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

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

Airbnb In NYC

Room Prices, Reviews, and Availability

By Huggingface Hub [source]

About this dataset

This dataset offers a unique and comprehensive look into the expansive Airbnb industry in New York City. We capture 20,000+ Airbnbs with its associated data such as descriptions, rates, reviews and availability. Professionals researching this industry will find it an invaluable resource in providing insight to the ever popular Airbnb market that can be used for their advantage.

This dataset showcases some of the most important attributes for each listing: host name, neighborhood group, location (latitude/longitude coordinates), room type, price per night, minimum nights required to book a stay at this listing , total number of reviews and ratings received by guests over time (including reviews per month and last review date), calculated host listing count (indicates how many listings are offered by each host) along with 365 days worth of availability score. With all these parameters one can understand dynamics of demand & supply & further utilize them accordingly to maximize returns or occupancy greeting never before seen transparency into NYC’s Airbnb scene

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How to use the dataset

This dataset can be used to gain a comprehensive understanding of the Airbnb market in New York City. The data offers descriptions, rates, reviews and availability for over 20,000 Airbnbs in NYC.

Here are few tips on how to use this dataset: - Use the latitude and longitude coordinates to visualize the variety of Airbnbs located across all five boroughs of New York City using mapping programs like Google Maps or ArcGIS. - Determine the versatile price ranges offered by Airbnb listings by looking at the “price” column available for each listing . - Analyze reviews scored by guests who have used an Airbnb in order to better understand customer experience with different services through columns such as “number_of_reviews” and “last_review.
4 Understand how often properties are made available for booking based on their popularity through columns like “availability_365 and reviews_per_month. . 5 Investigate listing host data by looking into their description (host name) as well as number of listings they have booked (calculated host listing count)

Research Ideas

• Determining the listings with the highest satisfaction ratings for potential customers to book.
• Analyzing neighborhood trends in prices, availability, and reviews to identify hot areas of competition within the Airbnb market.
• Predicting future prices throughput examining properties such as review scores and availability rate to provide forecast information to AirBnB owners

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------------------| | name | The name of the Airbnb listing. (String) | | host_name | The name of the host of the Airbnb listing. (String) | | neighbourhood_group | The neighbourhood group the Airbnb listing is located in. (String) | | latitude | The latitude coordinate of the Airbnb listing. (Float) | | longitude | The longitude coordinate of the Airbnb listing. (Float) | | room_type | The type of room offered by the Airbnb listing. (String) | | price | The price per night of the Airbnb listing. (Integer) | | minimum_nights | The minimum number of nights required for booking the Airbnb listing. (Integer) | | number_of_reviews | T...

Data Description There are 2 datasets

searches.tsv - Contains a row for each set of searches that a user does for Dublin contacts.tsv - Contains a row for every time that an assigned visitor makes an inquiry for a stay in a listing in Dublin

searches dataset contains the following columns:

ds - Date of the search id_user - Alphanumeric user_id ds_checkin - Date stamp of the check-in date of the search ds_checkout - Date stamp of the check-out date of the search n_searches - Number of searches in the search set n_nights - The number of nights the search was for n_guests_min - The minimum number of guests selected in a search set n_guests_max - The maximum number of guests selected in a search set origin_country - The country the search was from filter_price_min - The value of the lower bound of the price filter, if the user used it filter_price_max - The value of the upper bound of the price filter, if the user used it filter_room_types - The room types that the user filtered by, if the user used the room_types filter filter_neighborhoods - The neighborhood types that the user filtered by, if the user used the neighborhoods filter

contacts dataset contains the following columns:

id_guest - Alphanumeric user_id of the guest making the inquiry id_host - Alphanumeric user_id of the host of the listing to which the inquiry is made id_listing - Alphanumeric identifier for the listing to which the inquiry is made ts_contact_at - UTC timestamp of the moment the inquiry is made. ts_reply_at - UTC timestamp of the moment the host replies to the inquiry, if so ts_accepted_at - UTC timestamp of the moment the host accepts the inquiry, if so ts_booking_at - UTC timestamp of the moment the booking is made, if so ds_checkin - Date stamp of the check-in date of the inquiry ds_checkout - Date stamp of the check-out date of the inquiry n_guests - The number of guests the inquiry is for n_messages - The total number of messages that were sent around this inquiry

Practicalities Analyze the provided data and answer the questions to the best of your abilities. Include the relevant tables/graphs/visualization to explain what you have learnt about the market. Make sure that the solution reflects your entire thought process including the preparation of data - it is more important how the code is structured rather than just the final result or plot. You are expected to spend no more than 3-6 hours on this project.

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.

Comprehensive Airbnb dataset for Tokyo, Japan 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.

These are the Airbnb statistics on gross revenue by country.

Leverage our Airbnb dataset to gain comprehensive insights into global short-term rental markets. Track property details, pricing trends, reviews, availability, and amenities to optimize pricing strategies, conduct market research, or enhance travel-related applications. Data points may include listing ID, host ID, property type, price, number of reviews, ratings, availability, and more. The dataset is available as a full dataset or a customized subset tailored to your specific needs.

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.

Global short-term rental intelligence sourced from leading Online Travel Agencies (OTAs). The OTA Real Estate Dataset provides a comprehensive view of the global vacation rental market by combining verified OTA property listings with valuation, tax, and physical asset data. Each record includes unique property identifiers, geolocation details, pricing indicators, and occupancy metrics—enabling robust market analysis and investment-grade insights.

