Nuveen Real Estate foreword

At Nuveen Real Estate, we believe in investing in tomorrow’s world. By aligning our investment strategies with structural drivers of real estate demand – technology, sustainability and demographics – we aim to deliver both investment excellence to our clients and have a positive impact in local communities. The real estate industry is embarking upon an initial extended period of technological transformation, which has seen an acceleration due to the coronavirus pandemic and the resultant necessity to evolve.

Data is increasingly important for all stakeholders in the value chain, from investors to end-users, and is a critical enabler to enhancing the user experience of real estate, as well as achieving climate and social goals. As we move into a future where the importance of data continues to grow, there are valuable lessons that the real estate industry can learn from other industries that have already experienced (or are experiencing) digital disruption and will need to become more sophisticated in how it commands data. We are proud to partner with Osborne Clarke on this white paper to discuss key opportunities and challenges that this presents for the industry.

Lesley Smith

Head of Legal, Europe & Asia Pacific
Nuveen Real Estate

Osborne Clarke foreword

Our clients’ businesses are being profoundly changed by the impacts of digitalisation, the imperative of decarbonisation and – for the real estate sector in particular – the changing dynamics of our cities and urban environments.

The coronavirus pandemic has created an era of extreme unpredictability. We’ve seen sudden radical shifts in day-to-day occupation and use of real estate – even of whole urban districts. Although there’s a strong sense that the way we live, work, travel and connect in our cities won’t revert to pre-2020 patterns, there’s not yet any real clarity about what our new habits will look like. How we make use of our offices in the future, and the impact of home working, is one area being very hotly debated. Faced with all of this fluidity, the visibility and insight that data can generate becomes an end in itself. But beyond that, we’re increasingly working with clients seeking to convert the raw material of data into new business models, efficiency for asset management, and deeper and more collaborative relationships between landlords and tenants.

We’re delighted to have had the opportunity to work with Nuveen Real Estate to develop the thinking that we present in this white paper. We hope you find it timely and engaging, and we are looking forward to discussing the content further with you.

Conrad Davies

Partner, Head of Urban Dynamics
Osborne Clarke

Section 1

Data has always been crucial in real estate. But the types of data available from real estate for use by parties such as investors, landlords and tenants are expanding exponentially. The value of data is being enhanced by new technologies able to collect, interrogate and exploit it.

The sector is seeing new data-driven opportunities for automated efficiency, new offerings and insights, and new ways to boost the value of investment portfolios, the quality of the built environment, and the experience of occupants. Other sectors offer inspiration and learning around what can be achieved, as well as the challenges to be countered.

A much-quoted statistic from IBM claims that 90% of the data in the world today has been created in the last two years alone. To say that this figure applies to real estate today would likely be an over-exaggeration, but the industry is faced with the same technological forces that are driving change in other industries. Real estate is entering into a new era of data.

The immediate impact of the coronavirus pandemic on the real estate sector was unprecedented. Lockdowns caused workplaces, retail and other venues to empty almost overnight and remain closed for months. It is too early to know what the long-term legacy of this disruption will be, but the sudden mass shift to distributed working is likely to have altered office occupancy patterns permanently. Moreover, the emphasis on monitoring health and safety issues – whether to do with individuals’ state of health or adherence to social distancing measures – is making workers accustomed to new levels of data collection. It is widely acknowledged that part of the impact of the pandemic has been an acceleration of adoption and acceptance of digital tools. If sustained, this impetus can be harnessed to elevate the new age of data in real estate.

A long-standing need for data

Real estate has always had a complicated relationship with data. Its worth has been highly dependent on its perceived impact on the value of the investment and on the point in time during the investment cycle.

When acquiring an asset, high-quality data is critical and worth investing in. The significant transaction costs of real estate are partially due to spending on collecting, verifying and analysing data about the asset and the local market. This due diligence phase is a well-trodden path and mostly indispensable.

Once an asset has been acquired and the focus turns to managing it, many of the original incentives to invest in data change.

Established use of data in a legal context

Legal due diligence in property transactions will include the usual title information, replies to enquiries, suite of searches and tenancy records. Notwithstanding discussions and pilots to innovate around this body of data, such as providing it instantly via an online dashboard, or moving Land Registry records onto a blockchain structure, the approach to investigating a property remains largely unchanged.

