names Tim Gamble a CRE Future Leader

Posted on
May 22, 2023

At 25 years old, HelloData Co-Founder and Head of Machine Learning Tim Gamble has already surpassed the career aspirations of many. As Enodo‘s first employee, Gamble earned recognition as ChicagoIand’s 25 under 25, and after Enodo was acquired by Walker & Dunlop in 2019, he advanced from data engineer to vice president of data engineering—and is now focused on his own venture,

In a recent Q&A, Gamble discussed his career thus far and the role of artificial intelligence within commercial real estate.

What sparked your interest in real estate and AI technology?

Gamble: Let’s rewind to when I was introduced to Marc Rutzen, one of my now co-founders at In my very first class at DePaul University, a professor connected me with him regarding a data engineering internship opportunity.

My interest in real estate and AI was immediately sparked when I realized just how much data existed on each real estate property across the country. I’ll never forget wrapping up my first data pipeline project, which processed the data on hundreds of millions of properties. There was so much information being processed—and I knew that, at this scale, the only way to pull out insights would be with AI.

During the internship, I enjoyed building a predictive analytics platform for the real estate space. Our goal with that platform was to streamline the underwriting process for real estate. But over time and working with clients, we found that extracting information from documents was very useful, such as pulling rent and occupancy data from rent rolls, or extracting financials from operating statements. I became the first employee at Enodo, and that’s where I met Nicolas Lassaux.

After the company’s acquisition, we continued to build on the technology, ultimately developing software that processed tens of thousands of documents a year. We primarily focused on improving operational efficiencies, working closely with underwriters and producers.

It continues to amaze me how quickly the AI field is moving. It makes it all the more exciting to see how the latest and greatest in AI can be used to help real estate companies operate more efficiently.

Today, I enjoy developing products that use both artificial intelligence and computer vision. For example, we know a picture is worth a thousand words, and with our product, clients are able to extract amenities, room type, curb appeal and interior quality of real estate in seconds by analyzing property photos. So, analysts and investors are able to do what they do best—look at more properties that fit their investment criteria each day rather than spending hours manually entering data from one place to another. There are other uses for our product, too, from virtual inspections and appraisals all the way to incorporating the score into valuation models to better understand the value of certain properties, which was a hard thing to be able to capture from just static data that you can find on properties online.

Another trend in our tech is meeting people where they are. Instead of building another platform with another login, all of’s products meet clients where they work, integrating with CRMs, email, and existing workflows, too.

How do you see commercial real estate evolving in the next 5–10 years, and how do you anticipate, or AI in general, playing a role in that evolution?

Gamble: Now that the appetite for AI has become much larger thanks primarily to ChatGPT, I believe AI will become the focus of most real estate companies over the next 5-10 years. Ten years from now, I would say all real estate companies will have AI incorporated into their workflows in some capacity.

To give a few ideas on where I think AI might pop up:

  1. Assisting in finding the best deals in target markets so the company can target their efforts on deals they are likely to win;
  2. Extracting valuable information from due diligence documents to help catch issues before they become a problem;
  3. All of the data that was previously locked in PDFs will be extracted and stored in databases ready to provide insights previously impossible to accomplish, something is working on right now;
  4. Improving comparable property analysis, using more than just year built, number units, EGI, and distance. With the work has been doing with image extraction and, companies can start using interior images to detect comparable properties. Similar properties may start being suggested in different states if the algorithm is able to perfectly understand the market conditions of both areas.

These are just a few areas where I see AI assisting the real estate industry, and is positioning itself as the company that is taking these new technologies and finding ways to incorporate them into real estate companies.

I think about how the residential real estate space is moving faster in these areas and as technology becomes more of a competitive advantage, it won’t be unthinkable for commercial real estate professionals to underwrite deals in minutes. I imagine 10 years from now it will be commonplace to enter all of your deal terms and upload all of your due diligence documents for AI systems to double-check, streamlining the process of getting financing very quickly.

What resources do you rely on to stay informed and up to date with the latest developments in both real estate and AI technology?

Gamble: I stay up to date primarily through the use of online communities and watching creators on YouTube. There are a number of industry-specific subreddits with active communities posting relevant information every day. I typically scroll through these daily and see if anything jumps out at me. I’ve also watched a lot of programming-specific YouTube videos over the years, so YouTube has started to suggest similar videos. It’s been interesting to see what is suggested since these are typically trending topics around software engineering.

What advice would you give to aspiring entrepreneurs who are interested in starting a similar venture, based on your own experiences?

Gamble: My advice would be to get started now and keep it simple. If you solve a simple problem well enough, people will want to work with you to make it better. If people are willing to help in its early stages, you know you are onto something and can continue to build solutions that will solve the entire problem.

Then, reach out to people on LinkedIn that you think would find value in the product you are building and set up calls with them. You would be surprised by how many people are willing to talk—just ask them. Ask them to make introductions and use what you learn in each call to build on your expertise. After a few rounds of this, you will have a great group of people to bounce ideas off of. These will be your early adopters, and they will help guide the direction of your product.

Don’t be afraid to pivot your original idea. It’s possible your initial hypothesis was enough to have conversations with these people, but maybe it doesn’t solve their biggest problem. Focus on the area where you can provide the most value.

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