AI and Generative AI among top 3 technologies expected to have the biggest impact on real estate
Record investment into AI-powered PropTech in 2022
1.6M sq m
Real estate footprint of AI companies by end of 2023 (U.S.)
PitchBook Data, Inc.; JLL Research
With new products like ChatGPT, AI’s potential to transform the global economy has captured the world’s imagination
AI has enormous potential to reshape real estate, with near and long-term impacts ranging from the emergence of new markets and asset types to innovations in investment and revenue models.
A rapidly expanding AI ecosystem and its supporting infrastructure will drive demand for real estate in different markets across the globe.
PropTech adoption has laid a solid foundation for AI integration in real estate. Organizations will need to consider how they can harness AI strategically and ethically, piloting applications before scaling to deliver value.
AI driving real estate transformation
AI is seen as a game changer by real estate professionals, but understanding of its capabilities is still low
The potential for artificial intelligence (AI) to transform businesses, industries and society has been mounting for decades. But recent advancements, have moved the science from niche to mainstream. The technology’s proficiency in writing, drawing, coding and composing has compelled corporate leaders to consider both the opportunities and threats that AI presents for their future.
For commercial real estate, it’s clear that strategically embracing AI could transform the sector. In JLL's 2023 Global Real Estate Technology Survey*, AI and generative AI were ranked among the top 3 technologies that were expected to have the greatest impact on real estate over the next three years by investors, developers and corporate occupiers. Less clear are the details of what exactly comes next. Respondents indicated the least understanding of AI and generative AI, when compared to other surveyed technologies such as blockchain, virtual reality and robotics.
Still, acutely aware of the impending change, real estate industry leaders are continuing to explore ways to harness AI’s transformative possibilities.
*JLL's 2023 Global Real Estate Technology Survey will be published in September
AI vs Generative AI – what is the difference?
First, it’s important to clarify the scope of discussion by differentiating the definitions of AI and generative AI. AI, in a broad sense, uses machine learning and deep learning algorithms to perform tasks that require the ability to learn from experience, understand complex concepts, recognize patterns, interpret the nuances of natural language and independently make decisions.
Generative AI is a subset of artificial intelligence that focuses on what its name implies – generating new content, designs or solutions. It employs advanced algorithms to create outputs, including synthetic data, images, text and music.
Both AI and Generative AI will have an impact on commercial real estate. In this article, we will focus on AI in a broader context, while specifically emphasizing the role of generative AI where applicable.
As new technology and innovation boost productivity, more people are freed to create new areas of opportunity
AI has been deemed a revolution with widespread impact
According to OpenAI, around 80% of jobs are exposed to AI disruption. This number has sparked a wave of concerns about a potential upheaval in the real estate market because of the impending changes in the labor market.
Anxiety around such change is not new. In 1589, Queen Elizabeth of England refused to grant the inventor of the mechanical knitting machine a patent out of fear that it would put knitters out of work. Now, it is universally acknowledged that mechanical knitting machines spearheaded the first industrial revolution that led to explosive economic growth and real estate market expansion.
AI is expected to boost productivity
When technology allows fewer people to achieve the same productivity level, more people are freed to create new areas of opportunity. Goldman Sachs draws on a study by MIT economist David Autor to reveal that more than 85% of employment growth in the U.S. over the last 80 years is explained by the technology-driven creation of new positions.
Additionally, according to Microsoft CEO Satya Nadella, AI service providers are making the conscious choice to explore a human-centric approach, developing “co-pilot” products designed to assist people, as opposed to “auto-pilot” products that aim to entirely replace human roles. This positions AI as a significant productivity booster. On an aggregated level, the increase in productivity is projected to augment global GDP by 14% by the year 2030.
Commercial real estate is set to feel these effects. Historically, there have been five adaptive ways that real estate responded to technological changes over time:
With AI, we anticipate a similar five-fold impact in the long run. While it remains to be seen how AI will be applied to specific sectors like healthcare and how much this growth will generate space demand, some influences are already emerging.