An established area of data-centric tools for transactions is the use of data rooms and document management solutions. These are increasingly becoming ‘smarter’, as well as moving towards providing value beyond the point of due diligence and acquisition into the asset management phase. Workflow software solutions allow deal pipelines to be managed on a centralised platform, which may allow trends or issues to be spotted that might otherwise have been overlooked. For example, perhaps a particular sales team consistently takes longer to close deals than others – do they need additional training? Is there an HR issue in the team?

Traditionally, for successful asset management, the emphasis has been to spend as little as possible, while maximising net operating income. The landlord’s approach to data – or areas that it enables, like property management – has often not been high on tenants’ priorities during the leasing process compared with major factors of asset location, floor plate design, rent, and so on.

Once the asset is leased, the focus turns to generating returns. In a world of long-term, fixed-rent leases, it is hard to justify a lot of spending on collecting, structuring or storing data that does not have a clear, short-term return on investment. This is not to say that real estate businesses spend nothing on data – buildings have to be kept running – but the conversation around data has historically been driven primarily by what is needed to keep the building operational (for example, data linked to repairs, maintenance, smooth operation of services, etc.), rather than how data can enhance the experience of tenants and end-users to generate additional value.

However, this is now starting to change.

Section 2
New incentives to use data – plus legal considerations

There are a number of structural trends that are disrupting traditional approaches to real estate, almost all of which are directly or indirectly elevating the importance of data:
  • Leases are getting shorter, creating greater alignment between landlords and tenants as it becomes easier for tenants to leave, increasing the focus on tenant retention. New, flexible ‘as a service’ business models for office space measure value by reference to occupancy rates rather than secure, long-term revenue streams (as, for example, has long been the case with the hotel sector).
  • Traditional landlord-tenant relationships are changing, with landlords playing a more active role within the tenant’s demise, blurring the lines between where the landlord’s role ends and where the tenant’s role begins. This might include the landlord being invested in the tenant’s performance within that demise (through turnover-derived rents and the subsequent challenge of multi-channel purchasing), or playing a greater role in the physical design and fit-out of the workspace, the digital and communications infrastructure, meeting room and desk booking.
  • The balance of power is shifting towards end-users (employees and customers), and their experience within the space (including wellbeing) is becoming increasingly important. Property management is evolving from being solely focused on the operation of the building to enhancing the end-user’s experience of the space.
  • New technologies are enabling the capture of more data types with increased detail, enabling greater insights and services to be built, paving the way for smart buildings, workplaces and stores with the ultimate end point of integrating them into a smart city.
  • There is an increasing prevalence of public sector organisations as tenants within privately managed real estate, particularly within mixed-use schemes.
  • Climate change is increasing the focus on sustainability and energy efficiency for both real estate investors and tenants. Data-driven technology is supporting environmental objectives both within a building and in the ways in which it is powered.

Collectively, these changes are shifting the incentives for landlords and property managers towards understanding their tenants’ needs and patterns of use to a much greater degree, as well as how the end-users of the space are using the building (including inside the tenant demise) on a day-to-day basis. These insights can play directly into returns and revenue streams by minimising void periods and reducing reasons to consider exercising a break provision, or to justify higher service charges or a premium rent.

For example, data might indicate that a particular part of the floor plan is infrequently used – does this suggest that the area is not configured in a way that matches the tenant’s needs? Can the landlord facilitate reconfiguring that area, for example with a capital contribution? Such insights can directly impact on the outcome of rent renegotiations, etc.

Building information modelling (BIM) data from the construction phase can be built out and added to, to become a digital replica and resource for the building. Future occupiers could have clear information about hidden service conduits, or links to operating manuals for installed appliances.

In addition to using sensors, cameras or software to collect data about building usage, there is a growing data marketplace where third-party datasets can be accessed or purchased. It may be possible to boost proprietary data by combining it with external datasets. A smart heating control system might be integrated with weather predictions so that, for example, the blinds on a building are automatically lowered to reduce the building’s temperature in anticipation of an expected hot afternoon.

Equally, for ‘as a service’ occupancy-based business models, understanding patterns of use and user preferences, and having real-time visibility of how the space is used, becomes central to maximising the attractiveness of the space and the value of the asset. Aggregated insights about actual use can be fed into future design decisions as new assets are developed or existing spaces refurbished.

Landlords of residential schemes might use data and digital platforms to drive convenience, such as keyless/touch-free or programmable access using an app, or smart home functionality such as smart thermostats and lighting.

