Pricing Strategy Configurations

When we onboard a new listing, we build a pricing strategy from the ground up. Every configuration is based on market data specific to your listing's location, property type, competitive set, and seasonal demand patterns.

Before any configuration is set, we determine whether your listing should be managed on a conservative or aggressive model.

The model is based on your listing's maturity - primarily your review count, review score, and presentation quality. A newer listing with less social proof is positioned more conservatively, because it has less ability to compete at higher rates or absorb risk on open nights. A well-reviewed, well-presented listing can support a more aggressive approach.

This model influences several settings across the strategy. It's not a fixed label - as your listing matures and performance builds, the approach can shift.

The base price is the anchor from which your dynamic pricing is built. It's not your average rate - it's the reference point the system uses to calibrate everything else.

We set it by analyzing the average daily rate in your market, scoring your property against comparable listings based on its competitive positioning, and applying an adjustment for the strategy model.

The goal is to position your listing correctly relative to the market - not so high it kills conversion, not so low it leaves money behind.

The minimum price is the floor rate your listing can sell for during soft demand periods.

We set it using a data-driven process: analyzing the lowest seasonal ADR in your market, benchmarking your property against the competitive set, and adjusting for the strategy model.

For a full breakdown of how minimum prices work, see Understanding Minimum Prices.

We identify a set of comparable listings to benchmark your pricing against the right properties. Comps are selected based on proximity, bedroom count, quality level, amenities, and review standing.

This comp set informs the base price, minimum price, and occupancy model. It's how we make sure your pricing is calibrated against what guests are actually comparing you to when they search.

Not all days perform equally. Most markets see higher demand on certain days and softer demand on others.

We analyze historical occupancy by day of week and apply pricing adjustments accordingly - pushing rates up on high-demand days and pulling them back on softer ones. This is calibrated to your specific market, not a generic weekend premium.

Beyond the base and minimum, we layer in a set of dynamic rules designed to capture bookings at the right time and at the right rate.

Last-Minute Discounts

As stay dates approach without booking, we apply a gradual discount that increases as the window shortens. The goal is to capture last-minute demand rather than lose the night entirely. Conservative listings discount less aggressively than mature ones.

Far-Out Pricing Adjustments

For bookings made well in advance, we apply a gradual premium. If a guest is booking months out, demand is strong enough to support a higher rate. As we get closer to the date, pricing adjusts based on how the calendar is pacing.

Gap Night Discounts

Short gaps between existing reservations are difficult to fill at full rates. We apply targeted discounts to those specific nights to maximize occupancy without discounting the broader calendar.

Adjacent Stay Discounts

We apply discounts to nights immediately surrounding existing reservations, primarily on weekdays. This encourages back-to-back bookings and keeps the calendar filling efficiently rather than leaving isolated weekday nights stranded.

We build a tiered minimum stay structure based on your market's seasonal demand patterns and booking windows. The structure relaxes as the stay date approaches - longer minimums early on to capture higher-value bookings, shorter minimums closer in to protect occupancy.

For a full breakdown of how this works, see Dynamic Length of Stay Rules.

We configure an occupancy model that adjusts your pricing based on how your calendar is filling relative to expected pace at each booking window.

The model is built using your market's booking behavior as the baseline, with occupancy targets defined at different points in time. If your calendar is filling faster than expected, rates adjust upward. If it's filling slower, pricing comes down to stimulate demand.

This is what allows the strategy to respond to real-time booking behavior rather than follow a fixed pricing curve.

We research upcoming local events - concerts, conferences, sporting events, festivals - and apply temporary markups for periods where demand is expected to spike. Markup levels are based on event size and proximity to your listing. Markups are set to expire automatically once the event window closes, so they don't linger after the opportunity has passed.

Holidays are handled separately from standard seasonal pricing. We analyze historical occupancy patterns around each major US holiday and apply specific markup and minimum stay configurations based on how demand actually behaves in your market during those periods.

Several settings are configured at the OTA level rather than through the pricing software.

OTA Discounts

We apply platform-native discounts on Airbnb and other channels at controlled levels. These function as conversion and algorithmic tools, not revenue giveaways. For detail on how and why we use them, see OTA Discounts and Custom Promotions.

Cleaning Fee

We analyze cleaning fee norms in your market and set yours competitively - typically toward the lower end of the market range to support conversion without underselling.

Cancellation Policy

We review what comparable listings are doing and recommend a policy that balances booking volume with appropriate protection. We typically recommend including a non-refundable option alongside the standard policy, which tends to convert a specific guest segment and can improve overall revenue.

Additional Guest Fees

For listings with a defined base occupancy, we set an additional per-guest fee based on what's common in your market.

The configurations in this article reflect a methodology built on market data, years of pattern recognition, and what we've seen work across a wide range of properties and markets. Every decision has a reason behind it.

That doesn't mean the strategy is fixed. Markets change, goals evolve, and there are times a client wants to approach something differently. We respect that - it's your business and we want the strategy to reflect your goals as much as the data.

What we ask is that broader strategy changes - minimum price, base price, length of stay rules, or anything that affects the overall model - come through us before they're made. Reach out with what you want to change and the reasoning behind it. We'll share our perspective, explain what we'd recommend, and if a revision makes sense we'll make sure it's implemented correctly so it doesn't create unintended effects elsewhere in the strategy.

For date-specific changes, you're welcome to make a direct override in PriceLabs if you have access. Just leave a note referencing the change so our team doesn't overwrite it as part of the normal management cadence.

If you have questions about your pricing configuration, reach out to our team at [email protected].