Week 3: Building My AI Strategy for Real Estate Opportunity Zones

This week, I began introductory research in earnest on my project centered on the utilization of artificial intelligence in identifying real estate investment opportunities. In particular, my area of interest will focus on determining the best lots in Opportunity Zones in Brownsville, Canarsie, and Sunset Park on which to build. I believe there is a real chance here to build a model that will get a jump on the competition because AI technology is relatively new and rapidly evolving, and many real estate firms aren’t studying these minute-by-minute advancements.

As I’m especially interested in areas that have not seen development in a long time, which might represent investment opportunities that have gone overlooked, I spent most of my time working on my methodology for collecting the data I plan to feed into an AI model designed to analyze patterns and evaluate possible investments. To do this, I began poring over available public records, permits, zoning, and census data. Principally, I collected data throughout Opportunity Zones in these three neighborhoods, where federal tax incentives are meant to encourage development in historically underinvested areas.

On Thursday, I spent time with Sam, the owner of the office I’ve been working with. What stood out to me in our conversations was how he stressed the human aspect that still prevails in real estate despite the increase in AI tools. There are always intangible factors to be taken into consideration, he pointed out, like neighborhood perception, informal knowledge, and even gut instinct. I’m going to have to figure out how to account for these as I iron out my approach.

I also discovered during data collection this week that the data itself is quite fragmented. It is much more inconsistent on a block-by-block basis than I hoped, even though I was well aware of this possibility, because permit records, zoning maps, and census data are often organized using inconsistent naming conventions and even geographic units.