What if room rate or occupancy changes?
An interactive illustration with fictional data. One business question, transparent assumptions and an understandable result.
Your scenario
This is a mathematical calculation of your assumptions, not a demand forecast or a pricing recommendation.
Expenses, profit and break-even
- Total expenses
- ₪1,230,500
- Estimated operating profit
- ₪232,000
- Operating profit margin
- 15.9%
- Break-even occupancy
- 55.3%
Variable expenses: ₪405,000 · Commissions: ₪175,500 · Fixed expenses: ₪650,000
| Metric | Baseline | Your scenario | Change |
|---|---|---|---|
| Room revenue | ₪1,462,500 | ₪1,462,500 | ₪0 |
| Total expenses | ₪1,230,500 | ₪1,230,500 | ₪0 |
| Estimated operating profit | ₪232,000 | ₪232,000 | ₪0 |
| Operating profit margin | 15.9% | 15.9% | 0 pp |
| Break-even occupancy | 55.3% | 55.3% | 0 pp |
Assumptions, formula and limitations
100 rooms × 30 nights × (occupancy ÷ 100) × average room rate = room revenue.
Total expenses = occupied room-nights × variable cost per occupied room-night + revenue × commission rate + monthly fixed expenses. Commission is a blended percentage of all room revenue. Variable cost excludes commissions and fixed expenses.
Estimated operating profit = revenue less total expenses; profit margin = profit ÷ revenue. Break-even occupancy = fixed expenses ÷ [3,000 × (rate × (1 − commission rate) − variable cost)] × 100. Commission rates in this formula are decimal fractions. Above 100% means break-even cannot be reached at this capacity; with no positive room contribution, there is no operating break-even (except with zero fixed costs and zero contribution). With no revenue, profit margin is undefined.
The original baseline remains 75% occupancy and ₪650 per room. The extension adds fictional assumptions: ₪180 per occupied room-night, ₪650,000 fixed expenses and 12% commission. Comparisons always use this fixed baseline; differences in percentages are shown in percentage points.
Room capacity is fixed. This simplified monthly model excludes taxes, financing, depreciation, ancillary revenue, cancellations, seasonality and price–occupancy relationships. Costs are linear. All values are fictional, not business forecasts; no client information, live systems or external APIs are connected.
How can AI extend scenario analysis?
AI can help identify trends, suggest data-informed assumptions, explain results and compare alternatives, depending on the information and models available within the organization. This demo does not run a live AI model.