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NYC 2019 Short-Term Rental Listing Activity
Data Science and Analytics
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About
Captures the state of the New York short-term rental market for the year 2019. It includes detailed metrics necessary for prediction modelling and deep data analysis. The data enables users to explore geographical distribution, understand host activity levels, and forecast pricing trends. It is an ideal resource for anyone looking to learn about the various hosts and areas within NYC, and to investigate noticeable differences in traffic across different neighbourhoods.
Columns
id: The unique room identifier from the website.name: The name associated with the listing.host_id: The identifier for the host.host_name: The name of the host.neighbourhood_group: The primary neighbourhood division (e.g., Manhattan, Brooklyn).neighbourhood: The specific neighbourhood location (e.g., Bedford-Stuyvesant, Williamsburg).latitude: The geographical latitude coordinate of the listing.longitude: The geographical longitude coordinate of the listing.room_type: Indicates the type of room offered (e.g., Entire home/apt, Private room).price: The price of the room, ranging up to 10000.minimum_nights: The minimum number of nights required for a booking.number_of_reviews: The total count of reviews.last_review: The date of the most recent review.reviews_per_month: The frequency of reviews per month.calculated_host_listings_count: The calculated number of listings the host maintains.availability_365: The number of days the listing is available over the next year.number_of_reviews_ltm: The number of reviews received in the last twelve months.license: Licensing information.
Distribution
The underlying data file is typically found in a CSV format, specifically noted as
New York Airbnb_4 dec 2021.csv, with a size of 5.73 MB. The data structure contains 18 columns. The majority of columns feature approximately 38.3 thousand valid records.Usage
This data is perfectly suited for several applications, including practising price prediction models, developing robust regression models, and conducting spatial analysis to determine the factors influencing listing popularity and price variation across different New York City areas. It allows users to answer questions such as identifying the busiest hosts and exploring traffic disparities between areas.
Coverage
The dataset focuses entirely on New York City, NY, USA. It describes listing metrics for the year 2019. Geographical scope includes all five neighbourhood groups, with Manhattan making up 44% of the listings and Brooklyn 38%. Review data ranges temporally from December 2010 up to December 2021.
License
CC0: Public Domain
Who Can Use It
Intended users include data analytics professionals, machine learning practitioners, and individuals interested in real estate market dynamics. It is also suitable for beginners seeking practical experience with data analysis and regression techniques.
Dataset Name Suggestions
- NYC 2019 Short-Term Rental Listing Activity
- New York City Airbnb Market Metrics
- Geospatial Analysis of NYC Airbnb Listings
Attributes
Original Data Source: NYC 2019 Short-Term Rental Listing Activity
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