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Global Real Estate Outlook 2026: Real estate in the age of AI - Mapping tomorrow’s landscape
8 min read 2 Dec 25
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Introduction
Real estate has been, and always will be, heavily influenced by structural changes. The rise of Artificial Intelligence (AI) is an increasingly important theme – one which is beginning to dramatically change our world and have widespread consequences for the sector.
The release of ChatGPT in November 2022 is widely seen as a pivotal moment for making AI mainstream. Over the three years since then, there have been rapid advancements in the capability of AI which has, in turn, created a global rush to integrate AI solutions across industries.
When people consider what AI means for real estate, their first thought might be about the requirement and growth potential for data centres alongside wider digital infrastructure. But data centres simply house and make accessible the computer power required to enable AI to be used by people and businesses in the wider world, in a whole range of settings and a whole range of places. This is the bigger picture – how AI will increasingly be used in our everyday lives and which therefore will influence and change when, where and how real estate of all kinds will be used and required.
We can look at different areas of people’s lives and contemplate how AI will affect each of these areas. In this paper, the well-established triptych of “live, work and play” is supplemented by a fourth category: “thrive”.
- Work – What job types and economic sectors are impacted by the adoption of AI and how will economic opportunity be distributed in the future? As professional roles are transformed and yet navigating ambiguity, trust and collaboration becomes imperative, we foresee talent and businesses congregating in fewer but larger global hubs at the expense of secondary locations. Bifurcation of office markets will intensify.
- Live – What are households’ preferred living locations and accommodation types within a shifting employment landscape? For the living sectors – including student housing, multifamily and senior living, structural supply-demand imbalances will persist or even increase in locations with abundant jobs, culture and experiences, while extended independent living boosts demand for amenity-rich senior communities.
- Play – What consumer habits will define spare time preferences in a period of further AI integration? Augmented reality will make retail predictive and personalised, driving demand for smarter, more immersive stores. AI companions may increase social isolation and therefore the need for authentic, in-person leisure. Automated logistics and autonomous vehicles could enhance the value of peripheral distribution mega-hubs and lead to the repurposing of centrally located car parks.
- Thrive – With ageing populations, what impact will AI have on longevity and the provision of healthcare services? We anticipate a growing active senior population due to AI-led healthcare breakthroughs, changing how health services are provided, an increase in residential demand globally and more fractional, part-time or advisory professional roles.
The four components clearly have overlap and interaction. The links between “work” and “live” locations, for example, may be altered by AI, but there will still be a fundamental connection via commuting patterns. “Thrive” and “play” interact across a range of locations and activities, such as in mixed-use developments which could combine retail and healthcare occupiers.
The advent of AI is a growing and unique industrial revolution, with likely profound impacts in both the short and long term across societies and on the real estate used by individuals and businesses. Even in an increasingly fractured world, businesses and talent maintain some degree of mobility and will converge in locations that offer attractive end destinations for a more AI-integrated future.
This paper explores the potential impacts of AI across people and corporations, influencing how they interact with real estate. Not all effects are obvious and there are many uncertainties over how events play out, but real estate owners should start thinking about how these new structural shifts impact their portfolios and investment strategies moving forward.
Work: The evolution of employment
Key takeaways
- Professional roles will be transformed as routine tasks are automated but demand rises for skills in navigating ambiguity, building trust and creative collaboration.
- Arguably more flexible, hybrid work weeks will be adopted.
- Fewer but bigger key hub cities as talent and businesses congregate for face-to-face idea exchange and events. These cities would invest heavily in AI infrastructure, data and intellectual property rights protection.
- Bifurcation of office demand and rents to intensify.
With AI’s advancing capabilities and widespread adoption, there has been ongoing debate about its impact on the labour market, particularly whether it is beginning to supplant human labour. According to an IMF study, 60% of jobs in advanced economies are exposed to AI1. Of these, half are expected to be negatively impacted, while half stand to benefit from higher productivity from AI integration or augmentation. On a global scale, nearly 40% of all jobs are likely to be affected as the technology is increasingly assimilated in business processes.
