Competitive Set Strategy for Hotels: Building the Right Compset for Smarter Pricing

To build a competitive set that drives smarter pricing decisions, hotels must abandon static, property-centric models and implement a dynamic, multi-tier compset strategy. Rather than benchmarking solely against five geographical neighbors, revenue managers should divide rivals into distinct primary, secondary, and seasonal sets, while using automated rate shoppers to track hybrid inventory (such as premium vacation rentals and corporate apart-hotels). This allows you to position your rates against the exact alternatives your target guests are actively comparing at any given moment.

What Is a Hotel Competitive Set?
How to Select the Right Comp Properties
Primary vs. Secondary Competitive Sets
Common Compset Selection Mistakes
Monitoring Tools: Lighthouse and Beyond
Using Compset Data in Pricing Decisions
Propeter’s Competitive Intelligence Feature
Frequently Asked Questions

Every pricing decision a hotel makes is, implicitly or explicitly, a competitive decision. Setting your rate $10 above the market average is a statement about your perceived value differential. Setting it $20 below signals aggressiveness or desperation, depending on context. The problem is that most hotels base these competitive positioning decisions on a compset that was assembled somewhat arbitrarily — the five nearest hotels, or the properties a general manager happened to think of, rather than the true set of alternatives a guest would genuinely consider.

A well-constructed competitive set is one of the most valuable assets in a hotel’s revenue management toolkit. It provides the market context that makes pricing intelligence meaningful, the benchmark against which performance is measured, and the signal source that drives the most critical reactive pricing decisions. Building it right — and maintaining it as the market evolves — is foundational work that pays compounding dividends.

4–6
Optimal primary compset size for most hotels — signal-rich without dilution

What Is a Hotel Competitive Set?

A hotel’s competitive set (compset) is the defined group of properties that compete directly for the same guests in the same market. It is the lens through which competitive pricing intelligence is gathered, interpreted, and acted upon. When a revenue manager checks what competitors are charging for a given date, or when an AI system calculates a market rate index, the compset is the reference point that makes those comparisons meaningful.

The compset is not simply “the other hotels nearby.” A city centre market might contain dozens of hotels within a two-kilometre radius spanning budget hostels, mid-market business hotels, boutique properties, and five-star luxury resorts. A 3.5-star family hotel and a 5-star business hotel in the same building would not be in each other’s compsets — they are competing for entirely different guests with different willingness to pay and different purchase criteria.

What a Compset Is Used For

Rate benchmarking: Measuring your ADR and rate index relative to comparable properties.
Competitive positioning: Setting deliberate rate positioning (premium, parity, or discount) relative to the compset average.
Demand signal detection: Identifying compset availability closures as leading indicators of strong market demand.
Market share analysis: STR and similar services use your compset to benchmark your RevPAR performance relative to the market.
Pricing strategy calibration: revenue management systems such as Propeter use compset rates as an ongoing input to demand and price-elasticity models.

How to Select the Right Comp Properties

Compset selection should be a rigorous analytical exercise, not an intuition-based exercise. The core question to answer for every potential comp property is: “When a guest considers staying at my hotel and then decides not to, where do they go instead?” The properties that capture your lost bookings are your true competitors — and those are the properties that should be in your compset.

The Five Core Selection Criteria

1. Geographic proximity. Comp properties should be close enough that a guest would consider them a genuine alternative for the same trip. In a dense urban market, this might mean within 1–2 kilometres. In a resort market, it might mean within the same micro-destination — the same beach strip, the same ski resort access point, the same national park entrance. Properties far enough away that a guest would consider them a materially different experience are not true competitors.

2. Star rating and service level. The most robust guidance is to include properties within one star category of your own. A 4-star hotel’s compset should contain 3.5-star to 4.5-star properties. Guests considering a 4-star property rarely cross-shop 2-star budget options as genuine alternatives — the product is too different. Including budget properties in a mid-market compset creates a rate index that is artificially low.

3. Comparable ADR range. Even within the same star category, significant ADR variation can exist. A boutique 4-star property with a design-forward product might average $200/night in a market where other 4-star properties average $140. Including the $140 properties in the boutique hotel’s compset is misleading unless those properties genuinely compete for the same guests. ADR bands provide a useful filter: limit your compset to properties within 30–40% of your own ADR.

4. Room count and scale. A 20-room boutique and a 500-room convention hotel have fundamentally different demand dynamics. The convention hotel fills and closes to availability at times and demand levels that have no relevance to the boutique’s inventory management. Large-scale properties make poor comps for small properties, and vice versa.

