How to set up your base price?

Setting your base price

Base price is the average rate you would charge across the year. It serves as a starting point, with market factors, demand patterns, and customizations applied on top. The base price also reflects the quality of your listing—for example, a luxury two-bedroom will have a higher base price than a basic two-bedroom.

Base Price Help

The Base Price Help tool analyzes nearby listings and provides a suggested starting point for your base price. It considers properties with similar bedroom counts to help match your competition.

To use this tool, click "Help me choose a base price" from the Review Prices page in your Pricing Dashboard.

💡 Pro-tip: You can also access this tool from the Multi Calendar under the More Actions column!

Options When Setting a Base Price

1. Imported Base Price

  • If a listing is newly imported, PriceLabs does not recommend a base price immediately. Instead, Imported Base Price which reflects the prices synced when the listing was connected is an available option.

  • If the listing has been performing well with these prices, you can adopt the Imported Base Price as a starting point.
  • If the listing is new and it’s unclear if the imported prices are competitive, consider using a Market-Based Price instead.

2. Market-Driven Price

  • This option sets the base price based on local market data.
FACTORS CONSIDERED IN THE MODEL
The Market-Driven base price model considers several groups of factors when determining the ideal price for your listing. You can see how these factors are weighted and distributed in the Base Price Helper modal (see screenshot below).
                                                           

Understanding the Price Adjustments
Before diving into the specific factors, here is a quick overview of how the price is calculated:
  1. Factors vs. Sub-factors: Each main Factor (like 'Performance') is made up of smaller, specific Sub-factors (like 'Past 30 days Pickup').
  2. Market Comparison and Adjustment: For every sub-factor, your listing is compared against the local market average.
    1. If your performance on a sub-factor is better than the market, the model applies a positive adjustment to your base price.
    2. If your performance is worse than the market, the model applies a negative adjustment (penalty).
    3. Example: If your "Past 30 days pickup" shows a positive adjustment, it means your listing is performing better than the market average for this metric.
  3. Impact of a Sub-factor: Each sub-factor has a pre-determined Impact level: High, Medium, or Low. This level determines how much the model's adjustment (positive or negative) will affect your base price.
    1. Example: If the Impact of 'Rating' is Low, having a slightly lower rating than the market won't penalize your base price as much as it would if the Impact was High.
💡 Tip: You can hover over the info icon (i) next to the price for any sub-factor in the modal to get a more detailed explanation of what it means.
                                                      

On to understanding the factors now...
The Market-Driven Base Price is calculated by starting with a base value and applying adjustments based on the following factor groups:
  1. Starting Price:
    This is the initial market-derived base value the model uses before applying any specific adjustments for your listing's performance or features.
    1. Calculation: The adjustments for all other factors are applied on top of this value.
    2. Example: In the pricing breakdown (see screenshot below), the starting base price is 219.
    3. Note: The starting price is dynamic and can change if you adjust selections like bedroom count or property segment in the tool.
                                          
  2. Performance: There are 3 sub-factors within performance
    This factor assesses how well your listing is currently performing in the market.
    1. ADR (Average Daily Rate) - Compares your listing's past and future ADR against your Compset.
    2. Occupancy
    3. Past 30 Days Pickup - Assesses your market competitiveness by looking at the number of future bookings secured in the last 30 days relative to the market average.
                         
  3. Review & Ratings: There are 3 sub-factors within physical parameters
    1. Review Count: Compares the total number of reviews on your listing (e.g., on Airbnb) to the average review count of listings in your Compset
    2. Overall Reliability
    3. Review Rating: Compares your listing's star rating (e.g., on Airbnb) to the average star rating of listings in your Compset
                           
  4. Physical Parameters: There are 3 sub-factors within physical parameters
    This factor considers the intrinsic features and quality of your property.
    1. Amenities: We analyze a curated list of high-impact amenities and compare the presence of these in your listing versus your comp-set, assessing their impact on revenue.
    2. Property Type: The model assesses your listing's type (e.g., entire cabin, apartment, chalet, house, loft, etc.) against the distribution in your comp-set and ranks its revenue impact.
    3. Number of Bathrooms: Compares the number of bathrooms in your listing to other listings in the Compset.
                       
  5. Fees: Fees are crucial for an apples-to-apples comparison because they affect the final price the guest sees.
    1. Cleaning Fee:
      1. We compare the final price, including your cleaning fee structure, against that of your comp-set. This determines your price competitiveness.
      2. How do we get your cleaning fee?
        1. To get the Cleaning Fee for your listing, we rely on a manual input from your end. For some cases, we receive the cleaning fee from your PMS/Channel Manager, but we always recommend you to review your cleaning fee, and in case you are changing it, we recommend you to update in the Base Price tool also.
                                        
    2. Service Fee: Similar to Cleaning Fee, we look at which airbnb fee commission model you are on, compared to listings in your compset, and how does it impacts the final prices that guest see. This is essential for doing an apples-to-apples comparison of your price, and also to understand the impact of your cleaning on your price competitiveness in the market.
                                       
  6. Others: These are platform-specific trust and quality indicators that lead to positive adjustments.
    1. Guest Favorite: Listings with the "Guest Favorite" badge generally receive a positive price adjustment.
    2. Superhost: Achieving Superhost status generally leads to a positive price adjustment
                             
Final Base Price Calculation
The Final Base Price is the result of adding the Starting Price and all adjustments (positive and negative) from the factors listed above.

