If a competitor's move triggers your rate change, that is market-based pricing, not dynamic

A hotel where most rate changes follow a competitor's move is running market-based pricing, whatever the rate meeting calls it. That is the most useful line in a ten-strategy revenue management guide Lighthouse writer Joe Hanly published September 8 on Hotel News Resource, a piece that puts dynamic pricing fed by real-time data at the top of the list, ahead of the segmentation and data-driven decision work the rest of the guide covers.

The guide links to Lighthouse Pricing throughout, so read it as vendor content. Its practical value for a general manager or asset manager is narrower than the headline: it lays out four ways a hotel can set a rate, names what each one ignores, and gives an owner a vocabulary to work out which of the four a property is actually running.

Four ways to price a room, and what each one leaves out

Hanly describes cost-plus pricing as the traditional method: total fixed and variable costs, add a markup. Simple, and blind to both fluctuating demand and competitor activity, according to the Hotel News Resource piece.

Market-based pricing aligns with or undercuts the competitors a hotel monitors, and Hanly calls it effective up to a point before warning that leaning on it too heavily can trigger price wars and erode revenue. Open pricing is the more complex option: frequent monitoring and adjustment, usually automated through a revenue management system, and only as good as the data feeding it.

Dynamic pricing, in the guide's telling, pulls in competitor behavior, local events, booking pace and market trends, adjusts in real time, and aims to lift ADR while holding occupancy. Both approaches watch competitors; only one treats the competitor's rate as one input among several.

Which of the two a property is running shows up in what triggers its rate changes. If the answer is usually a competitor, the strategy is market-based, and Hanly's caution about price wars applies to it no matter what the strategy is called internally.

What Lighthouse feeds into the model, including short-term rentals

Lighthouse describes its own Lighthouse Pricing product as a rate-shopping tool that combines live competitor pricing with market trends, local event data and short-term rental insights, per the Hotel News Resource piece. That is the company's description of an announced capability, not an observed result. Hanly's prescription for avoiding common pricing mistakes is a tech stack configured for dynamic pricing plus continuous monitoring of performance against revenue goals.

Short-term rentals appear in both of Hanly's recent pieces. The September guide lists them as a data feed. An April explainer he wrote for Hospitality Net tells revenue managers to add any short-term rental inventory that is pulling demand in their key segments to the competitive set itself. Lighthouse is consistent on this point, and it would matter most for a property whose leisure segments are losing bookings to rentals rather than to the hotel across the street.

Hanly's list of inputs, competitor behavior, local events, booking pace and market trends, leaves out factors that published research puts near the top. A study in SAGE's Journal of Computational Methods in Sciences and Engineering, working from a Kaggle dataset of hotel listings, found hotel type, rating and location were the predominant factors in room pricing, with review count also playing a role. The analysis was correlational, using ANOVA, chi-square tests and regression on listing data, so it describes what prices correlate with rather than what a rate change does to bookings. It does suggest that rating and review volume belong somewhere in a pricing conversation, alongside the event and pace data Lighthouse emphasizes.

Why Hanly says audit the compset and rate plans before touching a rate

The April Hospitality Net explainer is the necessary companion to the September guide, because it deals with what a pricing engine sits on top of. Hanly frames a new revenue manager's job as two pressures. Ownership and the general manager want results quickly against a budget often set before the manager arrived, while the property carries a history of embedded decisions that shaped its performance in ways nobody fully understands on day one.

Most revenue managers, he writes, focus on the first and underestimate the second. There is a built-in tension there. The audits he recommends are slow, and the owner's call comes fast.

The compset audit starts with a single test: would a guest booking this hotel today consider each listed property a genuine alternative? Hanly's method, per Hospitality Net, is to search the main OTAs filtered for key amenities, location and hotel size, because that is how many guests are weighing options, then check each competitor's current positioning, room-type mix and channel strategy. Hotels reposition, renovate and change hands, so a compset built two years ago may no longer describe who the property competes with.

The rate-structure audit is where the inherited problems hide. Hanly's examples, all from the Hospitality Net piece: a rate plan built for a corporate account that stopped booking two years ago, a floor rate set in a softer market and never raised after recovery, and a minimum length-of-stay restriction on peak weekends that now blocks higher-value bookings instead of protecting them. Properties without a structured review in a few years often have more active rate plans than anyone can meaningfully track, he writes.

His fix is to pull the full rate-plan list and ask three things of each plan: is it still being booked, does the discount still drive the behavior it was designed for, and do its restrictions reflect current demand? He points to Lighthouse's business intelligence product, Lighthouse performance, as the way to see how much each plan produced over the past year. Those legacy settings sit under any pricing engine a hotel adds later, so a stale floor or a misapplied stay restriction could be reproduced faithfully by the automation meant to replace it.

The 10-day lead time that hides two different guests

Hanly's sharpest operational point in the Hospitality Net piece is about averages. A hotel with a 10-day average lead time may in fact have leisure guests booking two weeks out and corporate arrivals booking within 48 hours. The average flattens that split, and the split is where the inherited strategy either worked or didn't.

For a property with a mixed corporate and leisure base, that makes segment-level lead time the number to measure before setting pickup expectations, and before judging whether a rate change moved bookings or simply coincided with a segment's normal booking window. He also recommends mapping where demand peaks, where pace consistently falls short, and whether business builds steadily or arrives in bursts.

Virginia McShane, Senior Manager of Commercial Strategy Services at Lighthouse, adds a caution in the same piece: historical data still matters for forecasting, but the way people travel has changed, so inherited data should be read with that in mind rather than taken as a template.

Neither piece puts a price on the tooling or an hours estimate on the audits, and both are written by Lighthouse's own content team. What Hanly does offer is a standard of evidence. Owners and operators come back to specifics, he writes: when a discount was compressed, when a rate moved and what triggered it. That record is what settles the larger structural conversations when they arrive, and it is also the record that shows whether a property's rate changes were following competitors or the wider set of inputs the guide describes.