How Property Types Can Impact Performance in the Short-Term Rental Market

Updated : Dec 16, 2025

When evaluating a short-term rental investment, one of the most overlooked—but most influential—decisions is property type. A studio, one-bedroom apartment, or three-bedroom home may exist in the same city, yet each can deliver vastly different results in terms of occupancy, pricing power, and overall revenue. For hosts with 1–5 listings, choosing the right property type often matters more than simply choosing the right market.

In this article, we’ll walk through how to compare property types for Airbnb earnings using PriceLabs, while grounding every insight in real short-term rental performance data. You’ll see how studios, multi-bedroom homes, and amenity-rich listings perform differently across markets—and why assumptions like “bigger earns more” don’t always hold true.

By the end, you’ll have a repeatable, data-backed framework to evaluate property types, understand what drives performance in your market, and make smarter acquisition or pricing decisions using PriceLabs’ Market Dashboards and comparison tools.

Getting Started with PriceLabs for Property Comparison

Comparing Airbnb earnings across different property types—or even between two cities—requires more than surface-level averages. Market conditions, guest behavior, and competitive density all shift based on location and property configuration. This is where a data-driven revenue management platform like PriceLabs becomes essential.

PriceLabs analyzes pricing and performance data from hundreds of thousands of short-term rental listings across global markets, helping hosts move beyond guesswork. Instead of assuming that a larger home or a specific bedroom count will perform better, you can evaluate actual occupancy trends, average daily rates (ADR), and revenue potential by property type before making decisions.

At its core, PriceLabs uses dynamic pricing, which means nightly rates are automatically adjusted based on real-time demand, seasonality, and competitor behavior. For hosts comparing property types, this matters because demand patterns differ significantly between studios, one-bedrooms, and larger homes—and static pricing often hides those differences.

A typical property comparison workflow in PriceLabs looks like this:

• Create a PriceLabs account and connect your Airbnb listings
• Build competitive sets (Comp Sets) based on similar property types
• Use Market Dashboards to analyze occupancy, ADR, RevPAR, and booking patterns
• Compare how different property types perform within the same market
• Adjust pricing and strategy based on data—not assumptions

This structured approach allows hosts with small portfolios to answer high-impact questions confidently, such as whether a studio will outperform a two-bedroom in an urban market, or if investing in a larger home with premium amenities is justified in a vacation destination.

Connecting and Importing Your Airbnb Listings

Accurate comparisons start with clean listing data. PriceLabs makes it easy to connect and sync your Airbnb listings in minutes.

How it works:

• Create a PriceLabs account
• Securely connect your Airbnb account
• Import your listings automatically

Once connected, PriceLabs continuously updates pricing and performance data. This ensures your property type comparisons are based on real, current market behavior—not static averages or assumptions.

Building a Competitive Set for Accurate Benchmarking

To compare Airbnb earnings by property type meaningfully, you need the right benchmark. This is where Competitive Sets (Comp Sets) come in. A Comp Set is a group of similar listings used to evaluate how your property—or a potential investment—performs relative to direct competitors.

Rather than comparing a studio to the entire market, Comp Sets allow you to isolate performance by property type, location, and quality level. This removes noise and highlights what’s actually driving revenue differences.

When building Comp Sets in PriceLabs, focus on relevance over volume:

• Select 5–10 similar listings for reliable comparisons
• Match by bedroom count (studio, 1BR, 2BR, etc.)
• Ensure listings are in the same neighborhood or demand pocket
• Prefer listings with strong ratings and active calendars

For property-type analysis, it’s best to create separate Comp Sets for each configuration. For example, one Comp Set for studios and another for two-bedroom homes within the same market. This makes it easier to compare occupancy, ADR, and revenue potential side by side.

By anchoring your analysis to well-built Comp Sets, you ensure that differences in performance reflect property type dynamics, not mismatched locations or quality levels.

Performance Insights Based on Property Types In New York City 

1. Studio Dominance in NYC

The data presents a clear pattern: the studio consistently outperforms other New York City market property configurations.

Studios have achieved higher occupancy rates over the past 12 months. This shows that larger spaces do not consistently deliver better returns and suggests that efficiency and affordability often trump additional square footage in dense urban environments.

Entire guest suites and private rooms in townhouses record high occupancy rates in New York City, suggesting that privacy and independence are key drivers.

