This dataset, prepared for Final Datavidia 9.0, provides an extensive collection of Airbnb listing information, offering a rich resource for deep analysis into short-term rental markets. With over 260,000 entries and a diverse range of features, it enables researchers, data scientists, and enthusiasts to explore various aspects of the Airbnb ecosystem, from pricing dynamics to host behavior and geographical distribution.

Dataset Overview

The dataset comprises 261,894 individual Airbnb listings across multiple cities, described by 55 distinct features. It captures a snapshot of properties available, details about their hosts, location specifics, pricing structures, availability, and comprehensive review scores. The data types span categorical, numerical (float64, int64), providing a versatile base for various analytical and machine learning tasks.

Key Features and Columns:

The columns in this dataset can be broadly categorized as follows:

• Listing Identification & Details:

  • id: Unique identifier for each listing.
  • name, description: Textual descriptions of the listing.
  • property_type: Type of property (e.g., apartment, house, private room).
  • room_type: The type of room offered (e.g., Entire home/apt, Private room).
  • accommodates: Number of guests the listing can accommodate.
  • bathrooms, bathrooms_text, bedrooms, beds: Details on the property's physical attributes.
  • amenities: A list of amenities provided by the listing.

• Host Information:

  • host_id, host_name: Unique identifiers and names for hosts.
  • host_since: Date when the host joined Airbnb.
  • host_location, host_about: Information about the host's location and self-description.
  • host_response_time, host_response_rate, host_acceptance_rate: Metrics on host responsiveness and booking acceptance.
  • host_is_superhost: Indicates if the host is a "Superhost."
  • host_neighbourhood: The neighborhood where the host resides (if provided).
  • host_listings_count, host_total_listings_count: Number of listings by the host.
  • host_verifications, host_has_profile_pic, host_identity_verified: Verification status of the host.

• Location Data:

  • latitude, longitude: Geographical coordinates of the listing.
  • neighbourhood, neighbourhood_overview, neighbourhood_cleansed: Information about the listing's neighborhood, with neighbourhood_cleansed likely being a standardized version.
  • city: The city where the listing is located.

• Pricing & Availability:

  • price: The nightly price of the listing.
  • has_availability: Indicates if the listing has any availability.
  • availability_30, availability_60, availability_90, availability_365: Number of available days in various future periods.
  • availability_eoy: Availability at the end of the year.

• Review Scores & Activity:

  • number_of_reviews, number_of_reviews_ltm, number_of_reviews_l30d: Total reviews and reviews in the last 12 months (ltm) and 30 days (l30d).
  • number_of_reviews_ly: Number of reviews in the last year.
  • first_review, last_review: Dates of the first and last reviews.
  • review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value: Detailed breakdown of review scores.
  • reviews_per_month: Average number of reviews per month.
  • estimated_occupancy_l365d, estimated_revenue_l365d: Estimated occupancy and revenue over the last 365 days.

Potential Use Cases:

This dataset is ideal for a wide range of analytical and predictive modeling tasks, including but not limited to:

• Price Prediction: Building models to predict Airbnb listing prices based on features like location, amenities, and property type.
• Market Analysis: Identifying trends in listing availability, host behavior, and property distribution across different cities and neighborhoods.
• Host Performance Analysis: Evaluating factors that contribute to host success, superhost status, and review scores.
• Geospatial Analysis: Visualizing listing density, pricing heatmaps, and neighborhood characteristics.
• Recommendation Systems: Developing systems that recommend listings based on user preferences or similar properties.
• Demand Forecasting: Analyzing availability trends to understand demand patterns over different periods.
• Feature Importance: Determining which features are most influential in determining price, reviews, or occupancy.

The presence of non-null counts indicates varying levels of data completeness across columns, which may require data imputation or careful handling during analysis.

Explore the booming Vacation Rental market analysis, revealing key insights, market size, CAGR, drivers, and future trends for 2025-2033. Discover growth opportunities in apartment rentals and private home rentals.

The top 100 Airbnb markets in 2025 are: 1. Los angeles - Strict regulations, 11,250 listings, 67% occupancy rate, $213 daily rate. See other 99 places.

Discover the booming vacation rental market! Explore key trends, growth drivers, and regional insights for 2025-2033. Learn about leading companies like Airbnb and Booking.com and understand the future of short-term rentals. Get the data-driven analysis you need to succeed.

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The Airbnb Listing Data 2023 from Website dataset contains information about Airbnb listings in over 190 countries. The dataset includes information about the location of the listing, the price, the number of bedrooms and bathrooms, the amenities offered, and the reviews of the listing. The dataset was scraped from the Airbnb website using a web scraping tool.

The dataset is a valuable resource for researchers and businesses who are interested in the short-term rental market. The dataset can be used to analyze the trends in the short-term rental market, to identify the most popular destinations for short-term rentals, and to compare the prices of short-term rentals in different locations.

