Market Leasing Assumptions: The Inputs Behind a Pro Forma

Market leasing assumptions are the guesses about future leasing that sit underneath every commercial pro forma. They answer one question: when a lease ends, what happens next? The rent, the downtime, the cost to get the space leased again. Change those inputs a little and the value of a building can change a lot.

What Are Market Leasing Assumptions?

Market leasing assumptions, often shortened to MLAs, are the set of inputs an analyst uses to project cash flow after each existing lease expires. Instead of assuming every tenant stays forever at today’s rent, the model assumes the space will either renew or turn over, and it prices both outcomes.

A typical set of market leasing assumptions includes:

• Market rent: what the space would lease for today, per square foot per year.
• Rent growth and escalations: how market rent grows over time, and the annual bumps written into new leases. Around 3% annual bumps is common in many leases.
• Lease term: how long a new or renewal lease runs, often 3 to 5 years for smaller spaces.
• Renewal probability: the odds the current tenant stays.
• Downtime: months the space sits vacant between tenants if the tenant leaves.
• Free rent: months of abated rent given to land a tenant.
• Tenant improvement (TI) allowance: dollars per square foot the landlord spends to build out the space.
• Leasing commissions: what the landlord pays brokers to get the lease signed.
• Expense structure: whether the lease is NNN, where the tenant pays its share of taxes, insurance and maintenance, or modified gross, where the landlord carries some of those costs.

The Renewal Probability Shortcut

The key idea is blending. Most models do not pick one outcome. They weight the renewal case and the new tenant case by the renewal probability.

Blended assumption = (renewal probability x renewal value) + (1 minus renewal probability) x new tenant value

Here is an illustration with round numbers. These are example figures only, not current market data.

Say you have a 10,000 SF flex space with a lease expiring. You assume a 75% chance the tenant renews.

1. Downtime: 0 months if they renew, 6 months if they leave. Blended: 0.75 x 0 + 0.25 x 6 = 1.5 months.
2. TI allowance: $3/SF on a renewal, $10/SF for a new tenant. Blended: 0.75 x $3 + 0.25 x $10 = $2.25 + $2.50 = $4.75/SF, or $47,500 on 10,000 SF.
3. Lost rent from downtime: at $12/SF a year, the space earns $10,000 a month. 1.5 months of blended downtime is about $15,000.

That one rollover costs the owner roughly $62,500 in expected TI and lost rent before commissions or free rent. If you leave it out of the pro forma, your projected cash flow is too high.

How Escalations Stack Up

Rent bumps are the other big input. Using the same example, a new 5 year lease at $12/SF NNN with 3% annual bumps looks like this:

• Year 1: $12.00/SF, or $120,000
• Year 2: $12.36/SF, or $123,600
• Year 3: $12.73/SF, or about $127,308
• Year 4: $13.11/SF, or about $131,127
• Year 5: $13.51/SF, or about $135,061

Total base rent over the term is about $637,096. Leasing commissions are often calculated as a percentage of that total, so the commission assumption depends on the rent assumptions too. Everything is connected.

Where Pro Formas Go Wrong

In my experience the most common problems are not math errors. They are optimistic assumptions. I see a few patterns:

• Market rent set at the highest asking rent anyone has seen, not what spaces actually lease for.
• Renewal probability set high with no downtime at all for the tenant who leaves.
• No TI or commissions modeled at rollover, so every lease renewal looks free.
• Gross and NNN rents compared as if they were the same number.

Any one of those can make a building look better on paper than it will perform. When I review a seller’s pro forma, the market leasing assumptions are the first thing I check.

Setting Assumptions for North Idaho Buildings

Much of the inventory around Coeur d’Alene, Post Falls, Hayden and Rathdrum is smaller industrial, flex, retail and office space, often with local tenants and shorter leases. That means rollover happens more often, so these assumptions matter more, not less. A small building with two or three tenants can swing from fully leased to half empty with one move out.

The best inputs come from actual recent leases on comparable space in the same submarket, not from national averages. A flex unit in Post Falls and a retail space in downtown Coeur d’Alene can have very different downtime, TI and rent. I would rather build a model on a handful of real local comps than on a clean spreadsheet full of guesses.

If you are underwriting a building and want to pressure test the leasing side, send it over and I will take a look. What assumptions are you using now?