Data Analysis : Analyzing AirBnB listings data

As part of my Udacity Data Science course, I’ll be answering some questions about this AirBnB data for Seattle, WA, USA.

First question: Are listings similar for each weekday and month in the data?

I’ve generated bar charts to represent the number of listings for each day (Fig. 2) and each month (Fig. 3) below.

Fig. 2 show listings per weekday are similar at about 200,000 and Fig. 3 shows number of listings is also similar for each month with only slight deviations.

Next question: Are listings filled at similar rates on each weekday?

I’ll define the variable Utilization as the percentage of filled listings vs the total number of listings.

Fig. 4 below shows a plot of each weekday by utilizations on those days. This shows that listings overall in a year get filled at similar rate on each weekday.

Final question, are listings filled equally per month in a year?

Fig. 5 below shows the result of similar utilization calculations for each month.

This shows listings in January are filled at a higher rate of over 40% compared to other months in a year. Also, you can see the gradual decline in utilization from July to December.

In conclusion, this data suggests that in Seattle, WA, January is probably the most profitable month for AirBnB owners.

Please follow this link to the project GitHub