Sectioning Airbnb Neighborhoods in Tokyo: Survey the Surroundings
By: Fuaddi Yustindra
Tags: Clustering, Travel, Tokyo, Geospatial, Airbnb

Preface

Despite rising tension with the government authority in recent years, Tokyo has been a growing market for property owners renting out their space for the public; 民泊 or minpaku as they would call in Japanese. Airbnb, just as in other major cities, is among the popular platforms to find one, especially for foreigners who possess little or no local language understanding.

When arriving at Airbnb, people tend to wonder what are some good spots nearby. Hidden underrated sushi bars, authentic local ramen stalls, or maybe those ‘grammable Japanesque cafes are waiting to be discovered. Quick Google search will do, but what if we could explore what kind of places are there in which area before even booking the place? Or find out areas with similar characteristics but cost less? We probably could save more time on deciding where to go and even plan our trip better. This light project aims to answer that question. It’s not only targeting travelers, but also might be useful for those interested in running the lodging or related business around the proximity, or even for people who consider living in the world’s most populous metropolitan city and wonder what the neighborhoods look like.

Prepping the Data

Inside Airbnb has one of the most updated databases for any Airbnb cities in the world, and it’s free; thanks to their scraping tool that managed to extract texts from the relatively complex HTML structure of the Airbnb webpage. After grabbing only the necessary data for the analysis, the data consisting of 13367 listings (as of November 2019) would look like this:

Dataset 1: Tokyo Airbnb listings

Then I aggregated the data to find out the number of listings, average price, and latitude/longitude coordinate of each neighborhood. To add some more spice, I did a quick BeautifulSoup scraping on the Special Wards of Tokyo Wikipedia page containing population and area data of the city’s administrative 23 wards.

Dataset 2: Aggregated attributes for each neighborhood

Subsequently, based on the coordinates, I retrieved a maximum of 100 recommended places for each area within walking distance radius (less than 1km) using Foursquare API. There might be some areas that are less concentrated than the others, therefore having fewer spots surrounding. I found it interesting to see the obtained results as I didn’t know some quite nice places around despite living within the area for years now. Displayed below are the first few rows of 2216 records collected. We would look up more on the venue category to have a glance at the surroundings and answer the project question. So let’s get going to further analysis.

Dataset 3: List of recommended spots in each vicinity

Inspecting the Neighborhoods

Brief Check

The chart below sums up all the information and gives us a glimpse of the comparison between the wards. I am using Plotly for the visualization, so feel free to hover around.

Note: The size of the circle indicates the number of population. The x-axis is log-scaled.

Overall, Tokyo listings cost 12k~15k JPY ($110~140) per night on average. It’s relatively equal or lower than average in other world’s major cities but it’s been actually getting pricier since last year due to rising demand ahead of Olympics Games this summer.

Area and population seem to be well-correlated as Nerima, Adachi, Edogawa, and Ota are in roughly equal size and color. It’s also interesting to see Bunkyo as the most expensive ward although it sits at bottom 3 for the number of listings. Besides, a spike on Edogawa price despite being on par in terms of population and area with 2 cheapest regions, is probably due to its easy access to Tokyo Disney Resort as well as to the city center. 5 outliers are easily identified for having ±1000~ listings; there seems to be an apparent reason for that which we’ll see in the next few lines.