One thing attracted Charlie Clark to Marquette above every other school: applied learning.
“I work a lot better when I can take things into the real world,” Clark says. “Information sticks in my brain more after I do something with it, rather than just sitting in a lecture hall taking tests.”
Clark, a junior double majoring in real estate and Accelerating Ingenuity in Markets, has ample opportunity to use what he’s learned. He is working with faculty members and external partners to create an artificial intelligence-powered real estate sentiment index to help developers make better investment decisions. His AI prowess also scored him an invite to an exclusive summit at Columbia University in New York City, alongside top graduate students from around the country.
You are concentrating specifically on fintech in real estate. What led you to that choice?
I started out on the Chartered Financial Analyst track in the AIM program, but when I started working on the AI project, I realized that I could provide a lot more value to real estate firms if I had knowledge of how AI and tech work. It’d be a tighter niche than just being a real estate analyst.
Plus, fintech is a lot more applied — you get a lot more creativity. You’ll learn the basics, and then you’ll be able to apply them to solve real-world challenges.
What is this real estate sentiment index meant to measure? How would people in the field use it?
The idea is that we’re going to look at the top 50 cities in America by population and judge how the market is feeling about commercial real estate based on geography. We take a lot of news articles, group them by source and by asset class, then use the tone of the articles to create a sentiment graph that you can see over time.
Investors would be able to use this in pitch books when they’re trying to pitch a property to an investment committee. You’d be able to see which geographic areas and asset classes people are optimistic about and which ones people are more pessimistic about, then factor that into investment decisions.
You mentioned that you built the index with “no-code coding.” Explain what that means.
Before artificial intelligence, you really had to understand each language you wanted to code in at an advanced level to build something that worked. AI models work on the same idea as coding: you’re giving it instructions that will translate to a certain output. However, you are now doing it in English. You give it a goal, you tell it how you want each piece to go and then it will actually develop the code for you.
As someone who’s just getting into the real estate field, how do you anticipate using AI once you’ve graduated?
AI is automating a lot of underwriting, so if you look at a particular property, you can automate the generation of financials, internal rate of return, equity multiples — all the things that analysts used to spend a lot of time on. It’s so much faster than typing everything out yourself.
Artificial intelligence is great at acquiring, applying and storing knowledge, but it has a hard time creating knowledge. The business roles that are going to be in demand are going to be a lot more on the creative side.
Why is hands-on learning so important to you?
I think about it like racing. You can read about how cars work, you can read about the engineering behind them and how fast you should go on different parts of the track. That’s like classroom learning.
Then you have applied learning where you can get on a simulator and drive through the track. You develop the muscle memory of where to turn, where not to turn, when to hit the gas, when to hit the brake. I like pushing myself beyond my comfort zone to where I’m going to get real, instant feedback when I mess up.
I really like that Marquette is so big on applied learning, and it will be cool to see where it goes from here.
