Commercial Real Estate Should Stop Overlooking Qualitative Data

Quantitative data drives and informs decision-making in the multitrillion-dollar commercial property sector, but what ultimately decides the outcome of an investment lies in the vastitude of qualitative data.

The answers to a successful investment have never been limited to tangible, quantitative market or performance data, like rent or occupancy rates, assessed property values, comparable sales, or an asset’s projected return on investment or internal rate of return. 

Qualitative judgments can be elusive, but they dictate how a property is perceived and the fate of an investment. This data provides a lens into the past and present story of an area surrounding a property, and is invaluable to understanding how the life cycle of an investment may unfold. The stories told through this information can shape risk profiles and ultimately determine where capital is deployed and which markets get written off too quickly. 

Artificial intelligence is transforming how commercial real estate can gather this information, and market participants would do well to not overlook it. 

In New York, a developer can change the name or address of a building to capture an easy win. When landlords rebranded the likes of 708 Third Ave to 10 Grand Central or 1250 Broadway to NoMad Tower, they changed how the market perceived these properties and captured considerably higher rents.

Many smaller investors and developers across the country often don’t have the luxury of simply rebranding a building in such a way. In most cases, an investment is reliant on a bevy of qualitative factors that outline the inherent story behind a plot of land, a neighborhood or a submarket. Historically, finding and parsing such information has been arduous, if not inaccessible, and dependent on intuition, which adds to underwriting risk. It has also not been scalable. New technologies are changing that. 

Much of the data needed to confidently identify qualitative risk in a real estate investment is hidden in plain sight. It lives in historical news reports, environmental assessments, permit filings, local ordinances, transit plans, crime data, or the records of zoning board hearings, school board meetings or neighborhood forums. These factors can provide measures for current and future economic activity, neighborhood safety, transit accessibility or even school quality, among other things. 

While this information has traditionally been dismissed as soft sentiment because it couldn’t easily be dropped into a spreadsheet, advanced analytical tools can now parse these disparate records. It is finally becoming possible to turn evasive neighborhood signals into hard data that underwriters can actually test.

The consequences of failing to find and test these stories are clearest in markets where the narrative moves faster than the fundamentals. Austin and San Francisco offer two very different examples in recent years of what happens when investors let a narrative do too much of the underwriting.

In Austin, the pandemic created a powerful narrative around the city’s future growth, as corporate relocations, tech expansion, population gains and investor enthusiasm reinforced one another. But belief in that growth helped justify a wave of speculative office development the market could not absorb. Developers built nearly 14 million square feet of new Austin office space between 2020 and the end of 2025, pushing citywide vacancy to 29 percent and downtown vacancy to 32.4 percent.

San Francisco shows the other side of the same problem. Around 2022, the “urban doom loop” narrative became the dominant story about the city. Office vacancies were rising, retailers were leaving downtown, and public safety concerns made national headlines.

By 2025, the market revealed buying into those narratives meant missing out on opportunity. San Francisco posted its strongest year for office leasing since 2019, finishing last year with more than 1 million square feet of positive net absorption in the fourth quarter. While the city still has challenges, the narrative lagged the signals that actually drive value.

In Austin, too much supply chased a growth story, and, in San Francisco, real estate investors risked missing a recovery because the decline story seemed permanent. Both examples show how narratives can lead investors to make shortsighted decisions.

In short, real estate investors have had a tendency to underwrite based on vibe, sometimes overlooking the value of qualitative data, which can be the secret sauce that decides the success of an investment. 

But in a data-rich multitrillion-dollar industry like commercial real estate, should so much of that judgment still run on instinct?

Used carefully, AI can help turn softer, more elusive signals into something underwriters can test. It can scan public meetings for recurring concerns about safety or infrastructure, compare how often local businesses are opening or closing, track whether permit activity is clustering around a corridor, or identify when tenants and brokers are describing a district differently than they did six months earlier. That information gives underwriters a way to see whether the story around a neighborhood matches the underlying data.

San Francisco’s office market shows why that matters. The story investors often tell about office demand is relatively simple: the newest, highest-amenity buildings in the most established business districts will win. But some of today’s fastest-growing tenants are complicating that assumption. In San Francisco, AI startups are increasingly clustering in smaller, mixed-use neighborhoods and converted industrial spaces rather than defaulting to Financial District towers. LangChain and unitQ, for example, signed leases at 501 Second Street in SoMa, a former warehouse. 

Companies like these are looking for spaces that aren’t easy to sum up into a single narrative that traditional underwriting can capture. That kind of preference can look subjective until it starts changing demand. A converted warehouse may read as secondary space in a traditional model, while the tenants driving the next leasing cycle may see it as exactly the kind of place they want. 

There’s a story in every piece of real estate, and the industry has rewarded those who could read a block beyond the spreadsheet. The difference now is that more of those signals can be tested, compared and challenged before investors commit significant capital.

Steven Song is the founder and CEO of Diald AI, an agentic AI platform focused on automated underwriting and due diligence.