Deep-dive guide
Short-Term Rental Shock Simulator
Executive industry analysis
A short-term rental is a small hotel with one room and a very high fixed cost. Rent or mortgage, utilities and insurance are due whether the property is booked for three nights or twenty-five, so the business behaves like an airline: most of the cost is committed in advance and profit depends on how full the calendar is. That is why the simulator starts with demand. Two-thirds occupancy can look comfortable and still leave very little, because each additional booked night adds revenue with almost no extra fixed cost, while each empty night costs you the full price of holding the property.
Turnover is the cost that surprises new hosts. Every checkout means cleaning, laundry, supplies and a gap in the calendar. A three-night stay spreads that cost over three nights, while a one-night stay carries all of it in a single night, so average stay changes profit even when occupancy and price do not move. Longer minimum stays often raise net income despite a slightly lower occupancy because cleaning and supplies fall per booked night. The turnover node shows the cost per night so you can see when short bookings are quietly taking your margin.
The hidden layer is made of many small items. Occupancy and tourist taxes, linen and consumables, a reserve for guest damage, maintenance and wear on a property that is used constantly, and the software and time needed to manage listings and messages together take a double-digit slice of revenue. Hosts who track only rent, cleaning and platform fees routinely overestimate profit by a third. Seasonality adds another difficulty: a property that earns well in summer can lose money in winter, so a single average month hides the periods when cash is short.
The scenario comparison shows the trade-off between price and structure. A lower rate and longer minimum stay give up some revenue per night but win more bookings and cut turnover cost, and in the twenty-four-month view the seasonal swing makes the difference visible: the alternative plan lifts the weak months more than it lowers the strong ones. The breaking point shows how far occupancy or rate can fall before a normal month stops covering its own cost, which is the number to check before signing a lease or buying a property for rental income.
Formula and methodology blueprint
The model computes one month of operations from eight inputs and projects 24 months with a seasonal occupancy swing of plus or minus twenty percent. Constants for taxes, supplies and reserves are typical host assumptions and are listed so you can judge them.
- Booked nights and stays
- nights = 30 × occupancy; stays = nights ÷ average stay
- Longer stays mean fewer turnovers for the same number of nights.
- Revenue
- revenue = nights × nightly rate
- Before any platform fees or taxes.
- Visible costs
- platform = revenue × fee %; cleaning = stays × cleaning cost; plus rent and utilities
- The costs most hosts track.
- Variable and hidden costs
- maintenance = revenue × %; tax = 5% × revenue; supplies = 6 × stays; damage = 1.2% × revenue; software and time = 35 + 1% × revenue
- Typical assumptions for tourist tax, supplies and reserves.
- Seasonality
- occupancy(month) = occupancy × (1 + 0.2 × cos(2π (month − 6) ÷ 12))
- Peaks in summer, dips in winter, same average across a year.
- Net outcome
- net = revenue − all costs above
- The monthly cash flow the breaking point drives to zero.
Cascading elasticity
Cascading elasticity: rent and utilities are fixed, so every lost booked night falls straight through to profit, while platform fees, tax, maintenance and damage fall with revenue. Occupancy and nightly rate usually top the sensitivity ranking, followed by housing cost.
Three real-world case studies
Bootstrapped starter
Bootstrapped starter
A spare flat with low rent and modest prices. Fixed costs are small enough that decent occupancy leaves a steady side income while the host learns pricing and reviews.
Scaling mid-market
Scaling mid-market property
A larger, better-located home with a high nightly rate and bigger housing cost. Revenue is much higher, but a few points of occupancy now move hundreds a month.
Black-swan crisis
Black-swan crisis
A local rule change, new taxes and a demand slump push occupancy to the high thirties with shorter stays and costlier cleaning. Load it to see what must change for the property to cover its cost again.
Industry benchmark matrix
| Metric | Danger zone | Industry average | Top 10% elite |
|---|---|---|---|
| Occupancy | Below 50% | 60–70% | Above 75% |
| Average stay | Under 2 nights | 2.5–4 nights | Over 4 nights |
| Cleaning and supplies per booked night | Above 20% of rate | 10–15% | Below 8% |
| Revenue versus rent and utilities | Below 1.6× | 2–2.5× | Above 3× |
| Net margin | Below 0% | 10–20% | Above 28% |
Variables and levers
Nightly rate
Average price per booked night.
Lever: Use dynamic pricing around local events and weekends; a few percent higher on peak nights usually beats discounting quiet ones.
Occupancy
Share of nights in the month that are booked.
Lever: Improve photos and the first lines of the listing, respond within an hour and open the calendar further ahead before cutting price.
Average stay
Nights per booking. Longer stays mean fewer turnovers.
Lever: Set a minimum stay on busy periods; fewer, longer stays cut cleaning and turnover cost per night.
Cleaning cost per stay
What you pay after each checkout, net of any cleaning fee guests pay.
Lever: Shop around for a cleaner on a flat per-stay rate and separate the cleaning fee in the listing so guests contribute.
Platform fee
Share of revenue taken by booking platforms.
Lever: Encourage direct repeat bookings and compare host-only and split-fee models.
Rent or mortgage per month
What it costs to hold the property.
Lever: Renegotiate rent or refinance if the property cannot cover its holding cost in ordinary months.
Utilities per month
Electricity, water, internet and heating, including empty nights.
Lever: Fit smart thermostats and reduce idle heating or cooling between guests.
Maintenance and wear
Share of revenue set aside for repairs and replacements.
Lever: Preventive maintenance and durable furnishings cost less than emergency repairs between bookings.
How to use this simulator
- 1Start with the Realistic preset or enter your listing's real numbers.
- 2Read the five nodes to see whether demand, turnover or coverage turns red first.
- 3Press Find my breaking point to see how far occupancy or rate can fall before cash flow reaches zero.
- 4Compare the longer-stay plan with today's plan across a full seasonal cycle.
- 5Share the scenario link with a co-owner or lender.
Mistakes to avoid
- Planning from the best month. Occupancy swings with the seasons, so a property that earns well in summer can lose money in winter; check the whole year, not the peak.
- Leaving out small recurring items. Taxes, linen, supplies, damage and software together take a double-digit share of revenue, and hosts who skip them overestimate profit by about a third.
- Cutting the nightly rate to fill the calendar. A lower price with the same cleaning cost per stay can leave a booking worth less than an empty night, so test longer minimum stays first.
- Underestimating the holding cost. Rent or mortgage and utilities are due on empty nights too, so occupancy has to cover them before any night produces profit.
Frequently asked questions
What occupancy do I need to break even?
Use the breaking point. It walks occupancy downward in 1,000 steps and shows the level at which a normal month ends at zero, given all your other inputs.
Why does average stay change profit?
Because each stay carries a cleaning cost and supplies. The same number of booked nights split into fewer, longer stays means fewer turnovers and less cleaning cost per night.
Why is there a seasonal swing in the timeline?
Short-term rentals rarely earn the same every month. The model moves occupancy by up to twenty percent above and below average, peaking in summer, so the timeline shows the strong and weak months.
How accurate are the numbers?
They are a planning model with stated assumptions, not tax or accounting advice. Local taxes and rules vary, so check yours and use your own figures.
What do the Monte Carlo results mean?
The tool reruns the scenario 1,000 times with every input varied randomly by around fifteen percent and shows how often a month ends at zero or below, with the range of likely outcomes.
This is an educational planning model with stated assumptions. It is not financial, tax or legal advice. Replace the inputs with your own figures and verify important decisions with a qualified professional.