AI in Real Estate: What Comes Next?

Artificial intelligence (AI)¹ has moved from experimentation to day-to-day execution across the real estate industry. It's now increasingly used to search and summarize documents, extract structured data from unstructured files, forecast outcomes from historical patterns, and in some cases initiate actions within defined workflows. These capabilities are valuable in a sector defined by fragmented data, document-heavy processes, and time-sensitive decisions.

How AI Has Evolved

AI adoption has accelerated as foundation models)² have improved interpreting language, generating content, and handling multi-step reasoning. In real estate, this directly addresses common bottlenecks: leases, diligence packages, rent rolls, service logs, construction documentation, and recurring reports. Meanwhile, improved infrastructure has made AI faster and less expensive to deploy at scale. Because models evolve quickly, durable implementations require flexibility. A model-agnostic architecture³ allows systems to upgrade as new models emerge without complete rebuilding.

AI vs. Programmatic Automation

A common misunderstanding is the difference between AI and traditional automation. Rules-based automation follows explicit logic and works reliably with structured, predictable inputs. AI is probabilistic, interpreting unstructured data like leases, PDFs, and emails to generate context-aware drafts and identify patterns. The best results often combine both: use rules-based automation for stable, predictable tasks, and AI for interpretation, summarization, prediction, and workflow acceleration.

Where AI is Being Used in Real Estate Today

Most high-value real estate use cases fall into four categories: retrieve and understand, predict, generate, and act⁴. These patterns are consistent across asset classes.

The use cases mentioned in this article are provided as examples of current applications in the market. The market is evolving rapidly - new solutions emerge frequently, and existing ones continuously improve.

1. Operations, Leasing, and Revenue Protection

AI is improving leasing conversion, streamlining lease administration, protecting revenue, and enabling predictive maintenance through applications that:

• Provide resident-facing chat and automated service request triage
• Personalize outreach and optimize property marketing workflows
• Automate lease abstraction by extracting key clauses like escalations, renewals, and liabilities into structured data
• Streamline lease creation and draft workflows for property managers
• Perform ongoing lease audits comparing leases to rent rolls to flag revenue leakage
• Monitor equipment sensor data to forecast equipment issues and reduce downtime
• Analyze historic and real-time data to predict property risk signals like tenant defaults or lease breaks

2. Investment, Underwriting, and Portfolio Management

AI is compressing deal timelines, accelerating underwriting, and streamlining portfolio reporting through applications that:

• Apply AI to property data for deal sourcing and opportunity identification
• Extract and structure data from unstructured diligence documents
• Automate first-draft investment memos summarizing fundamentals like rent comps and projected returns (IRR)
• Apply AI-driven credit and risk models to assess suitability and identify red flags
• Ingest and organize portfolio data to enable analysis and actionable insights
• Generate consistent first drafts of monthly narratives, variance explanations, and executive summaries

3. Development and Construction

AI is helping developers and construction teams reduce rework, optimize schedules, and improve job site monitoring through applications that:

• Automate takeoffs, cost forecasting, and early scope validation for preconstruction planning
• Use real-time data and machine learning to track construction progress and predict potential issues
• Use computer vision⁵ to monitor job sites, track headcount, and identify safety risks
• Propose alternative building designs to meet constraints like zoning and budget

4. Industrial & Hospitality Operations

AI is optimizing staffing, pricing, and guest experience while reducing operational burden through applications that:

• Optimize staffing based on work order forecasts and facility activity patterns
• Support predictive maintenance by analyzing equipment sensors and data
• Apply AI to fleet operations for route planning and performance monitoring
• Adjust pricing based on real-time demand, competition, and local events
• Analyze guest data to personalize marketing and support conversions
• Automate back-office tasks and operations workflows

The Future of AI in Real Estate

AI adoption follows a spectrum of human involvement. Many workflows start with "Human in the Loop" (HITL), where AI suggests outputs for human review (e.g.: AI flags potential revenue leakage → asset manager investigates). As systems mature, they shift to "Human on the Loop" (HOTL), where AI executes tasks while humans monitor and intervene when needed (e.g.: AI updates rent rolls based on signed leases → accountant spot-checks weekly). In limited cases with strong safeguards and governance, some workflows may reach "Human out of the Loop" (HOOTL), where AI operates end-to-end without real-time oversight.

Near-term expansion will likely come from Agentic AI - systems that plan and execute multi-step tasks toward a goal rather than responding to single prompts. In real estate, an agentic workflow might retrieve a clause, draft a response, update a system record, and create a follow-up task, all with logging and oversight built in.

Navigating Challenges

Responsibly scaling AI requires addressing key constraints including:

• Accuracy, Hallucinations, and Auditability – AI can generate plausible but incorrect outputs when not anchored to source data. High-stakes workflows need controls like confidence thresholds, review gates, and audit trails that record inputs, outputs, and actions for traceability.
• Data Readiness and Trust – AI effectiveness depends on data quality. Inconsistent document structures, fragmented systems, and varying definitions limit results. Strong data governance covering privacy, security, ownership, and quality standards is essential.
• Exceptions and Edge Cases – Real estate involves many exceptions: bespoke deal terms, irregular property histories, and nonstandard documentation. Exception-handling processes and human oversight are critical to managing these edge cases.
• Math and Deterministic Outputs – As of now, many AI models are not reliable for exact math on their own. For spreadsheet-grade accuracy, route calculations to deterministic tools⁶ and use AI to orchestrate workflows rather than perform computations directly.
• Adoption and Change Management – AI represents an operating shift, not just a technology change. Organizations that build practical AI fluency⁷ through pilots, iteration, and hands-on use progress faster than those treating AI as a planning exercise. Practical experience helps teams identify where AI excels, where it needs guardrails, and where humans must retain control.

How Citrin Cooperman Can Help

AI in real estate is no longer optional. Organizations implementing AI now gain measurable advantages in speed, accuracy, and efficiency, while those that delay risk operating at a structural disadvantage.

Citrin Cooperman's Digital and Cloud Services Practice offers an AI Readiness Assessment covering organization, strategy, technology, data, governance, and culture. The assessment identifies practical use cases, defines vision and targets, and shapes a roadmap addressing technical readiness, training, and rollout. It also aligns initiatives to measurable outcomes and scales what works.

If you're evaluating AI for leasing, asset management, investments, lending, or development, an AI Readiness Assessment can help prioritize high-impact workflows and ensure your data, controls, and operating model are ready for responsible scaling.

Ready to explore what AI can do for your organization? Contact our team or reach out to Jigar Shah for more information.

¹Artificial intelligence (AI) refers to systems that perform tasks requiring human-like intelligence, including learning from data, recognizing patterns, generating language, and making predictions.

²Foundation models are broadly trained AI systems that can perform multiple tasks, including language understanding and generation. Examples include OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini, Meta's Llama, xAI's Grok, and DeepSeek.

³Model-agnostic architecture refers to system design that can switch between AI models without requiring a full rebuild, enabling organizations to upgrade as technology improves.

⁴These categories represent core AI capabilities in business workflows: finding and interpreting information, forecasting outcomes, drafting content, and initiating workflow actions.

⁵AI techniques that interpret images or video to detect patterns, changes, or conditions relevant to a workflow.

⁶Systems that produce consistent, exact outputs from the same inputs (e.g., spreadsheets, databases, calculators)

⁷Working knowledge gained through direct use, helping teams understand AI's strengths, limitations, and appropriate applications.