Several market-specific factors likely contribute to this trend:

• High real estate costs in NYC drive demand for an efficient use of space
• Domestic and business travelers arrive in large numbers in the city
• Shorter average length of stay compared to other destinations
• The premium on convenient locations over spaciousness as people prefer being outside to inside

2. Kitchen is a Decisive Performance Driver

Listings with kitchens (19,972 active listings) significantly outnumber those without one (3,009 active listings) in New York City. The market has already realized this is important for city travelers.

The ones with kitchens demonstrate substantially higher occupancy rates than those without: 53% vs. 41%. This 12% difference represents 44 more booked nights annually.

Let’s assume your average daily rate is $200. The 12% occupancy difference represents approximately $8,800 in additional annual revenue. Even if you account for ongoing maintenance costs for kitchen facilities, the ability to charge premium rates is overwhelming. 

How to Use Market Dashboard to Analyze Performance?

Once your Comp Sets are in place, PriceLabs Market Dashboard help you understand how different property types actually perform within a market. Instead of relying on assumptions, you can see clear trends across key revenue metrics.

Market Dashboards provide a high-level view of supply, demand, and pricing behavior for each property type, making them especially useful when comparing studios, apartments, and larger homes.

Using Market Dashboards, you can analyze:

• Occupancy rates to understand booking consistency by property type
• Average Daily Rate (ADR) to see pricing power across different configurations
• Revenue and RevPAR to evaluate overall earning potential
• Booking windows and length of stay to identify guest behavior patterns

For example, data from major urban markets shows that smaller units like studios often achieve higher occupancy, while larger properties may command higher nightly rates but experience more variability. These insights help explain why a higher ADR doesn’t always translate into higher total revenue.

By filtering dashboard views by bedroom count or listing type, you can compare performance side by side and identify which property types align best with your market’s demand profile.

1. Inventory Growth Assessment

Looking at inventory growth patterns (the growth in active listings in the market) will help you understand the market’s supply and demand patterns and competitive pressures.

For example, in Miami:

March 2024: 1,150 active listings; Current: 1,065 active listings (7% decrease)

Read more: How To Find Out Airbnb Demand In An Area: Complete Guide

High-performing listings (4.8+ ratings): 540 active listings (41% difference from listings that aren’t premium)

Read more: How to Get More Reviews on Airbnb?

This pattern of declining overall inventory but growing premium inventory suggests increasing competition in the luxury rental segment, where factors affecting property value extend beyond basic configuration to include premium amenities and exceptional guest experiences.

2. Comparative Market Analysis

In the Market Dashboard, you can also compare different property types with the key performance metrics of listings with those property types to identify performance patterns. 

Following the previous example, you can further analyze listings with 4.8+ ratings.

Here, you can further analyze how 2-bedroom listings are charging their properties. You can evaluate seasonal performance variations by property type and track RevPAR to determine overall revenue potential. 

This comparative approach is essential because affecting property value often varies significantly between markets: what works in NYC might not work in Vegas or Miami. 

3. Amenity Impact Analysis

You can use PriceLabs Market Dashboard’s Amenity graphs to identify the top-performing amenities in the market. With this graph, you can locate the amenities you can add to your property. This graph will provide the amenities that are the most desired in the market and also the most common ones.

You can then analyze specific amenities in the comp set to provide critical insights into how particular features influence property performance. 

• Identify high-impact amenities that justify investment.
• Quantify the revenue differential between properties with and without specific amenities.
• Calculate ROI for potential property improvements.
• Compare amenity preferences across different markets.

Let’s examine whether adding a hot tub to your property in Phoenix, Arizona, is profitable. The number of active listings with hot tubs has increased by 30% in the last year, making up over 17% of the overall market.

The average daily rate of listings with a hot tub on their property is 14% higher than that of listings without a hot tub. 

4. Booking Pattern Analysis

With the same Market Dashboard, you should be able to understand how different property types impact booking patterns. 

For example, our analysis shows us that:

• Studios often attract shorter stays but higher occupancy
• Larger properties typically command higher rates but may experience more seasonal volatility
• Different property types may attract guests from different geographic regions
• Lead time for bookings often varies by property configuration

Going with the previous example of Phoenix, properties with a hot tub in their property receive maximum booking 2-4 weeks in advance, and most of their stays are short stays for around 3-4 days. 

Analyzing by property type reveals an interesting trend—especially for listings with a hot tub.

Although 4+ bedroom properties are limited (with just 43 active listings, making up 23% of the market), they generate the highest revenue. These larger properties can charge nearly 200% more than the market average—a substantial increase.

However, while the potential profit is impressive, it’s important to factor in higher investment costs and expenses, which can also be significant.

