Custom Data Requests Don’t Have to Stymie Commercial Real Estate Teams

An asset manager opens an operating report, an equity partner asks for a custom slice of debt service coverage across a regional portfolio, a lender wants a specific view of trailing-12 occupancy, factoring in signed leases not yet occupied.

Despite decades of commercial real estate software investment, fulfilling specific reporting requests remains an exhausting, manual bottleneck. Nearly half (49 percent) of enterprise chief financial officers cite process automation as their top operational priority, specifically to free teams from routine manual tasks so they can focus on strategic decision-making, according to research from Deloitte’s CFO Signals Survey. 

Plenty of data is stored and available across systems such as Yardi, MRI and Argus, as well as custom Excel models and static PDF loan agreements. The problem is that as portfolios scale and capital structures stack up, every stakeholder demands a different cut of the underlying numbers.

For years, enterprise software pitched a simple solution: Force every business unit into a single standardized dashboard. That playbook ignored how complex real estate platforms actually operate.

At a national enterprise operating across ownership, property management, brokerage and financing, the property management team defines “occupancy” by physical move-ins. Asset management views it through economic collections, while underwriting tracks committed square footage against debt covenants.

When software forces these disciplines into one rigid template, teams do what they’ve always done: They pull out raw numbers, build custom formulas in Excel, and manually adjust the output. What on the surface looks like a clean digital platform from the C-suite is usually held together by disconnected workbooks and copy-pasting. When an owner or lender asks a new question, an analyst spends three hours stitching together a one-off presentation.

The goal was never to trap every department inside the same reporting template, but to give teams an infrastructure where they can reliably build and modify workflows themselves without breaking governance or losing track of the underlying sources. That means that, instead of buying static software, operators are building context-aware layers beneath their systems.

Consider Friedman Real Estate, which operates across thousands of units and millions of commercial square feet. Managing multiple service lines means constant, distinct reporting demands from lenders, institutional partners and internal leadership.

Rather than layering another static dashboard over their stack, the company focused on establishing underlying AI infrastructure, which is connected directly to core accounting systems while capturing the firm’s specific business logic and verification steps. By putting the business logic into the infrastructure itself, their teams run automated reporting workflows today while keeping the flexibility to adapt those workflows tomorrow when a lender needs a new view.

Fixing reporting is as much about saving analysts from repetitive manual tasks as building an operational knowledge base. When a system understands how a firm defines its metrics, maps entity structures, and validates numbers against primary sources, that context compounds. The same layer powering weekly operating reports can naturally extend into underwriting, quarterly budgeting, document review and investor packages.

Commercial real estate is inherently dynamic as capital markets shift, debt terms fluctuate, and investors demand higher granularity. Operators that navigate this best will be giving their teams the infrastructure to answer the next question quickly and accurately.

Arunabh Dastidar is the co-founder and CEO of real estate AI infrastructure platform Leni.