The evolution of real estate legal advice

Real estate lawyers are finding that it is no longer enough to be an expert in the sale and purchase of properties and asset management such as the grant of leases. As purchasers acquire more data with a property and landlords offer wider commercial services to occupiers, property lawyers are increasingly expanding their expertise and collaborating more frequently with other specialists (in relation to data, cybersecurity, etc.) in order to deal with provisions that might more traditionally have been found in a services agreement.

New data-driven legal tools

There is current private and public investment in efforts to create a secure data platform that could revolutionise access to real estate data. The approach seeks to use AI to create a common data model without the need for property owners or investors to change their existing systems. It could also enable data to be aggregated from multiple other sources, and to be analysed through ‘knowledge graphs’ to unearth and map the often unseen valuable connections across the various types of data.

Professional service providers, funders, insurers, counterparties and others could all use the platform to access their client’s property data (augmented with data from third-party sources) that they need, whether for review in delivering their services, to use in documentation, or to input into their own models. This could clearly help improve efficiency in transactions as well as throughout asset management and operations.

Data protection

Any data that concerns an identifiable individual is subject to the protections of data privacy legislation. It must be ‘processed’ (i.e. stored, used, transferred, etc.) in accordance with these laws. Individuals must be given specified information about this processing, and their data disclosed to them on request. The law is often backed up by heavy sanctions, and is continuing to develop, meaning that it can be challenging to adhere to.

The legislation also means that personal data can only be processed if it comes within one of the listed allowable legal bases for how the business is using that data. This must all be worked out in advance, and documented.

Some data, on the other hand, is not personal (e.g. the number of people accessing a particular area of a development), or can be collected or processed in a way that avoids it being personal (e.g. scanning shoppers’ feet to measure footfall rather than using facial recognition technology, or aggregating data so that it ceases to be about individuals).

Care is needed to consider whether privacy regulations are engaged by data-capture and processing tools, and to ensure compliance where needed. Businesses introducing new ways of capturing and using personal data will usually need to undertake formal data protection impact assessments, reviewing what data they have, what they use it for, how long they keep it, and whether they are taking the optimal approach to preserving individuals’ privacy.

The key is to sort out the data protection position at the beginning of a data project, at a stage when most issues can be identified and resolved in a way that does not risk breaching the law.

We are moving beyond the traditional focus on gathering data about the financial and operational condition of the asset towards harnessing the output from sensors and interfaces capturing greater quantities of data about real-time conditions in and around the building and making ever more extensive measurements about the end-users of real estate. As more and more smart buildings exist within a city, ultimately we can envisage their integration into smart city systems. These will present a new challenge for management due the significant increase in the complexity of available data, but will also create an extraordinary opportunity for the industry to use this data to create insights, new services and new business models.

Of course, the flip side of the benefits of data and the connectivity that generates it is the increased risk. Data is often called ‘the new oil’ but, if mismanaged, it can become a liability instead of an asset, such as the situation with an oil spill. Cybersecurity becomes a proportionately more significant area of risk with greater use of digital tools and collection of data. Security is needed not just around digital databases and operating systems but in relation to physical network cabling as well (particularly where it passes through and/or is located in shared or common areas). Added to this, globalisation and the use of cloud-based systems will often necessitate data transfer beyond one jurisdiction.

Data security

The deployment of data-driven software and storage of databanks often involves third-party suppliers, particularly where cloud services are used. Every digital connection to a third party represents an extension of the ‘attack surface’ – another digital back door into the business. It is essential to include cybersecurity issues in procurement due diligence and to make sure that suppliers’ cybersecurity practices and accreditations are checked and rechecked over time. Cybersecurity insurance is available but, even where it is in place, a robust contractual framework dealing with the apportionment of liability in supply arrangements will be an essential risk-management tool.

Similarly, the data governance practices of suppliers to which data will be transferred need to be rigorously reviewed, and contractual liability and controls agreed. For example, it is important that a supplier is under a clear obligation to notify the customer if a data breach or other security incident occurs that could impact on that customer or on the individuals whose data is held by the supplier. More generally, where personal data of individuals is concerned, under the GDPR and other data privacy legislation, in many cases it will be mandatory to impose particular forms of contract wording.

As complexity of data collection and management increases, data governance considerations also become more critical. Some see data as a proprietary asset, conferring commercial advantage. Others see it as a raw ingredient to be shared as open data, for others also to benefit from. Some see it as a tradable asset, generating its own revenue stream. In truth, it is all of these and more.

