Week 2: Cracking down the Basics in Real Estate Data

This week I started to understand real estate data much more as a real estate professional. In the office, I saw agents taking action, handling property listings, comparing prices, and determining if something is a good deal.

I learned about things like price per square foot, rental income potential, and neighborhood trends. These are all important when deciding if a property is worth investing in. I also started thinking about how I can use AI to analyze these factors faster.

At the same time, the market has been one thing that I didn’t expect to be so unpredictable and volatile. When evaluating deals, some opportunities may initially seem promising, but there is often a lot of uncertainty and risk involved. Even when something looks like a good deal on paper, there is always a question of how risky it actually is. This made me realize that AI will not replace human involvement in these decisions. It may not replace human decision-making, but it can definitely support it.

I also started to experiment with AI tools and asked simple questions like “What are some of the qualities of a good real estate investment?” and “What are other aspects of property value?” This helped me think about how to design stronger prompts in the future and how to improve them over time. Overall, I feel like I am making progress. I’m starting to connect the real-world work I see in the office with the technology side of my project.