The dataset is also a valuable resource for travelers who are looking for a place to stay. The dataset can be used to find affordable and convenient accommodations in different locations.

The dataset is available for download on Kaggle. The dataset is licensed under the Creative Commons Attribution 4.0 International License.

Here are some of the potential uses of the dataset:

• Researchers can use the dataset to analyze the trends in the short-term rental market.
• Businesses can use the dataset to identify the most popular destinations for short-term rentals and to compare the prices of short-term rentals in different locations.
• Travelers can use the dataset to find affordable and convenient accommodations in different locations.

The dataset is a valuable resource for a variety of users. It is a valuable resource for researchers, businesses, and travelers.

Discover the booming short-term vacation rental market! This in-depth analysis reveals key trends, growth drivers, and regional market share, including insights into major players like Airbnb and Booking.com. Learn about the lucrative opportunities and challenges in this rapidly expanding industry, covering everything from 1-3 day rentals to longer business trips. Explore the future of STRs and unlock valuable strategic insights.

Discover the booming global housing rental service market! This comprehensive analysis reveals key trends, growth drivers, and challenges impacting short-term and long-term rentals, along with insights into leading companies and regional variations. Explore market projections to 2033 and uncover lucrative investment opportunities.

Comprehensive Airbnb dataset for Winter Park, United States providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

In New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of ** nights per stay, with an average price of *** U.S. dollars per night. Meanwhile, the average income per property was ***** U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in *************. This was followed by *************************. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in *************. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than **** thousand properties accommodating to the different needs of visitors to the city. There are various types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered for longer-term stays, in light of city regulations on housing.

Discover the booming rental housing market! Explore key trends, drivers, and challenges impacting this multi-trillion dollar industry. Learn about top players like Airbnb and Zillow, regional market share, and future growth projections to 2033. Get insights to inform your investment or business strategy.

Discover the booming online home rental market! Our analysis reveals a $150 billion market in 2025, projected to grow at a 12% CAGR through 2033. Explore key trends, regional insights, and leading companies shaping this dynamic sector. Learn how to capitalize on the opportunities in short-term rentals, vacation homes, and more.

Description

This dataset provides extensive information about Airbnb properties listed in Los Angeles, California. It offers a wealth of details suitable for analyzing short-term rental trends, exploring traveler behavior, and studying pricing dynamics within one of the most vibrant tourism markets in the U.S.

Dataset Context and Purpose

As Airbnb continues to impact urban rental markets, this dataset allows analysts, researchers, and real estate professionals to investigate how the short-term rental market shapes the local economy and influences housing availability. Users can leverage this dataset to perform location-based analysis, identify seasonal occupancy trends, and explore the popularity of amenities and property types.

Content

id: Unique identifier assigned to each property listing.

name: Property listing name as provided by the host.

host_id:Unique identifier assigned to the host of the property.

host_name:Name of the host associated with the property.

host_since:Date on which the host joined Airbnb.

host_response_time: Typical response time of the host to guest inquiries.

host_response_rate:Percentage of guest inquiries that the host responded to.

host_is_superhost: Indicates whether the host is a Superhost (True/False).

neighbourhood_cleansed: Neighborhood name where the property is located.

neighbourhood_group_cleansed: Standardized neighborhood group or district where the property is located.

latitude: Geographic latitude coordinate.

longitude: Geographic longitude coordinate.

property_type: Type of property.

room_type: Type of room offered (e.g., Entire home/apt, Private room, Shared room).

accommodates: Maximum number of guests that the property can accommodate.

bathrooms: Number of bathrooms in the property.

bedrooms: Number of bedrooms in the property.

beds: Number of beds in the property.

price: Total price based on minimum nights required for booking.

minimum_nights: Minimum number of nights required for a booking.

availability_365:Number of days the property is available for booking in the next 365 days.

number_of_reviews: Total number of reviews received for the property.

review_scores_rating: Average rating score based on guest reviews (5 is maximum value).

license: License, if applicable.

instant_bookable: Indicates whether guests can book the property instantly (True/False).

Inspiration

• Host Insights: Analyze host behavior, response times, and Superhost status to understand their impact on guest satisfaction and property performance.
• Property Characteristics: Identify popular property types, room types, and amenities, and how they correlate with pricing and occupancy rates.
• Neighborhood Analysis: Explore neighborhood-level trends in pricing, occupancy, and guest reviews to identify popular areas and potential investment opportunities.
• Pricing Strategies: Analyze factors influencing pricing, such as property type, location, amenities, and seasonality.

