Airbnb Comp-Set Secrets: Build, Track, and Beat Your Real Competitors

Why Comp Sets Decide Your Revenue Ceiling

Your comp set is the small group of listings you benchmark against when making pricing, minimum-stay, and investment decisions. Done right, it becomes a live dashboard of what your real competitors are doing—and a roadmap to beating them.

Done wrong, you’ll underprice, chase the wrong guests, and misread your market.

This guide breaks down, step by step, how to:

Build a tight, high-signal comp set
Use PriceLabs/Wheelhouse market dashboards to track price and availability
Use Guesty exports to compare your performance
Run a disciplined weekly pricing cadence that compounds revenue gains over time

Comp Selection Criteria: How to Build a Real Comp Set

Step 1: Define your “product” before you pick comps

Before looking outward, document your own listing in a simple worksheet:

Property profile (fill this out first):

Location:
Micro-area (e.g., “Downtown – walkable to convention center”)
Distance to key drivers (beach, stadium, hospital, trailhead, metro)
Size & capacity:
Bedrooms / bathrooms
Max guests
Number and types of beds
Property type:
Entire home, apartment, condo, studio, tiny house, etc.
Design & quality tier:
Budget / Standard / Upscale / Luxury
Amenities:
Non-negotiables: parking, Wi‑Fi quality, A/C or heating, washer/dryer
Demand drivers: hot tub, pool, view, pet-friendly, workspace, EV charger, outdoor space
Policies:
Minimum stay, max stay, cancellation policy, pet rules
Booking profile:
Typical LOS (length of stay), top booking channels, main guest avatars (families, contractors, couples, corporate, etc.)

This becomes your baseline. Every comp must be judged against this profile.

Step 2: Core comp selection criteria

Use Airbnb’s filters plus tools like AirDNA, PriceLabs Market Dashboards, or Wheelhouse to shortlist properties.

Filter using these non‑negotiable criteria:

Location (micro, not macro)

Same neighborhood or usage zone: “beach walk zone” ≠ “10 minutes drive to beach.”
Same demand drivers:
Convention center corridor vs residential outskirts
Ski-in/ski-out vs “10-min shuttle”
Use map view to stay within the same micro‑cluster, not just same city.

Property type & capacity

Entire home vs private room vs shared—do not mix.
Within ±1 bedroom and similar max guest count.
Avoid comparing a 1BR with sofa bed that sleeps 4 to a 2BR that sleeps 4; bedrooms matter.

Design & quality tier

Scan photos: finishes, furnishings, styling.
Classify into:
Budget: basic furniture, older finishes, minimal decor.
Standard: clean, moderate finishes, cohesive but not styled.
Upscale: modern, high-quality furnishings, clearly styled.
Luxury: designer-level, exceptional architecture or views.
Only compare inside the same tier or immediately adjacent (Standard vs Upscale) if your supply is limited.

Amenity profile
For a comp, the headline amenities must match:

If you have a hot tub / pool / sauna / rooftop deck / EV charger / dedicated office, comps need those or similar “hero” amenities.
If you’re pet-friendly, prioritize other pet‑friendly listings; pet status materially affects pricing and demand.
In PriceLabs comp tools, use amenity filters to narrow to your amenity set.

Policy & usage match

Similar:
Minimum stay
Cancellation policy flexibility
Check‑in (self‑check‑in vs hosted)
If you target mid-term stays (21+ nights), include similarly configured mid-term listings in your comp set.

Step 3: Build 3 layers of comps

Aim for 15–30 listings, broken into:

Primary comps (8–12):
Very close match on location, size, design tier, and amenities. These drive your day-to-day pricing.
Secondary comps (8–12):
Slightly off on one dimension (e.g., 1 extra bedroom; or 1–2 blocks further from the beach). Useful for understanding how upgrades or downgrades are priced.
Aspirational comps (3–6):
Same location/size, clearly nicer design or amenities, and significantly higher ADR. These show what is possible if you upgrade.

