A rental property can look fantastic on a spreadsheet when every unit is occupied, every tenant pays on time, and every lease renews immediately.

Real properties rarely behave that perfectly.

Apartments sit empty between tenants. Office leases expire. Retailers close stores. Some tenants receive concessions, while others simply stop paying.

Even a relatively small difference between expected and actual occupancy can have a surprisingly large effect on cash flow and property value.

That is why understanding how vacancy assumptions influence real estate investment returns is an important part of professional property underwriting.

Vacancy affects more than rental income. It changes net operating income, capitalization-based valuations, debt-service coverage, cash-on-cash returns, and sometimes refinancing capacity.

NCREIF treats vacancy alongside NOI growth and cap rates as a core property operating indicator, while Fannie Mae’s multifamily underwriting guidance specifically calls for historical physical and economic vacancy information when assessing cash flow.

The goal is not to predict occupancy perfectly. It is to use assumptions conservative enough that the investment can survive when reality turns out slightly worse than expected.

1. Vacancy Directly Reduces Effective Rental Income

The easiest way to understand vacancy risk is to begin with potential gross rent.

Suppose a property could generate $120,000 per year if every unit remained occupied for the full twelve months.

That $120,000 is theoretical.

At a 5% vacancy assumption, expected rental income falls to:

$120,000 × 95% = $114,000

At 10% vacancy:

$120,000 × 90% = $108,000

That extra five percentage points of vacancy reduces annual revenue by another $6,000.

It may not sound enormous, but property expenses do not automatically fall by the same amount. Property taxes, insurance, landscaping, security, and many maintenance costs continue whether a unit is occupied or empty.

This creates operating leverage in the wrong direction.

Vacancy reduces revenue while much of the expense base remains relatively fixed.

That is why an overly optimistic occupnacy assumption can make a property’s expected profitability look much stronger than it really is.

2. Physical Vacancy and Economic Vacancy Are Not the Same

A property can appear almost fully occupied while still losing meaningful income.

This happens because physical vacancy and economic vacancy measure different things.

Physical vacancy generally refers to space or units without tenants.

Economic vacancy looks more broadly at income that could have been earned but was not actually collected. That can include vacant units, concessions, delinquent rent, collection losses, and leases priced below potential market levels depending on the underwriting approach.

Imagine a 100-unit apartment property with 96 occupied units.

Physical occupancy is 96%, which looks healthy.

But suppose several tenants received one month of free rent, while another group is behind on payments.

The actual cash collected could be lower than the 4% physical vacancy rate suggests.

Fannie Mae’s underwriting guidance requests both physical and economic vacancy history and emphasizes using historical operating performance alongside anticipated conditions when estimating property cash flow.

For investors, the lesson is simple: occupancy alone does not equal cash collection.

Analyse what tenants actually pay.

3. Vacancy Can Have a Large Effect on NOI and Property Value

Vacancy becomes especially powerful because it flows directly into net operating income.

Return to the property with $120,000 of potential annual rent.

Assume operating expenses are $42,000.

With 5% vacancy:

Effective Income: $114,000
NOI: $72,000

With 10% vacancy:

Effective Income: $108,000
NOI: $66,000

The difference in NOI is only $6,000.

Now value the property using a 6% capitalization rate:

$72,000 ÷ 0.06 = $1.20 million

At the higher vacancy assumption:

$66,000 ÷ 0.06 = $1.10 million

A $6,000 difference in annual NOI has produced a $100,000 difference in implied value.

CBRE defines stabilized cap rates using stabilized NOI relative to acquisition price and considers normal market vacancy when characterizing stabilized properties.

This is one reason small underwriting mistakes can become expensive.

When income is capitalized into property value, a modest error in sustainable NOI may translate into a much larger valuation error.

4. Higher Vacancy Can Weaken Debt-Service Coverage

Vacancy becomes even more important when the property uses debt.

A useful measure is the debt-service coverage ratio:

DSCR = NOI ÷ Annual Debt Service

Suppose annual debt payments are $52,000.

Using the previous example, a $72,000 NOI produces:

$72,000 ÷ $52,000 = 1.38x DSCR

If NOI falls to $66,000:

$66,000 ÷ $52,000 = 1.27x DSCR

The mortgage payment did not change.

The property’s safety margin did.

Push vacancy higher again and the investment could approach the point where property income barely covers scheduled debt obligations.

This matters for both existing owners and prospective buyers.

A highly leveraged property that works beautifully at 3% vacancy may become surprisingly fragile at 10%.