Areas and roles involving tasks such as coding, language understanding, and logical reasoning are likely to be more impacted. For example, according to the recent AI Index Report released by Stanford University2, AI systems in 2023 could solve just 4.4% of coding problems on SWEBench, a widely used benchmark for software engineering, but performance increased to 71.7% in 2024.
Uneven impact on the workforce
Jobs like data entry, scheduling and the elements of customer service which entail repetitive data processing face near-term obsolescence as AI’s accuracy and scalability improve. Other jobs that involve innovation with a fair bit of complexity such as breakthrough R&D could be more resilient (albeit AI will increasingly be involved). AI is also likely to struggle when it comes to replicating roles that require emotional intelligence, human trust and navigating ambiguity.
Based on company-level analysis by Seyed Hosseini and Guy Lichtinger at Harvard University, early evidence suggests that AI adoption at the workplace has coincided with a material decline in junior hires, while senior employment remains unchanged. The decline in entry level roles is concentrated in occupations highly exposed to generative AI, with low-exposure occupations less impacted. This could mean that AI’s widespread adoption may shift work away from entry-level tasks, potentially narrowing the bottom rungs of internal career ladders.
Winner and losers
While it is still early stage in the AI adoption cycle, the impact on jobs will largely depend on how employers – and employees – ultimately put the technology to best use. With AI likely to have a significant impact on job security and income propensity, we expect highly mobile skilled professionals to migrate towards hub cities that harness, invest and regulate AI, to maximise their earnings potential.
These cities would invest into AI infrastructure, carefully adopt the latest available tools and proactively regulate intellectual property rights, data integrity and privacy issues well. It would be natural for companies and capital to follow the flow of talent to these advanced economies in order to compete. Capital begets capital, and talent begets talent, creating a positive feedback loop. Once a city becomes like a London or a Tokyo, where there is a constant flow of capital and talent, it is challenging to unseat due to its incumbency.
As such, it will be fundamental for real estate investors to track how countries are proactively engaging with AI such as implementing the right policies and fostering the ecosystem for AI to ride this wave of change, and invest in “future-ready” cities.
Bifurcation to persist, and even strengthen
With the adoption of AI tools in redesigned work processes and increased gains in productivity, more organisations could adopt flexible, hybrid work weeks such as normalising shorter work schedules. For example, Microsoft Japan recorded a 40% productivity gain in a 2019 four-day work week pilot that closed offices on Fridays and halved meeting times – and they continue to offer this to their employees to this day3. This would mean that employees are also more likely to return to workspaces for tasks that involve in-person collaboration, knowledge-sharing and are typically challenging to perform remotely.
With the eventual inevitable rise in deepfake related fraud, face-to-face interactions would be almost a prerequisite in building trust and relationships. This shift could elevate the importance of central business districts (CBDs) in these global hub cities given their strong transport connectivity and sheer agglomeration of talent and businesses for idea exchange, meetings and events. Essentially, these CBDs facilitate the establishment of in-person relationships because headquarters, elite universities, research facilities, think tanks, clients, regulators, competitors and the media are typically disproportionately concentrated in these locations. Talent would naturally gravitate towards these key business districts for work that require the development of personal rapport and familiarity. As such, offices in secondary cities and/or non-core locations could be less relevant over time.
This could further exacerbate the bifurcation of office markets. In the last few years, clear bifurcation of demand and rents have already emerged in many growing cities.
For instance, in Sydney, where new office supply has been made available, net absorption has focused mainly in prime districts in 2022-2025, in contrast with previous cycles. The gap between prime and secondary rents has also widened to a record 50% in 2025 from 30% in 2022. Across European markets, recent data indicates core CBD leasing volumes now represent three-quarters of total space taken, compared to 60% prior to the pandemic, accelerating the obsolescence of non-core sub-markets4. This stronger tenant demand has translated into an outperformance on the rental side, with prime rents (typically representing new buildings within CBDs) growing at twice the rate of average office rents over the last five years (4.6% p.a. vs 2.3% p.a.)5. This trend could well continue over the next few years, meaning there could be bigger central business hubs alongside a smaller number of decentralised employment districts as they diminish in importance.
