HAL 9000 made generations of moviegoers wary of artificial intelligence, but AI can have its uses, especially if you work in commercial real estate.
New artificial intelligence (AI) functions are constantly being developed that will soon impact every sector of commercial real estate. More and more (CRE) firms are getting on board with the technology. But the problems that AI has begun to solve are increasing in complexity and accelerating the pace of business operations. As a result, an introduction to AI may be useful to those unfamiliar with the subject.
AI and Generative AI (GenAI) were ranked among the top three technologies expected to have the most significant impact on real estate over the next three years by investors, developers, and corporate tenants, according to JLL’s 2023 Global Real Estate Technology Survey. IBM defines AI as technology that simulates human learning, comprehension, problem-solving, decision-making, creativity, and autonomy. At its most basic level, it enables computers and machines to solve problems for people and organizations.
As tools using AI work their way into businesses and society, they will drive significant increases in global GDP, increasing productivity in the process. Goldman Sachs economists Joseph Briggs and Devesh Kodnani estimate a 0.4 percent growth boost to GDP per year from AI in the U.S. AI has enormous potential to reshape CRE by streamlining and optimizing many real estate functions, potentially resulting in more transactions. More transactions mean more money.
In an interview for CRE development organization NAIOP, Sean Ward, executive vice president at CRE services and investment firm CBRE, maintains that the amount of time analysts spend underwriting deals for brokers can be dramatically reduced using AI. Ward believes the appraisal, lease administration, and property management sectors will all be completely changed by AI as well.
“Companies that figure it out first will put themselves far ahead of the pack.” said Ward.
The broad concept of AI originated in the 1950s, while several subcategories of the field have emerged only in the past few years. Colliers, a professional CRE services and investment management firm, lists six subtypes of AI that are currently being used or likely to be used in the near future.
Natural Language Processing. Understands, generates, and interacts with human language. It bridges the gap between human communication and computer understanding, making technology more accessible and intuitive.
Expert Systems. Mimics decision-making abilities of a human expert in a particular domain. They automate tasks that typically require specialized human expertise.
Generative Models (GenAI). Creates original content in the form of text, videos, images or even music from large datasets in response to a request by users. (Examples are OpenAI’s ChatGPT, Microsoft’s Copilot, and Google’s Gemini.)
Machine Learning. It can learn from data, adapt, make informed decisions, and improve its performance over time without being explicitly programmed.
Predictive Analytics. Analyzes historical data to make predictions about future trends. It is designed to help companies anticipate future scenarios to optimize decision-making and formulate long-term strategies.
Computer Vision. Analyzes and interprets visual information. It’s intended to replicate humans’ ability to see and understand the visual world.
A more detailed discussion of these subcategories is available from Traction Technology.
Public accounting and advisory firm BPM lists a number of ways CRE individuals and firms can begin using AI technology to enhance their businesses. They include:
Apprehension by CRE workers that AI will replace humans is real. However, a survey by the Altus Group suggests most CRE professionals view AI in a positive light. In its Q4 2023 CRE Industry Conditions and Sentiment Survey, Altus Group surveyed 197 CRE participants from 51 different firms across the U.S. The survey asked, “In what way do you think AI will affect the CRE industry?” Respondents could choose any or all of the following responses:
Overall, 72 percent of respondents viewed AI positively. However, 20 percent of participants did express concerns about AI’s potential threat to CRE jobs and industry standards.
Confidence in AI increased with firm size but decreased with years of experience. Furthermore, owners, operators, and service providers saw far more practical applications for the technology than those in brokerage and lending who depend more heavily on personal relationships.
The more positive response from larger CRE firms could be due to several factors, including more resources at their disposal that facilitated an earlier adoption of AI tools. For a more in-depth discussion of survey results, read “The Real Estate AI Debate: Risk or Revolution.”
Technology disruption in CRE has been gradual, accelerating in recent years with the increased use of the Internet. Online listings, virtual tours, and mobile apps have improved communication between the various CRE participants, but AI will still need human direction and input. Workers will be needed to define the right goals and metrics, identify the appropriate data sources, ensure data quality, and select the appropriate AI tools for solving a given problem. Humans will also be necessary to test, analyze, validate, and adjust the results produced by AI to ensure it is correctly solving a given problem. Harvard Business School professor Karim Lakhani summed up the more likely reality that lies ahead: “AI won’t replace humans, but humans with AI will replace humans without AI.”
Although future articles will discuss specific AI benefits and challenges in more depth, below is an overview of some of the ways AI can impact different CRE sectors.