Use a spreadsheet or Google Sheet to track them, or pull them into tools like:

Misfit-Comp Red Flags: What to Exclude Immediately

Misfit comps will wreck your pricing signals. Use this “red flag” list when curating your set.

Misfit-comp red flag checklist

Exclude or heavily discount any listing that:

Serves a different primary demand driver
You’re in a nightlife district; they’re by a hospital hosting travel nurses.
You’re ski-in/ski-out; they’re “15 min to base area by car.”
Has a structurally different property type
Condo in a building with gym & pool vs standalone house with yard.
Basement unit vs top‑floor penthouse.
Is a totally different design/quality tier
Your high-end, fully renovated townhouse vs tired 90s unit with mismatched furniture.
Your luxe cabin with hot tub and views vs basic cabin in the woods.
Has major amenity mismatch
They have a pool and hot tub, you don’t (or vice versa).
They sleep 10 with 3 bathrooms; you sleep 4 with 1 bath.
They allow pets and you don’t, in a pet‑heavy drive‑to market.
Has abnormal pricing dynamics
Listed far under market because it’s a new host chasing reviews.
Perpetually underpriced due to owner-occupied “hobby host” behavior.
Abnormal cleaning fees or bundled utilities that distort nightly rate.
Is not actively in your channel mix
If a “comp” only lives on niche mid‑term platforms but not on Airbnb, its demand pattern will differ.

If you “need” to keep a questionable listing (because the market is thin), clearly flag it in your worksheet with a –25–50% weighting in any averages you calculate.

Market Dashboards 101: Turning Data Into Strategy

Market dashboards compress thousands of listings into a handful of KPIs you can act on. Tools like PriceLabs and Wheelhouse are built specifically for this.

Key tools to use

PriceLabs Market Dashboard
Live market KPIs: ADR, occupancy, RevPAR, booking window.
Amenity & policy analysis.
Custom comp sets.
Read more: PriceLabs Market Dashboard overview.
Wheelhouse Market Reports
Competitive set performance, seasonal curves, “recommendation bands.”
See: Wheelhouse Market Dashboard help.
Channel / PMS analytics (e.g., Guesty)
Guesty financial and performance reports
Export CSVs to match against your comp set.

Core KPIs to monitor

From your market dashboards, watch:

ADR (Average Daily Rate)
By bedroom count, property type, and comp set.
Compare your ADR vs market for the next 7/30/90 days.
Occupancy rate
Your last 30 / 60 / 90 vs market averages.
Forward-looking occupancy: next 7, 14, 30, 60 days.
RevPAR (Revenue per Available Rental night)
Best for apples-to-apples performance comparison.
Aim to beat market RevPAR while staying roughly at market occupancy.
Booking window
Average days between booking date and check-in.
Use to time aggressive vs conservative pricing.
Length of stay patterns
Median LOS per month or season.
How weekends, weekdays, and peak periods differ.
Amenity premiums
In PriceLabs/Wheelhouse, identify which amenities support higher ADR (e.g., hot tub may add 15–20%, dedicated workspace adds 5–8% in some urban markets).

These KPIs are your “instrument panel” for weekly decisions.

Weekly Review Cadence: The Operating Rhythm of a Pro Host

A consistent weekly process beats sporadic big changes. Aim for a 30–60 minute revenue management session once a week.

Weekly workflow overview

Review past performance (last 7–30 days)
Check forward-looking demand vs comps (next 7–60 days)
Update rates & LOS rules
Document changes and notes

You can manage this workflow across Guesty, PriceLabs/Wheelhouse, and your comp-set sheet.

Step 1: Past performance review (15 minutes)

Use your PMS or channel data:

In Guesty:
Export or review reports for:
Occupancy %
ADR
Revenue
Nights booked or cancelled
See: how to export performance data from Guesty.

Compare to your targets or market averages (from dashboards):

If occupancy is low and ADR is at or above market, pricing might be too aggressive.
If occupancy is high and ADR is below market, you are likely underpriced.

