REAL ESTATE & GENERATIVE AI

BY Realty Plus

Generative AI (gen AI) is maturing at an auspicious moment for the real estate industry. Investors have mountains of both proprietary and third-party data about properties, communities, tenants, and the market itself. This information can be used to customize existing gen AI tools so that they can perform real estate–specific tasks, such as identifying opportunities for investors at lightning speed, revolutionizing building and interior design, creating marketing materials, and facilitating customer journeys while opening up new revenue streams.

Although gen AI has only recently captured the public’s imagination, AI has been fundamentally changing the way the world does business for decades. This more familiar version of AI—also known as analytical AI—is goal oriented and focused on activities such as predicting values for a future forecast or assigning categories to segment customers. It is already embedded in parts of the business world: AI-assisted forecasts, for example, have altered how investment professionals think about the future, and dynamic pricing models have changed how several industries charge for goods and services. One industry in which AI’s transformative power has been missing, however, is real estate, a historically slow adopter of new technologies.

Gen AI represents a fresh chance for the real estate industry to learn from its past and transform itself into an industry at technology’s cutting edge. Gen AI has not replaced analytical AI; instead, its open-ended and creative nature introduces a new frontier of use cases that analytical AI does not address. Based on work by the McKinsey Global Institute (MGI), we believe that gen AI could generate $110 billion to $180 billion or more in value for the real estate industry.2

For all the hype that gen AI has received to date, many real estate organizations are finding it difficult to implement and scale use cases, and thus have not yet seen the promised value creation. This is not surprising: deriving competitive advantage from gen AI is not as simple as just deploying one of the major foundational models, and many things have to go right in an organization to make the most of the opportunity.

Real estate can benefit from gen AI in a multitude of use cases

Gen AI’s strengths generally fall within one of four categories, which we dub “the four Cs.” The first is customer engagement, which can be supported by tools such as conversational chatbots that answer questions and remove doubt from customer decisions. The second is creation, in the form of tools that generate new creative content, including text and images. The third is concision: gen AI excels at synthesizing insights from unstructured data, interpreting conversations, and querying large data sources. The fourth is coding solutions, of which gen AI offers many, including interpreting, translating, and generating code.

In our own work with AI, we have seen real estate companies gain over 10 percent or more in net operating income through more efficient operating models, stronger customer experience, tenant retention, new revenue streams, and smarter asset selection. Here are five examples of how businesses can apply gen AI’s four Cs to specific real estate issues.

Sifting through mountains of leasing documentation (concision)

Gen AI can be applied to a repository of lease documents, which can be dense and filled with bespoke terminology, making it difficult for owners of many properties to sift through and find information at scale. A gen AI–powered tool can summarize key themes across the leases, such as how much rent is expected monthly or what market forces (such as local environmental, social, and governance compliance laws) could affect leases. Additionally, the tool can scan across leases for a particular parameter (for example, all leases with a rent price per square foot below a certain level) and generate tables of information. At that point, professionals can examine the information the AI tool has compiled.

Copiloting real estate interactions (concision and customer engagement)

Gen AI can be used to create a powerful copilot (a gen AI–powered bot) for a variety of real estate interactions, including managing tenant requests and lease negotiation. Simple requests from tenants, such as for routine maintenance, can prompt the copilot to directly contact a building’s maintenance staff. The copilot can identify a more complex question and flag it for a specialist at a property management company. As the specialist interacts with tenants, gen AI can observe conversations and written responses and suggest ways to improve communication. For high-stakes moments—such as a commercial lease negotiation with an office, warehouse, or retail tenant—a gen AI tool can take in all the information about a tenant, the property, and the market and craft a negotiation transcript. If communications and calls are recorded or turned to text, the copilot can monitor these interactions at scale, providing coaching while reminding specialists to refrain from using certain terms that could incite moments of risk.3

Enabling visualization and creating new revenue streams (creation and customer engagement)

Today, when a prospective office tenant looks at raw space on a tour or a potential resident views picture of an apartment on a listing site, they see an empty unit or photos filled with someone else’s finishes and furniture. Virtual reality tours have helped, but these static, noncustomizable simulations usually only go part of the way toward showing the end user what the result could be.

