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Don’t Expect AI to Revolutionize Real Estate Capital Markets
A relative lack of data and connectivity in the industry makes it difficult to leverage technologies like predictive and generative AI.
Recent advances in artificial intelligence, particularly generative AI, have prompted real estate professionals to ask how these powerful new technologies will impact real estate capital markets and the way they work day to day.
The blogosphere generally asserts that AI will “change everything.” Technology vendors have rushed to make exaggerated claims about the application of AI within their solutions. Many real estate professionals, meanwhile, remain skeptical. Thus far, the tangible effects of AI have been underwhelming.
The earliest impacts of AI on real estate will likely come from changes in other industries. As the demand for AI services increases and some jobs become less human intensive, the world will need more server racks and fewer desks and meeting rooms. This will create opportunities for investment in data centers and infrastructure while adding to the pressure on the office sector.
Regions with high levels of employment in industries that are either bolstered or disrupted by AI will see rents influenced as economic activity in those regions is positively or negatively impacted.
The timing of physical space market changes is unlikely to correspond with the investment cycle. As has happened in previous “hype cycles,” asset prices in the affected sectors will probably overshoot economic fundamentals, resulting in an initial rally (or slide), followed by a correction, followed by a more gradual growth or decline in prices over the long term.
Unraveling Predictive and Generative AI
Before predicting how AI might change the working lives of real estate investors, lenders and others in the market, the term “AI” must be clarified. When people speak of AI, they often conflate very different technologies with different trajectories and potential impacts.
Predictive AI
Predictive AI systems have existed for a long time and have improved steadily over the past few decades, along with growth in computing power and the availability of data. Predictive systems are essentially statistical models, and “data science” is merely an evolution of statistics that harnesses new technologies and practices developed to work with very large data sets.
Predictive AI models can find patterns and predict outcomes based on vast amounts of structured data. These abilities can, in theory, be used to identify markets that look like other markets that have performed well or predict future rents and asset values.
While recognizing the potential of these applications, it is important to note that recent advances in AI have not meaningfully improved the efficacy of predictive analysis within the realm of real estate investing and are therefore unlikely to accelerate adoption. Rather, adoption of these technologies and approaches has been limited primarily by the lack of accurate, timely and consistent (“apples to apples”) information to feed predictive models.
Because real estate transactions are completed via the exchange of unstructured documents, typically over email, rather than anything similar to the electronic platforms commonly used in other sectors, it is impossible to obtain complete, consistent and accurate information on these transactions. Without this data, the application of predictive AI within real estate capital markets remains impractical.
Generative AI
Generative AI, the source of the recent buzz, is not designed to do the type of analysis discussed above. As the name implies, generative AI is designed to create, not analyze, and as such has a very different set of potential applications within real estate investing. “GenAI” has improved by leaps and bounds over the past 18-24 months, demonstrating an ability to significantly enhance many tasks previously considered the exclusive remit of human experts.
Fields like law, medicine and education — all of which, at their core, are based on digesting, synthesizing, structuring and presenting large sets of information — look likely to be severely disrupted.
But what about real estate investing? One could argue assessing a real estate investment is similar to assessing a contract or diagnosing a patient’s condition. Certainly, there are aspects that are similar. For example, understanding a real estate asset’s potential value involves analyzing a significant amount of information about the asset, the tenants, operating costs, local market context and so on.
However, the job of a real estate investor differs in important ways. Investors operate in a dynamic environment in which the outcomes of their decisions depend on the decisions of others outside their firms. Developments in real estate capital markets depend on a set of ever-changing factors in both the market for physical space and the market for capital, not a set of relatively static facts as in the case of law and medicine.
This isn’t to say that real estate investors won’t leverage generative AI to increase their productivity. The ability to rapidly synthesize unstructured data and query that data using natural language will allow investors to churn through analysis much faster and spend more time considering qualitative and competitive dynamics.
Is AI a Threat to Real Estate Investors?
Some have argued that the power of new GenAI models will result in analysts being replaced by agents who can more quickly consume, digest, process and present information about prospective investments. Altrio, a real estate investing management software company based in Toronto, disagrees. By increasing the productivity of real estate investors and lowering the barriers to entry (i.e., the effort/cost required to participate in the market), AI should lead to an increase in demand for real estate investment professionals.
Consider a simple example from outside the industry — a burger restaurant where, without the assistance of technology, a single line cook can produce 50 burgers per hour. If the installation of a new machine allows the same cook to produce 100 burgers per hour, the restaurant owner will need only half the number of cooks. It would appear the new technology will reduce the demand for grill cooks. But what happens next? The owner can now generate more profit per grill cook, which creates an incentive to hire more cooks and buy more machines and perhaps open more restaurants. As the business grows and the number of locations increases, the total number of cooks needed to operate the machines across the chain of restaurants rises.
When the marginal productivity of a resource increases, so does demand for that resource. As real estate investment analysts and associates begin to leverage AI to do certain parts of their jobs faster and better, they will become more productive, and demand for their services will increase.
What Sets Real Estate Capital Markets Apart
In some industries, generative AI will make it possible to completely automate jobs that were once performed by humans. When that happens, the marginal productivity of those resources will drop sharply to zero, resulting in mass unemployment in those sectors.
However, it will be difficult to completely automate the job of real estate investing because much of what occurs during the execution of a real estate transaction happens offline. Investors, lenders and brokers use electronic forms of communication, but the lack of established standards and protocols, combined with the unstructured nature of most of the information involved in the transactions, means the capital markets are not “networked” in the same way many other markets are.
AI is an application that must run on a platform. The power of an AI agent will always be limited by the capabilities of the platform on which it runs — specifically its ability to access real-time structured data and interact with others based on a common set of rules.
The offline, fragmented nature of the real estate capital markets and the absence of accurate, timely data places significant limitations on the potential impact of AI agents within the commercial real estate development industry, just as these limitations currently frustrate the efforts of human agents who operate in the market.
Raj Singh is CEO of Altrio.