Log these in your worksheet:

Last 7/30 days:
Your ADR vs market ADR
Your Occupancy vs market Occupancy
Your RevPAR vs market RevPAR

Step 2: Forward-looking demand check (20 minutes)

In PriceLabs or Wheelhouse:

Open the calendar / dynamic pricing view for the next 60–90 days.
Overlay:
Suggested rates
Market demand curves
Picked comp-set average pricing

Focus on four time buckets:

Next 0–7 days

Goal: maximize occupancy at healthy ADR.
If significantly more expensive than comps and you’re not booked, adjust down quickly.

Next 8–30 days

Goal: keep pace with market; fill 60–80% in advance in high season, 40–60% in shoulder/low.
Fine-tune based on events, school holidays, etc.

Next 31–90 days

Goal: price toward the top of market, adjusting occasionally as demand becomes clearer.

Peak periods and events

Cross-reference with:
Local events calendars
AirDNA or market dashboards for surge patterns
Ensure you are above typical ADR for those dates and apply stricter minimum stays.

Step 3: Apply changes via rate rules and bulk actions

Use your dynamic pricing tool plus Guesty:

In PriceLabs:
Adjust minimum/maximum price, last-minute discounts, orphan day rules, and weekend premiums.
See: PriceLabs documentation for detailed rule settings.
In Wheelhouse:
Modify strategy aggressiveness, floor/ceiling, and special event overrides.
Read: Wheelhouse pricing strategies guide.
In Guesty:
Apply bulk pricing or LOS changes to multiple listings or date ranges.
Learn how: Guesty pricing & restriction adjustments.

Document what you changed and why in your worksheet so you can connect decisions to results next week.

Rate & LOS Adjustments: Tactics That Move the Needle

Core pricing levers

You have four main daily levers:

Base rate / target ADR
Day‑of‑week premiums/discounts
Last‑minute adjustments
Event and peak period overrides

And three primary LOS levers:

Minimum stay
Maximum stay
Arrival/departure constraints (e.g., no Sunday check-in)

Tactical playbook: price vs occupancy

Use this matrix for quick decisions:

Situation	Signal	Core Moves
Low occupancy, high ADR vs comps	Guests choosing cheaper neighbors	Lower prices or add discounts; consider more flexible LOS
High occupancy, low ADR vs comps	You’re filling too easily	Raise base rates, reduce discounts, tighten LOS
High occupancy and high ADR	Sweet spot	Nudge rates up, especially for peak days
Low occupancy, low ADR	Market weakness or misfit listing	Rebuild comp set, improve listing, adjust LOS and price floor

Smart weekday / weekend structures

In many markets:

Weekends can support 10–30% higher ADR.
Weekdays might need 5–15% discounts, especially in leisure‑heavy areas.

In PriceLabs/Wheelhouse:

Set weekend premiums and midweek discounts as rules.
Use market data for your comp set to back into the spread.

Example:

Market ADR:
Fri/Sat: $250
Sun–Thu: $190
Your ADR (goal):
Fri/Sat: $245–255
Sun–Thu: $185–195

Last-minute pricing rules

Last-minute discounts work best when data-driven, not “panic-driven.”

Typical pattern (adapt to market):

0–2 days out: Up to 25–35% discount vs standard rate if still vacant.
3–7 days out: 10–20% discount.
8–14 days out: 5–10% discount or small incremental reductions.

Use tools:

PriceLabs Last Minute Discounts or Wheelhouse Proximity Adjustments to automate this rather than editing daily.

LOS strategies that boost revenue

High-demand weekends and events

Set 2–4 night minimums to avoid one-night “blocking” stays.
For major events, require stays that bridge shoulder nights (e.g., Thu–Sun).

Slow season or midweek gaps

Drop minimum stay to 1 or 2 nights where allowed to scoop up spontaneous demand.
Use orphan night discounts in PriceLabs/Wheelhouse to fill 1–2 night gaps between bookings.

Pursuing longer stays

Offer weekly (5–10%) and monthly (10–25%) discounts if your market supports extended stays.
Use Guesty to set channel-specific LOS rules if you differentiate between Airbnb, Booking.com, etc.

Protecting your calendar

Use arrival/departure rules (e.g., no Saturday checkouts in peak season) to avoid hard-to-fill fragments.

