Test the revenue response

A higher rate helps only if enough guests still book. Change the assumptions to see where the recommendation pays.

16–30 September 2026, every night · Booking.com search pages, rendered in a browser and captured in full, one page per stay-night

Price changes only work if enough guests still book

The model covers the 5 repriced nights in the fortnight. Hold, closed and conditional nights are excluded. Revenue totals below relate to those repriced nights; they are not total hotel revenue for the window.

Occupancy and elasticity are assumptions, not measured. Room count is disputed between sources (121 vs 157). Use the inputs below to substitute actual figures. Where Booking.com shows rooms left, the reprice applies only to that allotment.

Set the assumptions

Results update as inputs change

Available rooms

121 rooms 157 rooms Custom

Custom room count

Select a published count or enter the actual room count.

Occupancy at current rates 72%

Blanket assumption applied to every repriced night, unless nightly inputs are enabled.

Price elasticity -0.9

Elasticity means how much bookings respond to price: -0.5 = guests barely react to price; -1.5 = very price-sensitive.

Optional: occupancy by night

Enter the occupancy expected at current rates for each repriced night. Saved nightly entries are retained when the option is turned off.

Use per-night occupancy instead of the blanket assumption

Expected occupancy at current rates
Night	Occupancy
23 Sep	Occupancy on 23 Sep %
26 Sep	Occupancy on 26 Sep %
28 Sep	Occupancy on 28 Sep %
29 Sep	Occupancy on 29 Sep %
30 Sep	Occupancy on 30 Sep %

Result across the 5 repriced nights

121 rooms · 72% occupancy · elasticity -0.9

At these inputs

+$1,143

Estimated change across the repriced nights.

If guests do not react (ceiling)

+$18,208

The same rooms sold at the recommended rates.

Break-even elasticity

-0.977

The negative elasticity where the estimated revenue change is zero, holding these occupancy inputs fixed.

Repriced nights · expected room-nights sold and room revenue in AUD
Night	Rooms sold as-is	Rooms sold recommended	Revenue as-is	Revenue recommended	Change
23 Sep	6.0	5.5	$1,764	$1,771	+$7
26 Sep	87.1	67.9	$20,386	$20,702	+$316
28 Sep	87.1	74.0	$14,898	$15,177	+$279
29 Sep	87.1	72.4	$18,469	$18,758	+$289
30 Sep	87.1	77.0	$18,469	$18,722	+$252
Total, 5 repriced nights	354.5	296.9	$73,986	$75,130	+$1,143

Room figures are expected room-nights and may be fractional. Displayed amounts are rounded; totals are calculated before rounding. The model estimates room revenue, not profit, and does not model the separate effect of changing cancellation terms.

The demand response is measured against the market median: occupancy moves with the change in Rex’s rate relative to the median, so the effective price elasticity on a night is the input elasticity multiplied by Rex’s starting position (rate ÷ median). A night already above the median (21 Sep, 1.17× the median; effective −1.05 at the −0.9 input) loses revenue on a raise, while the far-window nights start near 0.9× the median (effective about −0.8) and gain. Break-even elasticity is quoted for the five repriced nights together.

Nights held after modelling: 21 Sep — raising $226 to the $239 target at 121 rooms, 72% occupancy and elasticity -0.9 changes revenue by −$86.

How much does price sensitivity change the answer?

The curve holds rooms and occupancy fixed. A more negative elasticity means more guests leave when the rate rises.

Precomputed uncertainty range

Fixed source scenarios

Monte Carlo means repeated simulated scenarios with varied assumptions. These supplied ranges remain fixed when the interactive inputs change. They are scenarios, not measured probabilities from booking outcomes.

P10 is the outcome with 10% of simulated outcomes below it; P50 is the outcome with 50% of simulated outcomes below it; P90 is the outcome with 90% of simulated outcomes below it.

Fortnight revenue change · precomputed scenarios · AUD
Room count	P10 change	P50 change	P90 change	Chance of a gain
121 rooms	−$3,959	+$1,162	+$6,757	60.8%
157 rooms	−$5,185	+$1,543	+$8,773	60.7%

The annual case is pricing consistency

The annual gain comes from consistency: removing erratic price swings across the year, not from any single night. This annual model is a separate supplied scenario, not an extrapolation of the fortnight change. The fortnight is a test of price sensitivity; actual rooms sold determine whether the annual opportunity is credible.

121 rooms

Annual room revenue · AUD
Scenario	P10	P50	P90
No fix	$6,705,467	$7,289,482	$7,865,093
Consistent pricing	$6,833,311	$7,492,268	$8,170,697
Annual gain	−$8,043	+$206,252	+$438,257

Modelled chance of an annual gain: 89.4%.

157 rooms

Annual room revenue · AUD
Scenario	P10	P50	P90
No fix	$8,697,073	$9,438,928	$10,183,922
Consistent pricing	$8,863,510	$9,694,498	$10,565,824
Annual gain	−$12,996	+$264,339	+$554,576

Modelled chance of an annual gain: 89.2%.

The source reports the annual gain distribution separately. It cannot be recovered by subtracting the same-labelled percentiles of the revenue distributions. The underlying annual sampling process is not included in the source.

The model, stated exactly

For each repriced night, current and recommended prices are expressed relative to that night’s competitive-set median. Occupancy is entered as a share; ε is the selected price elasticity.

r0 = current / med
r1 = recommended / med
roomsA = rooms × occ
occB = min(0.97, max(0.05, occ × exp(ε × (r1 − r0))))
revA = roomsA × current
revB = rooms × occB × recommended
delta = revB − revA

exp is the exponential function. The recommended occupancy is bounded between 5% and 97%. The ceiling holds occupancy unchanged; break-even is solved numerically across negative elasticity values. Read the method and limitations.