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...

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

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

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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 ...

Dataset

This dataset was created by Raja wahaj

Contents

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Dataset

This dataset was created by Abdelaziz Sami

Released under Apache 2.0

Contents

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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

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