A one-point change in vacancy can materially change a property’s NOI, debt-service coverage, and value. Yet many underwriting models still treat vacancy as a plug: a flat percentage copied from an offering memorandum or a prior deal. Knowing how to model vacancy assumptions means separating what is vacant now, what may become vacant, and how long it will realistically take income to return.
For multifamily investors, vacancy is usually a recurring operating friction tied to tenant turnover, seasonality, unit condition, and local demand. For office, retail, and industrial investors, it is often a lease-event risk that can appear suddenly when a major tenant rolls. The modeling discipline is the same, but the timing and evidence behind the assumption must match the asset.
Start with the vacancy definition
Before setting a percentage, decide what your model means by vacancy. Physical vacancy measures unoccupied space or units. Economic vacancy measures lost revenue, which can include vacant space, concessions, bad debt, employee units, model units, and sometimes collection loss. These are related but not interchangeable.
A 5% physical vacancy factor does not automatically mean a property loses 5% of gross potential rent. A multifamily property may have 3% vacant units but another 2% of economic loss from concessions and delinquency. Conversely, a fully occupied retail center can have meaningful future vacancy exposure if a tenant’s lease expires during the hold period.
In a clean model, show the components separately when the available data supports it. For a stabilized multifamily property, that may mean a physical vacancy and credit loss line plus a concessions line. For a multi-tenant commercial property, it may mean contractual rent by tenant and suite, followed by a specific vacancy and downtime assumption for each lease rollover. Separate lines make it easier to explain the underwriting to a lender, equity partner, or client.
How to model vacancy assumptions from evidence
The best vacancy assumption is not the most optimistic market statistic or the most conservative round number. It is the assumption that is supported by the property’s current condition, the competitive set, lease structure, and business plan.
Establish the in-place starting point
Begin with trailing operating results and current rent roll data. Calculate actual economic vacancy using collected revenue, not only scheduled rent. If a property reports 96% occupancy but collections consistently trail billed rent, the real income loss may be higher than the occupancy report suggests.
For multifamily, review at least 12 months of occupancy, notices, turn times, concessions, delinquency, and bad debt. Monthly data matters because annual averages can hide a soft leasing season or a recent demand decline. If occupancy has fallen from 97% to 93% over four months, underwriting a 3% vacancy rate because that was last year’s average ignores the current trajectory.
For commercial assets, map every tenant’s lease expiration, renewal option, square footage, rent, and credit profile. Current occupancy is only a snapshot. A property that is 100% occupied today may be riskier than an 85% occupied property with stable long-term tenants and a credible lease-up plan.
Compare the property with its actual competition
Market vacancy data is useful context, not a substitute for property-level analysis. A metro-wide multifamily vacancy rate may include new Class A deliveries, older workforce housing, and submarkets that do not compete for the same renter. The same problem appears in office and retail when broad market reports blend very different locations and quality tiers.
Build a relevant comp set. For apartments, compare unit size, condition, amenities, asking rents, effective rents, concessions, and availability. For commercial space, compare location, suite size, building quality, parking, tenant improvements, and lease economics. Ask a practical question: if a prospective tenant or resident says no to this property, where do they go instead?
If comparable properties are offering six weeks free rent, assuming your property will maintain zero concessions needs a clear reason. If your asset has materially better access, finishes, or rents below the market, the assumption may be defensible. The point is not to follow competitors blindly. It is to document why the property should perform differently.
Match the assumption to the business plan
Vacancy should change when the business plan changes. A light value-add multifamily deal may experience temporary vacancy as units turn for renovation, even if long-term demand is strong. The model should reflect that disruption before showing rent premiums and stabilization.
For example, an investor planning to renovate 10 units per month should consider the average days each unit is offline, the expected renovation duration, make-ready time, leasing time, and any delay between lease signing and move-in. Modeling a flat 5% vacancy factor while also assuming aggressive renovations can understate lost rent.
For office, retail, and industrial, model vacancy by suite and by period whenever lease expirations are material. A 20,000-square-foot tenant rolling in year two should not disappear inside a blended 8% vacancy rate. Underwrite a renewal probability, expected downtime if the tenant leaves, market rent at re-lease, tenant improvements, leasing commissions, and free-rent exposure. The model may show no vacancy if the tenant renews, or several months of lost revenue if the space goes dark. That is the risk the investment committee needs to see.
Build vacancy into the timeline, not just the annual total
Annual vacancy assumptions can produce clean-looking forecasts while hiding cash-flow pressure. Monthly or quarterly modeling is especially valuable during acquisition, renovation, lease-up, and major rollover periods.
A practical approach is to model three stages: in-place operations, transition, and stabilized operations. In-place performance reflects actual recent results. Transition captures known vacancy, renovation downtime, lease expirations, or marketing periods. Stabilized operations use a long-term economic vacancy rate based on the competitive market and the asset’s positioning.
For a multifamily acquisition, a stabilized vacancy rate might be 5%, but the first six months may warrant 7% to 9% if turnover is elevated, rents are being repositioned, or the property is carrying deferred maintenance. For a leased commercial asset, stabilized vacancy may be less relevant than the timing of individual lease events. It depends on the lease profile.
Do not forget that vacancy affects more than base rent. Empty units may reduce utility reimbursements, parking income, pet fees, and other ancillary revenue. Vacant commercial suites can also trigger landlord-paid utilities, security costs, leasing expenses, and tenant improvement outlays. A complete model follows the income and expense consequences of lost occupancy.
Test the assumption with simple downside cases
No vacancy assumption is certain, so the model should show what happens when leasing takes longer than planned. This does not require an overly complicated sensitivity table. A few targeted cases often provide more decision value than dozens of scenarios.
Test a realistic downside case against the base case. For multifamily, increase economic vacancy, extend unit turns, reduce renewal rates, or add concessions. For commercial property, shorten a major tenant’s renewal probability, extend downtime, reduce re-leasing rent, or increase tenant-improvement costs. The right stress test reflects the property’s most likely failure point.
Then measure the effect on NOI, debt-service coverage ratio, cash flow, and exit value. Because direct capitalization values are tied to NOI, a vacancy miss can affect returns twice: it reduces operating cash flow during the hold and can reduce the income used in the sale valuation. If the deal only works at a vacancy rate below what comparable properties are achieving, the issue is not a spreadsheet formatting problem. It is a pricing or strategy problem.
Common errors that create false confidence
The most frequent error is using the seller’s trailing vacancy without adjusting for current leasing conditions. Another is applying a market average to an asset with a different quality level, rent position, or tenant base. Investors also often double-count or omit economic loss by mixing vacancy, concessions, bad debt, and credit loss without defining each line item.
A separate issue is assuming immediate stabilization after renovations or lease expiration. Space does not usually move from vacant to fully productive in one month. There may be construction time, marketing time, free rent, and a lag before collections normalize. Conservative timing is often more useful than a conservative percentage because timing drives real cash flow.
Finally, avoid burying vacancy inside an unexplained percentage of effective gross income. A simple model is good. An opaque model is not. Your assumption should be easy to trace back to rent roll data, property history, market evidence, or a specific lease event.
A vacancy assumption earns confidence when it tells a believable operating story. If you can explain who may leave, how long income may be offline, what it will cost to replace it, and why the property should stabilize, you are no longer using vacancy as a plug. You are underwriting the risk that matters.
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