A ~220-unit short-term-rental operator in Miami brought Pacer in as their embedded revenue management function. The figures below are same-store, drawn from the 90 units managed across both full years, so growth from added inventory is stripped out and only revenue management shows through.
The operator had a desirable Miami book and healthy rates, but the calendar told the real story. Same-store occupancy sat at roughly 50 percent. Soft mid-week and shoulder dates went unfilled, rate moves were reactive, and the booking window was short and last-minute. There was real revenue between the rate they were getting and the demand they were not converting.
Daily rate adjustments tied to booking velocity versus the comp set. Soft dates were priced to move and peak dates were protected, lifting occupancy from 50 to 77 percent while ADR still rose 6 percent. Occupancy was earned, not bought.
By opening the calendar further out, layering early-booking rate structures, and laddering minimum stays on peak dates, the operator pulled reservations in earlier and reduced its dependence on last-minute fills. The same-store advance booking window lengthened year over year, giving the team more pacing visibility and more room to optimize.
Length-of-stay discounting and minimum-night logic built per unit, shifting the mix toward longer, higher-value bookings on the dates that warranted it and staying flexible where one-night demand was real.
Fee architecture aligned to unit value, and channel mix actively managed across OTAs so the calendar filled from the right demand at the right price, not just the cheapest channel.
| Metric (same-store) | Before | After | Change |
|---|---|---|---|
| Adjusted RevPAR | $42.73 | $70.08 | +64% |
| Occupancy | 50% | 77% | +27 pts |
| Average Daily Rate | $86 | $91 | +6% |
| Same-store revenue | $1.40M | $2.27M | +62% |
This is what an embedded revenue management function looks like. Same 90 units, same Miami market, a 64 percent lift in same-store RevPAR driven by occupancy, not by buying volume with discounts. The kind of result an operator cannot reach with a pricing tool alone, and would otherwise need a full in-house RM team to chase.