Unit 3 · Creating Your Pricing Strategy · Lesson 3.16
Case study: Burleigh Heads, Australia
A worked example of the whole pricing framework on one listing: a new two-bedroom home in Burleigh Heads, Australia, with its comp set already built. The steps run in order: load the market exports, set the length of each step from booked nights by booking window, choose the low season, set weekday and weekend minimums for the hedge, control and test steps, then nominate the seasonal dates.
The full lesson text below is an edited transcript of the video, published 2026-08-24. The complete course is free at the playbook.
The listing and the data
The subject is a two-bedroom listing in Burleigh Heads, Australia. It is a new listing, and that shortens the work in one specific place: with no booking history of its own, there are no past payouts to run through the Airbnb fee calculator, so that step is skipped and every price decision rests on market occupancy and market prices alone. Its comp set was already built, so the case starts at the data.
Two exports feed the strategy sheet, both pulled with the comp set applied so the numbers describe the actual competition rather than the whole market. The first is the trends by booking creation date file from the KPI reports, which shows when bookings in the market were made rather than when the stays fall. The second is the pacing export, aggregated daily across a custom range that starts two years before the current date and ends one year ahead.
Both files load into a new strategy sheet, and the check that they landed is the three-step pricing tab: market occupancy and comp set prices appear there once the upload has worked. Confirm that before analysing anything, because a silent upload failure looks exactly like a market with no data.
Setting the length of each step
The length of each step comes from the length of stay versus booking window graph in the market dashboard, the market-level view a dynamic pricing tool such as PriceLabs provides, switched to the view that splits booked nights by both length of stay and booking window.
The outliers come out first. In this market they were the 15 to 28 night stays and the 29 night and longer stays: bookings long enough to distort the picture of ordinary demand. They are taken out by entering them as negative values against the booking window they sat in, which here meant minus 24 and minus 30 nights inside the 7 to 13 day window, minus 18 in the two to four week window, and minus 43 in the two to four month window.
With the outliers accounted for, the graph reverts to its plain booked nights view and each booking window’s total goes into the sheet. For this market that was 70, 42, 112, 93, 170, 232, 78 and 62.
The sheet’s first recommendation came back as a hedge step only 0 to 1 days long, which is very short. The lever for lengthening it is the share of nights booked allocated to the hedge step, which sits at 10% by default; raising it to 15% extended the hedge step to a 0 to 6 day booking window. The control step then ran from 7 days out to 62 days, and the test step started at 63 days.
Choosing the low season
The low season comes from the occupancy graph in the same dashboard, read on the finalised line for the trailing 365 days rather than on what is currently on the books. The finalised line shows where each part of the year genuinely settled, which makes a stretch running clearly below the rest of the year easy to pick out.
In this market one period stood out immediately, and the dates recorded were May 8 through June 5.
Those are prior-year dates, and they were chosen deliberately over the same window ahead. That period was still some way off from the current date, and prices for dates that far out tend to sit above where the market eventually lands. Taking the future window would have produced higher prices, and therefore higher minimums, than the market actually supports.
Hedge step minimum prices
With the low season fixed, minimums get set for each step, weekdays and weekends separately. For a new listing the decision rests on two inputs: the occupancy the market delivers across the low season, and the market prices across it. Here that was 45% on weekdays and around 63% on weekends, which is decent occupancy for a low season.
The sheet recommended $205 for weekdays and $269 for weekends at the hedge step, and both were set below that. The immediate goal for a new listing is occupancy, and the hedge step covers the closest dates, where perhaps only 5 to 10% of the market’s demand is still to come. Being conservative there makes the listing more attractive for the few bookings left in the market.
So the weekday minimum went in slightly under the market’s 25th percentile, at $175. The weekend minimum went in under the 25th percentile as well, at $210, on the same reasoning: for the last-minute weekend stays, a competitive position is worth more than a proud one.
Control and test prices
The control step covers dates where more booking activity should already be visible, so the positioning moves up with it. Weekdays were set between the 25th and 50th percentiles at around $210, and weekends closer to the 50th percentile at $250.
The test step, the furthest and longest of the three, was aligned with the percentile equivalent of the market occupancy the low season delivers. Against 45% weekday occupancy, $240 sits just under the 50th percentile, which the market put at $248. Against 63% weekend occupancy, $290 sits between the 50th and 75th percentile prices. Both are incremental increases on the control numbers, which is what the test step is for: room to test a higher position while demand for those dates is still arriving.
Nominating the seasonal dates
Seasonal dates come from reading the forward occupancy and forward price graphs together, with the prior year’s finalised occupancy still on screen for reference. What you are looking for is dates that stand out against the rest of the calendar on both graphs at once: occupancy elevated, and prices elevated with it.
The holidays were obvious, and the useful move was to split them rather than treat December and January as one block, because the pricing graph showed real variety across the period. That produced a shoulder from December 15 to December 21, the peak itself from December 22 to January 6, a second shoulder from January 7 to January 20, and a further week from January 21 to January 27, which still carried elevated occupancy and prices.
A second period ran from late March into April, with elevated occupancy matched by higher prices. The pricing graph put the start at around March 30; the range was set to begin on March 29 instead so that the Friday was included, and it ran through to April 23. A third ran from June 23 to July 6, again with occupancy and prices rising together.
The rest of the calendar gets read the same way. Every stretch where both graphs agree that demand sits above the ordinary run of the year becomes its own seasonal date range, with the shoulders split out separately wherever the price graph shows the peak and the days either side of it behaving differently.