Continuously sourced from major OTA platforms and refined through proprietary data-cleaning models, this dataset ensures accuracy, consistency, and comparability across regions. Available globally with flexible delivery via API or flat files, it serves as a foundational dataset for those analyzing market performance, forecasting development potential, or conducting housing research.

Key Highlights: Granular Real Estate Intelligence: Combines OTA listing data with valuation, tax, and property attributes for a holistic view of market activity.

Global and Standardized: Harmonized schema and coverage across countries, cities, and neighborhoods for cross-market comparability.

High-Fidelity Data: Proprietary normalization removes duplicates and outliers to ensure analytical precision.

Flexible Access: Delivered through API or CSV, updated regularly for timely decision-making.

Ideal For: Real Estate Investors: Identify high-performing short-term rental markets and assess yield potential.

Developers & Urban Planners: Evaluate spatial demand patterns and inform development feasibility studies.

Financial Institutions: Integrate standardized OTA data into underwriting, risk, and valuation models.

Tourism Economists & Market Researchers: Quantify the impact of vacation rentals on local housing and tourism dynamics.

Use It To: Benchmark short-term rental performance by region or property type.

Analyze shifts in rental demand and pricing over time.

Support market-entry, site-selection, and feasibility studies with real OTA-backed data.

Enhance research and policy analysis with consistent, globally comparable property-level insights.

Comprehensive Airbnb dataset for Santa Pola, Spain 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.

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

The Airbnb business model has revolutionized the hospitality and tourism industry by leveraging the power of the sharing economy. Established in 2008, Airbnb provides a platform for homeowners to monetize their extra space by renting it out to travelers seeking unique lodging experiences. This innovative approach no

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.

The dataset used in this project is a publicly available dataset containing Airbnb listing data for New York City. It provides comprehensive details about various aspects of Airbnb listings, such as neighborhood, room types, prices, availability, and host information.

Key Features of the Dataset Here are some of the main columns included in the dataset and what they represent:

id: A unique identifier for each listing. name: The name or title of the Airbnb listing. host_id: The unique ID of the host. host_name: The name of the host. neighbourhood_group: The borough where the listing is located (e.g., Manhattan, Brooklyn, Queens, Bronx, Staten Island). neighbourhood: The specific neighborhood within the borough. latitude and longitude: The geographic coordinates of the listing. room_type: The type of room being offered: Entire home/apt Private room Shared room price: The price per night to stay at the listing. minimum_nights: The minimum number of nights required for booking. number_of_reviews: The total number of reviews received by the listing. reviews_per_month: The average number of reviews the listing receives each month. availability_365: The number of days the listing is available for booking in a year. calculated_host_listings_count: The total number of listings managed by a host. Dataset Characteristics Timeframe: The dataset represents a snapshot of listings and reviews within a specific time period (usually the latest available at the time of collection). Geography: Includes all five boroughs of New York City: Manhattan Brooklyn Queens Bronx Staten Island Diversity: The dataset captures a diverse set of listings, from luxury apartments in Manhattan to budget-friendly shared rooms in the Bronx. Why This Dataset? This dataset is ideal for analysis because:

It allows us to explore trends in NYC's Airbnb market, such as pricing patterns, popular room types, and host activity. It offers valuable insights into neighborhood preferences and pricing strategies for hosts. It helps identify potential areas of improvement, such as boosting listings in underrepresented neighborhoods like Staten Island and Queens. Dataset Source This dataset is commonly hosted on platforms like Kaggle or Inside Airbnb, a project that compiles publicly available information on Airbnb listings. It is designed to provide transparency and insight into Airbnb activity across cities.

Discover the booming vacation rental market! This in-depth analysis reveals key trends, growth drivers, and leading companies shaping the future of short-term rentals. Learn about market size, CAGR, and regional insights for informed investment decisions.

Discover the booming housing rental platform market! This in-depth analysis reveals market size, growth trends (2019-2033), key players (Airbnb, Booking.com, etc.), regional insights, and future forecasts. Learn about the impact of short-term rentals, long-term leases, and emerging technologies. Invest wisely in this rapidly expanding sector.

The Airbnb Reviews dataset provides structured, multilingual guest feedback from Airbnb listings worldwide. Each entry includes the full review text, star rating, reviewer profile, location, timestamp, and language. Ideal for sentiment analysis, reputation monitoring, travel market research, and AI/ML training, this dataset allows country-level filtering for the US, EU, and Australia. Continuously updated and scalable to millions of reviews, the data is exportable in CSV, JSON, or JSONL formats. It is ready for analytics pipelines, NLP applications, recommendation engines, and travel trend analysis.

You can request the large dataset at: Airbnb Reviews
To get a custom data quote, visit: Get quote

Key Features:

• Full review text with star ratings and verified stay flags.

• Metadata including reviewer name, profile, property type, location, timestamp, and language.

• Multilingual coverage for global analysis.

• Country-specific filtering for US, EU, and AU markets.

• Continuous updates to include new reviews and listings.

• Export formats: CSV, JSON, JSONL.

• Scalable for millions of reviews.

Use Cases:

• Train AI models for sentiment analysis and review classification.

• Monitor property and host reputation across regions.

• Build semantic search engines for Airbnb reviews.

• Conduct global or regional travel market research.

• Feed review summarizers and QA models with structured review data.

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 ...

Comprehensive Airbnb dataset for Paris, France 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.