How to Build an STR Comp Set: A Complete Revenue Manager’s Guide
In short-term rentals (STR), pricing decisions often look simple on the surface: check nearby listings, compare rates, and adjust accordingly. But that approach leaves significant revenue on the table.
High-performing operators don’t build pricing strategies around nearby properties—they build them around guest behavior.
That’s where a competitive set, or comp set, becomes essential.
A comp set is not simply a list of similar listings in your neighborhood. It is a carefully selected group of properties that guests would realistically consider instead of booking yours. Built correctly, it becomes the foundation for pricing decisions, occupancy strategy, inventory planning, and long-term revenue growth.
This guide explains how revenue managers build and maintain effective STR comp sets, the filters used to qualify properties, and how operators can turn market data into better pricing outcomes.
What Is an STR Comp Set?
A short-term rental comp set is a curated group of approximately 8–15 properties used as benchmarks for:
Pricing strategy
Minimum-stay decisions
Occupancy management
Revenue forecasting
Market positioning
The key distinction is that these properties represent real alternatives from the guest’s perspective.
Unlike hotels—which often build competitive sets around brand category and chain positioning—short-term rentals require a different approach.
Guests compare properties based on:
Amenities
Design and aesthetic
Location behavior
Experience quality
Price expectations
A guest choosing a modern cabin with premium design features may never compare it to a traditional family cabin nearby—even if both have similar bedroom counts.
That difference changes everything.
Why Most STR Comp Sets Fail
Most comp sets fail because they are built from the seller’s perspective instead of the buyer’s perspective.
Hosts commonly create competitive sets by asking:
“Which listings look like mine?”
Revenue managers ask a different question:
“Which listings would guests actually choose instead of mine?”
Those answers are rarely identical.
A comp set based only on proximity and visible similarities often creates pricing decisions that feel correct but underperform financially.
When operators benchmark against the wrong properties, calendars may appear healthy while revenue quietly underachieves.
The Guest-First Approach to Building a Comp Set
The correct way to build an STR comp set is to start with guest substitution behavior.
Imagine a design-forward two-bedroom cabin.
A traditional approach might compare every nearby two-bedroom listing.
A guest-first approach asks:
Which properties appeal to the same traveler?
Which listings match the same expectations?
Which alternatives appear during the same search process?
That perspective creates a more accurate picture of competition.
The 7 Filters Every Comp Must Pass
Every property in a candidate pool should pass seven qualification filters.
1. Bedroom and Bathroom Match
Bedroom count is one of the strongest competitive signals.
A 2-bedroom property generally does not compete directly with a 3-bedroom property.
Bathroom count matters as well because it affects guest convenience and travel group dynamics.
2. Guest Capacity and Sleeping Configuration
Two properties with identical bedroom counts may serve completely different audiences.
Sleeping arrangements and maximum occupancy influence who books.
Capacity should align closely.
3. Sub-Market Location
Competition is driven by guest behavior—not city boundaries.
Different neighborhoods inside the same city often attract different travelers.
Define sub-markets according to guest search patterns and booking intent.
4. Amenity Tier
Amenities reshape competitive positioning.
Examples include:
Hot tubs
Dedicated workspaces
EV charging
Pet-friendly accommodations
Private pools
Guests filter based on amenities, so comp selection should too.
5. Property Type and Design Style
Design is increasingly part of market competition.
A modern, curated rental attracts different demand than a rustic traditional stay.
Style affects substitution behavior.
6. Listing Quality and Photography
Presentation matters.
Listings with weak photography or poor conversion performance may not actually compete for guest attention—even if specifications match.
7. Price Range Overlap
Price creates market segmentation.
If average nightly rates differ dramatically, guests may not compare those listings at all.
Comp candidates should remain within a realistic pricing band.
Three Questions Every Comp Should Pass
Before including any listing, ask:
Substitution Test
Would the guest choose this property if yours were unavailable?
Amenity Match Test
Does this listing compete in the same experience category?
Behavioral Market Test
Do guests view these listings as interchangeable choices?
If the answer is no, remove it.
The 7-Step Framework for Building a Strong Comp Set
Step 1: Define Your Property Honestly
Evaluate your property as guests see it—not how you wish to position it.
Step 2: Map Your Behavioral Sub-Market
Identify where your guests actually search and compare.
Step 3: Build a Large Candidate Pool
Start with approximately 30–50 properties.
Gather broadly before narrowing.
Step 4: Apply the Seven Filters
Remove candidates aggressively.
The objective is not inclusion—it’s accuracy.
Step 5: Validate with Booking Data
Listing appearance alone is insufficient.
Look at:
Occupancy trends
Booking pace
Performance consistency
Properties outside your performance tier should be removed.
Step 6: Lock the Final Set
Maintain a comp set of 8–15 properties.
Too few creates volatility.
Too many dilutes insights.
Step 7: Refresh Quarterly
Markets evolve.
New supply enters.
Properties renovate.
Guest behavior changes.
Quarterly review should become operational discipline.
Which Metrics Actually Matter?
Comp sets become useful only when paired with meaningful metrics.
ADR (Average Daily Rate)
Helpful context—but never sufficient alone.
RevPAR (Revenue Per Available Night)
RevPAR is the strongest benchmark because it combines rate and occupancy performance.
Occupancy
Occupancy must always be interpreted alongside revenue.
High occupancy does not automatically mean stronger performance.
Booking Pace
Monitor how quickly properties fill:
30 days out
60 days out
90 days out
Pacing reveals market demand patterns.
Length of Stay
Stay duration influences pricing and minimum-night strategy.
Minimum-Stay Rules
Minimum stays can create competitive advantages and unlock different booking segments.
Review Velocity and Ratings
Reviews influence ranking, conversion, and pricing power.
Monitor review growth and recent quality.
Using Comp Sets in Daily Pricing Decisions
Comp sets are benchmarks—not instructions.
Use them to:
Compare Market Position
Understand where your pricing sits relative to alternatives.
Identify Demand Gaps
If competitors fill faster, investigate why.
Adjust Minimum-Stay Strategy
Open booking windows competitors cannot access.
Monitor Event Pricing
Study how competitors react to local demand spikes.
Common Comp-Set Mistakes
Survivorship Bias
Only analyzing active listings creates incomplete market understanding.
Aspirational Benchmarking
Pricing against properties outside your tier reduces bookings.
Sample-Size Errors
Too few creates noise. Too many reduces clarity.
Stale Comp Sets
Static sets become inaccurate quickly.
Ignoring Guest Perception
The guest—not the host—defines competition.
When Should You Refresh or Rebuild?
Refresh your set when:
Amenities change
Photos improve
Market supply shifts
Demand patterns move
New seasons begin
Rebuild completely when:
Your property changes tiers
Market conditions fundamentally shift
Final Thoughts
A comp set is not simply a spreadsheet exercise.
It is the foundation of revenue management.
The biggest shift is learning to stop viewing competition from the property outward and start viewing it from the guest inward.
Properties do not compete because they share features.
They compete because guests treat them as alternatives.
Build the comp set correctly, revisit it consistently, and every pricing decision becomes more informed, measurable, and profitable.