Lenders know this, which is why professional underwriting generally does not assume permanent perfect occupancy. Fannie Mae, for example, explicitly incorporates economic vacancy into multifamily underwriting rather than relying entirely on gross potential rent.

Investors should apply the same discipline.

5. Vacancy Assumptions Should Reflect the Market and Property Type

There is no universal “correct” vacancy percentage.

A stabilized apartment property in a supply-constrained city can have very different occupancy economics from a suburban office building facing structural tenant departures.

Current market conditions provide useful context.

CBRE reported a 4.3% U.S. multifamily vacancy rate in Q2 2026, while its midyear outlook expected vacancy around 4.9% for the broader 2026 period.

That does not mean every apartment model should automatically use 4.3% or 4.9%.

Local conditions can differ dramatically.

A newly built property competing with several nearby developments may need aggressive concessions despite a healthy national market. An older building in an established neighborhood could maintain better occupancy because relatively little new supply exists.

Property type matters too.

Office, industrial, multifamily, retail, healthcare, and hospitality assets have different lease structures and tenant risks.

NCREIF therefore tracks vacancy separately by property type and region rather than assuming one national rate applies everywhere.

Use market statistics as a starting point, then adjust for the actual asset.

6. Vacancy Should Change Across the Investment Period

Another common modelling mistake is using exactly the same vacancy percentage every year.

Real estate rarely operates in such a straight line.

Suppose a property has a major tenant whose lease expires in Year 4.

Years 1 through 3 may reasonably have 3% vacancy. Year 4 could require a much larger assumption because the tenant may leave, renovations may be necessary, and the space could remain empty while a replacement is found.

Vacancy might then normalize later.

The same logic applies to apartment lease-up.

A newly developed property may initially have substantial vacancy before reaching stabilization.

Fannie Mae’s 2026 guidance for near-stabilized properties requires detailed monthly underwriting operating data, lease-up schedules, rent comparables, and analysis of competing supply.

That illustrates an important principle: vacancy is dynamic.

Advanced property models should reflect expected lease expirations, new competing supply, tenant quality, seasonal patterns, and future demand.

Using one permanently low vacany assumption may make the spreadsheet cleaner, but not necessarily more accurate.

7. Stress-Test Vacancy Instead of Relying on One Forecast

A strong investment model should not contain only one vacancy assumption.

Build several.

Suppose your base case assumes 5%.

Create another scenario at 8% and a severe case at 12%.

Then watch what happens to effective rental income, NOI, DSCR, annual cash flow, and estimated property value.

You might discover that the investment remains perfectly manageable at 8% vacancy but becomes cash-flow negative at 12%.

That information is extremely useful.

It shows how much operating cushion exists before the investment starts creating problems.

Also test vacancy together with other adverse conditions.

An economic downturn may simultaneously increase vacancies, slow rent growth, increase tenant concessions, and push cap rates higher.

Those variables are not always independent.

NCREIF’s operating reports track vacancy, NOI growth, expense ratios, and cap rates together precisely because property performance is influenced by the interaction between these measures.

A consistant underwriting process should therefore examine multiple variables at once rather than changing vacancy in isolation.

Build the Assumption From Evidence

Start with actual property history.

Review at least several years of occupancy, tenant turnover, concessions, and bad-debt experience when available.

Then study competing properties and local supply.

If thousands of new apartment units are scheduled for completion nearby, assuming vacancy improves immediately may be difficult to justify.

CBRE’s 2026 multifamily outlook illustrates how supply conditions can differ substantially across regions even when national fundamentals appear relatively stable.

Finally, add a margin of safety.

If the deal only produces an acceptable return at 98% occupancy, the purchase price may simply be too aggressive.

The strongest investments usually do not require perfect conditions.

Vacancy assumptions can quietly change almost every important real estate investment metric.

Higher vacancy reduces effective income, pressures NOI, lowers debt-service coverage, and can materially reduce property value when lower income is capitalized.

Economic vacancy can also be worse than physical occupancy suggests because concessions and unpaid rent create additional income loss.

The answer is not to automatically use an extremely pessimistic number.

Instead, base the assumtion on historical performance, local supply and demand, tenant quality, lease structure, and property type. Then stress-test several weaker scenarios.

Before purchasing your next income property, calculate returns at your expected vacancy rate – and then run the same model with vacancy several percentage points higher.

If the investment still works under less favorable conditions, your expected return is probably built on a much stronger foundation.