What happens to cash flow if occupancy drops 5%? How to model it quickly

An operator-friendly way to stress-test rent, NOI, and cash-without rebuilding your spreadsheet.

You are in a weekly review and someone asks the question that matters more than any IRR slide:

"If occupancy drops 5%, what happens to cash flow?"

In real estate, that one change can ripple through:

• rent collected (obvious)
• recoveries / service charge income (sometimes)
• turnover/leasing costs (often)
• bad debt / arrears (in stress cases)
• and-most importantly-debt service coverage and cash runway

The trick is answering it quickly and consistently, without duplicating files or tearing up your model.

This guide shows a fast, spreadsheet-first approach you can implement in minutes.

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First: clarify what "occupancy drops 5%" actually means

People use "5%" in two different ways:

1. 5 percentage points (pp): 95% -> 90%
2. 5% relative: 95% -> 90.25% (95% - 0.95)

In property conversations, it is usually percentage points (pp). If you do not clarify internally, you will produce answers that sound precise but are mismatched.

In the steps below, I will assume -5 pp unless stated otherwise.

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The 10-minute model: a quick cash impact answer

This is the fastest "good enough" approach for portfolio reviews.

Step 1) Start from gross potential rent

Set up (or reference) the base case:

• Units (or NLA / rooms)
• Market / passing rent
• Gross Potential Rent (GPR)

Example:

• GPR = Units - RentPerUnit

If you already have a rent roll, your GPR is basically "rent if fully occupied."

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Step 2) Apply occupancy as a single multiplier

Define:

• Occ_base (e.g., 0.92)
• Occ_delta (e.g., -0.05 for -5 pp)
• Occ_stress = MAX(0, MIN(1, Occ_base + Occ_delta))

Then:

• Effective Rent (billings)
  Rent_billed = GPR - Occ_stress

This alone answers the headline question: "What happens to top-line rent?"

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Step 3) Convert billings into cash (collections)

In the real world:

• billed rent - cash received
• arrears can rise when occupancy drops (or when tenant quality deteriorates)

Add a collections factor:

• CollectionRate (base might be 0.99; stress might be 0.97-0.98)

Cash in:

• Rent_cash = Rent_billed - CollectionRate

If you skip this step, you will often understate how quickly cash tightens in a stress case.

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Step 4) Adjust the costs that actually move with occupancy

Not all opex changes when occupancy falls. Most fixed costs stay fixed.

But a few items often move:

• letting / leasing fees
• unit turnaround costs (cleaning, minor repairs)
• utilities in some operating models
• bad debt provision (if you model it separately)

A quick way is to split opex into:

• Fixed opex (unchanged)
• Variable opex (linked to occupancy or to move-outs)

Simple stress approximation:

• Opex_total = Opex_fixed + (Opex_variable - Occ_stress)

Or even more conservative:

• keep opex flat (worst case for NOI), and only reduce income.

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Step 5) Flow it through to cash runway (the "so what")

Now connect to the two outflows that decide survivability:

• Debt service (interest + principal)
• Capex (if ongoing)

Cash movement:

• NetCash = Rent_cash - Opex_total - Capex - DebtService

Then track:

• ClosingCash = OpeningCash + NetCash

Your answer becomes:

• "NOI drops by -X per month"
• "Cash bottom is -Y in month Z"
• "We breach DSCR/ICR in months A-B (if applicable)"

That is the minimum set of outputs stakeholders actually act on.

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A simple numeric example (so you can see it instantly)

Assume monthly:

• GPR = -100,000
• Base occupancy = 95%
• Stress occupancy = 90% (-5 pp)
• Collections = 99% (base), 98% (stress)
• Opex fixed = -25,000
• Opex variable = -5,000
• Debt service = -40,000
• Capex = -5,000

Base case

• Rent billed = 100,000 - 0.95 = 95,000
• Rent cash = 95,000 - 0.99 = 94,050
• Opex = 25,000 + (5,000 - 0.95) = 29,750
• Net cash = 94,050 - 29,750 - 40,000 - 5,000 = -19,300

Stress case

• Rent billed = 100,000 - 0.90 = 90,000
• Rent cash = 90,000 - 0.98 = 88,200
• Opex = 25,000 + (5,000 - 0.90) = 29,500
• Net cash = 88,200 - 29,500 - 40,000 - 5,000 = -13,700

Result: a -5 pp occupancy move reduces net cash by -5,600/month here-before any knock-on effects like higher leasing costs, longer voids, or covenant traps.

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The 30-minute model: make it realistic without making it heavy

If you want something still fast, but closer to reality, add two upgrades:

Upgrade 1: ramp the occupancy drop over time

Occupancy typically does not drop overnight across the whole asset.

Create an activation factor:

• Step change: Active = IF(Month >= StartMonth, 1, 0)
• Ramp: Active = MIN(1, MAX(0, MonthsFromStart / RampMonths))

Then:

• Occ_stress_t = Occ_base_t + (Occ_delta - Active)

This stops your forecast from looking like a cliff.

Upgrade 2: model the cause of occupancy drop (voids vs price vs churn)

A 5 pp occupancy drop can come from:

• a few leases expiring with slower re-letting
• downtime from refurb works
• deliberate vacancy while repositioning
• weaker demand / higher incentives

Your cash answer changes depending on the cause.

A lightweight way to capture this:

• add a "void duration" assumption (e.g., 1 month -> 2 months)
• add a "leasing cost per move-in" assumption
• optionally add a "concession" or "incentive" line

This produces a much more believable cash story: not just "rent down," but "rent down and leasing costs up."

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The spreadsheet pattern that makes this fast every time

If you only implement one modelling improvement, implement this:

Use a scenario table + a single scenario selector

Instead of rewriting assumptions in-line, keep a small table like:

• Scenario name
• Occupancy delta (pp)
• Collections rate
• Start month
• Ramp months

Then all your modules reference the selected scenario.

This gives you:

• Base / Downside / Severe downside instantly
• No duplicate files
• Fewer formula errors
• Repeatable answers in meetings

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Common mistakes that make occupancy stress tests misleading

1. Mixing up % vs percentage points (95% -> 90% is -5 pp, not -5%)
2. Reducing opex too aggressively (many costs are fixed)
3. Ignoring collections/arrears (cash tightens before NOI tells you)
4. Forgetting downtime/capex interaction (refurbs often cause occupancy dips)
5. Not checking covenants / cash traps (sometimes cash is restricted even if "portfolio cash" looks fine)

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What to report after you run the stress (the 5 outputs people care about)

When someone asks "what happens if occupancy drops 5%," a useful answer is:

1. NOI impact (-/month and -/year)
2. Minimum cash balance and when it happens
3. Peak funding requirement (if cash goes negative)
4. Debt service coverage impact (DSCR/ICR proxy)
5. Which SPVs/assets drive the downside (portfolio concentration risk)

That last one is where multi-entity portfolios struggle: the portfolio number is easy; the explainable breakdown is hard.

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How we help teams do this across every SPV (without rebuilding spreadsheets)

In SPV-heavy portfolios, the modelling challenge is not one spreadsheet-it is consistency across entities:

• one-stop visibility across multiple Xero or QuickBooks SPVs
• standardised COA mappings so rollups reconcile cleanly
• portfolio dashboards with drill-down
• FP&A and rolling forecasts
• "what-if" scenario planning (occupancy shifts, rates, refurb programmes) tied directly to cash flow and returns

Drop-in CTA copy:
If you want to answer "what happens if occupancy drops 5%?" in minutes-at portfolio level and by SPV-without rebuilding your model each month, we can show you a scenario-ready setup that stays reconciled and drillable.