Unlock the full potential of the short-term rental market with our comprehensive Airbnb Listing Data. This dataset provides a granular, 360-degree view of listing performance, property characteristics, and market dynamics across key global geographies. Designed for Real Estate Investors, Property Managers, Hedge Funds, and Travel Analysts, our data serves as the backbone for data-driven decision-making in the hospitality sector.

Whether you are looking to optimize pricing strategies, identify high-yield investment neighborhoods, or analyze amenity trends, this dataset delivers the raw intelligence required to stay ahead of the competition. We capture high-fidelity signals on listings, availability, pricing, and reviews, allowing you to model supply and demand with precision.

Key Questions This Data Answers Our data is structured to answer the most pressing commercial questions in the short-term rental industry. By leveraging our granular fields, analysts can immediately address:

Market Composition: What is the exact distribution of property types (Entire Home vs. Private Room vs. Shared) in a specific market? Understand supply saturation instantly.

Amenity ROI: Which amenities are most common in top-performing listings? Correlate features (e.g., Pools, Hot Tubs, Wi-Fi speeds) with Occupancy Rates and ADR (Average Daily Rate) to determine the ROI of renovations.

Pricing Intelligence: How does nightly price vary by neighborhood, seasonality, and property type? Visualize price elasticity and identify arbitrage opportunities between sub-markets.

Geospatial Density: What is the density of listings in different geographical areas? Pinpoint "hot zones" for tourism and identify underserved areas ripe for new inventory.

Performance Benchmarking: How do my listings compare to the top 10% of competitors in the same zip code?

Comprehensive Use Cases 1. Market Analysis & Competitive Positioning Gain a competitive edge by understanding the landscape of any target city.

Competitor Mapping: Track the growth of listing supply in real-time. Identify which property managers control the market share.

Saturation Analysis: Avoid over-supplied markets. Use density metrics to find neighborhoods with high demand but low inventory.

Trend Forecasting: Analyze historical data to predict future supply shifts and market saturation points before they occur.

1. Pricing Strategy & Revenue Management Move beyond static pricing. Our data enables dynamic pricing models based on real-world market conditions.

Attribute-Based Pricing: Quantify exactly how much a "Sea View" or "King Bed" adds to the nightly rate.

Seasonality Adjustments: Optimize calendars by analyzing historical price surges during holidays, events, and peak seasons.

RevPAR Optimization: Balance Occupancy and ADR to maximize Revenue Per Available Room (RevPAR).

1. Real Estate Investment & Valuation For investors and funds, this data acts as a fundamental layer for asset valuation.

Cap Rate Calculation: Combine our revenue data with property values to estimate potential yields and Cap Rates for prospective acquisitions.

Investment Scouting: Filter entire regions by "High Occupancy / Low Price" to find undervalued assets.

Due Diligence: Validate seller claims regarding income potential with independent, third-party data history.

1. Property Type & Amenity Distribution Analysis Understand what guests actually want.

Amenity Gap Analysis: Identify amenities that are in high demand (high search volume) but low supply in specific neighborhoods.

Renovation Planning: Data-driven insights on whether installing A/C or allowing pets will significantly increase booking conversion.

Data Dictionary & Key Attributes Our schema is designed for financial modeling and granular analysis. We provide over 50 distinct fields per listing, including calculated financial metrics for Trailing Twelve Months (TTM) and Last 90 Days (L90D).

Listing Identity & Characteristics:

listing_id: Unique identifier for the listing

listing_name & cover_photo_url: Title and main visual

listing_type & room_type: Property classification (e.g., villa, entire home)

amenities: Comprehensive list of offered features

min_nights & cancellation_policy: Booking rules and restrictions

instant_book & professional_management: Operational indicators

Property Specs & Capacity:

guests, bedrooms, beds, baths: Full capacity details

latitude, longitude, city, state, country: Precise geospatial coordinates

photos_count: Quantity of listing images

Host Intelligence:

host_id & host_name: Primary operator details

cohost_ids & cohost_names: Extended management team details

superhost: Quality badge status

Financial Performance (TTM - Trailing 12 Months):

ttm_revenue & ttm_revenue_native: Total gross revenue generated

ttm_avg_rate (ADR): Average Daily Rate achieved

ttm_occupancy & ttm_adjusted_occupancy: Raw vs. Adjusted (excluding owner blocks) occupancy

ttm_revpar & ttm_adjusted_revpar: Revenue Per ...

These are the Airbnb statistics on gross revenue by country.

