A property owner can look at revenue and feel pretty good about the month. The harder question is whether each property is earning its keep after the costs that quietly eat into the result.
Synthetic portfolio simulation · 10 properties · 30-day review window · USD
The portfolio's clearest opportunity is not “get more bookings.” It is finding where demand is already strong enough to support better pricing, where operating costs are eroding otherwise good properties, and where low demand needs diagnosis before a pricing change.
My first three moves: test pricing on P05, investigate turnover economics on P06, and diagnose demand/cancellations on P09.
The contribution model currently includes a 3% platform fee and a cleaning cost assumption based on one turnover per three booked nights. It does not include utilities, maintenance, supplies, taxes, management fees, financing, or other fixed costs. The result is a contribution view, not a full property P&L.
Instead of treating every property equally, the review looks for decision-worthy differences: high demand with weak pricing, strong rates with expensive operations, and low demand with cancellation risk.
Click a property. The position tells you what kind of problem you may be looking at.
Click one of the labeled points above to see the diagnosis and the action I would investigate next.
Click a row. The ranking is based on contribution, not revenue alone.
| Property | Occupancy | ADR | Contribution | Margin | Priority |
|---|
Click each stage to understand what the deduction means operationally.
Gross revenue is useful, but the deductions show where operational decisions can change the result.
91% occupancy + $132 ADR suggests the property may be leaving rate on the table.
Action: controlled price test.Strong $225 ADR, but $88 cleaning cost creates a different problem.
Action: investigate turnover economics.42% occupancy + 12% cancellations makes the high $246 ADR less reassuring.
Action: diagnose before repricing.The analysis is reproducible; the SQL is intentionally readable.
Occupancy, ADR, cleaning cost, and cancellation rate are the core synthetic inputs.
Booked nights are adjusted for cancellations. Revenue is reduced by modeled cleaning and a 3% platform fee.
The model is deliberately simplified. It points to where deeper analysis should happen; it does not claim to be a full P&L.
These are the recommendations I would bring to the property owner based on the modeled results.
The project includes the synthetic data, SQL, and project notes. Download them directly below.