What you can learn from Shitrentals.org 💩 using ChatGPT

"Your most unhappy customers are your greatest source of learning." - Bill Gates

I’ve recently seen a bit of chatter in the mainstream media and on socials about a website called shitrentals.org.

If you’re unfamiliar with the website, it’s by Jordan van den Berg, a lawyer, social media influencer and tenant advocate, who says he is aiming to level the playing field between renters and landlords.

Through the site, he wants to give renters insights into landlords, property managers, and potential rental properties, the purpose being to enable renters to make well-informed choices.

It works by allowing tenants to submit reviews about landlords, property managers, and rentals via Google form, and once approved, they are uploaded to a publicly available Google Sheet.

I’m not here to debate the validity of the idea, the website, or the legals.

It does, however, occur to me that there might be a thing or two to be learned from the data, given that tenants can be a great source of referrals into your business - which, if you get it right, means growth.

Turning lemons 🍋 into lemonade

Some of you who have been around here for a while might have seen me do an analysis of some mock customer gauge data using ChatGPT’s code interpreter.

Code interpreter is now called “Advanced Data Analysis” - same tool, different name.

But here is an opportunity to use real data, so I’m going to redo this tutorial to see what we can learn.

There are a plethora of questions you could ask the data; here are some I asked in this video:

  1. I want to find out what the data says about what tenants really want.

  2. Can you give me by state the 1-star ratings?

  3. What are the most common complaints about properties?

  4. What type of repairs are we talking about here?

  5. Are private landlords receiving better or worse reviews than agencies?

  6. Is the dissatisfaction mostly financial (rent, bond) or operational (maintenance, communication)?

  7. Based on the data, what are the top 10 tenant grievances, and how might I fix them?

  8. For the agencies that got high ratings, what were some of the things they did right?

  9. Could you please give me a list of all the questions I asked (obviously, so I could put them here for you guys!)

Of course, this is non-specific data - you would likely get a way better result using your own data and pinpointing your own areas of improvement.

Happy Hunting 🚀