Live: Housing preferences across life stages
Key takeaways
- Elite universities with strong networks and AI-resistant skills will thrive, while weaker institutions, and the student housing linked to them, are likely to struggle.
- AI enables location freedom, but creative collaboration and human nature favour proximity. Expect tenant demand to concentrate in cultural and experience hubs with expanding commuter catchments.
- Extended independent living means older generations can choose lifestyle over necessity, unlocking demand for amenity-rich communities and mobile living.
If AI changes how – and where – we work, it will also create a shift in where people choose to live at every life stage. For real estate investors, understanding these emerging patterns isn't just interesting, it's essential.
Student living: A market in flux
The student housing sector may face its most profound challenge as AI offers new, viable alternatives to how – and what – we can learn as well. This could be increasingly important as the skills required for traditional white-collar graduate roles – the mainstay of mass-market university education – start to shift with AI becoming more embedded in the workplace.
Against this backdrop, students will need to stand out – not only from an increasingly competitive pool of graduates but also from AI-driven automation. In our view, this will mean gravitating towards prestigious and globally-renowned universities offering genuine value-add credentials and strong networking opportunities or those that specialise in courses that emphasise AI-complementary skills, creative thinking and innovation.
Meanwhile, institutions that rely on offering degrees in easily-automated fields or that fail to innovate by swapping dry, easily-replicable lectures for active collaboration sessions may struggle to fill places. Evidence of demand-led polarisation is already visible: in the UK, applications to high-tariff universities (those requiring high grades for entry) are now 65% higher than those to low-tariff institutions, compared with near parity a decade ago6. This reflects a focus on greater value for money from higher education amid falling graduate earnings’ premia and heightened scrutiny of course quality. AI could now accelerate a new wave of polarisation across the global markets as it disrupts the traditional skill sets that are associated with career success.
The investment implication is stark. Student housing linked to the wrong universities could face significant challenges and possible obsolescence. Institutional relevance will become the decisive factor in whether student accommodation succeeds or fails.
Working life: The great dispersion vs re-clustering
For working-age adults, AI's impact on residential will likely be nuanced and led by lifestyle choice more than ever before.
AI-enhanced remote work and improved logistics could enable unprecedented flexibility around where you live, with digital nomads potentially representing one emerging segment: highly mobile professionals given the choice of moving between ski resorts, beach locations and cultural hotspots based on lifestyle preferences, not office locations. This suggests growing demand for fully-furnished, short-lease properties with hotel-like amenities, perhaps delivered through global subscription services with smart technology that personalises each space before arrival.
Yet not everyone craves this freedom from place. Humans still value stability and proximity and history suggests new technologies often strengthen, rather than weaken, global cities. Railways, telephones and the internet all reinforced clustering in major hubs, despite expectations to the contrary.
The most resilient white-collar jobs that AI cannot fully replace (like those requiring high-value creative thinking and in-person collaboration) will continue to anchor talent in experience-rich urban centres where innovation happens and like-minded communities form. Cities such as London, Paris, Beijing and Tokyo offer a combination of talent, innovation and experience that could see them consolidate their status as global living hubs, supporting sustained growth in multi-family demand.
There is also a ‘Goldilocks’ alternative: an expanding commuter belt. Combining the need for occasional urban collaboration with other AI technologies like autonomous vehicles and improved delivery/logistics capabilities make longer commutes and life away from urban centres more viable. Families seeking space, affordability and good schools can now live further from city centres without sacrificing career potential. Meanwhile, AI can even help them identify communities that align perfectly with their values and interests. This would suggest an acceleration of the doughnut effect, boosting the growth of suburban hinterlands at the expense of closer, but expensive, alternatives.
