A widening technology divide between lenders and the rest of the commercial real estate industry is becoming harder to ignore—and harder to justify—as market pressures mount.
While billions of dollars in PropTech investment have transformed how investors, operators and brokers source and manage deals, much of the lending ecosystem remains rooted in manual processes. That imbalance is now coming into sharper focus as lenders face mounting time constraints and a wave of maturing debt.
"PropTech has poured billions into tools for investors, operators, and brokers," Vijay Mehra, CEO of LenderBox.ai, tells GlobeSt.com. "The lending side, community banks, regional lenders, and private credit, is still largely running on Excel and Word."
According to Mehra, the growing volume of loans coming due is exposing inefficiencies that lenders have long managed to work around.
"The large maturity wall facing the industry is forcing that gap into the open, and the institutions that close it first will have a structural advantage that compounds," he says.
Mehra's perspective is shaped by years spent building deal-management platforms for brokerages, investors and REITs—segments of the industry that have been quicker to adopt new technologies.
"I sort of had firsthand experience in selling SaaS [or software as a service] software into a lot of these organizations where the appetite was quite big," he says.
"A lot of these folks would see what their competitors were doing across the way, and they were able to make decisions very quickly and say, 'Hey, let's move and adopt this because they wanted to stay competitive in the market.'"
Lenders' business models are shaped by regulatory oversight and risk management structures that make rapid adoption far more difficult. Institutions typically have leadership roles focused on compliance and risk and banks in particular must answer to federal regulators such as the Federal Deposit Insurance Corporation and the Office of the Comptroller of the Currency.
"And so now you've got extra scrutiny to ensure that you're staying within guidelines," Mehra says.
That scrutiny reinforces a cautious approach to change.
"By nature of the organization and how it's architected, they're going to be risk-averse," he adds. "When you introduce new technologies into a lending organization, you're already introducing another element of risk, as opposed to what's worked for so long."
Even when lenders do adopt new tools, regulatory shifts can quickly complicate implementation, requiring ongoing updates to ensure compliance. Technology providers also face a balancing act as they integrate generative AI into underwriting workflows. Systems must be powerful enough to improve efficiency but constrained enough to avoid the hallucination issues associated with broader AI models like ChatGPT or Claude.
Still, standing is becoming less of an option. As deal volume increases and timelines compress, lenders face growing pressure to process more opportunities with limited resources. The inability to efficiently evaluate deals not only slows decision-making but also constrains potential returns, making the cost of inaction increasingly difficult to ignore.
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