How do successful Airbnb properties in Sofia actually perform? Analyze occupancy rates across 3,700+ listings, understand measurement methodology, and discover how Superhost and Guest Favourite badges correlate with booking success.
Beyond Ghost Properties: Understanding Occupancy Rates in Sofia's Competitive Airbnb Market
We're continuing our deep dive into Sofia's short-term rental market during Q1 2026. Our first analysis exposed the prevalence of 'ghost' properties – inactive listings that artificially inflate the market. Our second installment examined pricing strategies and how different segments command different nightly rates. Now, we turn our attention to a metric that separates thriving properties from struggling ones: occupancy rates.
Occupancy is arguably the most important indicator of true market health. Unlike listing counts, which can be misleading, or average prices, which vary by location and quality, occupancy reveals one fundamental truth: Is the property actually booking guests? In this analysis, we'll explore multiple methods to calculate and filter occupancy data, systematically removing noise and ghost properties to understand the real competitive landscape in Sofia.
What is Occupancy Rate and Why It Matters
At its core, occupancy rate is elegantly simple: the number of days a property is booked, divided by the total available days in the period. For our Q1 2026 analysis, that's 90 calendar days across over 3,700 properties.
But simplicity shouldn't fool us into thinking occupancy is easy to interpret. With 3,700+ listings to analyze, a histogram is the most effective visualization tool – it shows us the distribution across all properties at once, revealing patterns that matter.
What we see is striking: an enormous spike at 100% occupancy. This mirrors what we discovered in our ghost properties analysis – and for similar reasons. Experienced hosts know that true 100% occupancy across an entire quarter is virtually impossible. Properties require maintenance days, experience cleaning turnover between guests, and even the most successful listings have natural booking gaps. When we see this concentration at the extreme end, it's not a sign of success – it's a red flag that we're measuring something other than active guest bookings.
The Measurement Methodology: Understanding What We're Actually Seeing
To properly interpret our data, we need to explain a critical technical detail about how Airbnb availability works – and how we measure it.
Airbnb hosts can configure a "same-day booking cutoff" time. For example, a host might disable same-day bookings after 15:00 local time. This means that if someone tries to book the property for that same day after 15:00, they'll see it as unavailable – even though the property might not actually be occupied by a guest.
Our data collection methodology checks each property's availability between 19:00 and 21:00 Sofia local time daily. This is well after most same-day booking windows close. The result? When we see a property marked as "unavailable" at our check time, we cannot distinguish between:
- The property being genuinely occupied by paying guests
- The property being blocked for cleaning or maintenance
- The property being restricted due to same-day booking cutoff policies
Complete details on our data collection methodology are available here.
This measurement challenge is why the 100% occupancy spike exists. It's not all genuine bookings – it's a mix of truly occupied properties and blocked properties, all appearing identical in our snapshot.
Progressive Filtering: Removing the Noise Layer by Layer
To move beyond this surface-level picture, we need to systematically filter out properties that are clearly not actively competing for bookings. Our approach is progressive – each filter targets a specific type of noise.
Filter 1: Eliminating Zero-Review Properties
In our first article, we applied strict criteria: neighborhoods with at least 5 properties AND properties with 6+ reviews in Q1. This was necessary to isolate the most active market segment.
For this analysis, we're taking a gentler approach with Filter 1: removing only properties with zero reviews across their entire Airbnb history. If a property has never received a single guest review – ever – it almost certainly hasn't received bookings. This first pass removes the clearest case of inactive listings.
The results are revealing. The histogram shows a 10-bin distribution (each bin represents 10 percentage points of occupancy). Comparing to our raw data, we notice the most dramatic changes at the extremes: the 10% bin and the 100% bin both shrink. We removed approximately 400 properties – mostly concentrated in the extreme ranges.
However, the 100% occupancy spike remains stubbornly large. This tells us something important: most ghost properties do have some guest history. They're not completely inactive – they're just not actively booking.
We also tested adding price-based filters (targeting properties over 150 euros per night), but this yielded minimal improvements. Only 50 properties dropped from the 100% occupancy segment. This suggests that high pricing alone doesn't explain the spike – the issue is more systemic.