5. Guest segment overlap. The most important criterion, and the hardest to quantify directly. Properties with significant overlap in their target segments — leisure, corporate, group, extended stay — are genuine competitors. Properties primarily serving segments you do not target are not. A corporate-focused business hotel and a leisure-oriented boutique in the same postcode may not be each other’s primary competitors even if they are adjacent.

Primary vs. Secondary Competitive Sets

Most sophisticated revenue managers maintain both a primary and a secondary competitive set. The primary compset — typically 4–6 properties — contains the closest, most directly comparable competitors. These are the properties monitored most intensively, tracked in daily rate shopping, and used as direct pricing benchmarks.

The secondary compset — an additional 4–8 properties — includes properties that are comparable but less directly competitive. Perhaps they are slightly further from your location, in a slightly different price band, or serving a partially overlapping but not identical segment mix. Secondary compset data provides market context without the noise that comes from including too many primary comps. It is particularly useful for detecting broader market trends that are not yet visible in the primary compset.

The Aspirational Compset Trap

A common mistake is building an aspirational compset — including properties that the hotel aspires to compete with rather than ones it actually competes with today. If a 3-star property benchmarks itself against 5-star properties, its rate index will always be low, its apparent market share will always look weak, and the competitive intelligence it generates will have no practical value for pricing decisions. Build your compset honestly, based on reality.

Common Compset Selection Mistakes

The most common compset mistakes all share a root cause: using criteria that are easy to measure (proximity, brand name, physical size) rather than criteria that reflect actual competitive dynamics (guest segment overlap, booking cross-shopping behaviour).

Including brand-name hotels purely for prestige: Being in the same market as a major chain brand does not make that brand a relevant comp if it serves a materially different segment.
Using proximity as the only criterion: Hotels 500 metres apart with completely different product offerings and guest bases are not competitors — the guest who books a $400 luxury suite is not cross-shopping with a $90 budget room down the road.
Never updating the compset: Markets change. Hotels reposition, renovate, close, or change ownership. New properties open. A compset built three years ago may contain properties that no longer compete with you and miss newer properties that do.
Making the compset too large: Including 12–15 properties in a compset creates noise and dilutes the signal from the properties that actually matter. The competitive average of 15 dissimilar properties tells you very little about what any individual competitor is doing.
Not accounting for distribution channel differences: A property that sells primarily through one OTA may price very differently on other channels. Monitoring their rates only on Booking.com when your guests primarily use Expedia creates a distorted picture.

Monitoring Tools: Lighthouse and Beyond

Once a compset is built, it needs to be monitored continuously to generate actionable intelligence. The monitoring infrastructure is as important as the compset selection — a perfectly constructed compset monitored once per day via manual rate checking is less valuable than a good compset monitored several times a day through an automated rate-shopping tool.

Lighthouse (formerly OTA Insight) is a widely used rate-intelligence platform for compset monitoring. It aggregates rate data from the major OTAs for your defined compset, stores historical rates and presents forward rate calendars showing competitive positioning across the booking window. Typical compset metrics include rate index (your ADR as a percentage of the compset average), rate rank (your position in the compset by rate on a given date) and availability signals (when compset properties show sold-out or restricted availability). Other rate shoppers offer similar data; what matters is that the data reaches the people and systems making pricing decisions.

What Lighthouse Data Tells You

Current and historical rate positioning relative to your defined compset
Availability status across all compset properties — critical for detecting demand surges
Rate change frequency and patterns for each competitor
Parity monitoring across your own channels and compset channels
Market demand index derived from search and booking data

If your hotel subscribes to Lighthouse, Propeter can pull that compset data into its pricing recommendations, so competitive data is actively used in pricing decisions rather than sitting in a reporting interface waiting to be reviewed.

Beyond Lighthouse, Propeter collects public rate listings through a third-party data provider, which can include direct-only rates and promotional offers that do not surface in OTA-only monitoring.

Using Compset Data in Pricing Decisions

Compset data becomes valuable only when it drives concrete pricing actions. The four most important uses of compset data in pricing decisions are: rate positioning calibration, demand signal detection, competitive response triggers, and market share benchmarking.

Rate Positioning Calibration

Your target rate position relative to the compset should be a deliberate strategic choice, revisited periodically and adjusted based on your product’s competitive standing. A hotel that outscores its compset on TripAdvisor by two full points can justifiably maintain a 15–20% rate premium. A hotel that scores at or below the compset average should position at parity or below until product investment improves the quality differential. Compset data is the measurement tool that tells you whether your actual rate position matches your intended position.