1. Geolocation: AI companies and investments have been observed to cluster around established tech markets. Going forward, growth is likely to be concentrated in locations where AI talent is available, namely tech hubs, innovation centers and universities.
2. Altered demand among assets: AI development calls for more and better data centers, energy grids and connectivity infrastructure.
3. New asset and product types: the birth of the ‘real intelligent building’ is imminent. AI-compliant infrastructure will become a default just as internet connections are a default feature of current buildings. AI will also help deliver net-zero buildings with high sustainability performance.
4. Revenue and investment: AI-powered underwriting and processes will enable faster transactions and more efficient understanding of properties and markets, catalyzing investments at a global scale. AI-compliant infrastructure and the ability to plug in multiple systems could also enable the expansion of ‘space as a service’ models and new revenue streams for landlords and developers.
5. Design and space function: AI will allow for experience-driven design and highly customizable environmental settings.
“JLL is embracing the AI-enabled future. We see AI as a valuable human enhancement, not a replacement. The vast quantities of data generated throughout the digital revolution can now be harnessed and analyzed by AI to produce powerful insights that shape the future of real estate.”
Chief Technology Officer, JLLT
The growth in AI is likely to translate into increased real estate demand
The growing AI ecosystem will continue to expand in selected tech hubs
Foundation model developers, such as OpenAI, are just the tip of the iceberg when it comes to the AI ecosystem. Companies involved in semiconductor hardware, cloud computing platforms, model hubs and application development all represent a growing occupier segment. The commercial real estate opportunity looks even brighter when considering the additional companies that will emerge as pre-trained foundation models are modified for specific use cases.
In 2022, the AI focus area with the most investment was medical and healthcare; followed by data management, processing and cloud computing; and Fintech. Additionally, enterprise use cases for generative AI represent a substantial opportunity, with projections reaching US$42.6 billion in 2023. Anticipating a 32% compound annual growth rate (CAGR), the market is expected to grow to US$98.1 billion by 2026 (PitchBook. Vertical Snapshot: Generative AI; 2023). According to our research, the growth in AI is likely to translate into increased real estate demand from this sector in the short term, reaching 1.6 million square meters by the end of this year in the U.S. alone.
37% of AI companies are based in the U.S.
Existing AI companies prefer specific locations. In the U.S., for example, 42% of AI companies are concentrated in the San Francisco Bay Area, followed by Boston, Seattle and New York. AI startup growth is expected to continue to center around these major tech hubs in the near future.
U.S. sector distribution of AI company occupied
JLL research shows there is an accelerating demand for AI talent, with AI job postings increasing by over 250% since the beginning of 2021. In the longer term, this means growth is likely to be where AI talent is available, namely primary and established secondary tech hubs, innovation centers and universities.
Training and using AI requires considerable resources and supporting infrastructure
Generative AI is built upon vast computing power and extensive resources. Training and inferencing AI requires infrastructure such as computing hardware, high-speed connectivity networks, power supply, cloud infrastructure and data storage that all must be housed somewhere. Additionally, the continuous expansion of AI applications will drive the need for more power, more cooling facilities and more data centers. Manufacturers and vendors of GPU and network switches will also grow, and thus require space as occupiers.
Colocation, hyperscale and edge data center markets will continue to expand globally
According to the 2023 JLL Global Data Center Outlook, the global colocation data center market is forecast to grow at 11.3% p.a. (CAGR) from 2021 to 2026. The Hyperscale Data Center Market is expected to grow even faster at an approximately 20% CAGR.
AI infrastructure location criteria gives more weight to lower energy prices and lower land costs. Factors such as competitive energy pricing and energy consumption regulations are driving growth toward less crowded markets such as Atlanta in the U.S., Malaysia and Thailand. While edge data centers continue to grow near major cities to be close to the users, a more dispersed distribution is being observed in the infrastructure underpinning AI.
