Where is the data from?

The data was scraped from Immoscout24, the biggest real estate platform in Germany. Immoscout24 has listings for both rental properties and homes for sale, however, the data only contains offers for rental properties.
The scraping process is described in this blog post and the corresponding code for scraping and minimal processing afterwards can be found in this Github repo.
At a given time, all available offers were scraped from the site and saved. This process was repeated three times, so the data set contains offers from the dates 2018-09-22, 2019-05-10 and 2019-10-08.

Content

The data set contains most of the important properties, such as living area size, the rent, both base rent as well as total rent (if applicable), the location (street and house number, if available, ZIP code and state), type of energy etc. It also has two variables containing longer free text descriptions: description with a text describing the offer and facilities describing all available facilities, newest renovation etc. The date column was added to give the time of scraping.

Inspiration

Did rents increase over time? Which areas are the most expensive? Which areas saw the largest increase, which areas became cheaper? Are there any duplicates? How many? What could be gained from a text analysis of the free text variables?