Where revenue management began
Adjusting prices to better match demand — revenue management — is a form of price discrimination used across industries. The base price is a concept from hotel revenue management. Before dynamic pricing, a hotel room was a single rate regardless of day or season, because managing many price points was technologically prohibitive. That changed in the 1980s when connected computer reservation systems let software manage and distribute prices at a granular level, and the substantial gains spurred the science of pricing and forecasting.
What a base price is
Base price is one of the simplest revenue-management methods. A hotel’s single rate is too high off-peak and too low in peak; the manager picks a starting rate as the base and adds 30% in peak season or subtracts 25% in low season. Complexity grows from there — different adjustments by day of week, and separate strategies per distribution channel — all applied to that base.
How STR adopted it
STR borrowed the base-price method from hotels because it was close enough and already working. The STR twist is the “neighbor model”: applications watch competitors’ prices and, when a neighbor drops 9%, assume they’ve spotted a demand change and drop yours the same, applying the multiplier to your base price. It was a novel solution born of necessity — early STR data was too thin to power the reservation-data-hungry forecasting models hotels and airlines use, so the neighbor model still dominates.
The problem with base price
The model assumes you know the right base price. Apps recommend one from scraped OTA data or your historical reservations — but scraped data fails for unique properties and inherits your neighbors’ mistakes, and historical data fails if you priced poorly before. So base prices are often set on assumption and hope. Then they must be maintained: in a volatile market, this year’s base is unlikely to be optimal next year or even next month, forcing managers to continuously update every property — a tedious task easily neglected at scale.
Seasonality makes it worse. Lake houses and ski cabins can see 4× the demand in peak season, and a base-price method struggles to apply a 400% swing because it depends on neighbors making that exact move. One booking at a low rate during peak season is often enough to make a manager swear off dynamic pricing entirely.
The solution
QuibbleRM replaces the base price with an optimized price from a regression model that turns all relevant reservation data into a revenue-maximizing price automatically. Every new reservation feeds the model, and the price updates continuously in real time — small adjustments that keep moving toward supply and demand over the long run. An embedded forecast segments demand into season and weekday pools, generating a minimum of 21 unique optimization prices per property — far more granular than a single base price, and previously unmanageable by hand. The system tests six models for fit every run, so pricing improves over time without the revenue manager setting, maintaining, or worrying about base prices and seasonality at all.