3 Questions and 1 Prediction for Seattle AirBnB
By: Torin Rettig
Tags: Exploratory Data Analysis, Airbnb, Regression, Data Science, Machine Learning

Photo by MILKOVÍ on Unsplash

With this project I set out to use the AirBnB property listing datasets for Seattle in 2016 to answer 3 questions and make 1 predictive machine learning model that could be beneficial for their business:

Question 1: What is the average overall occupancy for all properties in Seattle over the course of the year?

Question 2: Does it pay to be a Superhost? How do the occupancy, prices and reviews of Superhosts compare to normal hosts?

Question 3: What neighborhoods have the highest occupancy rates?

Prediction: Create a machine learning model to predict the average annual occupancy rate for a given property listing.

Below I will explain my findings for this project.

Visit the Github repo to see the data, code and notebooks used in the project.

3 Questions to Answer

The analyses of these questions are meant to be a starting place to inform deeper explorations of each topic, so we can gain further insights that we can apply to help guide the business.

Question 1: What is the overall occupancy in Seattle over the course of the year?

Are there periodic shifts in the overall AirBnB occupancy in Seattle over the course of the year and if so what does this look like? This can help the company decide when and how to run promotions of various kinds and to work with hosts to help them get the most out of these time frames.

Dates on the x-axis and average occupancy of all Seattle AirBnB listings on the y-axis

The trends here are fairly easy to understand. There are three distinct periods where we see a dramatic build up of reservations followed by a leveling off. We’ll take a closer look at each region.

New Year

New Year’s would appear to be the time we see the greatest spike in occupancy for AirBnB in Seattle, as occupancy never gets close to that level throughout the rest of the year.

Spring Break

Looking more closely at the March-April time frame we see a spike around the beginning of April that would seem to correspond to the spring break time frame, which makes sense.

Summer Vacation

Again we see an occupancy spike in the June-July time frame, which would correspond with school being out and summer vacations.

Holidays

This last trend is somewhat interesting. The dramatic spike at the end of the year corresponds to New Year’s, but there is a pretty dramatic drop before this spike, right after Christmas.

Question 2: Does it pay to be a Superhost? How do the occupancy, prices and reviews of Superhosts compare to normal hosts?

Superhost is a special title that is automatically applied to listings where the host maintains high marks in many areas and has an established positive trend with AirBnB overall.

I explored whether there is a correlation between being a Superhost and other metrics, such as occupancy and rental prices to get a better idea if the Superhost program is effective for AirBnB. Our findings here can potentially help guide changes and improvements to the program.

How do occupancy rates compare between normal hosts and Superhosts?

Interestingly there really doesn’t seem to be much of a difference between normal hosts and Superhosts in terms of their ability to maintain occupancy.

Occupancy rates are actually slightly higher for normal hosts, but only by about 1.3%. This is somewhat counterintuitive, as you would expect superhosts would be in higher demand.

This may call into question the efficacy of the Superhost program, but this is only one means of evaluation. Others may reveal advantages for Superhosts.

How does average rental price compare between host types?

Given that occupancy rates are roughly the same for both host types, maybe average price of rentals is different between them and this is an area where Superhosts gain an advantage.

Here we see a distinct advantage for Superhosts. While the Superhost rental rate is slightly lower than normal hosts (-1.3%), the mean prices for their listings is about 8.2% higher.

This alone might make striving for Superhost worth the effort. However, we should also look at the spread of the prices. There could be some really high outlier prices that are driving up the average price.

How does the distribution of prices for the two hosts’ listings differ?

This plot shows the shape of concentration of prices for hosts and Superhosts. These spreads look quite similar.

Box plot showing concentrations of prices and outliers as the circles extending from the boxes. The box plot graphic shows the middle 50% of prices (25%-75%) as the blue and green boxes with the red line as the 50% mark.

This is a numerical representation of what we see in the box plot above.

The occupancy rates of Superhosts and hosts are very close to each other, within about 1%, with the occupancy rate of normal hosts being slightly higher.

However, the average price of a Superhost listing is notably higher than that of a normal host, about 8% higher, as we saw before.

So in general, it does not look like unusually high prices for Superhost properties explain the overall higher price. It seems as though on average Superhosts do indeed make more per listing than normal hosts.

However, I would recommend looking deeper into this trend to see if it is indeed the Superhost title that makes the difference or some other factor.

Question 3: What neighborhoods have the highest occupancy rates?

Are there significant differences in occupancy rate by neighborhood? If we find differences we can look deeper into why that is and perhaps extract useful data on what makes a neighborhood more or less attractive. This information could be passed on to hosts to help them be more effective at attracting guests.

Here are the occupancy rates for each of the 81 neighborhoods:

Neighborhood name on the x-axis and annual occupancy rate on the y-axis

While most are clustered in the 20–40% range there are clear examples of much higher and much lower occupancy.

Highest Occupancy Neighborhoods

There’s not a standard way to define the “top occupancy” so we’ll look at the chart above see where there is a distinct spike for the top performers. This seems to be the spike to over 50% occupancy, which would be the top 5 neighborhoods. This also corresponds to around the 95th percentile of occupancy.

Price vs Occupancy

Pricing may be a factor in occupancy so it would be good to see what the relationship is between occupancy and mean price of a listing.

Occupancy rate on the x-axis and listing price on the y-axis

This plot strongly indicates there is no relationship between occupancy and listing price. The trend line is almost flat and the points look randomly scattered. If there was a relationship we would see a more dramatic upward diagonal line, indicating that price and occupancy increase together or a downward diagonal line, indicating that one increases as the other decreases.

Since the most obvious differentiator, price, does not seem to be a factor in occupancy in a particular neighborhood there are undoubtedly other factors at work. This initial analysis provides a good starting point for further exploring the other factors that may be at work with per-neighborhood occupancy.