The Unknown Unknown in Commercial Real Estate
Introduction by: Miles Kirkpatrick, CRO for SITE Technologies
In my role as the Chief Revenue Officer for SITE Technologies, I have daily conversations with commercial real estate (CRE) owners, operators, and portfolio managers usually centered around a singular, relentless pressure: how to protect asset value in an increasingly volatile market. From the rising costs of labor, materials and insurance to fluctuating interest rates, volatility is the new norm. We commissioned this comprehensive research study to put hard numbers behind those conversations.
What the data revealed is both a stark validation of the daily realities on the ground and a wake-up call for an industry at a technological crossroads. My three biggest takeaways from the results are:
1. Validation from the field: There is a price to guessing
Every day, our team hears the same frustration from operators: they simply do not possess an objective, standardized method to measure and compare the physical condition of assets across a geographically dispersed portfolio. Without a reliable baseline, capital allocation meetings revert to informed guessing or allocating capital to the loudest voices in the room.
The survey data validates this completely, revealing that nearly one-third of asset managers regularly make significant capital decisions based on gut instinct or informal assessments. This visibility deficit isn’t just an operational headache—it is a direct hit to investment performance.
When you guess, you inevitably fall into a cycle of reactive, emergency spending that disrupts forecasting, inflates remediation costs by up to three to seven times, and drastically erodes asset-level Internal Rate of Return (IRR).
2. Practical uses of AI are hard to find
Over the last year, the buzz surrounding Artificial Intelligence in commercial real estate has been virtually inescapable. However, to this point, that conversation has been largely restricted to Large Language Models (LLMs) used for abstracting dense lease documents or automating tenant communications/requests. While valuable, these applications only scratch the surface of technology’s true potential in the built world.
There has been a noticeable silence regarding the power of computer vision, machine learning, and predictive AI models to evaluate physical damage objectively, continuously, and with a level of precision that far surpasses manual human inspection. It is my sincere hope that this white paper shines a much-needed light on this highly practical frontier. AI shouldn’t just summarize your paperwork; it should be used to inject absolute objectivity and operational efficiency into what has historically been a highly subjective, slow, and fragmented engineering process.
3. The Great Contradiction: The “Unknown Unknown” of CapEx Allocation
Perhaps the most fascinating—and telling—finding in the entire study is a glaring contradiction in how operators perceive their own readiness.
On one hand, 29% of respondents admit to regularly relying on gut instinct for capital planning , 69% have suffered inflated costs due to deferred maintenance, and 28% report that more than a quarter of their annual CapEx goes toward unplanned or emergency events.
On the other hand, the vast majority of these same respondents simultaneously expressed confidence that they possess a solid, reliable five-year CapEx projection across their portfolios.
This disconnect proves that inefficient CapEx allocation is an unknown unknown. Operators endure all the symptoms of a systemic problem—surprise repairs and budget overruns—yet fail to realize the root cause. According to the research, 33% were very confident, 48% were confident, and the remaining 19% were neutral or unconfident in their level of accuracy for their organization’s five-year maintenance or capital plan. They trust their five-year plans simply because the spreadsheets exist, blind to the fact that they are built on outdated foundations.
The Path Forward
The financial delta between operating reactively and managing capital through data-driven stewardship is worth millions of dollars in total portfolio value. The data infrastructure required to close this intelligence gap and transition from “gut instinct” capital allocation to predictive forecasting is no longer a futuristic concept—it is available right now.
I invite you to read through the following pages not just as a statistical reflection of the industry, but as a roadmap to fundamentally transforming how your organization collects, interprets, and acts on asset intelligence.