The losers in this reshuffled Living landscape? Cities lacking appealing social experiences or vibrancy, and outer urban areas that combine high costs and limited space with weak cultural amenities: expensive inconvenience without compensating benefits.
Senior living: Independence reimagined
The senior segment presents perhaps the most counterintuitive opportunity. On the one hand, AI will better enable ageing in place and delay leaving the family home. Smart home technology, autonomous vehicles and fall detection systems, among others, can extend independent living far beyond previous limitations, while AI companions can also combat rural isolation. The traditional driver – necessity – that would push seniors to downsize and focus on assisted housing near to family could weaken. Growing numbers of ‘stay at home’ seniors would exacerbate existing housing under-occupation issues, supporting an even greater need for appropriate new multi- and single-family housing for other generations, whether for sale or to rent.
Yet longer, healthier lifespans don't eliminate the need for purpose-built senior living. Instead, we may also see growing demand for independent living communities emphasising experience, connection and amenity, or, equally, more active seniors themselves embracing digital nomad lifestyles, spurred on by the ability to stay connected wherever they go. Key is that this demographic will be able to afford to prioritise experience and community over proximity to healthcare or family.
Destination living
The AI revolution might not simply change where people live; it could fundamentally alter why they choose specific locations. ‘Destination retail’ has been a growing theme as the impacts of e-tailing have unfolded, with successful retail now being defined by much more than just the shops themselves. Could AI mean we will now see the beginning of ‘Destination living’, where residential choice is driven less by proximity to traditional factors like work and more by access to innovation, experience and lifestyle?
Play: Accessing leisure and retail spaces
Key takeaways
- Elite universities with strong networks and AI-resistant skills will thrive, while weaker institutions, and the student housing linked to them, are likely to struggle.
- AI enables location freedom, but creative collaboration and human nature favour proximity. Expect tenant demand to concentrate in cultural and experience hubs with expanding commuter catchments.
- Extended independent living means older generations can choose lifestyle over necessity, unlocking demand for amenity-rich communities and mobile living.
As AI becomes our closest companion, it will redefine how we socialise, shop and experience the world. As the digital and physical worlds merge, this shift will ripple through real estate, converging the leisure, retail and logistics sectors. For real estate investors, recognising these shifts is critical: locations and asset types that thrive today may be obsolete tomorrow.
Retail: The end of excess, the rise of experience
Retail has already moved away from being confined to a place, but it will evolve to become a predictive, personalised experience that follows the consumer, not the other way around. AI will personalise every aspect of the shopping journey, from what we see, how we see it, to what we buy. For consumers, advanced technologies, such as AI-driven fit tools and virtual try-ons will reshape the shopping experience, making it hyper-personalised and more accurate, reducing the errors and returns. For retailers, AI enables precise forecasting of consumer behaviour and location-based demand. The implication is clear: smarter stores with reduced (more efficient) stockholding will create opportunities to repurpose space into immersive, experience-driven environments, further blurring the boundaries between retail and entertainment.
Yes, digital advancement and AI adoption will change how, when and where we shop, but it will not replace physical retail. In fact, its role will grow in importance. Physical retail will pivot to what a digital-only retail transaction cannot replicate, creating real value as retailers optimise the in-store experience through interactive technologies, including augmented reality (AR) to increase immersion and drive customer engagement.
We believe retailers and real estate investors should prioritise implementing AI across their flagship stores, transforming them into experiential hubs that attract and retain footfall. Those who fail to adapt run the risk of obsolescence in an experience-driven retail landscape.
Leisure: Digital dependence vs in-person experience
If retail shows us how AI influences what and where we buy, leisure offers a glimpse into how AI will shape how we socialise, interact and spend our time. AI companions will move beyond conversational tools and evolve into emotionally responsive systems. For isolated demographics, AI can fill social gaps, reducing the need to leave home, potentially driving the decline of cafes, gyms and pubs.
