Real estate financial reporting has come a long way since the 1980s. What was once a manual, calculator-driven process is now powered by connected systems that move data seamlessly from trial balances to final statements, improving both efficiency and accuracy. The next evolution is already underway as artificial intelligence and automation begin reshaping how real estate organizations process, analyze and communicate financial information. As market conditions, tenant expectations and regulatory demands continue to shift, these technologies offer new opportunities to accelerate reporting, improve performance and generate deeper insights.
For public Real Estate Investment Trust (REIT) CFOs, financial reporting remains a high-stakes responsibility. Reports must withstand Public Company Accounting Oversight Board (PCAOB) audits, address Securities and Exchange Commission (SEC) inquiries and remain consistent across earnings releases, 10-Qs, 10-Ks and lender reports. Below we explore how AI and automation can strengthen reporting processes without introducing new risks, providing a practical framework for improving efficiency, maintaining auditability and supporting effective governance.
In financial reporting, AI and automation are often discussed together, but they are fundamentally different and carry different risks. Traditional automation follows predefined rules where if X happens, the system does Y every time. It excels at routine, repeatable tasks such as approvals, reconciliations and recurring journal entries.
AI operates differently. Machine learning, a branch of AI, learns from data to identify patterns and make predictions without being explicitly programmed for every scenario. Generative AI goes further by creating new content, such as text or financial analyses, based on large datasets. Unlike automation, AI does not simply follow instructions; it generates outputs based on patterns and probabilities. This makes it valuable for reviewing documents, identifying unusual transactions and drafting financial narratives, but it also requires oversight because it can produce inaccurate or misleading results.
Most accounting teams can close a set of books easily. A primary challenge for a public REIT lies in developing a comprehensive and reliable financial reporting package within strict timelines that seldom allow for delayed input from external parties. Between property managers, joint venture partners, external valuation specialists, tax advisers, legal counsel and investor relations, the close process becomes a coordination problem as much as an accounting problem.
REIT CFOs also deal with the issue that the same results need to match across reports, from financial statements to board decks. When different teams use separate spreadsheets, mismatches are bound to happen. AI and automation can help if you have strong data, clear roles, and solid controls for audit and SEC review.
Real estate finance teams manage data from joint ventures, property managers and third-party systems, all while transaction activity accelerates. Information arrives in multiple formats, including PDFs, rent rolls, lease abstracts, construction draws and lender packages, and accounting teams must quickly turn it into consistent, actionable reporting.
For public REITs, reporting demands are relentless. Regulators and auditors require timely disclosures and strong controls, while investors expect transparency. As portfolios expand, reporting becomes a continuous process rather than a quarterly exercise. Manual workflows slow reporting, increase error risk and limit insight.
At the same time, technology has created new opportunities. Property management, treasury and consolidation systems now generate vast amounts of structured data, while cloud platforms make portfolio-wide analysis easier than ever. AI can extract and organize information from leases, invoices, engineering reports, lender packages and legal documents. The opportunity for CFOs is to unify these capabilities into a governed reporting ecosystem that delivers faster reporting, greater accuracy and deeper business insight.
The strongest results of using AI and automation come when data collection, subledger activity, consolidation, analytics, disclosures and investor reporting are designed as one system, with clear control points and clear ownership. The following considerations are essential when implementing AI and automation in the close process.
REIT reporting only works as well as the property level data on which it’s built. If every property manager sends reports in different formats or lease information is scattered in emails and PDFs, the accounting team spends more time cleaning data than actually using it. The first step isn’t high-tech AI. It’s consistency by ensuring everyone follows the same data structure for property hierarchy, chart of accounts, lease details, tenant IDs and ownership tables.
Once you have consistent data, AI can help by pulling key info from documents, sorting it into standard categories and checking for gaps. Every step should leave a clear record that identifies what came in, what was processed, what rules were used and how issues were resolved. AI should make things easier for employees and improve transparency.
REIT closes often become bogged down in reconciliations, whether cash, depreciation of fixed assets or any other account. Workflow automation can reduce time by turning reconciliations into standardized routines with built-in approvals and clear evidence of completion. Once the reconciliations are complete, AI can then add value by identifying anomalies early, including property-level variances that are inconsistent with leasing activity, or expense spikes that do not align to budget.
Joint ventures (JVs) are common for REITs, but they can make the close more complicated. The risk often comes less from the accounting rules themselves and more from practical challenges, such as late or inconsistent partner reporting, difficulty mapping JV balances back to the company’s books and records, and complex arrangements involving preferred returns or distribution waterfalls. Automation can streamline the intake of JV financials, keep mapping consistent, and track eliminations and intercompany activity. AI can flag unusual partner expenses, unexpected debt changes or distributions that don’t fit waterfall logic, and help organize important agreements.
For a REIT CFO, the goal is an integrated story, not just financial statements but accounting principles generally accepted in the United States of America (GAAP) results, non-GAAP metrics like funds from operations, key performance indicators, liquidity, and risk disclosures that stay consistent across quarters and earnings calls. Automation helps ensure numbers are reliably sourced, and generative AI helps draft management’s discussion and analysis (MD&A), explain variances, update risk factors, and check for inconsistencies between narrative and data. Consider the following items during the closing process:
The 10-K and MD&A. Successful public REIT 10-Ks are managed as ongoing processes, not one-off yearly events. They have clear owners with proprietorship, reliable data, documented decisions and a controlled drafting process.
