International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Augmented Real Estate Underwriting: A Practical Framework for Integrating Generative AI into Multifamily Pro Forma Development

Author(s) Sushmita Vinod Naik
Country United States
Abstract Real estate pro forma development remains one of the most time-intensive functions in property investment, typically requiring twenty to forty hours per multifamily project through manual research, Excel-based modeling, and iterative scenario analysis. While generative artificial intelligence demonstrates significant promise for efficiency gains across financial services, the real estate industry lacks systematic frameworks for integrating these tools into underwriting workflows where local market expertise and professional judgment remain critical. This research develops and empirically validates a three-phase framework for AI-augmented multifamily underwriting through controlled testing with ChatGPT-4 using a standardized 150-unit development scenario in Seattle's Greenwood neighborhood. The framework achieved seventy-one to ninety percent time reduction while maintaining analytical quality comparable to traditional methods. Phase One leverages AI for rapid market research aggregation and preliminary pro forma generation. Phase Two requires human-led professional validation to correct AI limitations, apply local market knowledge, and integrate risk factors. Phase Three employs AI for comprehensive sensitivity analysis while humans provide strategic interpretation. Testing revealed AI excels at computational tasks but consistently misses nuanced factors like new construction rent premiums and infrastructure proximity impacts, validating the framework's hybrid structure as essential for professional-grade underwriting.
Keywords Artificial Intelligence, Real Estate Underwriting, Pro Forma Development, PropTech, Multifamily Investment, Machine Learning, Financial Modeling.
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-26
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72553
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