The Startup
Published in

The Startup

Data driven insights of Seattle AirBnB listings

AirBnB logo
AirBnB Logo (Source: Internet)


Space Needle Tower and Seattle Downtown
Seattle Downtown (Source: Flickr)
  • listings.csv — includes full descriptions and average review score
  • calendar.csv — includes listing id and the price and availability for that day
  • reviews.csv — includes unique id for each reviewer and detailed comments We’ll be using all three files individually for analysis.
  1. “What factors highly influence the prices of listings in Seattle?”
  2. “What is the seasonal pattern of prices?”
  3. “What is the relationship of reviews with price?”

Part 1: Factors that Influence the prices of listings

  1. How Property Type affects prices?
Property Type Frequency
Price Distribution over property type vs room type
HeatMap for variation of prices with number of bedrooms for listings

Part 2: Seasonal Pattern Analysis of Prices

   listing_id  date       available  price
0 241032 2016-01-04 t $85.00
1 241032 2016-01-05 t $85.00
2 241032 2016-01-06 f NaN
3 241032 2016-01-07 f NaN
4 241032 2016-01-08 f NaN
Seattle AirBnB price trends over one year (2016–2017)
Seattle AirBnB price trends for each day in a week
Holidays and average price plot
Price Plot from July 4, 2016 to July 13, 2016

Part 3: Relationship between customer reviews and prices

Graph for Polarity Range vs Number of comments
No. of Reviews vs Price
  • From the graph, the reviews were most observed for the listings that have a price range around 100–300. The number quickly declines as the price goes up.
  • It shows that, there is no necessity for an expensive listing to have more reviews. Hence, the Prices have no relation with the Number of reviews.


  1. We gathered some of the factors that influence the prices of listings, which showed that number of rooms, neighborhood locality, and type of listing played a major role.
  2. We then looked at the seasonal patterns in the prices. This showed that prices of listings go up during weekends and especially in July and August months.
  3. Finally we looked at the relationship between customer reviews and prices. This analysis showed us that number of reviews does not have a relation with prices but these reviews and comments play a big role in attracting the attention of travelers.
  • What other factors boost the popularity of the listings ?
  • Time series forecasting of the prices.
  • Host analysis and recommending prices to the owners
  • Recommending user a better place to invest in order to obtain maximum revenue



Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +768K followers.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store