Generative AI could create between $110 billion and $180 billion in value for the real estate industry, but only if agencies rewire the way they work to make the best use of the technology.
According to research from McKinsey Global Institute, agencies will need to do more than simply learn how to use AI tools, including adopting agile ways of working like tech startups and collecting and and controlling unique data to generate valuable insights they can use to forward plan their future.
Although Generative AI (Gen AI) has only recently captured the imagination of the wider public, Artificial Intelligence has been around a long time.
Analytical AI, as it is known, is already well embedded in the business world in things such as AI-assisted forecasts, which have altered how investment professionals think about the future.
But traditionally, the real estate sector has been slower to take up new technology.
According to McKinsey, “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”.
But the research also notes that, so far, many real estate agencies have found it hard to implement and scale use cases, so they haven’t experienced the technology’s 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,” the research notes.
Four use cases for Generative AI in real estate:
McKinsey notes that Gen AI’s strengths are customer engagement, content creation, analysing data to develop insights into trends or patterns and coding solutions.
Here are four ways Gen AI can help in real estate:
- Sift through large volumes of dense leasing documents to extract key information at scale, like monthly rents or what market forces could affect leases. It can also generate summaries and tables to help you examine compiled data.
- Create an AI-powered bot to manage tenant requests, improve communications, and assist with high-stakes negotiations by monitoring interactions and providing suggested responses.
- Enable custom virtual visualisations of spaces to help prospective tenants envision finishes, furnishings and increase prospect-to-lease conversion rates. There are also potential e-commerce tie-ins by letting prospects order the visualised furnishings.
- Analyse internal and external data to identify and prioritise investment opportunities out of available properties, making faster and more informed decisions on assets worth further investigation.
McKinsey also outlines seven ways you can change the way you work to realise the full value of Gen AI.
1. Create a business-focused strategy for executives in a specific real estate sector
To thrive in the era of Gen AI, McKinsey says real estate CEOs need to adopt a forward-thinking approach, similar to successful startups and tech-native companies.
This includes prioritising technology, bringing in new skills, and adopting agile methods.
Embracing gen AI goes beyond just keeping up with the latest trends; it’s about staying strategically ahead and being ready to innovate and disrupt things yourself.
This transformation doesn’t necessarily mean a massive influx of new tech hires.
Instead, it calls for a small, but effective team of engineers and designers who are well-versed in gen AI, that should focus on creating real value in specific areas of the real estate value chain.
Leaders should first identify their position in the real estate value chain, which could be in development, operations, or investment. They should then reimagine how they can enhance the experiences of tenants, employees, and other stakeholders. This requires a shift in both roles and organisational structures to align with these new goals.
“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 organisation,” McKinsey recommends.
Gen AI necessitates a leadership-driven change in working methods, amplifying the capabilities of professionals at all levels and across various functions.
2. Adopt a laser focus on data
Real estate companies need to focus on collecting unique, proprietary data about tenants and properties.
The quality and structure of this data set, along with robust data governance, are crucial, according to the McKinsey research.
AI tools, trained on specific data like building maintenance or financial performance, can provide valuable insights and improve decision-making.
Internet of Things (IoT) sensors and computer vision technology can offer anonymised insights into how spaces are used, enhancing understanding of tenant behavior.
It’s important for real estate agencies to store this data in their own ‘data lakehouse’ to maintain control and allow for flexible interaction with various vendors.
Ensuring data ownership and easy accessibility should be a key consideration in choosing technology vendors, aiding in the development of effective future tech infrastructures in real estate.
3. Create a prompt library
McKinsey also recommends agencies develop a prompt library to help their team get the most out of foundational Gen AI.
Prompts guide the AI, and their effectiveness depends on how well they are formulated, particularly as the AI is refined with real estate-specific data.
An example prompt might direct the AI to use resident history and property data to draft a lease renewal email, followed by a personalised follow-up offering specific concessions based on the resident’s profile.
The effectiveness of the prompts hinges on nuances in syntax, detail, and framing, as slight modifications can lead to vastly different outcomes.
Since there’s no established way to predict the most effective prompts, McKinsey said a methodical approach, involving testing and refining, is needed.
4. Create digital tools that prompt action
McKinsey said agencies need to focus on creating digital tools that prompt action, not just provide insights.
While large language models have gained popularity for their ability to generate content, often what they create still needs further work to be truly useful.
For example, an AI tool might create marketing copy, but it often needs extra checking for grammar, brand compliance, and regulatory adherence.
Similarly, while a Gen AI model can suggest customer service strategies for real estate agents, it might need prompts at specific times during client interactions, along with explanations of the recommendations.
Design elements are also critical in gen AI tools.
Unlike traditional apps where color, style, and physical design patterns are key, in gen AI interfaces, your brand’s writing style, tone and pitch become more important for customer-facing avatars.
These need to be fine-tuned to appeal to the audience and encourage desired behaviors, marking an expanded definition of design that incorporates psychological aspects of interacting with algorithms and machines.
5. Invest in a modern technology stack to enable data use
For gen AI to be effective in real estate, investing in a modern, well-integrated technology stack is essential.
This tech stack must be secure, scalable, and user-friendly, encompassing the right infrastructure, feedback mechanisms, safeguards, and integration.
Gen AI introduces new requirements beyond traditional AI and machine learning, such as toxicity checks to prevent the creation of problematic content that might violate legislation, and guardrails against producing inaccurate responses.
Unlike traditional data science, gen AI demands unique elements in the tech stack to be functionally operational, which might be beyond the capabilities of current IT setups in many real estate agencies.
McKinsey said agencies that start adapting their technology stacks early, focusing on practical trials and future-use orientation, will be better positioned to capitalise on gen AI.
This involves thoughtfully integrating vendor systems and connecting various data sources, such as property management systems, customer relationship management, and maintenance portals.
6. Adopt an operating model that can scale
To accommodate the growth and diversification of a real estate portfolio, adopting a new operating model that can scale with gen AI technology is essential.
This upgrade will necessitate a redesign of operating models and roles to align with new work priorities, including the introduction of roles like prompt and data engineers for implementing foundational models.
Existing roles, such as agents or on-site staff, may shift to focus on specialised tasks and offloading their routine duties to gen AI tools.
In areas like marketing and investing, gen AI could transform the discipline itself, creating a need for new roles and skill sets.
“Companies need to be open to change, because the face of the IT or marketing organisation will not look the same with AI tooling, even if the objectives of the business unit remain the same,” McKinsey noted.
7. Recognise and mitigate mistakes
Gen AI in real estate is a developing field with unique risks, and McKinsey believes agencies that proactively identify and address these risks, will be better equipped to manage them.
“There may be biases in training data that are unintentional but create outputs with real consequences,” McKinsey said.
“There may also be questions about the intellectual properties feeding foundational models as the legal precedent around the space evolves.
“Marketing content, for example, may emerge from an algorithm trained on unlicensed images, catching the real estate business anawares.”
“Companies that identify risks early on and iterate to find improvements will be positioned to react effectively.”
