Marc Rutzen and his two co-founders put $5,000 apiece into HelloData in 2023. In April 2025 they sold the company to Grace Hill, and on the AI for CRE podcast this week, Rutzen said the sale came in at $70 million, roughly 10 times revenue at the time, with a team of 14 people serving about 30,000 users across its customers.
It was his second proptech exit, and both companies sold in under three years from start to finish. For the growing crowd of CRE professionals turning internal tools into products, his account runs against most of the standard assumptions about how this is supposed to work.
Rutzen is a proptech founder who came from the real estate side: Columbia’s MSRED program, affordable housing development in Chicago, then a data analytics seat at a brokerage. He co-founded Enodo in 2016, one of the first machine-learning platforms built to automate multifamily underwriting, and sold it to Walker & Dunlop in 2019, where he rose to chief product officer and built tools that cut underwriting times by more than 30 percent at one of the country’s largest CRE lenders. In 2023 he reunited with his original Enodo engineering team and started over, this time with no outside money.
The first version was a Google Sheet
HelloData launched selling raw multifamily data through APIs, which went badly, because enough real estate buyers responded with a version of the same question: what is an API? So the team exposed their data through a Google Sheet where you typed in an address, no validation, no interface. Rutzen calls it almost embarrassing how simple it was, and they charged $25 a month for it.
The sheet was the product for months. Customers sent back their BOV templates, their offering memo formats, and their Excel models, and the team worked backward from those finished deliverables, adding whatever the average of them required. The first real interface launched in late June 2023, built from the sheet’s feedback. The company didn’t make its first hire until April 2024, nearly a year in. By the sale, the full package ran about $700 a month, and the product the market wanted had been assembled from customer templates, one increment at a time.
Your ideal workflow is the biggest risk
Rutzen’s sharpest warning was for the operator with 20 years of experience and a perfect process. That process is what most CRE builders try to ship, and in his telling it fails on contact, because the prospect across the table has their own 20-year process and considers yours wrong on arrival.
His alternative: talk to 10, 20, 30 practitioners before building, find the common denominator that satisfies roughly 80 percent of how all of them work, and build that, with room to customize the rest. Customization by request, he warned, quietly eats margins, because you end up building a new product for everyone who comes in the door.
He would rather you didn’t raise
Rutzen has run the experiment both ways. Enodo raised $1.2 million out of the gate, hired early, bought data sets, and sold for what he described as close to $10 million all-in with the earnout. After paying investors back and splitting what remained among the founders, he called it far from a massive take for three years of work. In retrospect, he said, the three people who later became HelloData’s core team could have built Enodo’s MVP by themselves.
HelloData raised nothing, covered early payroll with consulting agreements the founders brought in, and was profitable from its first month. The constraint, he argued, was the advantage: when the only money is your own, every dollar gets interrogated, and his team validated before building because they could only afford to build once.
His verdict on the funding wave, verbatim: “I would venture to say most proptech companies that have been funded should never have gotten funding.” Many raised simply because rates were low and capital was easy, he said, and are struggling under the obligation now. His advice to builders was just as direct: do not raise unless it’s a genuinely winner-take-all market, like the AI platform race. “Validate the concept, build incrementally, and build a great product. And the market will spread it through word of mouth faster than you ever could.”
Learn enough code to be dangerous
For CRE professionals building with AI today, Rutzen’s baseline advice was to learn enough about how code works to supervise it. When something breaks, pleading with a chatbot to fix it goes nowhere without some grasp of what’s underneath. And the moment an internal tool becomes a product, a developer gets hired, and a founder who can’t specify architecture or a design library will, in his words, pay a lot and probably not get what they want.
He extended the same logic to the consulting wave. AI implementation, he said, is consulting with AI attached, the same army that once customized Salesforce. Much of what firms pay retainers for can be had by feeding a $20-a-month model your own workflows and asking it where AI fits. Try it yourself first, was his advice, and you’ll find you don’t need very much help.
The demo that closed on the spot
One story from the episode is worth retelling for anyone selling into this industry. Early on, a prospect cut Rutzen off mid-pitch: “You are the 12th AI for real estate company that’s demoed for me this week. What makes you special?” Rutzen pulled up the man’s own building in the platform, pointed at unit 101A, and asked whether that was the rent on the lease he’d signed a week earlier. It was, sourced entirely from public data. The prospect signed up during the call. The data closed him, and Rutzen’s read is that in a market this crowded, word of mouth and provable data are the only differentiators left standing.
Why he sold when he did
The decision came down to a small pool of buyers and compressing software multiples, what Rutzen calls the SaaS apocalypse. Some of HelloData’s own customers had written clauses into their contracts letting them walk if the company ever sold to certain large incumbents he declined to name, because they’d watched products they loved get acquired and die there. Grow much bigger, he reasoned, and even the acceptable buyers fall away, and he never wanted to IPO. At $70 million and 10x revenue, he took the deal, in a market he says now prices many software companies at low-single-digit EBITDA multiples.
Why Excel keeps winning
Asked why technology adoption lags in commercial real estate, Rutzen rejected the usual explanation that real estate people resist change. His answer was fragmentation: roughly 3,600 counties, each with its own zoning, taxes, and local practice, plus deal-level quirks no template anticipates. Every operator’s workflow is regional and personal, which is why the universal financial-modeling product keeps failing. His line: “The road to getting rid of Excel is littered with the carcasses of startups who failed trying to get there.”
Rutzen is now writing a book on the approach, working title The Risk-Free Startup, arguing that a company engineered to sell in under three years beats the raise-and-swing-big model for most founders. Both of his exits fit inside that window.
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