This is how I used real estate data and powered them using OpenAI Large Language Model GPT3
Image made by author using DALL·E3
This article starts with a personal story.
I’m from Italy, and I have been living in the United States for 4 years now.
I moved fresh out of my university in Rome, and I came here when I was 23 years old. I have a lot of dreams, a huge passion (the one for artificial intelligence), and a doctorate to get at the University of Cincinnati.
I lived the majority of my life in Italy, eating good food, drinking good coffee, and sitting for good hours in traffic (to go literally anywhere) 🙃 . Nonetheless, I’ve “adulted” (my wife taught me this term) so much in the United States, as I started doing things I have never done in Italy by myself, my parents being an ocean away. One thing that me and my wife are doing together for the first time is house hunting.
For my non-American followers, “house hunting” is trivially the practice of searching for your perfect house. The term “hunting” is used because there are so many houses and so many realtors and websites you can look for that it’s more than just “searching” for your house; it’s an active hunt. It’s about questions like:
What is the best website? What is the best offer? Where is the best neighborhood? How much taxes do I pay? How high is my insurance?
In physics, we would call that an optimization problem, where all the properties of the house of your dreams want to be met (with some room for negotiation) but limiting the budget to a certain value of x dollars:
Image made by the author
The market is so big, full of opportunities, and customizable that real estate agents try to work with the clients to get them the house of their dreams. For this concept, the figure of real estate in the United States is also usually linked with the concept of a “go-getter,” thus highlighting how human qualities and soft skills are…