AI also drives new data center design requirements. Bloomberg reported in 2022 that Meta paused its data center development pipeline in Denmark to rethink the design of its facilities to handle the workloads of AI. Data centers in the AI era need to accommodate evolving hardware needs, such as advanced liquid cooling facilities.
AI-powered solutions are already available for a broad range of real estate functions
The PropTech sector has laid a solid foundation for AI integration into real estate applications
The real estate industry has begun to proactively embrace and adopt new technologies. In JLL's 2023 Global Real Estate Technology* Survey, over 80% of real estate occupiers, investors and developers reveal that they plan to increase their real estate technology budget in the next three years.
This is powered by a maturing PropTech ecosystem. There are now technological solutions for almost every aspect of real estate functions, including investment management, design and construction, building and facility operations and portfolio management. A solid foundation has been laid for AI integration.
Globally, there are over 500 companies providing AI-powered services to real estate and already delivering value in terms of improved efficiency and cost-savings. Top use cases of AI include:
Document sorting and data standardization for portfolio data analytics and benchmarking
IoT data mining for automated facility management
Price modeling and prediction for investment management
Satellite image processing for asset valuation and risk management
Reality capture for construction site monitoring
Scheduling for construction and capital projects
Recommendation and matchmaking for leasing and investment transactions
Generative AI applications in real estate are still in the early stages
Emerging use cases cover client communication assistance in leasing and property management (such as chatbots to handle tenant queries), floorplan and design generation and summarizing unstructured documents to create reports. More products are expected to come onto the market soon. Large Language Models, in particular, provide the ability to extract insights from vast amounts of text-based documents in real estate, significantly reducing the complexity of multi-lingual, multi-national operations.
Transform how your buildings run
Technology driven by real estate expertise enables smart space utilization, data-driven decision-making, sustainability, worker productivity and high ROI
Royal London Asset Management, a leading UK investment firm, experienced significant improvements in HVAC operations and energy efficiency in an 11,600 square meters commercial office building. By implementing JLL’s AI-powered Hank technologies, the firm has reached a record ROI of 708% and energy savings of 59%, reducing carbon emissions by up to 500 metric tons per year.
Case studies: AI creating value for real estate
AI in PropTech will continue to grow. JLL research shows that in 2022, the total capital raised to fund AI-powered PropTech reached US$4 billion globally, almost double the total amount raised in 2021. Venture capital (VC) is the main driving force backing the development of AI products. Among all AI-powered PropTech companies, over 70% are VC-backed. About 20% of companies are in the very early incubator, angel or seed stage; 25% are at early-stage VC rounds; and 15% are at late-stage VC rounds. Overall, this ecosystem is young and energetic.
“AI is helping to streamline our industry. As venture capital investors, we have seen many experiments with the latest AI capabilities, and the key to making the leap from pilots to successful products hinges on data quality, workflow integration and intuitive output interfaces.”
Managing Partner, JLL Spark
Act Now: Harnessing AI strategically and responsibly
There are still considerable uncertainties about the future impact of AI, the full range of its rapidly expanding capabilities and how these capabilities will be assimilated into specific industry sectors. It is crucial for real estate investors, developers and corporate occupiers to stay informed and strategic, considering how to leverage the power of AI to support your business objectives and how to do it in a responsible and ethical way.
As the regulatory landscape for AI evolves to keep pace with its growth, businesses must be vigilant about three types of emerging regulations:
Market standards and protocols concerning data quality, IP rights, privacy and data security.
Regulations to mitigate societal risks, such as measures to protect the labor market from shock or safety standards for autonomous vehicles.
Environmental legislation, notably that aimed at mitigating carbon emissions from the growing digital economy.
Organizations will need to reflect on a number of key questions as they consider the right path forward: What does the growth in AI mean for your investment and location strategies across existing (or emerging) asset classes? What existing or future applications of AI do you need to be prepared for and pilot now? What are the potential business and societal risks?
Understanding how artificial intelligence will impact your business and creating a test and implementation strategy will be key to mitigating risk and harnessing the potential for growth.
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