Benchmarking Neighborhood and Seasonality Data

Property type performance doesn’t exist in isolation—it’s heavily influenced by where the listing is located and when guests are booking. This is why neighborhood-level and seasonality data are essential when comparing Airbnb earnings by property type.

With PriceLabs’ Market Dashboards, you can drill down into hyper-local insights to see how studios, apartments, and larger homes perform across different neighborhoods within the same city. Two listings with identical bedroom counts can show very different results depending on proximity to attractions, business hubs, or event-heavy areas.

Using neighborhood data, you can benchmark:

• Occupancy and ADR by neighborhood for each property type
• Seasonal demand shifts, such as peak vs. off-season performance
• Booking lead times, which often vary by property size and guest segment

Seasonality plays a major role in property-type comparisons. Smaller units may maintain steadier occupancy year-round, while larger homes often see sharper peaks during holidays, events, or vacation seasons. Without accounting for this, it’s easy to overestimate or underestimate a property’s true earning potential.

By combining neighborhood and seasonality insights, hosts can identify which property types perform consistently versus those that rely on short, high-demand windows—helping you align investment and pricing strategies with realistic revenue expectations.

Customizing Pricing Strategies by Property Type

Once you understand how different property types perform, the next step is aligning your pricing strategy with those insights. PriceLabs allows you to customize pricing rules at the listing level, which is especially important when managing multiple property types within the same market.

Different property configurations attract different guest segments—and pricing needs to reflect that. A studio competing on occupancy requires a different strategy than a multi-bedroom home targeting group travelers.

With PriceLabs, you can adjust pricing by property type using:

• Custom minimum and maximum rates for each listing
• Different minimum stay rules for studios versus larger homes
• Weekday and weekend pricing adjustments
• Seasonal pricing overrides based on demand patterns
• Event-based pricing for peak demand periods

For example, smaller units may benefit from lower minimum stays and more aggressive pricing to drive occupancy, while larger properties often perform better with higher minimum rates and longer stays to reduce turnover costs.

PriceLabs also supports launch pricing strategies, allowing new listings to start with discounted rates and gradually increase over the first few weeks as bookings and reviews build. This is particularly useful when introducing a new property type into an existing portfolio.

By customizing pricing at the property-type level, hosts can maximize revenue without sacrificing occupancy—ensuring each listing is optimized for how guests actually book.

Reviewing and Updating Your Comparison Regularly

Property-type performance isn’t static. Guest preferences shift, new listings enter the market, and seasonal patterns evolve—making regular review essential for accurate comparisons.

PriceLabs makes it easy to stay on top of these changes by continuously tracking competitor behavior and market performance. Instead of treating your analysis as a one-time exercise, you should revisit it at set intervals to ensure your strategy remains aligned with current conditions.

Best practices for ongoing review include:

• Reassessing Comp Sets quarterly to reflect new competitors or quality shifts
• Monitoring changes in occupancy and ADR by property type
• Identifying underperforming listings relative to their Comp Sets
• Adjusting pricing rules when demand or seasonality patterns change

Regular reviews help you catch early signals—such as studios becoming oversupplied or larger homes gaining pricing power—before they impact revenue. With PriceLabs’ dashboards and alerts, hosts can adapt quickly and keep each property type performing at its potential.

This ongoing optimization is what turns property-type comparisons into a sustainable revenue advantage, not just a planning exercise.

Frequently Asked Questions

How do I estimate which property type will generate the most Airbnb revenue?

Use PriceLabs to compare occupancy, ADR, RevPAR, and total revenue across different property types within the same market. Market Dashboards and Comp Sets let you see side-by-side performance so you can identify which configuration consistently earns more.

What key metrics should I compare between different property types?

Focus on occupancy rate, Average Daily Rate (ADR), RevPAR, booking window, and length of stay. Looking at one metric alone can be misleading—high ADR doesn’t always mean higher overall revenue.

How can I understand seasonal demand differences by property type?

PriceLabs’ Market Dashboards visualize seasonal trends and booking patterns by bedroom count and listing type. This helps you see which property types perform steadily year-round versus those that rely on peak seasons or events.

How often should I update pricing rules for each property type?

Review and update pricing rules at least quarterly, or sooner if your market sees rapid inventory changes, demand shifts, or new competition within specific property types.

How can I identify underperforming property types in my portfolio?

Compare each listing’s performance against its Comp Set in PriceLabs. If a property consistently lags in occupancy or ADR, the data will highlight whether pricing, minimum stays, or market fit needs adjustment.