MD&A links GAAP results to real operations and risks, making it both valuable and informative. Inconsistencies across MD&A, financials, earnings releases and supplements can damage credibility. AI and automation help by promoting consistency, speeding up drafting and maintaining records for every statement.
Start with Ownership. A common challenge for many REITs is that responsibility for the 10-K isn’t always clear. Individuals assume someone else is handling it, but it’s not spelled out. While accounting manages the GAAP close, creating the MD&A is a team effort that needs input from financial planning and analysis (FP&A), asset management, leasing, treasury, legal and investor relations. To make things run smoothly, the CFO should clearly define who oversees each part of the disclosures and also designate someone who is the person who can quickly provide backup for variances, property schedules, debt agreements, valuation documents or board materials whenever needed.
Make MD&A a Living Document. One of the easiest ways to reduce 10-K risk is to stop treating MD&A as a year-end writing project and capture changing leasing trends, rising property taxes, expanding capex programs, approaching debt maturities, and certain softening markets on an ongoing basis.
This is a natural place for AI to help. It can summarize quarterly variance narratives, extract recurring themes from management reports and maintain a controlled “disclosure library” of prior language so the team starts from approved content. The CFO goal is not robotic repetition but controlled evolution where changes are intentional, reviewed and supported by evidence.
Keep in mind investors generally read REIT MD&A the same way they would underwrite a loan by understanding (1) how the assets performed, (2) what changed and why, (3) what the forward risks are and (4) whether liquidity is sufficient to execute the strategy. A strong MD&A follows that line of reasoning and avoids generic boilerplate and overly technical details that fail to explain the reasons.
Critical Accounting Estimates. For REITs, critical accounting estimates reveal how management makes decisions. Typical areas include impairment reviews, purchase price allocations, revenue recognition for recoveries and variable rent, and fair value judgments. The goal is to ensure disclosures honestly reflect how management thinks in plain language, and that documentation is available and easy to reproduce.
Automation can help by tracking key assumptions such as cap rates, discount rates, rent growth, lease renewals, and credit risks with dates and approvals in a controlled log. AI can then draft memos and pull up the exact backup needed for each statement, such as valuation summaries or impairment logs. This saves time and keeps disclosures consistent.
Liquidity and Capital Resources. Liquidity disclosures can boost investor confidence. Investors expect clarity on cash flow, debt maturities, market access and management’s response options. However, this information is often scattered, making it easy for disclosures to fall behind as changes occur.
Automation can produce maturity schedules and covenant calculations from debt data and flag inconsistencies in terms or calculations. Reviewer approval remains essential, but a controlled system speeds up and lowers risk in liquidity reporting.
Risk Factors. Risk factors for REITs function as a public map of what can go wrong such as refinancing risk, tenant concentration, sector disruption, climate and insurance exposure, development execution, cybersecurity and regulatory changes. The finance organization has a role to play because the most credible risk factors are consistent with the numbers (for example, tenant concentration tables, maturity schedules and market-level performance) and with what management is actively monitoring.
AI can help manage this section in a very practical way by comparing the current draft to the prior year, indicating language changes and identifying where new risks emerged in management reporting but are not reflected in disclosures (or vice versa). This does not replace legal review, but it reduces unforced errors by ensuring the evolution of risk factors is deliberate, reviewed and consistent with the REIT’s actual risk posture.
Consistency Is a Control. Many SEC issues are not “big accounting mistakes” but, rather, inconsistencies such as a table in MD&A that does not match a note or a maturity schedule that differs from the debt footnote.
Automation can generate repeated tables from governed datasets, so the same number flows everywhere. AI can then act as a consistency reviewer by scanning drafts for mismatched figures, flagging unsupported claims, and checking that underlying analysis supports the key themes in MD&A. Think of this as a quality-control layer that strengthens disclosure controls and reduces last-minute rework.
Common SEC Comment Letter Failures. Many SEC comments stem from avoidable execution gaps, such as inconsistent numbers across sections, incomplete explanations of period-over-period changes or disclosures that do not clearly connect to the underlying accounting judgments. A modern reporting system can reduce this risk by enforcing standardized sources, maintaining a tie-out matrix and using AI to flag inconsistencies before filings go out the door.
Examples of common SEC comment-letter concerns include:
REIT growth is often achieved through purchases of properties, and each acquisition can create a parallel reporting workstream that runs on a different clock than your normal close. When a transaction is significant, Regulation S-X can require separate audited financial statements for the acquired real estate operation (Rule 3-14) or acquired business (think buying a hotel) (Rule 3-05), as well as separate financial statements for significant equity method investees (Rule 3-09). These requirements can be time-consuming, highly document-dependent and unforgiving on deadlines, commonly within 71 days after the initial Form 8-K reporting the acquisition.
