Wyndham priced rentals from guest choice, not just booking pace

Vacation-rental pricing has a moving target: each unit has different features, competitors change availability, and demand depends on how far ahead the guest books. A booking-pace rule can say whether sales are fast or slow, but it cannot explain which alternative the guest is comparing against.

The Wyndham Destinations application modeled guest choice directly. Conditional-logit estimates measured price sensitivity through the booking horizon; a nonlinear optimizer chose revenue-maximizing prices using current demand, company inventory, competitor availability and property features.

The system was tested in two Wyndham regions during part of the 2018 vacation season. The reported revenue per unit increased by 3.5% in one region and 5.2% in the other, showing that a choice model could outperform simpler pace-based pricing.

Price against the guest's actual choice set

The method priced the unit inside a live competitive set rather than treating demand as isolated.

Rolling-window estimation smoothed price sensitivity over the booking horizon.

The booking-curve heuristic kept optimization from chasing revenue at the expense of practical pace targets.

Dynamic pricing works better when the optimizer sees what the customer could choose instead of only watching your own booking curve.

The 2021 INFORMS Journal on Applied Analytics paper presented the Wyndham deployment as a successful vacation-rental revenue-management application. The case made a portable point for hospitality: price the customer's alternatives, not just your own remaining inventory.