Advanced Filtering: The Superhost and Guest Favourite Advantage
To make further progress, we need to shift our approach. Instead of filtering by negative indicators (zero reviews), let's filter by positive ones: badges that indicate actively managed, high-performing properties.
Airbnb awards two specific status badges: Superhost and Guest Favourite. These aren't random designations – they're earned through demonstrated performance.
Understanding Superhost Status
Superhost status is relatively straightforward. Airbnb evaluates this every quarter based on the previous 12 months of hosting activity. To qualify, a host must meet all four of these criteria:
- Maintain at least 10 completed stays OR 100 nights (across 3+ separate stays)
- Achieve a response rate of 90% or higher
- Maintain an overall rating of 4.8 stars or better
- Keep cancellation rate at 1.0% or lower (effectively 1 cancelled night per 100 stays)
These are measurable, objective criteria that directly correlate with active hosting and guest satisfaction.
Understanding Guest Favourite Status
Guest Favourite is more nuanced and operates differently. Airbnb evaluates properties daily using multiple criteria:
- Overall star ratings and guest review feedback
- Communication quality between guests and hosts
- Category-specific ratings (check-in, cleanliness, accuracy, host communication, location, value)
- Host cancellation rate
- Review removal patterns
- Quality-related incidents reported to Airbnb support
- Minimum 5 reviews in the past 4 years, with at least 1 in the past 2 years
As Airbnb notes, additional factors may apply. This makes Guest Favourite harder to achieve than Superhost – the criteria are broader and somewhat subjective. The status reflects not just volume, but quality of the hosting experience.
Our Filtering Strategy
Applying both badges to all properties would be too restrictive – we'd end up with only a handful of listings. Instead, we apply these filters selectively to properties with above 80% occupancy rate. Why 80%? Because the truly successful properties in any city should have earned these badges. If a property maintains 80%+ occupancy, it should either be a Superhost or Guest Favourite – or we should understand why it isn't.
This approach lets us examine three distinct market segments:
- Tier 1: Superhost AND Guest Favourite – The elite segment with both badges
- Tier 2: Superhost only – Strong performers, high consistency
- Tier 3: Guest Favourite only – Strong quality perception without volume history
The Market Tiers: Three Portraits of Success
Tier 1: Properties with Both Superhost and Guest Favourite Status
Our most elite segment contains 1,973 properties. This is a substantially cleaner picture than our raw data. The histogram shows a more balanced distribution – bins for 70%, 80%, and 100% occupancy are nearly equal in height, with 90% slightly lower. This is exactly what we'd expect from high-performing properties. Yes, some show 100% occupancy (likely the same-day cutoff effect), but it's no longer the dominant pattern.
The neighborhood heatmap on the right adds critical context. Notice that neighborhoods showing 100% average occupancy typically have very few properties – often just 2-3. This means they're statistical outliers, not representative of true neighborhood performance. The more populated neighborhoods show more realistic distributions in the 60-80% range.
The fact that this segment has the highest property count (1,973) while displaying the cleanest, most professional distribution is telling: properties achieving both badges represent the true competitive core of Sofia's market.
Tier 2: Superhost Properties Without Guest Favourite
This segment shrinks to 1,619 properties – about 18% fewer than Tier 1. Here, the distribution shifts noticeably. The 70% occupancy bin becomes the peak, and the 100% occupancy tail is significantly reduced. This is a healthier-looking distribution, closer to what we'd expect in a normally operating market.
The neighborhood data reinforces this picture: average occupancy rates tend to cluster in the 50-75% range, with fewer extreme outliers. These are hosts who have built strong operational consistency (meeting Superhost requirements) but perhaps haven't achieved the broader quality perception that Guest Favourite demands.
Tier 3: Guest Favourite Properties Without Superhost
Our third segment contains 1,556 properties – similar to Tier 2, just slightly smaller. The distribution resembles Tier 2 closely, with 70% occupancy as the dominant bin. The main differences appear in the 90% occupancy segment and neighborhood distribution patterns.