Revenue per available room (Rev PAR) is a key indicator in the hotel industry, calculating revenue in terms of how successfully rooms are being filled. In fiscal year 2024, for every room that was filled in a hotel in India, the revenue was 66 U.S. dollars. This was an increase compared to the previous year. Regional tourism impacting hotels The general push towards domestic tourism within India post-COVID, along with increasing disposable incomes and a rising middle-class has contributed to the recovery of the hotel industry in the country. RevPAR was highest among tier-1 cities including Hyderabad, Delhi, Mumbai, and Bengaluru. Business and corporate travel is a key contributor to growth in these cities, specifically among luxury hotels. Regions that are mainly holiday destinations (like Goa, Kerala, Udaipur) experience a more seasonal fluctuation – with higher occupancy rates during summer months and holidays. Furthermore, tier-II cities, and mid-range and budget hotels have recorded significant growth in recent years. Mid-range hotels disrupt traditional hotel market The impact of inflation on operating costs, rise of online travel agencies, and competitive pricing reflect in the fluctuations within the hotel market. This is especially prominent in the mid-range segments with players like Airbnb and OYO Rooms disrupting traditional hotel segments, forcing the country’s leading hotels to reassess, innovate, and adopt more dynamic methods to attract customers.

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.

In 2017, the occupancy rate of hotels in Paris was ** percent. Since 2011 the occupancy rate of hotels in the French capital has been oscillating between ** and ** percent. Hotel attendance in Paris remained relatively high despite a slight decline in 2016 after the terrorist attacks on the French capital in November 2015. In 2017, almost 34 million tourists were recorded at Parisian hotels.

Paris: the center of tourism in France

In 2019, there were more than ******* hotels rooms in France, of which the majority were located in the Ile-de-France region. That same year, the occupancy rate of hotels in the French capital is expected to reach ** percent, compared to **** percent for the net occupancy rate of bedrooms in hotels all over France.

Hotels in Paris

Paris is one of the cities with the highest revenue per available hotel room in Europe. In 2017, the revenue per available room in the French capital amounted to ***** euros, ranking Paris the second European city with the highest revenue per room that year. However, tourists have now other options such as the rental of private residences as offered by the Airbnb platform. Paris had ****** listings for rooms and apartments on Airbnb as of 2019.

The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

The need for hotels and motels depends heavily on domestic and international tourism levels, making these businesses susceptible to the overall economic environment. After the pandemic declines, rising inflation has forced some consumers to reduce spending on leisure activities, slowing revenue growth over the past two years. Nonetheless, the release of pent-up demand for travel has supported industry growth. In early 2025, the industry is expected to endure several significant setbacks from the newly imposed tariffs. Falling domestic consumer sentiment and slowing interest from international visitors, especially from Canada and China, are expected to hurt demand for US hotels and motels. Overall, the Hotels and Motels industry revenue is estimated to rise at a CAGR of 15.2% to $286.5 billion over the five years through 2025, including 0.5% growth in 2025. New tariffs have increased operational costs for hoteliers, causing industry profit to drop to 10.8% in 2025. The growth of alternative accommodation providers, including Airbnb and VRBO, threatens hotels and motels by increasing price competition. The highly competitive market leads hotels to include new services to maintain their revenue streams, although not all hotels have been able to. Specialty hotels, including extended-stay hotels, boutique hotels, spas, health retreats and resorts, are likely to experience higher growth as they offer unique features that differentiate them from others in the market. Many state and local governments regulate short-term rentals to protect their local economies, likely supporting hotel performance. Remote working trends continue to support demand for accommodation as employees choose to work remotely from hotels, boosting the need for the industry. Significant travel activity has enabled hotels to raise nightly rates, increasing profit. Large hotel chains are looking to expand to new foreign markets, lowering new investments within the United States and weakening revenue growth. As domestic and international economic conditions stabilize, hotels will likely experience growth. Further, major international events such as 2026 FIFA World Cup and 2028 Summer Olympics are expected to bolster US tourism during the outlook period, supporting demand for domestic hotels and motels. Overall, the industry revenue is expected to grow at an estimated CAGR of 1.6% to $310.8 billion over the coming years through 2030.

Airbnb Price & Room Analysis in Boston Using Tableau 📊

I recently worked on an Airbnb Boston dataset to analyze pricing trends and room details using Tableau. This project focused on understanding Airbnb pricing patterns, room availability, and geographic price distribution across different zip codes in Boston.

