BW #56: Rent increases

Apologies for the delay in publishing this week's BW questions. Between finishing up Pandas Workout, a lot of corporate training, and preparing for a family vacation, it took a bit longer to get this out than I had hoped.

I read an article in Sunday's Washington Post about how inflation in the US is higher than expected, in no small part because rent remains high — but, as the headline said, "there's something off in the data." (You can read the article at https://wapo.st/3SXVXEM .) Rent in the US seems to be declining by nearly every measure, except for the official numbers reported by the Bureau of Labor Statistics. According to the article, BLS numbers show that housing prices have risen, not fallen.

The article cites data from Apartment List, an online real-estate agency. I immediately went to https://ApartmentList.com, and found that they make their rental-survey data available to the public.

This week, we'll thus look at the housing data produced by Apartment List. Among other things, we can try to see whether rental rates, according to their estimates, have risen or fallen — and by how much.

Data and six questions

The data itself is downloadable as a CSV file from

https://www.apartmentlist.com/research/category/data-rent-estimates

Scroll to the bottom of the screen, and you'll be able to choose a research report to download. We'll use the "historic rent estimates, January 2017 - present.

Here are my six tasks and questions for this week. The learning goals include manipulating data frames, pivot tables, grouping, and plotting.

I'll be back on Thursday with my detailed solutions, including the Jupyter notebook I used to solve these:

• Read the rent estimates into a data frame. Create a line plot showing the estimated rent in each month at the national level, with a separate line for each number of bedrooms (1, 2, and any).
• Calculate, at a national level, the percentage change in rent from month to month. Create a line plot showing that percentage change.