“If I raise my price by ₩10,000, will revenue go up or down?” Every Airbnb host has wrestled with this question. Raising prices increases per-night earnings but may reduce bookings. Lowering prices fills more nights but shrinks per-night revenue. What if you could answer this question with data instead of intuition?
PriceBnb's revenue simulation uses real competitor data to calculate how your monthly revenue would change under 3 different pricing strategies before you make any changes. Instead of adjusting prices and waiting a month to see results, you can preview expected outcomes first and choose your strategy with confidence.
Why Simulation Matters
Most hosts set prices like this: “My competitor charges ₩150,000 so I'll match them” or “Bookings are slow, let me drop ₩10,000.” This intuition-based pricing has two problems:
- It takes a month to see results. After changing your price, you need at least 2-4 weeks to observe occupancy shifts. During that period, a wrong price costs hundreds of thousands in opportunity costs.
- Impact varies by segment. Raising ₩10,000 on weekdays versus weekends has completely different revenue effects. Weekend demand is strong enough to absorb increases, while weekday guests are price-sensitive. Even small weekday increases can cause significant occupancy drops.
Simulation solves both problems. Using actual competitor pricing and occupancy data, it pre-calculates expected revenue for each price change, split by weekday/Friday/weekend segments. No more waiting a month; preview results before committing.
Under the current 15.5% host fee structure, simulating on gross revenue is misleading. PriceBnb runs all simulations on net earnings (listed price x 0.845), ensuring you see the real impact on your actual income.
Three Strategy Simulations
PriceBnb simultaneously simulates 3 strategies for side-by-side comparison. Each serves a different objective, and the best choice depends on your current situation.
Strategy 1: Maintain Current
Projected revenue with no price changes, based on current pricing and occupancy trends. This serves as your baseline for comparing the other two strategies. It answers: “How much will I earn if I do nothing?”
Strategy 2: Balanced
Optimizes revenue while maintaining competitive positioning. Prices are set near the weighted median of competitors, targeting the sweet spot where occupancy stays healthy and per-night revenue is maximized. This is the recommended strategy for most hosts.
- Prices at or slightly below competitor median
- Occupancy maintained or slightly improved
- Best for stable, sustainable revenue growth
Strategy 3: Aggressive
Prices set at the competitor 25th percentile to maximize occupancy. Effective during off-seasons, for new listings building reviews, or when you need to boost search ranking. Per-night earnings decrease, but higher occupancy can drive greater total revenue and improved Airbnb algorithm visibility.
Critically, these 3 strategies apply independently to each segment. A mixed strategy like “Aggressive weekdays + Balanced Fridays + Current weekends” is entirely possible. PriceBnb analyzes competitive dynamics per segment and recommends the optimal combination.
| Segment | Current | Balanced | Aggressive |
|---|---|---|---|
| Weekday | ₩130,000 | ₩125,000 | ₩115,000 |
| Friday | ₩150,000 | ₩155,000 | ₩140,000 |
| Weekend | ₩200,000 | ₩210,000 | ₩185,000 |
| Est. Occupancy | ~55% | ~60% | ~72% |
| Est. Monthly Revenue | ₩2.43M | ₩2.61M | ₩2.67M |
In this example, the Balanced strategy projects ₩179,000 more monthly revenue than the current pricing. Notably, Friday and weekend prices actually increase while overall revenue grows. This is because a slight weekday reduction boosts occupancy, while higher weekend/Friday premiums capture additional value from strong demand.
The Aggressive strategy shows the highest gross revenue at ₩2.67M, but lower per-night earnings mean operating costs (cleaning, supplies) consume a larger share. Net profit may be similar to or even lower than the Balanced approach. Simulation helps you see these trade-offs clearly.
How the Simulation Works
The core of PriceBnb's revenue simulation is three-tier segment calculation. Averaging prices across all days produces distorted results, so calculations must be performed separately for weekdays, Fridays, and weekends, then summed.
Simulation Formula
Monthly Revenue = (Weekday Rate x Weekday Days x Weekday Occupancy)
+ (Friday Rate x Friday Days x Friday Occupancy)
+ (Weekend Rate x Weekend Days x Weekend Occupancy)
Net Earnings = Monthly Revenue x 0.845 (after 15.5% fee)
Days per Segment
Based on a 30-day month:
- Weekdays (Sun-Thu): approximately 17 days
- Fridays: approximately 4 days
- Weekends/Holidays (Sat + public holidays): approximately 9 days
Price Elasticity
When prices change, occupancy responds. This relationship is called price elasticity. PriceBnb estimates elasticity coefficients by analyzing competitor price-occupancy data. Common patterns observed in the Airbnb market:
- 5% price increase leads to roughly 3-7% occupancy drop (average elasticity ~-0.8)
- 5% price decrease leads to roughly 4-8% occupancy increase
- Weekends have lower elasticity (robust demand); weekdays have higher elasticity (price-sensitive)
Elasticity is applied independently per segment. Weekend and weekday price changes are calculated separately. After 4+ weeks of accumulated data, elasticity estimates become significantly more accurate.
