Barcelona Airbnb Listings by Kaggle | Finance and Economics

About this Dataset

Barcelona Airbnb Listings

Understanding Rental Prices and Trends in the City

By Kelly Garrett [source]

About this dataset

This dataset contains detailed information about Airbnb listings for the city of Barcelona, Spain, including reviews from guests and hosts, ratings, neighborhoods and more. With over 16000 observations collected from nearly 5000 unique listings, it offers great insight into the demand and popularity of different types of accommodation in Barcelona. It also provides detailed insights into the quality of each listing such as its exact location, number of bedrooms and Cleanliness Rating. Additionally, this dataset gives an opportunity to explore what kind of amenities each listing has to offer (such as parking or internet) and how they affect price range. Ultimately this data allows users to analyze different types of accommodations in Barcelona in order to discover key trends within the rental market - which locations are most popular amongst visitors? Which kinds amenities are associated with higher-priced rentals? How do ratings compare across neighborhoods?

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How to use the dataset

This dataset is a great way to gain insight into what Barcelona has to offer in terms of Airbnb listings. It provides information on over 19,000 listings throughout the city with details such as availability and pricing, as well as listings that include reviews and amenities offered. Through exploring this dataset you will be able to identify trends in Airbnb's presence in Barcelona and make better informed decisions when booking your stay.

To begin using this dataset:

Start by getting an overview of the data by considering the columns present in the dataset such as 'neighbourhood_group',‘room_type’, ‘price’, 'number_of_reviews' etc., and determining how each of these features influence your analysis or search for certain key properties that interest you.

Gain further insight about individual properties through exploring related columns such as 'amenities' or 'host_name'.

Identify geographic areas that have higher concentrations of Airbnb's using visualizations or clustering techniques to better understand which neighbourhoods have more activity or data points associated with them making for a potentially more enjoyable stay based on customer ratings & reviews etc,.

Use summary statistics and rankings (such as describing how far you are from main attractions) to examine overall prices across different neighbourhood components within Barcelona during different times of year taking into consideration factors like peak seasonality vs low seasonality before entering any booking agreement via online travel sites etc,.

By following these steps when utilizing this datasets potential it will allow users get a detailed overview of potential options prior to making any final decisions concerning their prized Airbnb stay!

Research Ideas

Analyzing the correlation between rental prices in different areas and various socioeconomic factors such as median household income, population density, and types of business establishments in those areas.

Examining differences in amenities offered at different price points to determine how much more a traveler would be willing to pay for certain amenities (ie luxury sheets, spa-like shower setup).

Analyze the changes in Airbnb listings over time - including number of new/cancelled listings, average nightly price increases or decreases - that can help inform decision making by tourists or local government on investment into the future of tourism in Barcelona

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

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Kelly Garrett.

Tables

Barcelona Listings

@kaggle.thedevastator_analysis_of_barcelona_airbnb_listings.barcelona_listings

28.89 MB
19833 rows
107 columns


CREATE TABLE barcelona_listings (
  "index" BIGINT,
  "id" BIGINT,
  "listing_url" VARCHAR,
  "scrape_id" BIGINT,
  "last_scraped" TIMESTAMP,
  "name" VARCHAR,
  "summary" VARCHAR,
  "space" VARCHAR,
  "description" VARCHAR,
  "experiences_offered" VARCHAR,
  "neighborhood_overview" VARCHAR,
  "notes" VARCHAR,
  "transit" VARCHAR,
  "access" VARCHAR,
  "interaction" VARCHAR,
  "house_rules" VARCHAR,
  "thumbnail_url" VARCHAR,
  "medium_url" VARCHAR,
  "picture_url" VARCHAR,
  "xl_picture_url" VARCHAR,
  "host_id" BIGINT,
  "host_url" VARCHAR,
  "host_name" VARCHAR,
  "host_since" TIMESTAMP,
  "host_location" VARCHAR,
  "host_about" VARCHAR,
  "host_response_time" VARCHAR,
  "host_response_rate" VARCHAR,
  "host_acceptance_rate" VARCHAR,
  "host_is_superhost" VARCHAR,
  "host_thumbnail_url" VARCHAR,
  "host_picture_url" VARCHAR,
  "host_neighbourhood" VARCHAR,
  "host_listings_count" DOUBLE,
  "host_total_listings_count" DOUBLE,
  "host_verifications" VARCHAR,
  "host_has_profile_pic" VARCHAR,
  "host_identity_verified" VARCHAR,
  "street" VARCHAR,
  "neighbourhood" VARCHAR,
  "neighbourhood_cleansed" VARCHAR,
  "neighbourhood_group_cleansed" VARCHAR,
  "city" VARCHAR,
  "state" VARCHAR,
  "zipcode" VARCHAR,
  "market" VARCHAR,
  "smart_location" VARCHAR,
  "country_code" VARCHAR,
  "country" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "is_location_exact" VARCHAR,
  "property_type" VARCHAR,
  "room_type" VARCHAR,
  "accommodates" BIGINT,
  "bathrooms" DOUBLE,
  "bedrooms" DOUBLE,
  "beds" DOUBLE,
  "bed_type" VARCHAR,
  "amenities" VARCHAR,
  "square_feet" DOUBLE,
  "price" VARCHAR,
  "weekly_price" VARCHAR,
  "monthly_price" VARCHAR,
  "security_deposit" VARCHAR,
  "cleaning_fee" VARCHAR,
  "guests_included" BIGINT,
  "extra_people" VARCHAR,
  "minimum_nights" BIGINT,
  "maximum_nights" BIGINT,
  "minimum_minimum_nights" BIGINT,
  "maximum_minimum_nights" BIGINT,
  "minimum_maximum_nights" BIGINT,
  "maximum_maximum_nights" BIGINT,
  "minimum_nights_avg_ntm" DOUBLE,
  "maximum_nights_avg_ntm" DOUBLE,
  "calendar_updated" VARCHAR,
  "has_availability" VARCHAR,
  "availability_30" BIGINT,
  "availability_60" BIGINT,
  "availability_90" BIGINT,
  "availability_365" BIGINT,
  "calendar_last_scraped" TIMESTAMP,
  "number_of_reviews" BIGINT,
  "number_of_reviews_ltm" BIGINT,
  "first_review" TIMESTAMP,
  "last_review" TIMESTAMP,
  "review_scores_rating" DOUBLE,
  "review_scores_accuracy" DOUBLE,
  "review_scores_cleanliness" DOUBLE,
  "review_scores_checkin" DOUBLE,
  "review_scores_communication" DOUBLE,
  "review_scores_location" DOUBLE,
  "review_scores_value" DOUBLE,
  "requires_license" VARCHAR,
  "license" VARCHAR,
  "jurisdiction_names" VARCHAR,
  "instant_bookable" VARCHAR,
  "is_business_travel_ready" VARCHAR,
  "cancellation_policy" VARCHAR
);