Property Management. AI can be used to enhance property management through predictive maintenance systems that analyze data from sensors and historical records. Potential issues can be addressed before they become major problems, reducing repair costs and improving tenant satisfaction. Automated tenant screening can streamline the process of background checks and credit scoring, saving time and reducing human bias in tenant selection. Finally, lease management can automate contracts, facilitate rent collection, and handle routine tenant inquiries, giving property managers additional time to focus on more strategic tasks.
Customer Experience. AI-powered virtual tours can provide immersive property-viewing experiences that allow potential buyers to visit properties remotely. AI-driven “chatbots,” software designed to simulate human conversation through text or voice conversations, can offer 24/7 customer support and quickly answer inquiries about properties, neighborhoods, or the buying process. AI systems can analyze buyer preferences, budgets, and behavior to provide custom property recommendations.
Investment Analysis. AI systems can analyze vast amounts of market data, including economic indicators, demographic trends, and local development plans to identify potential property investments. AI tools can help investors manage and optimize their real estate portfolios through data-driven insights that can be used in strategic decision-making.
Document Processing and Data Management. AI is streamlining document-related tasks such as lease abstraction, efficiently pulling key information from lease documents. Rent roll processing tools can process and digitize rent rolls quickly, resulting in more productive data management and analysis.
AI implementation is on a path of rapid adoption in commercial real estate.
In a 2024 report on global AI usage, Colliers found that 33 percent of CRE leaders plan to employ AI systems in their firms within the next two years, while 59 percent plan to employ systems even sooner. Only 7 percent had no specific timeframe in mind. As a result, CRE organizations may want to consider exploring new AI applications before scaling them up to deliver added value.
CRE professionals should be aware that investing in solutions incorrectly or deceptively marketed as AI can lead to financial losses. Purchasing ineffective tools can waste funds that could be allocated more efficiently. Employing or learning subpar systems also takes valuable time away from primary business activities.
How AI Is Driving Increased Demand For Power
Goldman Sachs Research estimates that U.S. data center power demand will increase by 160 percent by the year 2030, almost doubling their current percentage of total U.S. electricity usage. In Texas, comparatively low electricity rates and a large number of deregulated markets have turned Texas into a magnet for data centers. Research and policy center Environment Texas reports 329 data centers in Texas, with many more proposed.
Wider use of AI will drive further growth in data center power consumption going forward. According to Carboncredits.com, a single ChatGPT query requires about ten times the electricity needed for a Google search. AI applications already account for 10 to 20 percent of data center electricity usage.
Meanwhile, several ongoing challenges are hampering the development of sufficient electricity supply. Delays in building new transmission lines and the retirement of older coal, natural gas and nuclear baseload plants are part of the challenge. Replacement with wind and solar facilities adds new capacity, but they only work when the wind blows or the sun shines. As a result, data centers and other users requiring uninterrupted power are looking for alternatives.
One increasingly popular alternative is a “microgrid,” an onsite electricity generating system. Microgrids have several advantages. They offer a “bridge-to-grid” system where generators can be installed quickly to provide primary power until utility transmission lines are in place, a process that can take years. They can disconnect from the local utility grid and function autonomously as backup power for facilities during outages or emergencies to prevent any disruptions. They can also provide extra power to the utility grid in periods of peak power demand.
One such microgrid system powered by natural gas is in operation at Texas A&M University’s RELLIS campus, which is on track to house several multimillion-dollar state and national research facilities. The Texas A&M University System has entered a long-term agreement with Houston-based Enchanted Rock (founded by alumnus Thomas McAndrew) to provide RELLIS with uninterrupted power, ensuring critical services are available at all times.
Kevin McGinnis, A&M System Executive Director of Risk Management & Benefits Administration, noted that the backup capacity was obtained with minimal capital outlay compared to financing its own generation system. Enchanted Rock operates and maintains the system for the life of the agreement, allowing the RELLIS Campus to focus its resources on other initiatives.
Commercial brokers should be aware of AI’s insatiable power appetite. Competition for uninterrupted, available power will only increase. CRE professionals may need to consider this in underwriting, lease terms, and other factors.
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Harold D. Hunt, Ph.D. ([email protected]) is a research economist with the Texas Real Estate Research Center; Bucky Banks ([email protected]) is associate director and executive assistant professor for Texas A&M’s Master of Real Estate program in Mays Business School; and Stephen A. Ramseur is executive professor for Texas A&M’s Master of Real Estate program and holds the Julio S. LaGuarta Professorship in Real Estate.
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