Case Study · Puerto Vallarta / Banderas Bay, Mexico
+58% same-store Adj. RevPAR across a 40-unit Puerto Vallarta condo stack
A multi-family condo operator on Banderas Bay grew same-store revenue from $681K to $1.01M in 17 months, with occupancy climbing from 32% to 51% while nightly rate held flat. Source: KeyData adjusted RevPAR, same-store view.
RevPAR lift
+58%
Units
40
Same-store revenue
$681K → $1.01M
Engagement
17 months
Client snapshot
Market
Puerto Vallarta / Banderas Bay, Mexico (international beach market, strong Nov-Apr high season)
Units
40
Pms
Hostaway
Pricing Tool
PriceLabs
Engagement
17 months and ongoing
Scope
Full revenue management: rate strategy, stay-length pricing, low-season demand strategy, distribution mix, unit-level differentiation within the stack
The starting point
What we walked into
- 01A multi-family condo stack means dozens of near-identical units competing with each other on the same booking pages. Without deliberate differentiation, the building cannibalizes its own calendar and races itself to the bottom on price.
- 02Revenue was concentrated on two channels: Airbnb and Booking.com carried 80% of booked rent. Vrbo was barely producing and the direct booking engine was an afterthought.
- 03The low season was essentially written off. From July through October the entire 40-unit building was booking 120-190 guest nights per month, close to a dark building outside of peak.
- 04High season performed on rate but the calendar never filled: full-year occupancy sat at 32%.
The work
What Pacer did
Low-season demand strategy
Rebuilt the July-October playbook: structured rate ladders into the rainy season, loosened minimum stays where they were blocking short-window demand, and priced for the traveler who books inside two weeks. Low-season guest nights went from 602 to 2,280, nearly 4x on the same units.
Rate discipline while occupancy climbed
The easy lever in a soft calendar is discounting. Instead, average nightly rate was held ($160 before, $159 after) while occupancy rose from 32% to 51%. All of the RevPAR gain is filled nights, none of it is bought with rate.
Distribution expansion beyond the big two
Activated Vrbo properly (2.5x revenue growth on the channel) and revived the direct booking engine (3.3x), cutting dependence on Airbnb and Booking.com from 80% of booked rent to 75% while every channel grew in absolute terms.
Unit-level differentiation inside the stack
Identical floor plans do not have to be identical listings. Units were separated by floor, view, and configuration into distinct price positions so the building stopped competing with itself and started laddering demand across its own inventory.
Last-minute capture
The average booking window tightened from 19 days to 14 as the calendar opened to short-window demand the previous setup was structurally rejecting. In a market with heavy fly-in leisure traffic, the final two weeks are where soft nights get rescued.
Results
Same-store, year over year
Only units active in both the trailing 12 months and the prior 12 months. Pure revenue management impact, no mix-shift effects.
| Metric | Before | After | Change |
|---|---|---|---|
| Adj. RevPAR | $51 | $81 | +58% |
| Same-store revenue | $681K | $1.01M | +$331K |
| Occupancy | 32% | 51% | +19 pts |
| Avg. nightly rate | $160 | $159 | Held flat |
| Low-season guest nights (Jul-Oct) | 602 | 2,280 | +279% |
| Same-store units | 40 | 40 | Same cohort |
Takeaways
What this means for operators
- Every point of the +58% RevPAR lift came from occupancy at a held rate. This is the opposite of buying occupancy with discounts, and it is only possible with structured low-season strategy instead of blanket markdowns.
- The low season was the goldmine. Four months the operator had written off now produce nearly 4x the guest nights on identical inventory.
- Condo stacks are a distinct revenue management problem: near-identical units must be deliberately differentiated or the building competes with itself.
- Distribution concentration is fragile. Growing Vrbo and direct booking did not just add revenue, it reduced platform dependence.
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