Data governance

Ensuring appropriate governance around data is essential, whether or not it is subject to regulatory compliance requirements. There is growing public awareness of potentially problematic uses of data, especially in light of recent publicity around potential discrimination, bias or other unfair outcomes for individuals when data is manipulated by algorithms.

Data governance is important where data is shared, and is becoming a significant element in leases of tech-enabled space. There is not yet any settled approach to sharing data either in the real estate sector or more generally, but issues such as storage, deletion and access of/to the data need to be considered. For example:

  • Can the landlord have access to data collected by the tenant? And vice versa?
  • Is the data sharing remunerated? Financial provisions will need to consider the value of the raw data as well as the appropriate allocation of any new value derived as a consequence of sharing it.
  • What happens to legacy data on assignment or transfer of the property, or on grant of a new lease to a new tenant?

Some or all of these issues may need to be considered and agreed as part of a governance framework.

Governance frameworks are often contractual, particularly in relation to bilateral data-sharing arrangements. Legal structures such as data trusts are also developing to facilitate the pooling of data in multilateral sharing situations, usually with a view to promoting fairer access to data and ethical use of it.

The real estate sector needs to become more sophisticated in how it approaches data in order to avoid pitfalls and manage risks, seeking to unlock the benefits that this data can bring while balancing privacy, ethical and legal considerations.

Intellectual property rights and contracts

The legal status of data can be confusing. On the one hand, data is acknowledged to be an increasingly valuable asset. On the other, it is often said that pure information cannot be ‘owned’ in the way that property is owned. In fact, although coverage is patchy, intellectual property (IP) law can help to secure the value of data. For example, collections of data might well have database right protection, and will often be covered under trade secret or confidential information laws.

When embarking on any project involving potentially valuable data, the key is to set it up so as to optimise the position from the outset, making sure that there is a record of data collected or created, showing when it was obtained, and by whom, as well as documenting and enforcing access restrictions.

There is a flip side: depending on the situation in which data is acquired, accessing and using it without the right permissions may well infringe IP rights belonging to other businesses. Again, planning and preparation reduce the risk of being unable to use and exploit valuable data fully.

Contractual arrangements are fundamental to maximising the value and utility of data, especially given that formal IP protections are so important. The right contractual frameworks can ensure that, to the extent that there are IP rights in the data, as far as possible ownership is established and will not be contested, and the owner can control who else can use them. Or, if the IP rights are to be owned by someone else, the agreement can be designed to ensure that the necessary access rights are granted, including the ability to pass the data on to others if required.

As well as protecting the IP position, these contracts are also important for complying with data protection legislation.

Data-driven technologies

Digital data doesn’t exist in isolation. It is invariably part of – often the fuel for – a wider technology ecosystem.

A great deal of data is collected by Internet of Things (IoT) devices and systems. Room temperature, air quality, footfall, etc. may all be monitored in real time by networked sensors and relayed back to a central database. Data may also be collected from software systems – for example, access systems may record when a person entered and left, their movement through the building, use of lifts, etc. IoT technology and networks underpin smart buildings and their wider integration into smart cities. Sensors can also be used to collect real-time data to colour a ‘digital twin’ of a property. A digital twin is a virtual model of a physical asset. The twin replicates not only the design or shape of the asset but has additional layers of information and modelling built into it to the point that it mirrors many aspects of the operation of the physical asset. Changes, adaptations or new technology can be trialled and refined on the digital twin before making any changes to the actual asset.

Blockchain or ‘distributed ledger’ technology is very hyped and there are still only limited examples in any sector of it being in widespread use. But there is no doubt that it offers a secure yet transparent and auditable way to store and share data. It is often cited as being a particularly appropriate platform for collaboration between parties who do not trust each other, such as competitors. Blockchain also has the ability to carry or represent value: the potential for tokenisation of real estate to open up or ‘democratise’ property investment is much discussed. The value of an asset is split into much smaller fractions, and each fraction is represented by a cryptoasset or ‘token’. The tokens are tradable on a blockchain network – in a similar way that shares in a company are traded. However, as with the issue of tradable shares, tokenisation projects may well fall within the perimeter of financial regulatory requirements, which may be judged too burdensome to set up.

