How much can one make with Airbnb in Bristol, UK: an exploratory analysis
By: Lucas Machado Pontes
Tags: Python, Data Science, Exploratory Data Analysis, Airbnb

Airbnb has changed the way we travel, with more affordable values for guests, who can know real local customs while choosing the most appropriate option. Hosts also benefit from an increase in their revenues.

In July 2019 I visited the city of Bristol, UK. Bristol is the largest economic, cultural and educational center of southwest England. Historically the city’s development was linked to seaports, and more recently the economy has relied more on electronic and aerospace media creation, and the docks and ports in the city center have become historical and cultural heritage (https://pt.wikipedia.org/wiki/Bristol). This trip made me excited to analyze some data about Airbnb in Bristol.

Airbnb makes listing, reviews and calendar data available to download (http://insideairbnb.com/get-the-data.html) which is an interesting source to use in data science toy projects. The data was used to try to answer relevant business questions. The data was cleared using the pandas library on python. Columns with more than 60% missing data have been removed, categorical data has been transformed into dummy variables. Further details can be found on https://github.com/LPontes/Udacity_DSND.

Estimating income per month

Airbnb guests may leave a review after their stay, and these can be used as an indicator of Airbnb activity. A conservative estimate of monthly earnings can be obtained by multiplying the number of monthly reviews, minimum nights and price. However, the review rate is estimated to be between 50 e 72% (http://insideairbnb.com/about.html#disclaimers).

Figure 1. Histogram for minimum income per month, estimated with price, number of reviews and minimum nights.

Figure 1. Histogram for minimum income per month, estimated with price, number of reviews and minimum nights.

Figure 1 presents the histogram for the estimated income per month that is less than £ 2,000.00. It shows a right-skewed distribution, which means that there are much more properties with a small income and a few with higher income. The mean and maximum income per month is £ 236.58 and £ 4950.0 respectively.

Another interesting question is about the seasonality of Airbnb activity, which can be seen in Figure 2.

Figure 2. Number of reviews per month

From Figure 2 it is possible to observe that between December and February the number of reviews is lower than the other months, that is a good indicator of the Airbnb activity. This activity pattern should be related to the winter season.

Which neighborhoods have the highest income?

In Figure 3 we can observe the estimated income per month for each neighborhood.

Figure 3. The income per month for each neighborhood in Bristol, UK.

The neighborhood with the higher income is Hotwells and Harbourside which have a relatively high price (£ 92.7) and a reviews rate of 2 per month. On the other hand, Hartcliffe & Withywood has lower-income.

Figure 4, generated with the folium package on python, shows the heatmap of income per month, where red colors indicate higher income while blue means lower income.

Figure 4. Heatmap of income per month in Bristol, UK.

Figure 4. Heatmap of income per month in Bristol, UK.

Which features indicate greater income?

Besides neighborhood, which others can be related to income? This is an important question to identify the reasons that determine income amount, and to support feature selection to be used on machine learning models.

Initially I had considered using spearman correlation score, however, some of the dataset variables are categorical. Therefore, for categorical data bar graphs will be used and for numerical variables correlation indices.

As categorical variables we will consider the rent and property type, and if the host is a super host.

Airbnb enables two main types of rent: the entire home and private room. This is the main characteristic that defines the income per month, i.e. entire home type provides higher income (Figure 5).

Figure 5. The mean income per month for each room type.

Another relevant categorical feature is the property type (Figure 6).

Figure 6. The income per month by property type.

Super hosts seem to have a higher income than non super hosts (Figure 7).

Figure 7. The income per month for superhosts and non superhosts.

Amenities are another feature that is interesting to analyze, however, it will be addressed in a next post where I will use machine learning models that enables feature selection, such as random forest.

For numerical variables, it was used a log transformation to normalize the data and then the Spearman correlation score was calculated (Figure 7).

Figure 7. Spearman correlation for the normalized numerical features and income per month.

It is also possible to visualize the correlation between each numerical feature by using a matrix correlation heatmap (Figure 8).

Figure 8.

In this way, it is possible to conclude that the main features that are related to income per month are the rent type (entire apartment or private room), the property type, if the host is a superhost, the number os beds and bedrooms.

Are you an Airbnb host at Bristol? If so this information is accurate? How it can help you to improve your income?