Dynamic Pricing Simulator
Dynamic pricing simulator that adjusts your hotel base rate (BAR) up or down for on-the-books occupancy and lead time. Free, no sign-up, runs in your browser.
Inputs
Recommendation
When to raise rates vs when to hold
Dynamic pricing is about matching price to demand. This simulator uses occupancy and lead time to suggest a rate adjustment from your base rate (BAR). It’s especially useful for independent hotels that want a simple repeatable approach without complex forecasting models.
If you’re close to arrival and already high occupancy, raising rates is often safer because remaining inventory is scarce. If you’re far out and occupancy is low, try improving distribution and value first (better photos, clearer policies, packages) before discounting aggressively.
What this tool does not consider
- Local events and compression nights
- Competitor pricing changes
- Minimum length of stay rules and rate fences
- Cancellation and no-show patterns
What is dynamic pricing for hotels?
Dynamic pricing is a revenue management practice where a hotel moves its room rate up or down from the best available rate (BAR) as demand changes, instead of selling one fixed price for a season. It measures demand through signals such as on-the-books occupancy and lead time (days until arrival). Hoteliers use it to capture higher ADR when rooms are scarce and to protect occupancy when pickup is slow.
Formula
Suggested rate = Base rate × (1 + occupancy adjustment + lead-time adjustment)
Occupancy adjustment: +20% above 90%, +10% above 75%, −10% below 30%, otherwise 0. Lead-time adjustment (first matching rule only): +5% if under 2 days out with occupancy above 70%; −5% if under 7 days out with occupancy below 50%; +10% if more than 60 days out with occupancy above 20%; otherwise 0.
Worked example: a $160 base rate
- Start with the default inputs: base rate $160, occupancy 65%, 14 days to arrival.
- 65% is between 30% and 75%, so the occupancy adjustment is 0. At 14 days out no lead-time rule applies either, so the simulator returns $160 × 1.00 = $160.
- Change occupancy to 80%. That is above 75%, so the occupancy adjustment becomes +10%: $160 × 1.10 = $176.
- Keep 80% and set days to arrival to 1. The last-minute rule adds +5% on top: $160 × 1.15 = $184.
- Now set occupancy to 25% and days to arrival to 5. Both discounts apply: $160 × (1 − 0.10 − 0.05) = $160 × 0.85 = $136.
The same room moves across a $136–$184 range depending only on how full you are and how close the date is, which is the core idea behind dynamic pricing.
How to read your result
- A suggested rate equal to your base rate means none of the rules fired; demand looks normal for that lead time.
- Adjustments add together, so the largest possible move is +30% (over 90% occupancy and more than 60 days out) and the deepest discount is −15%.
- Thresholds are strict: exactly 75% or 90% occupancy does not trigger an increase. Test values just either side of a threshold before acting.
- "High" confidence appears only when occupancy is above 75%. The rules are simple heuristics, so treat any result as a starting point, not a final price.
Frequently asked questions
How often should a small hotel change its room rates?
Many independent hotels review rates for the next 30–90 days at least weekly and check the next 7–14 days daily. The right cadence depends on how quickly bookings come in and how much time you have to monitor them.
Is dynamic pricing the same as price gouging?
No. Dynamic pricing adjusts rates in both directions in response to normal supply and demand, and rates fall when demand is weak. Some jurisdictions limit price increases during declared emergencies, so check local rules.
What is the difference between BAR and a dynamic rate?
BAR (best available rate) is the reference public rate for a date and room type. A dynamic rate is what BAR becomes after you adjust it for current demand; in this simulator the base rate you enter plays the role of BAR.
Do I need revenue management software to use dynamic pricing?
Not to start. A simple rule set based on occupancy and lead time, applied consistently and reviewed against results, is a workable first step. Software becomes more useful as room count, channels and data volume grow.