Interestingly, Tier 3 is nearly as large as Tier 2. This suggests that Guest Favourite status can be achieved through quality perception and communication excellence without necessarily accumulating the volume history required for Superhost. These might be newer properties with excellent reviews, or properties with limited volume but exceptional guest experiences.
What The Tiers Reveal: Market Segmentation and Performance Insights
By layering these three perspectives, a coherent market structure emerges. These aren't arbitrary segments – they represent genuine differences in how properties perform and compete.
The filtering process proves what many suspected: the 3,700+ headline number drastically overstates true market competition. When we look only at properties with demonstrated quality badges and reasonable occupancy, we're examining roughly 5,000 properties across the three tiers (accounting for properties that appear in multiple scenarios). The truly competitive market is a fraction of the full listing count.
The Occupancy Benchmarks
For anyone considering Sofia as a market or evaluating a specific property, here are the baseline occupancy rates we observed:
- Tier 1 (Both Badges): Average 67.43% occupancy
- Tier 2 (Superhost Only): Average 61.79% occupancy
- Tier 3 (Guest Favourite Only): Average 60.76% occupancy
These should not be interpreted as guarantees – individual property performance varies based on location, quality, pricing, and seasonality. However, they serve as realistic benchmarks for properties competing actively in Sofia's market.
The gap between Tier 1 and Tiers 2-3 (approximately 5.6 percentage points) is revealing. It suggests that achieving both badges – the combination of volume consistency and quality perception – correlates with notably higher booking rates. For investors, this underscores the value of building toward both status levels. For hosts, it's a tangible performance target.Similarly, the minimal difference between Tier 2 and Tier 3 (just 1.03 percentage points) indicates that while these badges require different criteria, the practical occupancy outcomes are comparable. Both pathways lead to strong booking performance.
Seasonality and Weekly Patterns: How Occupancy Fluctuates Through Q1
Our final analysis shifts perspective from market segments to temporal dynamics. How does occupancy change day by day throughout the quarter? Understanding these patterns is critical for pricing strategy and inventory planning.
The daily occupancy graph for Superhost and Guest Favourite properties reveals several important seasonal and cyclical patterns:
Peak and Valley Dynamics
Occupancy rates spike dramatically around the New Year period – a pattern driven by holiday travel. However, this success is short-lived. Throughout January, occupancy rates decline steadily, while February and March show noticeably higher occupancy rates than January. This isn't random – it reflects genuine seasonal improvements as winter loosens its grip and travelers begin spring travel planning. March particularly shows strength, suggesting that early spring tourism is stronger than deep winter demand.
Embedded in this daily data is a consistent pattern we've observed before: weekends maintain higher occupancy rates than weekdays. This is predictable and expected.
These patterns validate what successful hosts already know: timing, seasonality, and day-of-week effects matter. Properties can't control global travel patterns, but they can prepare for them.
The Bigger Picture: Understanding Sofia's Rental Market
This analysis reveals a market with clear stratification. While 3,700+ listings exist, only a fraction compete actively for bookings. Among those that do, visible differentiation emerges based on host performance and guest perception.
For new hosts entering Sofia, this is neither discouraging nor over-optimistic. The market isn't saturated among actively managed properties. But it's also not a free-for-all – earning guest trust and maintaining consistent availability matter enormously.
The presence of such clear badge correlations suggests that data-driven, guest-focused hosting pays off. Properties that communicate well, maintain cleanliness, respond quickly, and keep promises about cancellations don't just earn badges – they achieve significantly higher occupancy rates.
Accessing Deeper Market Intelligence
These occupancy benchmarks are part of a larger market picture. To make confident investment or optimization decisions, you'll want to know: Which neighborhoods show the highest occupancy? How does seasonality affect different property types? What pricing strategy drives bookings in your specific segment?
Our detailed market reports provide exactly this property-level and neighborhood-level intelligence – the data you need to compete effectively in Sofia's rental market.
All data in this analysis is sourced from public Airbnb listings as of Q1 2026. This article is for informational purposes.