How to Build High-Performance Airbnb Comp Sets Using PriceLabs Market Dashboards

Most Airbnb operators make one major mistake when analyzing their market:

They rely on broad averages.

Looking at an entire city or vacation market may provide useful context, but it rarely tells you how your specific property should perform. A five-bedroom vacation home near the beach does not compete with every listing in the market. It competes with a very specific group of properties that match its size, location, amenities, and guest appeal.

That is where granular comp sets become critical.

In a Revenue Academy training session, Emile Sakhel and Adam from Pricing by Mira broke down how advanced operators use PriceLabs Market Dashboards to build more accurate competitive analysis systems and improve revenue management decisions.

The goal is simple: stop pricing against the average market and start pricing against the right properties.

Why Generic Market Data Creates Bad Pricing Decisions

Many STR operators analyze data across thousands of listings without narrowing the results to properties that actually compete with theirs.

That creates distorted benchmarks.

For example, a market may show top-performing properties generating over $250,000 annually. But if your property is fundamentally different from those homes, those numbers are not useful.

As Emile explained throughout the session, your property’s “identity” matters.

A property should be evaluated against listings that:

• Match its bedroom count

• Appeal to similar guests

• Compete in the same geographic pocket

• Offer similar amenities and experiences

• Operate at a similar quality level

Without that filtering process, pricing decisions become reactive instead of strategic.

What Makes PriceLabs Market Dashboards Useful

PriceLabs Market Dashboards allow operators to:

• Analyze historical and future market performance

• Build custom comp sets

• Filter listings by granular criteria

• Compare occupancy, ADR, RevPAR, and booking windows

• Study seasonality trends

• Export historical data for deeper analysis

Unlike simple revenue estimators, the dashboard functions as a broader market intelligence tool.

One of the most important capabilities is the ability to customize search criteria at a highly specific level.

Operators can filter by:

• Bedroom count

• Geographic area

• Amenities

• Occupancy characteristics

• Guest capacity

• Minimum stay requirements

• Property performance

• Pool access

• Beachfront status

• Guest favorite badges

• Active listing history

This creates a much more accurate picture of a property’s true competition.

Start With Geography Before Performance

One of the first steps discussed in the training was narrowing the market geographically.

Instead of analyzing an entire destination, operators can draw custom polygons around highly relevant areas inside PriceLabs.

This matters because guest behavior changes dramatically even within the same market.

For example:

• Beachfront homes compete differently than inland homes

• Walk-to-beach properties behave differently than resort condos

• Certain neighborhoods command higher ADRs

• Some areas attract longer booking windows

By narrowing the geography first, operators eliminate irrelevant listings before deeper filtering begins.

Bedroom-Level Filtering Changes Everything

One of the strongest takeaways from the session was the importance of bedroom-level analysis.

Adam demonstrated how filtering by four-, five-, and six-bedroom properties dramatically changed the competitive landscape compared to analyzing all listings together.

This matters because supply and demand vary heavily by property size.

For example:

• A market may contain thousands of two-bedroom listings

• But only a few hundred five-bedroom listings

That creates entirely different pricing dynamics.

Larger properties often:

• Face less direct competition

• Operate with longer booking windows

• Require different minimum stay strategies

• Generate stronger seasonal spikes

• Depend more heavily on group travel demand

Without bedroom filtering, operators miss these differences entirely.

Why Manual Review Still Matters

Even with advanced filters, the session emphasized that comp building is not fully automated.

Operators still need to manually review listings.

Some listings may:

• Be incorrectly tagged

• Lack proper amenities

• Hide direct-booking activity

• Operate under large management companies

• Misrepresent location quality

For example, some beachfront homes were not tagged correctly inside Airbnb data, which distorted filtering results.

That means revenue managers cannot blindly trust the dashboard.

The best operators combine:

• Data filtering

• Manual review

• Market intuition

• Guest perspective analysis

The key question becomes:

Does this property truly compete with mine from the guest’s perspective?

That mindset creates better comp sets than relying entirely on automated systems.

Why Multiple Comp Sets Can Be Valuable

One of the more advanced concepts discussed was building multiple comp sets for a single property.

This allows operators to evaluate pricing from different perspectives.

For example:

• A broad local comp set

• A luxury-performance comp set

• A beachfront comparison set

• A high-occupancy comp set

• A booking-window comp set

Each one reveals different insights.

Instead of relying on a single pricing lens, operators gain multiple strategic viewpoints for decision-making.

PriceLabs allows up to 30 comp sets within one market dashboard, giving advanced operators significant flexibility.

Understanding Seasonality Beyond ADR

The session also emphasized the danger of focusing too heavily on ADR alone.

A high ADR does not always indicate strong performance.

Revenue managers need to study:

• Occupancy

• RevPAR

• Seasonal demand swings

• Booking windows

• Available inventory

For example, winter months in some vacation markets may show:

• High ADRs on booked nights

• Extremely low occupancy overall

That means many listings remain empty most of the month.

This is where RevPAR becomes more important than ADR alone.

As discussed in the training, top operators often focus on increasing occupancy during weak seasonal periods rather than protecting inflated nightly rates.

That shift in mindset can dramatically improve annual revenue performance.

Booking Windows Reveal Competitive Opportunities

Another critical metric discussed was booking windows.

Booking windows help operators understand:

• When guests typically book

• How far in advance demand materializes

• When competitors begin discounting

• When pricing pressure intensifies

For example:

• If the median booking window is 100 days

• And operators panic at 60 days

• That creates opportunities for strategic pricing adjustments

Understanding booking windows allows operators to avoid reactive “race to the bottom” pricing behavior.

Instead, they can make proactive pricing decisions based on actual market timing patterns.

The Real Goal: Stay Ahead of the Market

Throughout the session, one theme repeated consistently:

The goal is not to follow the market.

The goal is to stay ahead of it.

That means:

• Building better comp sets

• Understanding seasonality deeper

• Studying occupancy behavior

• Analyzing booking windows

• Creating more granular market views

• Using data more intelligently than competitors

The operators who win long term are rarely the ones using default settings and average market assumptions.

They are the ones building systems that help them interpret demand more accurately than everyone else.

Final Thoughts

PriceLabs Market Dashboards become significantly more powerful when operators move beyond broad averages and start building truly relevant comp sets.

The combination of:

• Granular filtering

• Manual review

• Occupancy analysis

• RevPAR thinking

• Booking window analysis

• Seasonal interpretation

creates a much more strategic revenue management process.

For STR operators looking to improve pricing performance, the biggest opportunity often is not finding more data.

It is learning how to filter the right data.

Key Takeaways

• Broad market averages rarely reflect true property competition

• Bedroom-level filtering dramatically improves comp accuracy

• Geography matters more than many operators realize

• Manual review is still necessary even with advanced dashboards

• Multiple comp sets provide stronger pricing perspective

• RevPAR often matters more than ADR alone

• Booking windows reveal major pricing opportunities

• Top operators focus on staying ahead of market behavior