BW #39: WeWork
WeWork, one of the best-known startup stories from the last few years, has declared bankruptcy. This week, we look at some data about WeWork, the stock market, and commercial real estate.
In August of 2019, I received e-mail from a WeWork manager in Tel Aviv. His team was starting to use Python for data analysis, and that they were interested in a course in Python and Pandas. I said "yes," sent them a price quote, and didn't hear back from them. Which wasn't a huge surprise, because within weeks of my submitting a price quote, everyone in the business world was talking about how WeWork had pulled their plans for an IPO, now that everyone saw that the company was wildly unprofitable. (This is a good time to remind you that correlation is not causation; I don't think that my price quote was the final nail in WeWork's financial coffin.)
WeWork has been the subject of numerous books (https://www.amazon.com/Cult-We-Neumann-Startup-Delusion-ebook/dp/B08FHC77MT), podcasts (https://wondery.com/shows/we-crashed/), and articles, all of which point to huge investments, bad management, and a so-so business model. It's really quite wild to hear about this company, and how much misplaced faith the investors put in Adam Neumann, the founder and CEO. You can read a version of the history on Wikipedia (https://en.wikipedia.org/wiki/WeWork), but it's really more fun to learn about it elsewhere.
After Neumann left the company, WeWork got another chance. It even went public, thanks to a SPAC ("special-purpose acquisition company," https://en.wikipedia.org/wiki/Special-purpose_acquisition_company). But all sorts of things, including the depressed need for commercial office space, gave them additional trouble, and as of a few days ago, WeWork filed for bankruptcy. Their stock is no longer traded, and we'll see what, if anything, the company does in the future.
It's certainly bad news for WeWork's landlords, who might not get paid what or when they expected, as Axios wrote just earlier today: https://www.axios.com/2023/11/08/wework-bankruptcy-next-steps
This week, I thought that it would be fun to look at WeWork's stock price over its short period as a public company. I thought that it would also be particularly interesting to look at the S&P 500 stock index, as well as the state of the construction and office markets, and see if we can find any interesting or fun trends.
Data and seven questions
This week, we're going to read from four different data sources:
- WeWork stock information, which I downloaded from Yahoo Finance: I went to https://finance.yahoo.com/quote/WE/history, asked for the "max" time period, and then clicked on "download," which put downloaded a CSV file (WE.csv) to my computer.
- The S&P 500 index -- or actually, a proxy for it, since I cannot seem to download the S&P 500 directly from Yahoo. I went to https://finance.yahoo.com/quote/SPY/history, chose the "5 year" time period, clicked on "apply," and then downloaded the CSV file (SPY.csv) to my computer.
- From FRED, the amazing data portal at the St. Louis Fed, I downloaded data about spending on office construction in the US. The page is at https://fred.stlouisfed.org/series/TLOFCONS, and I retrieved the CSV file by clicking on "download" and choosing CSV. You could also decide to download the data directly via the URL.
- Also from FRED, I downloaded data about office rents in the US. The page for that data is at https://fred.stlouisfed.org/series/WPU43110101, and I (again) retrieved the CSV file by clicking on "download" and chosing CSV. You could, as before, also decide to download the data directly via the URL.
And now, here are my seven questions for this week. Learning goals include working with CSV files, plotting, working with time series, and joining data frames. As always, I'll be back tomorrow with my solutions, including the Jupyter notebook I used to solve the problems:
- Create a data frame for each of the four CSV files, parsing the date columns and using them as indexes.
- Get a new version of the WeWork data, in which each row represents the mean values from that month.