Data Visualization of Airbnb in New York
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Contributed by Amy(Yujing) Ma. She is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on her first project - R Shiny. (due on 2th week)
Why Airbnb?
Visiting NYC? Airbnb is a good choice of useful data to book unique accommodations.I have used Airbnb.com for almost 3 years, this website helps me spend my vacation as a local person, gain some fantastic experience!
To better explore its rental listings across New York City, I designed this app to answer some questions: How many of the listings are for an entire home versus a room in an apartment? How many are controlled by the same host? Why is tax Issue serious in Airbnb NYC? Should you think twice before trusting a review?
Click here to play: http://216.230.228.88:3838/bootcamp004_project/Project2-Shiny/amyma/
Data Source
Data source: Inside Airbnb
Description: Original dataset was compiled by on 09/01/2015, containing two datasets: 1. Listings (35,957 locations and 24,426 hosts); 2. reviews (366,453 reviews).
What can we learn from the app?
How many of the listings are for an entire home versus a room in an apartment?
There are 35,957 locations and 53.6% of them are entire homes/apartments. Most of the listings are in Manhattan.
How many are controlled by the same host?
Almost 20% listings are controlled by 7% of the host.
Why is tax Issue serious in Airbnb NYC?
At the second part of Listings, Neighborhoods, and Hosts, the app would show you the top n super hosts in NYC. The table shows that most of the super hosts are not local, some of them are not even a person.
For instance, Flatbook is a company to combine hotel and Airbnb together. That's why tax issue is really serious in Airbnb NYC. People are not just sharing their places, they actually rent entire homes/apartments this as a business.
Should you think twice before trusting a review?
Yes, be careful with those reviews contains great, nice and recommend! Based on the word cloud of reviews, people use great as frequent as stay! There are three possible reasons:
Why people tend to leave positive reviews? There are three possible reasons: 1. It's awkward to leave a negative one. 2. They are afraid of the hosts would leave a negative too. 3. Most of the Airbnb experience is really enjoyable.
Any suggestions for Airbnb and hosts?
1. To Airbnb, I would suggest them to send review reminder on Saturday, Sunday, and Monday. Do not send any emails during weekdays. Based on the plot, people tend to leave reviews after holidays (such as 01/02, marked as A) and on weekends (marked as B).
This plot shows that people more likely to leave reviews on Mondays and Sundays; less likely to leave one on Wednesday and Thursday since they are busy at work.
2. To hosts, I would suggest hosts choose long term rentals in February and November since the fewest people are booking during those months
And based on the word cloud of reviews, highlight their location with some keywords: such as subway, clean, restaurant and neighborhood.
Beyond my question, the app can answer much more. To answer your questions on Airbnb in NYC, you can play with my app.
How to play?
Data of Airbnb Listings in NYC
To gain the basic information about Airbnb Listings in NYC, the first tab would map the whole listings.
Every circle on the map indicates one listing and different colors indicate different room types (red-Entire Home/Apt, blue- Private Room and green- Shared Room). Click on each circle to find out the basic information about this location.
To get basic information on the average price in the particular neighborhood, for example, to show the average price in Manhattan, we can choose "Manhattan" on the left panel.
The right panel and map will change based on your input. In this example, I’ve selected “Manhattan".
Listings, Neighborhoods, and Hosts
The next tab shows some details about the Airbnb Listings:
To change the graphs, you can simply click the button "Make a change?"
Data on Review by Time
The third and fourth tab would help you discover the review related to Airbnb listing in NYC.
To help users better see patterns, trends for example, in the Number of Reviews Over Time plot, there are two methods:
Change the number in the text box at the bottom-left of the plot, which would average the number of reviews in specified number of days. For instance, changing the number into 7, would show a smoother trend:
Go directly to the bottom plot, and show data by specified time period
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Word Cloud-- Data Reviews
Thanks for reading
Thanks for reading, I hope you found this post and my app interesting. I want to improve this app to supply more information so if you have any suggestions please feel free to leave a comment or contact me directly.
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