The Javier Effect: How to Price Unique STR Inventory With PriceLabs

Javier Vasquez, one of Freewyld Foundry's revenue managers, joins Jasper to walk through a pricing strategy he built from scratch for a portfolio of glamping dome units near Toronto. These units run at 97-98% occupancy year-round with booking windows of up to 212 days and a waiting list. Because they attract destination travelers who book specifically to stay in those units, standard PriceLabs market factors did not apply. So Javier turned them off entirely and built a custom Portfolio Occupancy-Based Adjustment matrix instead. The result: ADR jumped from $493 in July to $565 in August. Jasper and Javier walk through the full setup live, including the screen share. You will learn: What Portfolio Occupancy-Based Adjustment is in PriceLabs, and why it is the right tool when your inventory does not follow regular STR market patterns Why Javier turned off both seasonality and demand factors entirely for this portfolio, and what that changes about how the base price works How the matrix calculates occupancy on a per-day basis rather than a date-range basis, and why that distinction is critical for high-demand destination inventory How to build separate matrices for different unit types and day-of-week patterns, including why Sundays need their own factor separate from weekdays and weekends What the Javier Effect looks like in the data: ADR from $493 in July to $565 in August within two months of implementing the strategy How to use the PriceLabs pacing dashboard to monitor whether a new strategy is working and where to adjust We also talk about: How to tell when your inventory warrants a custom strategy versus following standard market factors: the main tell is that your occupancy and booking patterns do not look like the market around you Why last-minute discounts are built directly into the matrix for this portfolio, eliminating the need for separate last-minute discount settings in PriceLabs The difference between regular occupancy-based adjustments (date-range basis) and portfolio occupancy-based adjustments (per-day basis), and why the distinction matters How booking windows shaped the date range columns in the matrix: 212 days for Forest Domes, 140 days for the middle units, and why a 366+ day column was added after October 2027 bookings started arriving What the pacing report shows heading into October: ADR $30 higher than the same period last year with occupancy also ahead Why most of the measurable impact so far is coming from weekday dates, and why the weekend impact will be clearer next year when weekend inventory was not already sold out before the strategy launched Mentioned in the Episode: Free Revenue Report from Freewyld Foundry: freewyldfoundry.com/report PriceLabs Portfolio Occupancy-Based Adjustment: enable through the PriceLabs control panel; requires a group to be set up before the feature appears VRMA Nashville October 4-6: Booth 1143, Tuesday workshop at 11:30 AM, Sunday mastermind for Freewyld Foundry clients Email Jasper: [email protected] Subscribe for new episodes every Monday on YouTube, Spotify, and Apple Podcasts. Hosted on Acast. See acast.com/privacy for more information.