🔹 Key Steps & Techniques: ✔ Data Cleaning & Preparation:

Used Data Interpreter to clean the raw Excel dataset. Removed duplicates and handled missing values for accurate insights. ✔ Data Joining:

Joined listings and calendar tables using a common key (ID) to combine pricing information with room details. Ensured correct relationship to avoid duplication and incorrect aggregations. ✔ Dashboard Insights: 📈 Revenue Trends Over Time – Visualized how Airbnb prices fluctuated over a year in Boston. 🏠 Price Per Zipcode & Bedroom Count – Mapped average prices across Boston zip codes to highlight expensive and affordable areas. 📊 Distinct Listings by Room Type – Explored how many 1, 2, 3, 4, and 5-bedroom listings are available in Boston.

🔥 Key Takeaways from the Boston Airbnb Analysis: 📌 Larger Listings Are More Expensive – As expected, the average price increases with the number of bedrooms, with 1-bedroom listings averaging $144 and 5-bedroom listings reaching $445. 📌 Certain Boston Zip Codes Are More Expensive – Prices vary significantly, with some areas averaging over $200 per night, while others remain below $50. 📌 Seasonality Impacts Pricing – The revenue trend shows fluctuations over time, suggesting that Airbnb prices increase during peak seasons and drop during low-demand periods in Boston.

🛠 Tools Used: ✅ Tableau for visualization & dashboard creation. ✅ Microsoft Excel for raw data handling.

Before the pandemic, hotels and motels benefited from rising incomes and population growth. However, hotel rooms were left empty when the pandemic shut down tourism, creating long-lasting financial and operational challenges. Long periods at home left consumers with savings and pent-up demand to spend on trips as travel restrictions lifted, leading to a rapid recovery at hotels between 2022 and 2023. Nonetheless, concerns about a recession and inflation partially stifled Canadian consumers' appetite for travel, lowering the full potential of revenue growth. In 2025, the threat of a potential trade war between Canada and the United States could have a negative impact on travel demand overall. Therefore, industry revenue is expected to grow at a CAGR of 13.3% over the past five years, totaling an estimated $30.9 billion in 2025, despite revenue is be expected to fall an expected 1.1%. This significant growth rate reflects the industry's rebound from its historical low in 2020. In the same year, profit is also anticipated to account for 18.4% of revenue. Rising competition is one of the main challenges facing hotels and motels. Short-term rental platforms have become a disruptor to traditional hotel stays. Airbnb has become a popular destination for travelers in Canada looking for unique experiences. However, recent efforts by the Canadian government could lessen Airbnb's influence moving forward. Housing shortages are prompting officials in Montreal and Toronto, two major tourist destinations, to attempt to remove illegal Airbnb units or ban the rental site altogether. At the same time, Canada's foreign home ownership ban, extending until the end of 2024, prohibits non-residents from purchasing residential property for personal use or renting as a vacation home. Hotels and motels will contend with labour supply issues over the next five years as access to temporary low-wage foreign workers become limited and domestic workers demand higher compensation, putting hoteliers in a difficult situation. Therefore, trends accelerated by the pandemic, like hotels' digital transformation, will permanently alter and benefit the industry. Innovation will be critical for hotels to manage operational challenges, strengthen profit and address guests' evolving preferences. Hotels and motels' revenue is expected to expand at a CAGR of 1.1% to $32.6 billion over the five years to 2030.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

The number of tourist arrivals in accommodation establishments in Budapest has increased steadily since 2020. Over *********** people stayed in Hungary's capital's accommodation in 2024, the highest value over the observed period. More than *********** of these were international tourists. Because of the travel restrictions imposed to prevent the spread of the coronavirus (COVID-19) pandemic, the number of arrivals at accommodation establishments in Budapest decreased to *********** in 2020. Accommodation in Budapest Visitors in Budapest can choose from a wide variety of accommodation types depending on their personal preferences. In 2024, there were over *******hotel rooms in the Hungarian capital, not to mention the numerous hostels, motels, Bed & Breakfasts, and Airbnbs. As of 2024, the revenue of hotels in Budapest per available room peaked at **** euros, following years of decline due to the pandemic. Airbnb in Budapest Since 2014, there has been a dramatic increase in the number of accommodations available on Airbnb in Budapest. In 2020, there were almost ****** available apartments on the platform, most of them being in District VII. Despite this, in the summer of 2020, new regulations were introduced in Hungary. These rules allowed local governments to limit the number of days apartments can be leased for, having a negative effect on Airbnb rentals.