Read “Navigating SEC Audits for REITs: Your Guide to Rules S-X 3-14, S-X 3-05 and S-X 3-09”
The REIT should standardize the way you assess significance, structuring your diligence requests to anticipate audit needs, and creating a data room that is audit-ready on Day 1 of the acquisition. AI can accelerate document intake and indexing; automation can enforce checklists and ownership; and analytics can help validate that acquired property activity reconciles to supporting schedules. The result is not just faster filing; it’s also reduced execution risk during the period when investors are most focused on your growth narrative.
In the current rate environment, debt is often the first question on a REIT earnings call. Investors and lenders care about maturities, fixed-versus-floating exposure, hedge coverage, covenant issues and refinancing plans. Yet many organizations still assemble these schedules manually from multiple sources (treasury, accounting, lender portals and spreadsheets).
Automation can centralize debt terms, generate consistent maturity and interest expense schedules, and produce covenant packages that reconcile to the general ledger. AI can add value by extracting key terms (maturity, spreads, floors, required reporting deliverables, covenant thresholds) and keeping them in a standardized repository.
Impairment analyses and fair value discussions combine judgment, market data and forward-looking assumptions. Whether you are assessing recoverability under ASC 360 or supporting fair value disclosures under ASC 820, the highest risk areas are (1) identifying triggering events timely, (2) ensuring assumptions are consistent with observable market information, and (3) documenting why the chosen assumptions are appropriate for your assets and markets.
Read “How to Know if Your Real Estate Entity Has Triggered an Impairment Loss”
AI can support this work by acting as an early warning system for changes in occupancy, market rents, Net Operating Income (NOI) trends, cap rate movements, refinancing conditions and capital project overruns, then identifying assets that warrant evaluation. It can also help organize the supporting evidence so the impairment memo is easier to assemble. The conclusion, however, should remain a management decision supported by qualified valuation input and a documented review process.
For most REITs, the market’s view of performance is shaped as much by supplemental reporting as by GAAP. Same-store NOI growth, occupancy, retention, capex and other operating metrics become part of the earnings narrative. The risk is that these metrics are often built in spreadsheets with limited governance, which can create inconsistencies across quarters, reduce confidence in the story you tell, and increase the burden of audit and SEC review.
A better approach is to treat key metrics as critical knowledge with definitions, responsible owners and change controls in place. When definitions change, AI can help document the rationale, approvals and impacts, reducing the time spent writing the changes while improving consistency.
The promise of AI in real estate reporting is not that it eliminates judgment. Instead, it removes repetitive work that keeps experienced professionals stuck in spreadsheets when they should be analyzing outcomes, identifying risk and communicating with stakeholders.
Public REITs operate under Sarbanes-Oxley Act (SOX) and SEC disclosure controls and procedures, and auditors will evaluate whether new technology changes the control environment. If an AI-enabled workflow touches financial reporting, expect questions about IT general controls such access or change management, segregation of duties, completeness and accuracy, and how you validated information produced by the AI tool.
An AI output used in financial reporting is information produced by the entity and falls squarely within internal control over financial reporting (ICFR) and disclosure controls and procedures. Auditors will ask how AI-generated outputs were tested for completeness and accuracy, how changes to models, prompts and configurations are controlled, and whether the outputs and the related reviewer signoffs are retained as audit evidence. The technology does not shift accountability: SOX Section 302 and 906 certifications remain the responsibility of the CEO and CFO and cannot be delegated to a tool.
Data confidentiality deserves equal attention. Drafting MD&A, risk factors or accounting estimate memos with generative AI means pre-release financial results and material nonpublic information are flowing into an AI tool. REITs should:
A clear AI-use policy, communicated to everyone involved in the close, is the simplest control against inadvertent disclosure.
Modernization efforts fail when they are framed as “technology projects” rather than performance improvements. A CFO scorecard keeps the program grounded in outcomes: speed, accuracy, controllership and stakeholder confidence. The right metrics also help you demonstrate return on investment to the board and create accountability across internal teams and third parties.
Key metrics for measuring the success of reporting modernization in real estate include the following:
These metrics help organizations track performance improvements, ensure accuracy and reliability, and demonstrate return on investment to stakeholders.
In real estate accounting, what’s on the books is just as important as what’s on the ground and, increasingly, how quickly and credibly you can explain the difference. AI and automation can modernize reporting by turning scattered documents and disconnected systems into a controlled, repeatable process. Done well, it elevates insight and frees leadership to focus on strategy rather than rework.
Organizations that stand to gain the most are those prioritizing the development of transparent, robust and scalable reporting systems, rather than simply adopting the latest tools. Establishing strong foundational processes, implementing purposeful automation, and integrating documentation and governance within the product framework are essential. In an environment where trust is paramount, advanced real estate reporting will serve as a key differentiator.
Contact Nick Antonopoulos, Gino Scipione or a member of your service team to discuss this topic further.
In this blog Cohen & Co is not rendering legal, accounting, investment, tax or other professional advice. Rather, the information contained in this blog is for general informational purposes only. Any decisions or actions based on the general information contained in this blog should be made or taken only after a detailed review of the specific facts, circumstances and current law with your professional advisers.