale matters.
The decision frequency is what makes scale matter here. If you're making this choice hundreds or even thousands of times a year, a systematic error in how you model tenant behavior doesn't just affect one unit, it compounds across your entire portfolio.
Use your own data to reveal patterns rather than relying on industry averages.
Where traditional models fall short
Both incentive types create the same tenant. Or so the model assumes.
Every analysis for leasing incentives starts the same way: calculate when the two options become equivalent. That approach is fine. It's the underlying assumption that isn't.
The model assumes both incentives produce the same tenure length. But a tenant with a monthly discount has something to lose by moving. Every month they stay, they're saving money versus market rate. That financial anchor extends their tenure, often materially. Meanwhile, offering a tenant free rent might maintain higher in-place rents, but that tenant does not face a financial penalty for leaving. This results in higher churn.
Without accounting for the different tenure lengths of each incentive, you're flying blind.
And this isn't one lease decision. This decision is made every time a tenant leaves, again, and again. A systematic error in how you model tenure doesn't affect just one vacancy. It might be made hundreds or even thousands of times a year shaping the value of the whole portfolio for years to come.
Two options with different trade-offs
Option A: Discount to Market
The tenant pays below market from day one. Revenue starts immediately, but at a lower rate.
Option B: Free Rent at Market
The tenant pays full market rate once free rent expires. Higher monthly rent, but no revenue during the incentive period.
The trade-off only resolves once you know your actual tenure numbers.
The standard approach is to calculate when one option overtakes the other, but it uses the same assumed tenure for both. What if tenants stay too long or churn too fast? Accurate analysis requires knowing the expected tenures. And that knowledge, applied to every lease-up across your portfolio, is the gap between your modeled NOI and your actual NOI.
Interactive tool
See the optimal incentive for any unit, in real time
The tool takes your turnover curve, current market assumptions, and the specific parameters of a vacancy, and tells an asset manager which incentive structure produces better long-term value and by how much. The leasing manager sees an actionable recommendation, not a spreadsheet.
Set once per building. Parameters flow down to all leasing decisions.
Real-time guidance for each unit. No spreadsheet needed.
How we help
Your leasing history is the answer you've been guessing at.
Your historical lease data shows exactly how tenants in your buildings behave at different rent positions, how long they stay when they're at market versus below market, and how that changes as their discount shrinks or grows over time.
We help you derive that relationship from your portfolio data. The result is a set of Turnover Curves made from the relationship between a tenant's discount to market and their likelihood of staying and specific to your markets and building types, not industry benchmarks or rules of thumb.
Apply those curves to any leasing decision, and you can calculate the expected tenure under each incentive type. The standard analysis stops being theoretical. It starts reflecting how your tenants actually behave.
Once that capability is in place, a recurring guess becomes a systematic advantage. By applying your data to every vacancy across every building, you can drive value across your portfolio.
What we deliver
- Turnover curves calibrated to your markets and unit types
- Incentive optimization analysis for any vacancy
- Asset manager tools for setting leasing parameters by building
- Leasing manager guidance for real-time decisions
- Ongoing refinement as your portfolio data grows
The operational advantage
Stop giving your leasing team a rule. Give them a tool.
The difference between "always maximize in-place rent" and evidence-based leasing guidance isn't one decision, it's the compounded outcome of every leasing decision you make across your portfolio.
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 composition, current leasing practices, and how frequently this decision gets made across your buildings. We'll show you how the analysis would work with your data and give you an honest assessment of whether it's worth pursuing.
45 minutes · FreeFree pilot engagement
Two questions determine whether this is worth pursuing. We answer both using data from four to six sample properties. You receive the analysis regardless.
- Is there signal in your data? Do tenants with discounts actually behave differently than tenants at market in your buildings?
- Is it economically meaningful? Does that behavioral difference translate into significant value across your portfolio?
Leasing tooling
Once value is proven, we deploy optimization tools for your asset management and leasing teams so every vacancy gets evaluated against your actual turnover data, not a rule of thumb.
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Asset managerSet parameters per building →
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Leasing ManagerReal-time guidance per unit →
Ready to find out?
Start with a 45-minute conversation.
We'll look at your portfolio's leasing patterns and give you an honest answer about whether your data is strong enough to reveal a real difference in tenant behavior. No cost, no commitment.