Gen AI tools can help a potential tenant visualize exactly what an apartment would look like in, say, their preferred midcentury modern style or in cherrywood versus walnut finishes. This data can then be fed back into a model to predict which types of furnishings and finishes work best for different customer segments, improving prospect-to-lease conversion and shaping future capital expenditure decisions.

There can also be e-commerce tie-ins: as a prospective tenant tours a unit, an app can virtually impose a variety of couches, window trims, or kitchen appliances that match a desired design style. If the prospective resident decides to buy or lease, these choices can be ordered and set up to coincide with the move-in. The resident benefits by moving into a home that already expresses their signature style, and the brokerage or apartment company benefits by reaping revenue from cross-selling.

Making faster, more precise investment decisions (concision)

Today, investment decisions are often informed through individual analysis of bespoke data pulls across sources. An investor interested in warehouses, for example, typically starts by performing a macroanalysis of markets that have attractive factors such as ports, airport locations, and high e-commerce volume. Then, they perform more granular analysis to locate areas of interest, pulling building information from local brokers or digital tools. As part of the decision-making process, the investor conducts discrete analyses to figure out how their investment hypotheses have panned out in the past.

With a gen AI tool that’s fine-tuned using internal and third-party data, an investor can simply ask, “What are the top 25 warehouse properties up for sale that I should invest in?” or, “Which malls are most likely to thrive in the future?” The tool can sort through the unstructured data—both internal (such as the performance of a company’s existing properties and the lease terms related to this performance) and third party (such as the US Census and publicly recorded, comparable sales). This multifaceted analysis can be overlaid on a list of properties for sale to identify and prioritize specific assets that are worth manual investigation.

Drawing architectural plans known to create desired outcomes (creation)

In website design, there are specific patterns and design choices known to generate e-commerce sales or higher click-through. Similarly, there are underlying design principles in the physical world that gen AI can unlock and use to draw architectural plans.

A gen AI–assisted process can introduce Internet of Things sensors and computer vision4 algorithms that collect data points on space use, such as how customers move through a store before purchase or when conference rooms are used in an office. This insight—along with outcome data about sales, customer loyalty, productivity, employee retention, or other areas—can then be fed to a gen AI tool. This information can be overlaid with spatial data about square footage, location, walls, furniture, and other architectural elements. The gen AI tool can then develop architectural plans that are optimized to create desired outcomes in a space. Human architects and designers can work from these plans to ensure art and emotion in the design, but with less guesswork over whether a space is purpose driven (illustration).

Actions real estate players can take to realize the full value of gen AI

Gen AI holds the promise of transformation, but real estate companies will have to do more than just learn how to use off-the-shelf products. Although foundational models are essential, they are just a small component of a real estate firm’s ability to realize value from gen AI.

To seize the opportunity, businesses in the real estate value chain can strive to outcompete by rewiring the way they work in the following ways. Although foundational models are essential, they are just a small component of a real estate firm’s ability to realize value from gen AI.

Align the C-suite around a business-led road map tied to a specific part of the real estate value chain. CEOs who want to lead in gen AI can prioritize technology, onboard new internal capabilities, and organize for agile delivery just as top start-ups and tech-native companies do. New ways of delivering technology are essential not just to gen AI delivery but also to ensuring modernity and staying ahead of the strategic curve. Winners are willing to experiment, iterate, and self-disrupt.

That starts with having capabilities that go beyond the traditional real estate IT organization. This does not mean leaders have to welcome scores of new tech hires into their companies. Rather, it requires investing in a nimble squad of engineers and designers who are familiar with gen AI and can be directed to focus exclusively on value-adding use cases.

C-suites can start by assessing which part of the real estate value chain they occupy—such as development, operations, or investment—and considering how the journeys of tenants, employees, and other stakeholders can be reinvented. Then, they can begin redesigning roles and structures to make the alignment happen. Getting value from gen AI requires that executives be willing to question the industry’s traditional hierarchies and operating models and, most important, to accept a new technology layer throughout the organization. Gen AI requires executive-led adoption of new ways of working that will elevate the power of professionals across functions and levels

Tags : Generative AI Matt Fitzpatrick Vaibhav Gujral & Associate Partners Ankit Kapoor Alex Wolkomir Mckinsey’s