AI in Commercial Real Estate: A Practical Guide for Industry Leaders

Investment managers have an excellent opportunity to re-design their investment management practices using AI to achieve investment outcomes that are often influenced by market uncertainty, data proliferation, and rapid advances in finance and technology.

This article will provide a practical guide to developing and deploying machine learning capabilities, highlighting the feature engineering process, model selection trade-offs, and machine learning operations (MLOps) best practices necessary to operationalize AI at scale.

The traditional process for modeling investment decisions among most real estate professionals relies on linear relationships, such as those between rent growth and vacancy, often overlooking the complexity caused by the heterogeneity of real estate markets and other key factors. However, using AI to identify and leverage pattern recognition from complex, non-linear relationships offer deeper insights into markets.

It is important to note that while more advanced algorithms enhance accuracy, they often reduce interpretability, whereas simpler algorithms improve interpretability at the expense of accuracy. The tradeoff between accuracy and interpretability can be