Artificial intelligence (AI) is both driven by data and is a powerful tool to realise its value. Machine learning systems are able to find patterns across big data, and to improve and refine their outputs with additional data. Indeed, the big data generated by the internet and mobile connectivity is one of the reasons for the current surge in interest in artificial intelligence applications. Equally, artificial intelligence tools can be used to take decisions based on past or real-time data. The timing of repairs and maintenance work can be optimised based on past patterns of wear and tear on a carpet, or on a part in an air-conditioning system. AI can also be used to draw on an individual occupant’s personal profile to generate personalised services (such as concierge offerings based on past requests or logged behaviours).

Augmented and virtual reality tools are increasingly being used as data-visualisation aids. A brownfield site can be brought to life for investors with a 3D digital model of what will be built, viewed through augmented reality headsets. The data that underpins these visualisations (architect’s drawings, interior designs, or plans showing the location and configuration of heating, ventilation, air conditioning (HVAC) and other infrastructure) may become correspondingly more valuable. Virtual and augmented reality are also being used to support training.

Section 3
Lessons from other sectors

As digitalisation sweeps across all commercial and industrial sectors, data is a consistently important ingredient. Lessons can be learned and ideas for innovation gleaned from other industries that are further ahead in their data journey than real estate.

Automotive industry

The automotive industry offers strong parallels, particularly in terms of how business-model change is leading to a fresh approach to data. The automotive industry has historically sold products (cars) rather than providing a service (mobility). This has meant that a lot of the focus of the industry has historically been on marketing and distribution. Once a car has been sold, the focus moves on to selling the next car, rather than to continually enhance the experience of the journey. Because of this, there was little need for car manufacturers to collect much data after the point of sale. If data was collected, it was typically focused on areas like pre-emptive maintenance rather than actively enhancing the driving experience.

However, in recent years this has begun to change. Cars are now competing on software as well as hardware. Customers can choose between outright purchase, various financing options or even short-term street hire. Data collection happens more strategically across the lifecycle of the vehicle, with a view to providing additional value to users of their cars after they have bought them, for instance data from cameras on the car, speed, accelerating, breaking, mileage, etc.

The automotive industry’s shift in focus is starting to be mirrored in the real estate sector, particularly in relation to office buildings. Historically, all the focus has been on selling a product (a long-term lease) rather than providing a service (a productive day at work). As leases become shorter and landlords become more of a ‘workplace partner’ to tenants, the incentives increase to capture granular data on how the space is used, which can be leveraged to enhance the consumer experience. This is ultimately because real estate is increasingly having to compete on services associated with a building, (technology, hospitality services, etc.) not just the physical building itself (location, floor plate design, etc.).

Although collecting data on the usage of a building (such as areas of highest traffic or customer footfall) is not new, analytics based on that data is becoming increasingly influential as decisions are taken around the amount of space dedicated to experiential factors such as fitness areas, breakout areas, catering areas, wellness areas and so on. In a pre-pandemic world, a building with multiple uses was certainly the direction in which things were headed. It remains to be seen what the long-term impacts are, if any, on demand for these types of buildings where space is designed to maximise user time spent there. Again, the data will illustrate any changes.

Financial Services

The Financial Services sector handles a vast amount of transaction and other financial data, as well as information about companies and individuals. This is a heavily regulated sector, with many traditional products and services and extensive legacy technology. But the Financial Conduct Authority also actively seeks to facilitate digital innovation through initiatives such as its regulatory ‘sandbox’ – often cited as one of the reasons for the global strength of UK fintech companies. At the same time, the more traditional retail and SME banking sub-sector is in the vanguard of the UK’s ‘smart data’ initiative. Regulation aimed at boosting competition has required the retail banks to build digital interfaces so that holders of a bank account can choose to pipe transaction data from their accounts out to a third party.

As a major investment asset class, known particularly for its reduced volatility, real estate has strong links with the Financial Services sector. But might there also be potential for the real estate sector to learn from the open-data initiatives in Financial Services, in releasing data collected about an individual at that user’s request? Although real estate is not regulated as such, property law and the associated institutions such as the Land Registry have a significant influence on the sector. Involvement of the Land Registry in supporting innovative projects is to be welcomed and has clear parallels with a ‘sandbox’-type approach.