Example: In the example (see screenshot below), the starting price placed the listing at the 50th percentile of the market. After applying all positive and negative factor adjustments, the final base price increased to 248, placing the listing at the 64th percentile of the market.

                                    

FACTORS YOU CAN REFINE TO ADJUST Market-Driven Base Price
The following factors allow you to fine-tune the comparison group (comp-set) the model uses to calculate your base price.
  1. Property Category: Economy, Midscale, or Upscale.
    This filter sets the quality segment of the properties you are being compared against.
    1. Default Setting: We auto-detect the segment your property falls into based on its performance relative to the local market.
    2. Your Action: You can rely on our default suggestion, or, if you are highly confident your property belongs to a specific segment, you can manually select it.
    3.                          
  2. Bedroom Categories:
    This filter allows you to choose which bedroom sizes to include in your competitor set to refine the market data.
    1. Default Setting:
      1. By default, we select the bedroom category that matches your listing.
      2. If your specific bedroom category has fewer than 50 listings, we automatically include listings from the $+/-1$ bedroom category (e.g., if you have 2 bedrooms, we include 1- and 3-bedroom listings).
    2. Your Action: You can manually select additional bedroom sizes if you believe your listing competes with them (e.g., a 2-bedroom property might compete with 1- and 3-bedroom listings in its area).
    3. Recommendation: We highly recommend selecting a set of bedroom categories that results in a comp-set of at least 30 listings. If there are too few data points, the conclusions drawn about market trends may be unreliable.
    4. Listings considered for this compsets are listing that are active for the last 1 year and next 1 year. We also remove outlier listings from the compset to make sure these listings don't distort the market trend.
                                        
  3. Market Map:
    The Market Map provides a visual representation of the listings included in the analysis and allows you to refine the geographical area used for comparison.
    1. Listings Considered in the Analysis:
      The comp-set includes listings that meet the following criteria:
      1. Activity: Active listings for the last 1 year and next 1 year.
      2. Data Integrity: Outlier listings are removed from the comp-set to ensure they do not skew the true market trends.
      3. Price Averages: The prices shown are based on the mean price observed for each listing over the past six months and the upcoming six months (an annual average). Listings active for less than six months will not appear on the map for comparison.
    2. Using the Map:
      1. Your Listing: Your property is represented by the square icon.
                                                             
      2. Comp-Set Listings: Other listings are colored based on their price trend.
      3. Details: Hovering over any listing will display its average price, bedroom count, and Listing ID.
      4. Fine-Tuning the Area: You can select an area on the map to fine-tune the geographical boundaries used to calculate the market-based values and percentile calculator.
                                                                                 

3. Recommended Base Price

  1. If a listing has been syncing prices consistently, a Recommended Base Price is generated after 14-21 days.
  2. This is based on the performance of the listing with regards to the market, the listing's occupancy, and its current base prices observed for a maximum of 60 days. Note that a higher weight is given to more recent trends. 
  3. Because the recommendations are based on your listing's performance with the current Base Price, the Recommended Price may appear blank and should be able to generate a new recommendation within a few days if the base price is recently updated.
Seasonal Base Price
  1. If the listing uses a seasonal base price or has used it recently, we recommend a percentage change to the base price instead of giving an absolute value. To implement the Recommended Base Price Change it should be applied to each Seasonal Base Price
  2. Once the Seasonal Base Price has been turned off, it takes 14-21 days for the algorithm to start recommending an absolute base price again.
     
How was this price calculated?
Below the recommended base price, we give reasons and show how these reasons impact the price (on a scale of 1 to 3). For example – if we suggest increasing the price for any given reason, we show it by green upwards pyramids (1 to 3 pyramids, depending on how much we grow it). For a decrease, we use red downward pyramids.

Recommendation in the presence of blocks
  1. Blocked dates are treated as booked when calculating a listing’s occupancy, except for certain PMS/Channel Managers where only actual booked nights are considered.
  2. For integrations that support block tracking:
    1. Occupancy calculations include blocked dates from the past 60 days and the next 30 days at the time of generating recommendations.
    2. If too many dates are blocked, occupancy comparisons with the neighborhood become difficult, making a recommendation unavailable.
    3. As blocked dates clear up, the algorithm will resume generating recommendations.

4. Custom Base Price

  • Select the Custom button to enter a base price manually.

