These data were accessed from Inside Airbnb on September 2, 2017. The version of the data that we will use in this class can be found here.

About Inside Airbnb

Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.

By analyzing publicly available information about a city’s Airbnb’s listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.

Data description

Inside Airbnb provides some visualizations of the NYC Airbnb data here, where you can see maps showing type of room, activity, availability, and listings per host for all NYC Airbnb listings.

After downloading both the “listings.csv.gz” and “listings.csv” files, the following code was used to create the provided dataset:


# Uncompress and load the detailed NYC airbnb data
# But only keep the 2 variables of interest
zz <- gzfile("listings.csv.gz", 'rt')   
airbnb_location_df = 
  read_csv(zz, header = TRUE) %>% 
  select(id, review_scores_location)

# Read in the summary data for NYC
airbnb_listings_df =
  read_csv("listings.csv") %>% 
  mutate(last_review = as.Date(last_review, format = "%Y-%m-%d"))

# Combine the two datasets
nyc_airbnb = inner_join(airbnb_location_df, airbnb_listings_df, by = "id")

# Save the data
save(nyc_airbnb, file = "nyc_airbnb.RData")

The resulting R data file nyc_airbnb contains a single dataframe nyc_airbnb with 40,753 rows of data on 17 variables:

  • id: listing id
  • review_scores_location: 0-5 stars converted into a 0-10 scale
  • name: listing name
  • host_id: host id
  • host_name: host name
  • neighbourhood_group: NYC borough
  • neighbourhood: NYC neighborhood
  • latitude: listing latitude
  • longitude: listing longitude
  • room_type: type of listing (Entire home/apt, Private room, Shared room)
  • price: listing price
  • minimum_nights: required minimum nights stay
  • number_of_reviews: total number of reviews
  • last_review: date of last review
  • reviews per month: average number of reviews per month
  • calculated_host_listings_count: total number of listings for this host
  • availability_365: number of days listing is available out of 365