Description:
The Cleaned_airbnb_barcelona.csv dataset is a meticulously curated collection showcasing various aspects of Airbnb listings within Barcelona. This dataset has been processed to ensure it is clean for analysis, making it an invaluable resource for understanding the short-term rental market dynamics in Barcelona. With 15 columns covering a wide range of information, this dataset offers insights into the listings' physical characteristics, amenities, pricing, availability, and host details.
Attribute Description:
- Index: Unique identifier for each row (Sample values: 10834, 3608, 2860).
- id: Unique identification number for each listing (Sample values: 30947869, 24469371).
- host_id: Unique number identifying the host (Sample values: 153943393, 123483933).
- host_is_superhost: Indicates if the host is classified as a superhost ('f' for no).
- host_listings_count: The number of listings the host has (Sample values: 1.0, 26.0).
- neighbourhood: The neighbourhood where the listing is located (Sample values: 'Sant Marti', 'El Raval').
- zipcode: Postal code of the listing location (Sample values: '8015', '8024').
- latitude and longitude: Geographical coordinates of the listing (Sample values for latitude: 41.37862, 41.39157).
- property_type: Type of property being listed (All sample values: 'Apartment').
- room_type: Type of room offered (Sample values: 'Private room', 'Entire home/apt').
- accommodates: Number of guests the listing accommodates (Sample values: 8, 4).
- bathrooms, bedrooms, beds: Number of bathrooms, bedrooms, and beds available (Sample values for bathrooms: 1.5, 2.0).
- amenities: List of amenities provided with the listing (Sample values include 'TV', 'Wifi').
- price: Listing price per night (Sample values: '$200.00', '$169.00').
- minimum_nights: Minimum number of nights required for booking (Sample values: 2, 1).
- has_availability: Indicates if the listing is available ('t' for true).
- availability_30, availability_60, availability_90, availability_365: Availability of the listing over 30, 60, 90, and 365 days (Sample values for availability_30: 7, 0).
- number_of_reviews_ltm: Number of reviews in the last twelve months (Sample values: 6, 0).
- review_scores_rating: Overall rating score of the listing (Sample values: 94.0, 98.0).
Use Case:
This dataset is particularly beneficial for individuals or organizations looking to analyze the competitive landscape of Airbnb listings in Barcelona, understand pricing strategies, or assess the influence of amenities on guest satisfaction and pricing. Researchers can use this data to study patterns in tourism, seasonal availability, and the economic impact of Airbnb in the region. Urban planners and housing authorities could also leverage insights garnered from this dataset to understand how short-term rentals are distributed across different neighbourhoods and their implications on local housing markets.