How PriceBnb Price Suggestions Work — Our Analysis Framework Explained

Data Points We Analyze

PriceBnb’s price suggestions result from multi-layered data analysis, not a single metric. Our proprietary data collection engine gathers these key data points weekly.

Competitor 3-Tier Prices (2 Weeks)

We collect prices for 5–10 expert-curated competitors across three tiers: weekday, Friday, and weekend/holiday. Rather than looking at just “this week,” we collect two weeks simultaneously (next week and the week after) to identify pricing trends. For instance, detecting that “Competitor C raised weekend prices by ₩15,000 for the week after next” enables proactive price adjustments before it’s too late.

Booking Rates per Listing (30 Days)

Through our real-time data pipeline, we collect each competitor’s booking status for the next 30 days. We distinguish booked dates from available dates and calculate tier-specific occupancy rates for weekdays, Fridays, and weekends separately. Even if overall occupancy is 70%, the breakdown might be 50% weekday + 95% weekend—this granularity is critical for accurate pricing.

Week-over-Week Price Changes

We compare each competitor’s prices between last week and this week to instantly detect changes. “Competitor A lowered weekday prices by ₩10,000” or “Competitor D raised weekend prices by ₩20,000”—these changes appear as insights in your weekly report. When a price change is detected, our AI analysis model evaluates its context (season, occupancy, etc.) and assesses its impact on your listing.

Seasonal and Holiday Calendar

We automatically factor in national holidays and extended weekends. During special seasons like Children’s Day or Chuseok holidays, demand surges dramatically, requiring different pricing strategies. PriceBnb automatically detects holidays in the target period and suggests appropriate premium pricing.

Additionally, we aggregate all competitors’ booking rates to determine overall market seasonality. When combined competitor occupancy falls below 50%, we classify it as low season; above 80%, it’s peak season. Strategy weights automatically adjust accordingly.

Net Earnings After 15.5% Fee

All analysis uses actual net earnings after the 15.5% Airbnb host fee. When you set ₩200,000, your net is ₩169,000 (₩200,000 × 0.845). Our suggestions don’t just say “set this price”—they show “set this price and your net earnings will be this amount.” The true optimal price is where net earnings (after fees) are maximized.

Price Position Analysis

After collecting data, our first analysis determines your listing’s price position. We answer the precise question: “Where does my price rank among competitors?”

Per-Tier Ranking

We rank all 6 listings (yours + 5 competitors) separately for each tier. If you’re ranked 3rd (middle) on weekdays but 6th (highest) on weekends, your weekend pricing strategy needs review. Only by analyzing all three tiers independently can you get an accurate picture of your competitive position.

In the table above, your listing ranks 3rd (middle) on weekdays but 6th (highest) on weekends. If your weekend occupancy is low, your weekend price being the highest among competitors is likely the culprit.

Median Deviation Analysis

Beyond simple ranking, we analyze the deviation from the median. Being 5% above the median is a completely different situation from being 30% above. In the example above, the weekday median is about ₩109,000, and your price at ₩108,000 is about 1% below—a healthy position. However, the weekend median is about ₩163,000, and your ₩180,000 is more than 10% above, creating significant pricing pressure for potential guests.

Price-to-Occupancy Efficiency

Cross-referencing price position with occupancy reveals “price-to-occupancy efficiency.” A listing maintaining high occupancy despite high prices occupies a “premium position,” while low occupancy at low prices signals issues beyond pricing (reviews, photos, location, etc.). PriceBnb’s position map (scatter plot) lets you visually inspect this relationship for each competitor.

Deriving the Optimal Price Point

After position analysis, we move to the core question: “What is the maximum price that won’t reduce my booking rate?”This is how PriceBnb defines the optimal price.

Maximum Price Preserving Occupancy

Raising prices increases per-night revenue, but beyond a certain threshold, occupancy drops sharply. Finding this “tipping point” is the core challenge. PriceBnb analyzes the price-occupancy relationship across competitors to estimate the price level where occupancy begins to decline meaningfully. Just below that threshold lies the optimal price.

Finding “Gaps” in Competitor Pricing

If five competitors are priced at ₩95,000, ₩105,000, ₩110,000, ₩115,000, and ₩120,000, there’s a ₩10,000 gap between the first and second. Exploiting these price gaps lets you avoid direct price comparisons while claiming a reasonable price tier. PriceBnb automatically performs this distribution analysis and suggests positioning in less contested price ranges.

Weighted Median-Based Pricing

Not all 5 competitors carry equal weight. We assign higher weights to listings with conditions more similar to yours, improving accuracy.

• Same base guest count: Weight 2.0x (most similar price structure)
• Same neighborhood: Weight 1.5x (similar location advantage)
• Similar rating (±0.3): Weight 1.3x (similar quality level)
• Similar room count (±1): Weight 1.2x (similar space)

After applying these weights, we calculate the weighted median—a more accurate benchmark tailored to your specific situation. This weighted median serves as the basis for the “balanced” strategy, while the 25th percentile drives the “aggressive” strategy and the 75th percentile drives the “premium” strategy.

Maximizing Net Earnings After Fees

Finally, we calculate net earnings after the 15.5% fee for every suggested price. What matters isn’t the listed price—it’s the money you actually receive. For example, if the balanced strategy suggests ₩155,000:

If your previous price was ₩140,000 (net ₩118,300), applying this suggestion yields ₩12,675 more per night. At 15 nights/month, that’s ₩190,125 additional monthly revenue.

How Suggestions Are Presented

PriceBnb’s price suggestions come in a format you can act on immediately. No abstract analysis—we give you the exact amount to enter in Airbnb.

Based on Host Set Price

Every suggestion uses the price you actually type into Airbnb as the reference. “Set your weekday base price to ₩108,000”—this specific, actionable recommendation is what you get. Simply enter that number in your Airbnb calendar.

Guest-Facing Price Shown Simultaneously

Under the current Airbnb model, the host set price equals the guest-facing price. When you set ₩155,000, guests see exactly ₩155,000. PriceBnb reflects this by clearly showing that set price = guest price, so you can evaluate your strategy from the guest’s perspective too.

Net Earnings (After Fee) Displayed

The most critical number—net earnings after the fee deduction—accompanies every suggestion.

This format lets you see three things at a glance: the amount you set, the amount guests see, and the amount you actually receive. Sound decision-making requires seeing all three numbers simultaneously.

Independent Per-Tier Strategies

PriceBnb suggests independent strategies for each of the 3 tiers: weekday, Friday, and weekend/holiday. A “balanced” approach might be right for weekdays while an “aggressive” stance is needed for weekends. During peak season weekends, a “premium” strategy can maximize revenue.

Each tier’s strategy is determined independently based on that tier’s occupancy and competitive landscape. This is precisely why per-tier pricing delivers significantly higher revenue than uniform pricing across all days.

Revenue Simulation

We also provide projected monthly revenue when our suggested prices are applied. We compare revenue under your current pricing, balanced strategy, aggressive strategy, and premium strategy, showing in concrete numbers which approach yields the highest returns. Revenue simulations are always calculated with 3-tier separation. Averaging prices across tiers produces significant deviations from reality.