Use cases of AI in commercial real estate
As AI technology rapidly advances, its applications in real estate and construction are becoming increasingly vital. Recognizing the potential at this stage, we are focusing on custom GPTs to facilitate everyday tasks in Commercial Real Estate (CRE) without coding. Although the most effective approach, we believe, is to develop bespoke GPTs tailored to specific business processes, these series of articles will delve into practical applications of publicly available GPTs, illustrating the concept of their use. Disclaimer: The GPTs mentioned are sourced from the GPT Store, and their outputs have not been independently verified by the author, as accuracy depends on their training.
In the constantly changing world of commercial real estate (CRE), AI is becoming a big deal in how underwriting is done. Custom GPTs are leading this shift, offering tools that automate risk evaluations by quickly analyzing massive datasets. This helps streamline processes, reduce errors, and speed up decision-making. But it's not all perfect—AI can make mistakes, and the complexity of CRE deals means human oversight is still crucial. As useful as AI is, relying solely on it could lead to oversights, especially in tricky transactions.
AI can serve as a powerful knowledge assistant in underwriting, acting as a 24/7 researcher that quickly retrieves relevant data, guidelines, and regulations. It streamlines the decision-making process by pulling and presenting information clearly and concisely, drastically reducing the time spent on manual research. This efficiency helps underwriters make more informed decisions, minimizing the risk of overlooking critical details. AI’s ability to provide precise, context-specific answers enhances the overall accuracy and ease of access to information.
For instance, a Custom GPT offers detailed, context-specific guidance during financial modeling, directly addressing user queries.
In financial modeling, AI could handle the tedious, data-heavy tasks that often slow down model development. It can automate complex calculations, ensuring data accuracy, and generate key financial components like matrices and ratios. By analyzing large datasets and identifying patterns, AI can provide actionable insights that streamline the modeling process. This reduces errors and frees up developers to focus on refining and optimizing their models. Overall, AI acts as an invaluable assistant, making the modeling process faster, more efficient, and highly accurate.
In this example, a Custom GPT helps generate specific elements to use directly in the modeling process.
The future for custom GPTs in CRE underwriting looks promising, with AI increasingly handling tasks that used to need human intervention. AI's ability to quickly process and analyze large amounts of data can speed up underwriting, making real-time risk assessments possible and fostering a more competitive market. However, there are still significant challenges. First and foremost, AI still makes lots of mistakes. The quality of AI insights depends heavily on the training and data it’s fed—bad data equals bad decisions, potentially reinforcing existing biases in lending. Plus, the regulatory environment hasn’t caught up with AI's rapid development, leading to concerns about accountability, transparency, and privacy. Finally, while AI might reduce the need for traditional underwriting roles, it also creates a demand for professionals who can manage and oversee these AI systems.
As the CRE industry adopts custom GPTs for underwriting, it’s clear that AI can significantly enhance efficiency, but it’s not a cure-all. Successful integration will require a balanced approach, combining technology with human oversight to get the best of both worlds. While AI can handle the heavy lifting, especially in research phases, the nuanced judgment of experienced professionals remains crucial. Moreover, newer professionals will be needed to integrate AI into real estate data. Moving forward, the focus must be on building robust data systems, ethical AI practices, and staying ahead of regulatory demands to fully realize AI’s potential in transforming underwriting. Balancing innovation with governance will be essential for creating a more efficient and fair underwriting process.
