What inputs should I use for an occupancy forecast?

Blend history (seasonality, past conversion), on-the-books (OTB) reservations, search and inquiry signals, event calendars, day-of-week effects, price changes, competitor/market trends, lead time and booking window, and policy constraints (minimum stays, CTA/CTD).

How far ahead should I forecast?

Common horizons are 14, 30, 60, and 90 days for operational decisions, and 6–12 months for budgeting. Use tighter, more frequently refreshed forecasts inside 60 days where pickup is most active.

How does occupancy forecasting connect to revenue metrics like RevPAR?

Occupancy forecasts inform price. If pace is ahead of forecast, protect rate and tighten rules; if behind, use dynamic pricing, value adds, or LOS discounts. Forecast × ADR planning rolls up to RevPAR targets.

Which methods work best—statistical or machine learning?

Both. Many teams start with time-series baselines (moving average, exponential smoothing) and layer features into models (regression, gradient boosting, simple neural nets). Keep models interpretable and stress-test with backtests.

How do I measure forecast accuracy?

Track error with MAE/WMAPE or MAPE and bias (over/under). Evaluate by date and horizon (e.g., D-60, D-30, D-7) to see where accuracy needs work, then recalibrate.

What about anomalies—renovations, closures, one-off events?

Annotate them. Tag out-of-service nights and one-time events so models don’t learn bad patterns. Normalize history or exclude those periods from training.

How often should I refresh the forecast?

Weekly is a good baseline; move to daily during peak seasons or event weeks. Always refresh after material price or policy changes and when pace deviates from plan.

Is an occupancy forecast the same as a booking forecast?

Related but different. A booking (pickup) forecast predicts net new reservations by day; the occupancy forecast reflects the final rooms sold for each future date after cancellations and changes.

Occupancy Forecast

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Definition: What is an Occupancy Forecast?

An occupancy forecast estimates how many nights you will sell over a future horizon—by date, week, or month—for a single listing, hotel, or portfolio. It powers day-to-day revenue moves, staffing, housekeeping schedules, and owner communications. In practice, teams blend historical seasonality with on-the-books (OTB) reservations, live demand signals, local events, and pricing effects to predict the path to a target occupancy rate and RevPAR.

In vacation rentals, occupancy forecasting is especially useful for lakeside and resort destinations with strong seasonality and event spikes. It helps hosts set rate floors and dynamic pricing rules, align turnovers, and order supplies—with fewer last-minute surprises.

Why It Matters

Revenue performance: Convert forecasts into pricing tactics that balance rate and volume to hit ADR/RevPAR goals.
Operational readiness: Staff cleanings, maintenance, and guest communications based on realistic occupancy, not hope.
Owner trust: Share forecast vs. actual and pacing insights to explain strategy and set expectations.

Signals to Use (Inputs)

History: Prior occupancy, ADR, and conversion patterns by day-of-week and season.
On-the-books (OTB): Current reservations, cancellations, and net pickup along the booking curve.
Demand indicators: Search interest, inquiries, and channel traffic (e.g., Airbnb, Vrbo, Google Travel).
Context: Local events, school calendars, weather seasonality, and regulatory changes.
Price & policies: Rate moves, booking window norms, lead time, minimum stays, CTA/CTD rules, and fees.

Common Methods

Baselines: Moving averages, exponential smoothing, or seasonal indices for a quick, explainable start.
Regression / ML: Models that include features for price, events, and pace (e.g., gradient boosting). Keep governance and backtests.
Pickup modeling: Forecast bookings added per horizon (D-90, D-60, D-30, D-7) and reconcile against capacity.

Build & Maintain the Forecast

Data hygiene: Keep your PMS clean—tag owner blocks, OOS nights, and event notes. Sync channels via your Channel Manager.
Cadence: Refresh weekly; daily during peak or event periods. Publish a simple dashboard: OTB vs. forecast, pickup, and pricing actions.
Accuracy loop: Track MAE/MAPE and bias by horizon; adjust model and rules when errors drift.

Examples

Holiday week compression: Pace is +12% vs. last year at D-45. Raise rate floors, add 3-night minimums, and tighten cancellation windows.
Shoulder-season softness: Pace lags at D-21. Relax arrival restrictions, add LOS discounts, and boost merchandising to stimulate pickup.

Related Terms

Frequently Asked Questions

What’s the fastest way to start forecasting if I’m new?

Begin with a seasonal baseline from last year, overlay on-the-books reservations, and adjust for known events and price changes. Then iterate with simple pickup models.

Daily, weekly, or monthly forecasts—what granularity is best?

Daily for pricing and operations in the next 30–60 days; weekly or monthly for longer-range planning and budgeting.

How do cancellations and rebooks affect the forecast?

Model expected cancels by horizon using history. Apply a cancellation factor to OTB and revisit more frequently close-in (D-14 to arrival) where volatility spikes.

Do I need competitor data?

It helps. Competitor rate indices and market listing counts provide context for price sensitivity and supply shifts—use directionally, not as the sole driver.

What are common pitfalls?

Relying only on history (ignoring current pace), failing to annotate anomalies, not measuring error by horizon, and moving price without checking conversion at each step.

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