Energy and Utilities

The Energy sector is similarly a regulated sector. Digital technology is proving critical to the decarbonisation of the sector. Renewable generation assets are far less controllable than traditional generation so data about weather forecasts, past use patterns, etc. is fed into tools to predict future supply and demand levels, facilitating the increased complexity of grid balancing. Data is also driving algorithmic trading in energy markets, and in turn is powering creative ways to supply power, such as virtual power plants and sophisticated power purchase agreements.

Although real estate assets tend to be unique, whereas one unit of energy is interchangeable with any other, the real estate sector might nevertheless be able to learn from the data-driven sophisticated flexibility of energy supply arrangements to end-users. This might be the case, in particular, where businesses move wholly or partly to a distributed workforce model following the disruption of the pandemic. Perhaps there is scope for real estate to be supplied in a similarly flexible way that matches (or ‘balances’, in energy sector language) available supply from a landlord’s portfolio with a specific tenant’s demand at any given point in time. Achieving this ‘balancing’ in real time, from moment to moment, might be unnecessarily ambitious for real estate space, but the concept would be an expansion of the existing concepts of ‘pop-up’ retail space, or flexible office space. Or perhaps data might support reconfigurable space that could readily take on different uses, depending on prevailing demand.


The Retail sector has been in a process of digital transformation for many years, with traditional retail facing extensive disruption since the advent of the internet and growth of e-commerce. One of the consequences of online sales is a huge body of data about customer transactions. Customer profiles are also built from tracking purchases via loyalty card registrations, etc. Some retailers or retail platforms have turned customer profiles into a revenue stream in themselves. More generally, customer profiles are used to create more personalised ‘customer journeys’, shaping offers to the customer’s known interests. Behind the scenes, data and digital technologies are used to boost the efficiency of manufacturing, to automate tracking of stock and product returns and to ease employee communications.

Retail real estate has suffered with the growth of e-commerce. The response has been to use data and digital tools to understand customer demand and preferences and to use that knowledge to drive new experienced-based offers to re-energise physical retail. Stores themselves are one of the front lines of innovation in digital management and monitoring of physical space. Although much of this innovation is going on inside individual stores, landlords of larger retail assets are increasingly investing in similar technologies to derive insights on their consumers and how they interact outside the store.

There are clear lessons for the wider real estate sector on creating spaces that are shaped by data, and about using that data to develop associated services that enhance the physical space and improve the experience of the user – for example, optimising space for a productive day at work. Post-pandemic, office-based working environments may no longer be the universal default, so landlords need to offer an attractive, inviting experience to outweigh the travel-free convenience of working from home.

Life Sciences and Healthcare

Life Sciences/Healthcare has a huge collective body of data about both patients and products (e.g. pharmaceuticals and treatments), but this is often siloed within each organisation and the value in such data can make sharing arrangements all the more complex to agree. A significant part of this sector is state-funded and so there is extensive interaction between public and private bodies at all levels of the sector. Much of this sector is subject to regulation. Collaboration is growing to unlock data held in public and private silos in order to drive drug development, healthcare provision and ultimately to benefit patients.

The Healthcare sector handles data that is extremely sensitive. Although this can make data sharing more complex, it is clear that individuals are happy to entrust their personal data to groups if they have clear information about the purposes that it will be used for and trust the governance of that data. In real estate, the reasons that data is being collected may not be as transparent in many cases and real estate actors have not yet earned the trust of being responsible data managers. This will have to evolve in the coming years. Individuals will need to understand the benefits to them of sharing their data.

Regulation and innovation

Regulation, as regulators and legislators themselves acknowledge, has to take care not to block innovation, or make compliance disproportionately burdensome and complex. It is often the case that regulation lags behind new technology and this can act as a drag anchor on innovation if misaligned rules have to be navigated. On the other hand, where new technology creates risks but is not (yet) regulated, innovators may face funding issues if investors are concerned that a business plan might be adversely impacted once the underlying technology receives regulatory attention.

As a matter of principle, regulation tends to be outcome- or principle-based, rather than prescribing specific actions or approaches. This creates space for innovation, with new practices or tools needing to comply with a generic principle or a particular customer/consumer outcome. On the other hand, it can be complex to understand quite what the impact of innovative technology will be – there are striking examples of the law of unintended consequences across the tech sphere. Sometimes, moreover, a lack of specific requirements can make compliance more difficult if businesses can’t be sure they’ve met the required objectives, as the regulator understands them.