Percentile Indicators

This indicator shows in what percentile of the selected market the newly selected base price will be. The percentile can be determined based on both bedroom category selection and map selection:                 

 
  • Market-based percentile: Always displays the 25th, 50th, and 75th percentiles.
  • Recommended and custom base prices: Show the exact percentile in which the selected price falls.
Percentile Score vs. Percentile Rank

When selecting the Market Base Price, we use Percentile Score. However, for Custom Base Price, we use Percentile Rank.

  • Percentile Score: Sets a benchmark based on the market. If $250 is the 75th percentile, it means that $250 is the price point that separates the top 25% of listings from the bottom 75%. This serves as a standard for the market.
  • Percentile Rank: Looks at how your specific price compares to all other listings. If $250 is at the 75th percentile, it means your price is higher than 75% of listings but lower than the top 26%.

📌 Key Difference: The Percentile Score focuses on where a price stands relative to the market as a whole, while the Percentile Rank measures how your set price compares to other listings. The same price can represent different positions depending on which metric you are using.

Seasonality Graph

Before confirming your selected base price, you can review the average monthly prices across the year to understand its potential impact.


🔹 Important Notes:

  • These averages serve as a reference only.
  • Actual recommended prices for high and low seasons may differ significantly due to:
    • Customizations (e.g., Far-out Premium)
    • Market effects (e.g., Demand Factor)

Base Price Nudges

We continuously monitor your base price and notify you if we detect a difference of more than 5% from our recommended base price using the Base Price Nudges. These timely updates help ensure that your base price remains optimized, allowing you to maximize earnings and stay competitive in the market.


Please note that these nudges do not automatically change your base price—you have the option to accept or reject them. Additionally, you can click the "See recommendation in Base Price Help Tool" link to access the tool, review our suggestion, and make adjustments as needed.

To learn more about nudges, check our guide here.

Base Price Override

  • To override your base price for specific dates, use the Date-Specific Override option in the Price Settings section.
  • This feature must be requested—contact support@pricelabs.co if you do not see it in your account.

Other Things to Consider

  • Don’t overthink your initial base price. Instead, refine and improve it over time. PriceLabs’ algorithm automatically adjusts rates based on your listing’s performance.
  • Review your base price regularly. Check it every couple of weeks at first, then every few months, to ensure it remains competitive in your market.
  • Make incremental adjustments. If you’ve used the same base price for more than a few weeks, adjust it gradually (5-10%) rather than making large, one-time changes to safely gauge the impact on revenue.
  • Factor in occupancy. If your occupancy is low, increasing your base price may not be ideal. Conversely, if occupancy is already high, decreasing the base price may be unnecessary.
  • Compare your prices with the market. After setting your base price, click “Save & Refresh,” go to the Neighborhood Data tab, filter by bedroom size, and check how your prices compare to similar listings. Adjust as needed within the 25th-75th percentile range.
  • Consider platform fees. If you’re not adding a separate markup for Airbnb host fees or OTA service fees, your base price already includes these costs—meaning your net payout will be slightly lower.
  • Exclude taxes from the base price. Taxes are typically charged on top of your nightly rate, so don’t include them in your base price calculation.
  • Set minimum and maximum prices. While daily rates are based on your base price, setting min/max limits ensures your prices don’t drop too low or spike too high.

Frequently Asked Questions

Q: How often should I change my base price?
A: Your base price should be a stable average that works year-round. Seasonal demand fluctuations are already factored into PriceLabs’ pricing. The Base Price Help tool evaluates occupancy trends from the past two months, giving more weight to recent data. If an adjustment is needed, you’ll receive a Base Price Nudge.

Q: Why does the recommendation say "low historical occupancy" even though my current occupancy is higher than the market?
A: The recommendation is based on your listing’s 30-day occupancy trend over the last 60 days, not just recent performance. Since recommendations update weekly and apply for seven days, there may be a lag before recent changes are reflected.

Q: Why isn’t my base price recommendation changing?
A: If your listing’s occupancy trends are stable and aligned with the market, the algorithm may determine that no change is needed.

Q: Why does the recommended base price seem to follow my manual changes?
A: When you manually change your base price, the algorithm temporarily reinforces that price while gathering new occupancy data. Frequent changes make it harder for the system to recalibrate, so we recommend keeping your base price unchanged for 2-3 weeks to allow the algorithm to adjust properly.

Q: Why is my recommended base price so high (or low)?
A: The Base Price Help tool nudges you toward an optimal price based on occupancy trends. If your recommended price seems off, start with a market-based or custom base price. After 2-3 weeks, the algorithm will adjust based on this new starting point.

Q: My base price is slightly below the median market price, but I’m not getting bookings. Should I increase it?
A: Generally, no. The Base Price Help tool focuses on market prices, but actual demand matters more. During low-demand periods, pricing below the market median may be necessary to attract bookings.

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