Paradoxically, the rise in digital companionship will elevate the value of authentic, in-person experiences. Sensory immersion, spontaneity and emotion are experiences AI cannot fully replicate; as the demand for genuine human connection increases, experiential formats will strengthen – immersive dining, interactive installations and AR/VR technologies will redefine how we engage with spaces. With millennials and Gen Z’s prioritising experiences over possessions, the winners should be those who build their offer around engagement and immersion, while standardised leisure formats quickly lose relevance. This has already started to play out in London and other major UK cities, with over 1.6 million square feet of prime space dedicated to competitive socialising and immersive venues7. However, this is already highlighting a clear divergence in market momentum, with London emerging as the dominant hub for immersive retail formats.
As the value of authentic connection grows, cities capable of staging meaningful interactions will lead. We expect demand will concentrate in adaptable global cities such as London, Paris, New York and Tokyo, places that offer compelling cultural, gastronomic and design-led experiences. These are the cities people will travel to and increasingly choose to live.
Blended realities will emerge as retail, leisure and hospitality converge. AI-enabled environments will transform shopping centres, hotels, restaurants and theatres into immersive, interactive playgrounds. These spaces become more than destinations; they evolve into epicentres where identity, community and experience collide, defining how we “play”. For investors, this polarisation is critical; value will concentrate in major global cities capable of delivering appealing immersive experiences. This shift will have clear implications for not only where and how people congregate, but also where they choose to live.
AI-driven delivery: Rewriting the rules of location
As retail and leisure shift towards personalised, experience-led spaces, AI will change the rules for logistics, making location and scale less predictable and opening up new possibilities for how and where goods move. Proximity to cities and reliance on human labour will matter less as goods move autonomously, reshaping where future demand and investment concentrate.
The global freight market is already under strain and is likely to worsen. According to the International Road Transport Union, Europe is expected to face a shortage of 745,000 truck drivers by 20288. AI is poised to fill this gap, with autonomous trucks stepping in where labour shortages are most acute. The rollout of self-driving trucks is likely to remain gradual compared to passenger vehicles, but momentum is building. Goldman Sachs forecasts there will be 25,000 autonomous trucks in the US alone by 20309. The question for real estate is not if this shift will happen, but where it will land.
Thrive: Health and longevity impacts
Key takeaways
- Hospital admissions and stays could be shortened with more patient interactions moved to outpatient facilities and medical offices.
- Earlier detection, faster drug discovery and lifestyle interventions could extend life expectancies materially in the next decade, increasing residential demand globally.
- Longer healthspans may mean an explosion of senior professionals securing fractional, part-time or advisory roles.
From hospitals to hubs
AI is beginning to reshape healthcare delivery in ways that could fundamentally alter demand for medical real estate. Predictive diagnostics, machine learning-assisted imaging and remote monitoring tools are already reducing the need for prolonged inpatient care, with early evidence suggesting AI-guided interventions can cut preventable hospital admissions by nearly 30% and shorten length of stay for discharged patients10. Over time, high-acuity treatment is likely to consolidate in specialised centres, while routine monitoring and preventive care become increasingly decentralised.
This shift creates a distributed footprint of care: more frequent, shorter patient interactions delivered in community-accessible, lower-cost environments. Under this model, activity moves away from traditional hospitals toward outpatient facilities and medical office buildings (MOBs), which offer flexible layouts for diagnostics, consultations and same-day procedures. In the UK, the NHS has explicitly acknowledged this trend in its 2025 long-term plan, calling for a shift “from hospital to community” and “from analogue to digital.”11
The US already illustrates the potential scale: a distinct, large medical office building (MOB) market with roughly 1.8 billion square feet of stock and an estimated US$490 billion in investable value, nearly 40% of which is investor-owned12. MOBs have proven easier to acquire, lease and manage than traditional hospitals, offering predictable income streams and operational flexibility. Europe and developed Asia have to date lagged behind the US – in part due to regulatory restrictions and public opposition to private ownership of healthcare assets. But a broader transformation in the composition of medical real estate, driven by AI-enabled models of care delivery, would help unlock these markets and expand global investment potential.
