Emerging technologies, most notably artificial intelligence, are reshaping how real estate professionals source opportunities, evaluate markets, underwrite investments, design projects, manage entitlement risk, coordinate construction, operate assets, communicate with investors, and govern complex organizations. Yet the most important real estate decisions still require judgment under uncertainty. AI can accelerate analysis, expand the range of alternatives, and improve organizational learning, but it can also create false confidence, amplify bias, obscure accountability, and produce elegant but unreliable outputs.

This seven-week elective module introduces students to the practical use of artificial intelligence across the real estate value chain. The course is built around direct engagement with industry practitioners actively deploying AI in investment, development, design, construction, asset management, operations, capital raising, climate-risk analysis, and organizational strategy. Each practitioner session is paired with a structured lab, critique, or applied assignment so that students learn not only what firms are doing, but how to critically evaluate whether those uses are reliable, ethical, profitable, and professionally responsible.

The course is not a computer science course and does not assume coding experience. It is a professional decision-making course for future real estate leaders. Students will learn to use AI tools, critique AI outputs, ask better questions of vendors and internal teams, design human-in-the-loop workflows, and produce more transparent, verifiable, and accountable AI-assisted real estate analyses.

Understanding the underlying data that powers AI analysis is foundational to this course. Students will learn that data quality, data governance, and data literacy are as important as tool selection and AI algorithm choice, often more so. The course emphasizes that poor data produces poor decisions, regardless of how sophisticated the AI model.

The course intentionally treats AI as an integrating technology rather than a stand-alone technical topic. Each class links AI to a core real estate function:
• Market selection and investment strategy
• Site acquisition and highest-and-best-use analysis
• Financial underwriting and capital stack design
• Design, programming, zoning, and entitlement strategy
• Asset management, operations, leasing, and tenant experience
• Climate risk, resilience, and social impact
• Governance, ethics, law, compliance, and organizational adoption

By taking this course, students connect AI capabilities to every domain of real estate practice and develop judgment about where and how AI should (and should not) be deployed.

Prerequisites
No coding background is required. Students should have working familiarity with core real estate concepts, including basic real estate finance, market analysis, development feasibility, and investment decision-making. Students with technical backgrounds may use more advanced tools for selected assignments, but all assignments can be completed using approved no-code or low-code tools.