Source

This dataset is part of Inside Airbnb, Los Angeles California on September 04, 2024. Link found here

The global housing rental service market is experiencing robust growth, driven by factors such as increasing urbanization, changing lifestyle preferences, and the rise of the gig economy. The market, valued at approximately $2 trillion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This significant expansion is fueled by several key trends, including the growing popularity of short-term rentals facilitated by platforms like Airbnb and VRBO, the increasing demand for flexible lease options catering to transient populations, and the emergence of innovative property management technologies enhancing efficiency and tenant experience. The market segmentation reveals a significant share held by the long-term lease segment, driven by stable rental income and predictable cash flows, while the short-term rental segment is witnessing rapid growth, propelled by the tourism and business travel sectors. Furthermore, the commercial segment is expanding, reflecting the growing need for flexible workspace solutions. Geographic distribution shows strong performance in North America and Europe, with emerging markets in Asia-Pacific presenting significant growth opportunities. However, the market faces certain restraints, including fluctuating interest rates impacting mortgage costs, potential regulatory changes affecting short-term rentals, and the challenges of maintaining consistent property standards across diverse portfolios. Despite these challenges, the long-term outlook for the housing rental service market remains positive, driven by continuous technological advancements, evolving consumer preferences, and the persistent demand for housing in rapidly urbanizing regions. Key players in the market, including Invitation Homes, Blueground, and Vacasa, are actively innovating to meet these changing demands and capitalize on growth opportunities within different segments and geographic regions. Strategic acquisitions, technological integrations, and expansion into new markets are crucial strategies for sustained success within this dynamic sector.

Comprehensive Airbnb dataset repository offering detailed vacation rental analytics worldwide including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

Introduction:

This dataset, titled 'Airbnb Market Analysis and Real Estate Sales Data (2019),' comprises a comprehensive collection of information pertaining to the Airbnb rental market and property sales in two distinct areas within California: Big Bear and Joshua Tree, along with their associated zip codes (92314, 92315, 92284, and 92252). The dataset provides monthly aggregated data, allowing for an in-depth analysis of rental and real estate market trends in these regions. It includes the following files:

Datasets:

Market Analysis:

This file contains listing-level information from 2019, aggregated on a monthly basis. It encompasses various metrics, such as unique property codes (unified_id), generated revenue, availability (openness), occupancy ratios, nightly rates, lead times, and average length of stay for reservations made each month. Additionally, it provides insights into property amenities.

Amenities:

This file indicates whether a listing has specific amenities, denoting their presence with a value of 1 or their absence with a value of 0. Notably, it identifies the availability of a pool or hot tub in each listing.

Geolocation:

This file contains latitude and longitude coordinates for each listing, enabling precise spatial analysis and visualization.

Sales Properties:

This dataset provides information concerning properties available for sale within the study areas. In the Joshua Tree region (zip codes 92284 and 92252), there are two separate files—one presenting the overall information about sales properties and the other focusing on properties with pools.

This dataset is a valuable resource for researchers and analysts interested in gaining insights into the real estate and Airbnb rental markets in California, particularly within the specified regions."

Potential Applications:

This dataset provides a strong foundation for Power BI reporting, enabling the creation of insightful reports and dashboards. Analysts can utilize joins on unique IDs to extract key factors and KPIs, facilitating data-driven decision-making. Whether it's optimizing Airbnb listings, making informed real estate investments, or shaping policies, this dataset serves as a valuable resource for Power BI users seeking to gain deeper insights and drive data-driven strategies in the California real estate market

As of January 2025, the majority of Airbnb properties in New York City in the United States were listed as longer-term rentals. Meanwhile, short-term listings, which are only rented for less than ** days, accounted for **** percent of rental properties available.

Comprehensive Airbnb dataset for Curitiba, Brazil providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

See the average Airbnb revenue & other vacation rental data in Pigeon Forge in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.

By analyzing publicly available information about a city's Airbnb's listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.

With Inside Airbnb, you can ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole. Questions such as:

• "How many listings are in my neighbourhood and where are they?"
• "How many houses and apartments are being rented out frequently to tourists and not to long-term residents?"
• "How much are hosts making from renting to tourists (compare that to long-term rentals)?"
• "Which hosts are running a business with multiple listings and where they?"

The tools are presented simply, and can also be used to answer more complicated questions, such as:

"Show me all the highly available listings in Bedford-Stuyvesant in Brooklyn, New York City, which are for the 'entire home or apartment' that have a review in the last 6 months AND booked frequently AND where the host has other listings."

These questions (and the answers) get to the core of the debate for many cities around the world, with Airbnb claiming that their hosts only occasionally rent the homes in which they live.

In addition, many city or state legislation or ordinances that address residential housing, short term or vacation rentals, and zoning usually make reference to allowed use, including:

• how many nights a dwelling is rented per year
• minimum nights stay
• whether the host is present
• how many rooms are being rented in a building
• the number of occupants allowed in a rental
• whether the listing is licensed

The Inside Airbnb tool or data can be used to answer some of these questions.

The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site.

The data has been analyzed, cleansed and aggregated where appropriate to faciliate public discussion.