Detailed Calculation Example
Here's the Balanced strategy broken down:
| Segment | Price | Days | Est. Occupancy | Segment Revenue |
|---|---|---|---|---|
| Weekday | ₩125,000 | 17 | 58% | ₩1,232,500 |
| Friday | ₩155,000 | 4 | 65% | ₩403,000 |
| Weekend/Holiday | ₩210,000 | 9 | 78% | ₩1,474,200 |
| Total (Guest Pays) | ₩3,109,700 | |||
| Net Earnings (x 0.845) | ₩2,627,697 | |||
Segment-level calculation reveals which periods drive revenue and which underperform. In this example, weekends/holidays account for 47% of total revenue. Weekdays have the most days (17) but contribute only 40% due to lower prices and occupancy. This analysis shows exactly where to focus pricing adjustments.
Real-World Example
Let's look at a practical simulation case. A host operates a 2-bedroom apartment (max 4 guests) in Seoul's Mapo district.
Current Situation
Weekday Price
₩130,000
Weekend Price
₩200,000
Overall Occupancy
~55%
Monthly Net
₩2.43M
PriceBnb's analysis found that the weekday price was slightly above the competitor median (₩120,000), while the weekend price was below it (₩215,000). In other words, the listing was overpriced on weekdays and underpriced on weekends — an inefficient pricing structure.
After Balanced Strategy
Key adjustments under the Balanced strategy:
- Weekday: ₩130,000 → ₩125,000 (-₩5K) → Occupancy 50% → 58%
- Friday: ₩150,000 → ₩155,000 (+₩5K) → Occupancy maintained
- Weekend: ₩200,000 → ₩210,000 (+₩10K) → Slight occupancy dip (80% → 78%)
Before (Monthly Net)
₩2.43M
After Balanced Strategy
₩2.61M
+₩179K/month
A ₩179,000 monthly increase translates to roughly ₩2.15M annually. Against PriceBnb's ₩9,900/month service cost (₩118,800/year), this represents approximately 18x ROI.
Month-over-Month Improvement
Simulation isn't a one-time exercise. PriceBnb updates competitor data weekly and recalculates simulations as the market shifts. Here's a 4-month improvement trajectory:
| Month | Strategy | Monthly Net | Cumulative Gain |
|---|---|---|---|
| Month 1 (Before) | Current | ₩2.43M | — |
| Month 2 | Balanced | ₩2.58M | +₩150K |
| Month 3 | Balanced (refined) | ₩2.66M | +₩230K |
| Month 4 | Balanced (optimized) | ₩2.74M | +₩310K |
Improvement grows over time because accumulating weekly data improves simulation accuracy and enables more responsive strategy adjustments. From week 4 onward, competitor price trend data feeds into recommendations, enabling precision like: “Competitor C raised weekend rates by ₩15,000 next week. You have room for a ₩10,000 weekend increase.”
Tips for Using Simulations Effectively
Practical tips to maximize the value of revenue simulations:
- Gradual changes beat big swings. Even if simulations look promising, start by applying 50-70% of the suggested change. After 1-2 weeks, if occupancy responds as projected, apply the rest.
- Use different strategies per segment. Low weekday occupancy? Go aggressive on weekdays. High weekend occupancy? Apply premium weekend pricing. Mixed strategies consistently outperform blanket changes.
- Adjust with seasons. Use aggressive pricing during off-seasons to maintain occupancy. Switch to premium during peak seasons to maximize revenue. PriceBnb auto-detects seasons and adjusts recommendations.
- Simulations are projections, not guarantees. PriceBnb marks all simulation figures with “~” to indicate estimates. Weather, events, review changes, and other external factors can shift actual results. Simulations are a starting point for decisions, not the decision itself.
Try Revenue Simulation Free
Ready to see how price changes would affect your revenue with data rather than guesswork? Start PriceBnb's 14-day free trial to experience it firsthand. You'll receive Instant Insights immediately upon signup, and once competitor analysis is complete, your first weekly report includes full 3-strategy revenue simulations.
Try Revenue Simulation Free
Start your 14-day free trial for competitor analysis, AI pricing suggestions, and 3-strategy revenue simulations. Cancel anytime during the trial at zero cost.
Try Revenue Simulation Free →Conclusion
Pricing changes are among the most impactful decisions an Airbnb host can make. But relying on intuition means a month of trial-and-error and hundreds of thousands in opportunity costs.
Revenue simulation eliminates that risk. With real competitor data, you can preview results before committing, compare 3 strategies side by side, and make an informed choice. By applying segment-specific strategies across weekday/Friday/weekend tiers, hosts can improve revenue by 10-20% from the same listing.
In the 15.5% fee era, protecting and growing your revenue depends on the power of data and simulation. Start now, and your revenue could look different as early as next week.