Where might these medical offices be built? Existing suburban office parks facing structural obsolescence could be repurposed for outpatient and diagnostic facilities, creating modular, flexible healthcare hubs. At a broader level, markets that successfully integrate predictive AI tools are likely to see more efficient, distributed care networks emerge. This, in turn, could channel capital into well-located MOBs and community-based facilities, effectively expanding the investable footprint of healthcare real estate.
AI and longevity
The advancing ‘silver wave’ of ageing populations stands as one of the defining socio-economic shifts facing developed economies. The integration of AI into healthcare research and delivery has the potential to reshape the dynamics of this trend, extending both lifespan and healthspan – the period of life spent in good health. AI is already transforming ageing research through tools such as biological age modelling, predictive interventions and accelerated drug discovery. These methods allow researchers to more accurately assess biological age from patient data, identify early markers of age-related disease and design targeted therapeutics that address the underlying processes of ageing13.
Global examples already demonstrate this potential in practice. In Singapore, an innovative trial using AI-powered retinal imaging has demonstrated the ability to predict cognitive decline and dementia risk up to five years in advance, enabling earlier intervention and care planning14. Collectively, advances like this offer the prospect of older adults to remain active, connected and economically engaged well into their 70s and 80s, redefining not only longevity but the very experience of ageing itself.
Longer healthspans will reinforce the shift toward extended working lives, helping to ease pressure on dependency ratios and fiscal balances by keeping more people economically active for longer. Older white-collar workers are well placed, being highly concentrated in roles that AI cannot replace: client-facing, advisory or managerial positions where experience and judgement carry greatest value. Fractional, part-time, or advisory arrangements are likely to grow, enabling highly experienced professionals to remain economically active while reducing the need for daily commuting.
Such roles will be predominantly anchored to major central office hubs rather than local back offices, but consistent with existing patterns of lower commuting frequency for older workers, they would likely visit these offices only once or twice a week at most. Therefore, the continued engagement of senior workers in the workforce via fractional work is likely to further entrench hybrid working models.
Longevity living
Patterns of senior housing are also likely to shift. One of the clearest effects will be a reduction in demand for traditional dependency-based care homes. While high-acuity and end-of-life needs are likely to persist, AI-enabled interventions and predictive healthcare will facilitate a broader shift from dependence to autonomy. Even in locations such as Southern Europe, which tend to favour traditional family care models, rising health and autonomy may gradually open opportunities for alternative housing formats.
Many older adults will choose to remain in their own homes or communities for longer though, supported by AI-enabled health monitoring, telemedicine and adaptive living technologies. This will exacerbate existing market inefficiencies, particularly surrounding the issue of under-occupied homes after children have left home, underscoring the increased need for more appropriate and flexible housing options.
Conclusion and implications
Even as the technology evolves apace, it is clear AI is going to change the way we live, work, play and thrive. There will undoubtedly be an evolution in demand – both positively and negatively – for different types of real estate, while locations could evolve into winners or losers. AI’s development, skill and usage are advancing rapidly and it is tempting to assume that dramatic impacts will be felt quickly. Yet history shows we often overestimate what will change in the short-term and underestimate what will change over the long-term.
As with previous technological shifts, expectations around AI often skew toward extremes – either overly optimistic or unduly fearful about immediate disruption. Yet the most profound impacts typically unfold over time. Real estate, by its nature, is a long-term investment, which means that even if near-term changes prove modest, investors must anticipate and plan for structural shifts – or even transformative revolutions – that are likely to emerge over a longer time horizon.
Authors and contributors
Richard Gwilliam, Head of Property Research & Chair of Property Investment Committee
Regina Lim, Head of Property Research, Asia
Emma Grew, Director: Property Research, UK
Tom Colthorpe, Associate Director: Property Research, Europe
Dickson Koh, Manager: Property Research, Asia
Jordan Rainey, Senior Associate: Property Research
Tom Kynaston, Senior Associate: Property Research
Artificial Intelligence, Contributor
