How AI Is Advancing Decision-Making In Corporate Real Estate

CBRE expects AI’s predictive analytics capabilities to enable greater price-forecasting accuracy, using non-linear data sources to inform transaction variables. In qualitative terms, AI will improve targeted customer segmentation, engagement and lead generation, creating efficiencies in the transaction process and occupancy strategies across the real estate life cycle.

AI is already making an impact on global talent markets. In the near term, companies across industry sectors are in a race to attract and retain AI-trained talent who will be able to create and implement AI tools to drive efficiency and gain market share. With the global demand for this talent far outpacing supply, a location-based talent strategy built on data is paramount. AI has initially engendered a spike in talent hiring, but long term, AI’s continued adoption will change many job responsibilities and eliminate the need for others, while creating demand for entirely new human-centric positions.

Today’s talent geography for AI-trained professionals is highly clustered. Two countries, the U.S. and India, comprise over half of the global talent pool, according to LinkedIn Talent Insights. Within the U.S., the San Francisco Bay Area, New York and Seattle have nearly one-third of the country’s total tech talent. While more tertiary markets are showing signs of growth and have some competitive advantages, companies looking to hire AI talent at scale will need to maintain a foothold in the country’s primary markets.

The current spike in AI-driven hiring is likely to impact real estate demand in a few of the largest global markets, such as San Francisco, Bangalore, New York, London and Paris.

AI promises to drive informed and innovative decision-making for investors or tenants with large real estate portfolios. In recent years, many organizations have sought to use a “big data” approach to align real estate activities with business objectives. Leaders today may have enormous amounts of data about all aspects of their business, which at times can be overwhelming. AI enhances big data’s capabilities and streamlines its application in several important areas of real estate:

Forecasting demand to prioritize employee experience. An organization’s business objectives and strategy drive space needs, which are dynamic and complex. AI can link data from multiple sources—like customers, suppliers, labor and real estate markets—to develop forecasting for a highly nuanced, agile and precise real estate strategy. AI-driven insights can answer questions about the right amount of space today and the contractual terms that will allow future flexibility. This will enable more efficient planning and resource allocation, benefiting both service providers and users. Corporate real estate departments will reduce business cost, allowing funds to be redirected to employee experience and engagement. Organizations where real estate and human resources teams work in tandem will see the greatest benefits from this approach.

Lease and transaction management will shift to strategy. Management of transaction flow, due diligence and property records are routine but important in real estate management. While often laborious, these activities must produce accurate and current data for business and legal purposes. Leading AI firms are developing hyper-intelligent and real estate-targeted tools to automate several aspects of these workflows, including lease extraction, document generation and processing for developers, developing risk scorecards based on proprietary lease agreements. CBRE has already begun implementing AI into lease abstraction and data management, reducing processing time by 25%. The long-term impact of integrating Large Language Models (LLMs) will result in traditional transactional real estate services evolving to a strategic consultative model. Real estate professionals who work with AI will contribute the greatest value in their creativity, rather than their quantitative analytical abilities.

In the real estate sector, AI algorithms have had the most significant impact in property-level applications, which are easiest to implement and test. More gains are expected, particularly in intuitive and customized on-site experiences that mirror the personalized ease of an individual’s online presence.

Smart buildings become predictive buildings. Building operations and utilities represent a sizable portion of real estate costs. Smart building software that predicts weather conditions and building occupancy levels lowers costs and improves operational reliability. These improvements reduce the carbon footprint of buildings and promote sustainable practices. However, the introduction of AI takes smart buildings one step closer to predictive maintenance by analyzing sensor data, improving equipment replacement cycles and reducing the potential for equipment failures. The future predictive building will not only analyze existing functionality but also recommend approaches for improvement through digital upgrades or user engagement. This will lead to a highly customized on-site experience for each user, much like the personalized digital experience Gen Z and subsequent generations expect.

The workplace is now a participant. As meeting rooms have become a participant in hybrid meetings, the future workplace as a whole will become a participant in creating the in-office experience. With most major office software firms developing AI-based tools to support employee collaboration, the future workplace will not only report who’s in the office, but nudge employee behaviors on when they should come in and why. Real estate leaders face a continuous quandary about the purpose of the office—a place for individual work or experiencing organizational culture—and AI can help answer these questions. However, this is not without risks. Guiding AI in decision-making is key to ensure that responsible use of the tool aligns with organizational values and priorities. Developing a defined governance structure to avoid biases during adoption and continuous management will be essential for success.

Design and space planning is accessible to all. From AI-generated interior renderings to automated space planning, real estate professionals will have greater access to tools that elevate design and planning for the end user. This will drive more precise and rapid buying or leasing habits, as investors and tenants can more accurately envision the end use and layout of a space. Even cost modeling will be generated more efficiently with Monte Carlo simulations (a mathematical algorithm to model all possible results and their probabilities), instead of only a few manually calculated scenarios that may limit cost optimization opportunities. Adequate regulation will be necessary to ensure appropriate buyer understanding of AI-generated designs. This will also redefine the role of designers and space planners from leading production to guiding users to translate their vision into an ideal workplace.

Technology’s impact is often overestimated in the short term and underestimated in the long term. To achieve AI deployment at scale, align AI use cases with business strategy. It is crucial to plan for the long-term shift that AI will have in the real estate industry and the short-term steps needed to succeed.

AI has significant potential to transform how work is done in many traditional real estate roles. To capture this opportunity, three key factors need to be addressed:

1. Invest in the right type of AI. With AI plug-ins being added in just about every tech product on the market, conducting the appropriate due diligence is not just prudent, but essential to investing in the right tool. Ensure there is an understanding of how data inputs are being used, what existing data has already been fed into the tool and to what degree third party plug-ins (that could compromise data security) are leveraged.
2. Define data sources. The average enterprise now has petabytes of data (one million times larger than a GB). Be discerning about which data sources to use, how that data is managed and where automation can be applied for greater accuracy and efficiency. Use the right framework to filter which data sources are most valuable versus those that are the easiest to obtain. Finding the right balance is essential for sustainable and cost-effective use.
3. Guide AI in decision-making. Numerous reports have already been published on bias in existing AI tools. AI is a nascent technology evolving at a remarkable speed, and our understanding is still in progress. Ensure the right governance is in place to manage AI-driven decision-making in your organization and the implications of such a decision. Certainly, examples exist of AI making “more informed” decisions than humans, but left to its own devices, AI can damage an organization’s culture and values without human oversight.

AI can free us of repetitive work and help us prioritize creative work and strategic thinking. To make this a reality we need to be innovative and thoughtful about how to apply AI in corporate real estate.