Description

This dataset provides extensive information about Airbnb properties listed in Los Angeles, California. It offers a wealth of details suitable for analyzing short-term rental trends, exploring traveler behavior, and studying pricing dynamics within one of the most vibrant tourism markets in the U.S.

Dataset Context and Purpose

As Airbnb continues to impact urban rental markets, this dataset allows analysts, researchers, and real estate professionals to investigate how the short-term rental market shapes the local economy and influences housing availability. Users can leverage this dataset to perform location-based analysis, identify seasonal occupancy trends, and explore the popularity of amenities and property types.

Content

id: Unique identifier assigned to each property listing.

name: Property listing name as provided by the host.

host_id:Unique identifier assigned to the host of the property.

host_name:Name of the host associated with the property.

host_since:Date on which the host joined Airbnb.

host_response_time: Typical response time of the host to guest inquiries.

host_response_rate:Percentage of guest inquiries that the host responded to.

host_is_superhost: Indicates whether the host is a Superhost (True/False).

neighbourhood_cleansed: Neighborhood name where the property is located.

neighbourhood_group_cleansed: Standardized neighborhood group or district where the property is located.

latitude: Geographic latitude coordinate.

longitude: Geographic longitude coordinate.

property_type: Type of property.

room_type: Type of room offered (e.g., Entire home/apt, Private room, Shared room).

accommodates: Maximum number of guests that the property can accommodate.

bathrooms: Number of bathrooms in the property.

bedrooms: Number of bedrooms in the property.

beds: Number of beds in the property.

price: Total price based on minimum nights required for booking.

minimum_nights: Minimum number of nights required for a booking.

availability_365:Number of days the property is available for booking in the next 365 days.

number_of_reviews: Total number of reviews received for the property.

review_scores_rating: Average rating score based on guest reviews (5 is maximum value).

license: License, if applicable.

instant_bookable: Indicates whether guests can book the property instantly (True/False).

Inspiration

• Host Insights: Analyze host behavior, response times, and Superhost status to understand their impact on guest satisfaction and property performance.
• Property Characteristics: Identify popular property types, room types, and amenities, and how they correlate with pricing and occupancy rates.
• Neighborhood Analysis: Explore neighborhood-level trends in pricing, occupancy, and guest reviews to identify popular areas and potential investment opportunities.
• Pricing Strategies: Analyze factors influencing pricing, such as property type, location, amenities, and seasonality.

Source

This dataset is part of Inside Airbnb, Los Angeles California on September 04, 2024. Link found here

According to data from Airbtics.com, domestic guests staying at Airbnbs in Spain reached a 13-month high of **** percent of total guests as of May 2020, compared to **** percent in May 2019. Domestic guests started to decrease in March 2020, reaching zero in April, as the COVID-19 outbreak restricted travel within the country.

According to data from Airbtics.com, international guests staying at Airbnbs in the United Kingdom reached a 13-month low of **** percent of total guests as of May 2020, compared to **** percent in May 2019. The ratio of international guests started to decrease in April 2020, as the COVID-19 outbreak restricted travel into the country.

According to data from Airbtics.com, international guests staying at Airbnbs in Portugal reached a 13-month low of **** percent of total guests as of May 2020, compared to **** percent in May 2019. International guests decreased in May 2020, as the COVID-19 outbreak restricted travel.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.

This statistic presents the results of a survey, asking British holidaymakers who travel on holiday in the UK how likely they are to use Airbnb to book holiday travel in 2017. Of respondents, * percent said they plan to use Airbnb for their holiday this year, while ** percent said they would consider Airbnb alongside other options. Between November 2014 and November 2015 the number of outbound Airbnb guests from the UK was *** million. Amore recent survey showed that the majority of consumers in 2017 had never booked flat share style accommodation such as Airbnb.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.