The Javier Effect: A Custom PriceLabs Strategy for Unique STR Inventory

Introduction

Jasper: Welcome back to another episode of Get Paid for Your Pad. Today we are talking about revenue management, and I have a special guest: Javier Vasquez, one of our revenue managers at Freewyld Foundry. He came up with a strategy that we have never talked about on this podcast before, so I wanted to bring him in to walk us through it in detail.

Javier: Thank you, Jasper. It is a pleasure to be here. I have been in the industry for about eight years, starting with multifamily units in Miami and Jersey City. I also worked at PriceLabs for a while, so I know both sides of the tool. Now I am managing several portfolios here at Freewyld Foundry, and I am really excited to talk through this custom strategy.

About the Portfolio

Jasper: One of the portfolios Javier manages has very unique units. They are glamping domes near Toronto, Canada. People travel specifically to stay in these units because they are so cool. This is destination travel, very different from the typical short-term rental pattern where someone decides where they want to go and then looks for a place to stay.

Javier: Exactly. These units even have a waiting list. When I took on the portfolio, the first thing I noticed is that they run at 97 to 98 percent occupancy throughout the year. They have some seasonality, with peak season in late summer and fall, but they are consistently at or near 100 percent. That means they are not following normal STR market dynamics at all.

Because of that, the standard seasonality and demand factors in PriceLabs were not useful for pricing these units. Those factors are built around how the broader market behaves. These domes operate in their own world.

Turning Off Seasonality and Demand Factors

Javier: The first thing we did, which is highly unusual, was turn off both seasonality and demand factors in PriceLabs entirely. This is the first time I have set up a property this way. Everything runs off the base price plus the Portfolio Occupancy-Based Adjustment matrix.

Jasper: The benefit of removing those factors is that your setup becomes much simpler to understand, monitor, and adjust. When you have seasonality at minus 10 percent and demand factor at plus 15 percent on top of your matrix adjustments, it becomes very hard to know which lever is doing what. Turning them off gives you clean control.

What Portfolio Occupancy-Based Adjustment Is

Jasper: In a standard PriceLabs setup, the regular occupancy-based adjustment works on a date-range basis. If your listing is 20 percent booked for the next 30 days, you add a discount or a premium to that range. Portfolio occupancy-based adjustment is different. It calculates occupancy on a per-day basis.

Javier: Exactly. If we have five units and four of them are booked on a specific date, that date is at 80 percent occupancy. The date right next to it might be at zero percent if no units are booked. They are calculated independently. This gives you very precise control over pricing on a day-by-day level, which is exactly what destination inventory like this requires.

To access Portfolio Occupancy-Based Adjustment in PriceLabs, you need to enable it through the control panel. The other important thing is that you must create a group for it to appear. It does not show up in the regular listings customizations view unless a group exists, even if it is a multi-unit listing.

Two Unit Types and Two Matrices

Javier: This portfolio has two categories of units. The Forest Domes are three premium units with a booking window of around 212 days for their peak months. The middle units are five units with a booking window of around 140 days. Because they book differently, I built a separate matrix for each unit type.

Within each matrix, I also built separate settings for weekdays, weekends, and Sundays. Sundays behave differently from weekends and differently from weekdays. They do not book as high as Fridays and Saturdays, but they do not go as low as weekdays either. So Sundays needed their own day-of-week factor to account for that middle position.

Weekend pricing premiums are built directly into the matrix rather than using a separate day-of-week pricing setting. Because everything is coming from the base price with no other factors, the matrix handles weekend premiums from the zero-occupancy row upward. This keeps the setup clean.

Last-Minute Discounts Inside the Matrix

Javier: We also do not use separate last-minute discount settings in PriceLabs for this portfolio. Those discounts are embedded in the matrix itself through the booking window columns. As the check-in date gets closer and occupancy has not built up as expected, the matrix starts applying discounts automatically.

For the Forest Domes, the matrix has a column for 0 to 14 days out, 15 to 30 days, and so on through the booking window. At zero to 14 days with no units booked, it applies a 15 percent discount. With two units booked, it applies a 5 percent premium, because these units have very high demand even at the last minute.

We also added a column for 366 days and beyond because bookings for October 2027 started arriving. The previous matrix only extended to around 365 days, so those ultra-early bookings were not capturing any premium. The extra column adds protection when bookings come in that far out.

The Javier Effect: Results So Far

Jasper: We are calling this the Javier Effect. When Javier took on this portfolio in May and implemented this strategy in mid-July, the ADR for bookings made in July was $493 Canadian. In August, it jumped to $565. That is a significant increase within about two months of the strategy going live.

One important caveat is that when we took over, the peak months were already heavily booked under the old flat pricing. Most of the impact we can measure so far is on weekday dates, because weekend slots were already sold out. The weekend ADR difference on the few available dates we did sell is around $250 Canadian higher than before. We expect the full impact to be clearer in November, December, and especially next October when we will have controlled the entire booking window from the start.

Javier: I am expecting roughly a 10 to 15 percent RevPAR increase for November and December compared to last year. If the pacing continues as it has been, it could be even higher. The most important thing now is monitoring. We implemented the hypothesis, and now we watch the data and adjust the levers as needed.

When to Use This Strategy

Jasper: When does this type of strategy make sense versus the standard approach?

Javier: The main tell is that your occupancy and booking patterns do not look like the broader market. If you are at 97 or 98 percent year-round, you are not following market demand. If your booking window is 200-plus days, you are operating in a different world from a typical STR. And if there is very little comparable inventory in your market, the market data PriceLabs pulls may not be relevant to your pricing at all.

For most standard inventory, you are better off using PriceLabs market factors and adjusting the occupancy-based settings from there. The goal in all cases is to keep the setup as simple as possible. The more levers you add, the harder it is to understand what is driving the price and harder to learn and improve over time.

Closing

Jasper: This has been a great walkthrough. We will definitely have Javier back in six months or so when we have a full cycle of data to show the real impact of the strategy.

If you are attending VRMA Nashville this year, stop by the Freewyld Foundry booth. We are just across from the wellness section. If you are doing over a million dollars in yearly bookings, we can create a free revenue report so you can see where you are leaving money on the table. I am also doing a workshop on building your own AI revenue management agent on Tuesday at 11:30 AM. And on Sunday, we have a live in-person mastermind for Freewyld Foundry clients.

If you need help with revenue management, you can request a free revenue report at freewyldfoundry.com/report. We will analyze your portfolio and point out the biggest opportunities. Thanks for listening. See you next time.