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Airbnb Chicago Market Data
Data Science and Analytics
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About
This dataset offers detailed information on Airbnb reservations in Chicago from 2017 to 2019. It provides valuable insights for analysis and prediction related to travel experiences. Compiled as an assignment for a data science class, the dataset is ideal for understanding market trends, factors influencing pricing, and host impact on popularity within the Chicago short-term rental market. It serves as an excellent resource for data visualisation and predictive modelling.
Columns
- id: The unique identifier for each Airbnb listing.
- name: The listed name of the Airbnb accommodation.
- host_id: The unique identifier for the host.
- host_name: The name of the host.
- neighborhood: The geographical area where the Airbnb is located.
- latitude: The latitudinal coordinate of the listing's position.
- longitude: The longitudinal coordinate of the listing's position.
- room_type: Categorisation of the accommodation (e.g., Entire home/apt, Private room, or Other).
- price: The daily price of the apartment.
- minimum_nights: The fewest number of nights required for a booking.
- number_of_reviews: The total count of reviews received for the listing within this dataset.
- last_review: The timestamp of the most recent review.
- reviews_per_month: The average number of reviews the Airbnb receives each month.
- calculated_host_listings_count: The total number of listings managed by the host.
- availability_365: The number of days the listing is available over a 365-day period.
Distribution
The dataset is typically provided in a CSV file format, specifically named
listings.csv. It includes various details about hosts, property locations, room characteristics, and pricing information. The current version (Version 2) has a size of 980.11 kB. Specific numbers for rows or records are not detailed in the available information.Usage
This dataset is well-suited for a range of analytical and predictive applications. It can be used for data visualisation, particularly for mapping Airbnb positions using latitude, longitude, and neighbourhood information. Ideal applications include exploring how neighbourhoods affect Airbnb prices, predicting trends related to locations, prices, and reviews, and assessing how hosts influence the popularity of their listings.
Coverage
The dataset focuses on Airbnb reservation data exclusively within the city of Chicago. It covers a specific timeframe from 2017 through to 2019. The information relates to various Airbnb listings, hosts, and traveller reviews during this period.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building predictive models, performing data visualisations, and conducting market analysis.
- Researchers: To study urban tourism, accommodation trends, pricing strategies, and the impact of hosts on listing performance.
- Travel Industry Analysts: To understand market dynamics, competitor pricing, and popular locations in Chicago.
- Students: As a practical dataset for data science assignments and learning projects.
Dataset Name Suggestions
- Chicago Airbnb Listings (2017-2019)
- Airbnb Chicago Market Data
- Chicago Accommodation Insights
- Airbnb Host and Price Analysis - Chicago
Attributes
Original Data Source: Airbnb Chicago Market Data
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