How to find the best place to stay in Seattle ?

Data-driven insights of Airbnb Data in Seattle


Being a avid traveller myself , I understand how taxing it can be to figure out the right location / property of your stay at your destination specially when you wish to chill or de-stress

Planning a trip must be a cake walk and engaging like this

and not a puzzle like this


Airbnb is an online marketplace that connects people who want to rent out their homes with people who are looking for accommodations in that locale. It currently covers more than 100,000 cities and 220 countries worldwide. The company’s name comes from “air mattress B&B.”

Airbnb hosts list many different kinds of properties — single rooms, a suite of rooms, apartments, moored yachts, houseboats, entire houses, even a castle — on the Airbnb website.

In this article, we will analyze Seattle Airbnb data posted on (

The data consists of 3 files

  • Listings, including full descriptions and average review score
  • Reviews, including unique id for each reviewer and detailed comments
  • Calendar, including listing id and the price and availability for that day

After reviewing the data I was intrigued to find out few things from the data

Time to Visit

  • What are the busiest times of the year to visit Seattle?
  • During what part of year do hosts signups the most?

Property / Room Types

  • What kind of property types are most popular in Seattle area?
  • What kind of room types are more popular in Seattle area?


  • Where in Seattle are the most hosts and super hosts (experienced hosts) located?

Part 1 : Best time to visit Seattle

I would like to know what is the best time to visit Seattle , so this can be derived from calendar file , where we a month wise analysis of availability of property

Next we also look at average price of listings month wise from the calendar file

We also look at the trend of number of hosts that signup month wise from the listing file

listed_month count
December 208
February 271
January 271
April 278
November 293
June 296
March 299
October 313
September 330
May 352
July 415
August 490
Name: listed_month, dtype: int64

Combining the 3 above observations , we conclude -

  1. demand is high during the month of June , July , August and so are the prices of the listing
  2. Maximum no of hosts signups , during the month of July,August

Hence month of July , August looks like the best time to visit Seattle

Part 2: Property / Room types

Next would like to explore what are the most prevalent property types in Seattle from listings file

We see that there are 16 property types out of which House & Apartment constitues about 90% of the listings

We also looked at the room types being rented and observed that ~65% of hosts are renting out the entire home/apt which tells us most of the hosts have a rental property where as about 35% of hosts either rent out a private room in the property they live in or share the property with the guests.

Part 3 : Host Trends

I would like to know which locations have the most number hosts and super-hosts. As you can see below % of super-hosts compared to hosts are pretty low. Broadway neighborhood has by far the most number of hosts and super-hosts. There are few neighborhoods like university districts and minor where there are no super-hosts, there are chances that hosts in these locations are part-time renters or just started. There are few places where all hosts are super-hosts.


In this article we took a look Seattle Airbnb data and analyzed 3 aspects: best time to visit , property types , host trends

  1. Best time to visit Seattle is July , August when you have max host signups , high availability but high prices of listing
  2. House and Apartment are the most prominent property types which account for ~90% of listings. 65% of the hosts rent out the whole place compared to 35% of hosts who rent out a room or share the room they live in.
  3. North of Seattle is the most popular location. These locations has the most number of hosts.

The findings here are observational and not a results for formal study.

To see more about this analysis, see the link to my GitHub :




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ankit kohli

ankit kohli

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