Practical Comp-Set Worksheet Template

Use this structure in a spreadsheet or database:

Sheet 1: Property Profile

Property name / internal ID
Location notes
Size & capacity
Design tier (Budget/Standard/Upscale/Luxury)
Amenity highlights
Policies (min stay, pet, cancellation)
Guest avatar(s)

Sheet 2: Comp Set Master List

Columns:

Listing name
Airbnb URL
Role (Primary / Secondary / Aspirational)
Distance from you (km/miles)
Bedrooms / baths / max guests
Property type
Design tier
Key amenities (hot tub, pool, view, pet, parking, workspace, etc.)
Min stay (weekdays/weekends)
Cancellation type
Notes (why included; red-flag caveats)

Sheet 3: Weekly Performance & Pricing

Rows by week, columns for:

Your occupancy %, ADR, RevPAR (last 7/30 days)
Market or comp-set occupancy %, ADR, RevPAR
Forward occupancy for next 7 / 30 / 60 days (you vs market)
Key rate / LOS changes made
Observations (e.g., “raised weekends +15%, maintained high occupancy”)

You can partially automate this by:

Exporting part of the data from Guesty and PriceLabs/Wheelhouse
Using simple formulas to compare your figures to your comp-set averages.

Using PriceLabs & Wheelhouse Effectively

PriceLabs: From market dashboards to listing-level rules

Key steps:

Market Dashboard Setup

Create a dashboard for your area.
Filter to your bedroom count and property type.
Use the “Comp Sets” feature to select your previously identified comps.

Dynamic Pricing Configuration

Set your base price relative to market ADR (e.g., 5–10% above if you’re clearly nicer than average comps).
Define:
Minimum/maximum price
Last-minute discounts
Orphan day rules
Seasonality adjustments

Rate Rules & Overrides

Configure day-of-week adjustments.
Add fixed overrides for:
Major events
Holidays
Personal blackout dates

PriceLabs documentation: Dynamic Pricing & Market Dashboards help center.

Wheelhouse: Strategy presets and comp insights

Wheelhouse emphasizes strategies:

Choose a Strategy

“Conservative,” “Recommended,” or “Aggressive” based on your risk tolerance.
Aggressive = higher ADR, lower occupancy; Conservative = reverse.

Refine Price Floors & Ceilings

Use market data and comp-set rates to set rational boundaries you’re comfortable with.

Use Market Explorer

Identify top-performing comps.
See typical seasonal price patterns.
Adjust your profile accordingly.

Wheelhouse’s guides: Wheelhouse help center.

Leveraging Guesty for Performance Tracking and Bulk Changes

Pulling performance data from Guesty

Use Guesty reports to:

Track:
Occupancy
ADR
Revenue
Booking window
By:
Listing
Time period
Channel

Steps:

Go to Reports in Guesty.
Download relevant financials and operational reports as CSV.
Import into your comp-set worksheet to compare against market/dashboard outputs.

Guesty support docs (helpful starting points):

Applying bulk pricing & LOS moves

Guesty lets you:

Bulk update:
Nightly rate adjustments
Minimum/maximum stay
Restrictions (like closed to arrival/departure)
Across:
Multiple listings
Channel-specific calendars

Workflow:

Derive decisions from PriceLabs/Wheelhouse and your comp analysis.
Use Guesty bulk tools to:

Apply consistent strategies across similar listings.
Roll out event pricing or seasonal changes portfolio‑wide.

Guide: Guesty pricing & restrictions help.

Advanced Comp-Set Tactics and Real-World Scenarios

Scenario 1: You’re 95% occupied, but your ADR lags the market

Signals:

Your occupancy > market by 10–20 points.
Your ADR is 10–15% under your primary comps.

Actions:

In PriceLabs/Wheelhouse:
Increase base rate 5–10%.
Reduce last-minute discount depth.
In Guesty:
Increase weekend premiums across next quarter.
Monitor:
If occupancy remains high, take another 5–10% rate increase step the following week.

Scenario 2: Bookings slow; comps are filling faster at higher prices