Demand Signal Detection Through Compset Availability

One of the most valuable uses of compset monitoring has nothing to do with rates — it is watching when compset properties sell out or close availability for future dates. A competitor closing to new bookings for a date 30 days out signals that demand for that date is strong enough to fill them before yours. This is a leading indicator that your own rates for that date should be higher, because the pool of available alternatives for arriving guests is shrinking.

Propeter’s Competitive Intelligence Feature

Propeter brings competitor rates into its pricing recommendations — from your Lighthouse subscription, if you have one, and from public listings collected through a third-party data provider. Competitor rates refresh hourly for the next 14 nights and progressively less often for dates further out. That competitor context is one of the inputs — alongside booking pace, events and seasonality — to the demand forecast and the rate recommendation for each date.

For each date in the booking window, Propeter shows your current rate position relative to the compset, availability gaps that signal demand pressure, and rate-change patterns that may indicate a competitor’s strategic move.

When compset availability is tightening on a future date, the demand forecast for that date is adjusted upward — not purely on historical patterns, but on current competitive market signals. The pricing engine then recommends a rate for that date, with the reasoning shown, and your team reviews and approves it before it is published through your connected PMS and distribution systems.

From Compset Data to Rate Action in Hours

With Propeter, a compset sell-out detected in the morning’s competitor data produces an updated demand forecast and a revised rate recommendation the same day. How quickly it reaches guests depends on how fast your team approves it and on your PMS and distribution connectivity — approved rates are published through the systems you have connected.

Frequently asked questions

How should a hotel track Airbnb and short-term vacation rentals within its competitive set?

Hotels should isolate premium short-term rentals into a separate, non-traditional compset using specialized data aggregators like AirDNA. Track professional multi-unit operators within a 2-mile radius that mirror your average room capacity and amenity stack. Instead of matching their daily rate fluctuations, map their occupancy compression trends to adjust your hotel’s weekend and seasonal pricing tiers.

How often should a hotel revenue manager review and change their official compset?

A hotel should conduct a formal compset audit at least annually — and whenever there is a material market change — to account for property renovations, brand conversions, or new market entries. However, you should never make frequent, impulsive changes to your primary benchmarking set, as this skews historical data and invalidates long-term RevPAR and MPI (Market Penetration Index) tracking.

How do you build a valid competitive set for a highly unique or boutique hotel?

If a boutique hotel lacks direct physical peers in its immediate neighborhood, it must build a concept-based compset rather than a geographic one. Identify properties in adjacent neighborhoods or comparable feeder markets that target the same psychographic guest segment, share a similar aesthetic, and sit within roughly 30–40% of your own ADR, the same band used for a geographic compset.

What is the difference between a pricing compset and a benchmarking compset?

A benchmarking compset is a fixed group of 4 to 6 properties used to track long-term market share via official metrics like STR reports. A pricing compset is highly fluid, often varying by day-of-week or guest segment (e.g., matching a corporate-heavy hotel on weekdays, but pivoting to a leisure-driven boutique resort on weekends to optimize dynamic pricing).

How many properties should be in a hotel competitive set?

Most revenue management practitioners recommend a primary compset of 4–6 properties — large enough to provide meaningful market signal, small enough that all properties are genuinely comparable. A secondary compset of an additional 4–6 properties can be monitored less intensively for broader market context. Including too many properties dilutes the relevance of the competitive signal; too few creates sampling bias.

How often should a hotel review and update its competitive set?

Competitive sets should be formally reviewed at least annually, and informally reassessed whenever there is a significant market change — such as a new hotel opening in the market, an existing competitor closing or undergoing major renovation, or a shift in your own hotel’s positioning or target segment. A compset built on market conditions that no longer exist will produce misleading competitive intelligence.

What is the difference between a primary and secondary competitive set?

A primary competitive set contains the properties that compete most directly with your hotel for the same guests on the same dates — the hotels a guest would genuinely consider booking instead of yours. A secondary competitive set includes properties that are comparable but slightly differentiated — perhaps in a different sub-market, at a slightly different price point, or targeting a partially overlapping segment. Primary compset data drives pricing decisions; secondary compset data provides broader market context.

How does Propeter use compset data in pricing decisions?

Propeter brings competitor rates into its pricing recommendations — from your Lighthouse subscription, if you have one, and from public listings collected through a third-party data provider. Competitor rates refresh hourly for the next 14 nights and progressively less often for dates further out. Propeter shows your rate position, availability gaps in the compset and competitive pricing trends for each date, and uses them as inputs to the demand forecast and to the rate it recommends — which your team reviews and approves.

Build a Smarter Compset with Propeter

Propeter brings your compset’s rates into every recommendation and shows you how they influenced it — so competitive data turns into pricing decisions your team approves, not reports nobody reads.

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