Clarity is needed on all sides. Part of the challenge can be the need first to educate a regulator about the new technology before compliance can be discussed. Regulatory bodies tend to be very aware of this and many invest significant effort and resource in building their own digital skills. The main regulator for individuals’ personal data – the Information Commissioner’s Office – is a good example, partnering with cutting-edge research organisations such as the Alan Turing Institute and running collaborative sandboxes with innovators, to ensure it can offer practical, informed guidance to businesses.

Section 4
Real estate has unique characteristics

Real estate has several unique characteristics when it comes to use of data, which can make data collection more complex. The lessons from other sectors needed to be viewed through the lens of the particularities of the sector: it is essential to be aware of, and proactively manage, the risks that collecting, storing and analysing data can create in a real estate context.

First, real estate has many layers of actors and the ultimate consumer of property (the occupants or a retail tenant’s customer) may be at some degrees of distance. Investors pass management of their portfolio to asset managers. There may be a number of intermediary tenancies between the landlord and the physical user of the building. Much of the data is ultimately collected on the activity of individuals (consumers or employees) – even if it is anonymised through aggregation or not personally identifiable. Although (as noted above) this is changing, there may not be any contractual provision to enable access to that data to others in the chain. This layered structure can present material challenges for complying with obligations to provide data protection notices, or where explicit consent is required for personal data collection. In practice, the difficulties of compliance with privacy legislation may mean that the better option is to design the digital system to avoid collecting data that triggers such obligations – although this may constrain what can be achieved.

Second, the physical nature of the built environment means that it may be much harder to provide appropriate information about personal data collection or to allow ‘opt-out’ of data collection (assuming privacy compliance obligations cannot be designed out of the application). Unlike a website (or a car), it may not be possible to create multiple different versions with different levels of data collection and use, depending on consents, etc. Opting out may only be possible by an individual not visiting the physical location. This can be especially challenging in real estate schemes that involve private management of public realm, typically in mixed-use environments. One solution might be to construct the digital services for the premises around an app, so that there is a digital interface with the individual that can be used to deliver the necessary notices, collect consents, etc., but this may not be practical (and would not address the ‘opt-out’ issue).

Third, real estate is inherently heterogeneous. Every real estate asset is unique, whether in location, tenant base, legacy technology stack, planning constraints, market norms, and so on. This means that it is less of a standardised environment in which to make top-down decisions regarding data strategy and build on value-added services. On the other hand, the typical cycle of development of sector-specific, ‘vertical’ software solutions would suggest that, from an initial fragmented ecosystem of start-ups and innovators building bespoke solutions for individual businesses, asset portfolios or even individual buildings, there will be consolidation in the longer term. Eventually, a much smaller number of versatile platforms will emerge. Big tech players may enter the market, offering tools from their cloud service offerings that can be used in this context. Data regulation compliance will be designed into the best of the early offerings and into the successful consolidated platforms. Overall, procuring technology to collect and generate value from data will considerably simplify (and reduce in price).

Fourth, real estate operates on much longer cycles than many other industries. Once built, a building’s lifespan will certainly be measured in decades and might stretch over a century. Most owners of institutional real estate hold their assets for between five and ten years. Despite continuing to shorten, recent surveys report that the average new office lease is over six years and the average lease in the market is still around ten years. In order to create a convincing business case for data investment, initiatives often have to be timed to coincide with leasing, asset acquisition/sale or construction, as these are the main mechanisms from which a return-on-investment calculation can be derived. The long cycles of real estate are also at odds with the fast cycles of innovation in technology, creating a higher likelihood of legacy technology path-dependency. Cloud-based software addresses this to some extent, with upgrades and updates rolled out centrally by the software as a service (SaaS) provider, but the requirement to periodically upgrade hardware and physical tech infrastructure remains.

Moving forward

Where will the new age of data in real estate take us?

The challenges around data collection and creating data-driven products, services and value for real estate assets are certainly numerous but, equally, far from prohibitive. Incentives are shifting in the sector so that these investments are increasingly warranted and becoming necessary. Lessons from other sectors offer shortcuts and insight, not least about tackling legal and regulatory constraints. Data will power better, more sustainable and convivial environments in our buildings, new and valuable related services for occupants, and greater insight for investors to drive value and returns.

Perhaps the greatest challenges will be the complexities of building collaboration around data. Integrating smart buildings into smart cities will need compatible technology, commercial agreement to share data, robust governance arrangements for all aspects of the collaboration, and strong cybersecurity in every aspect of the infrastructure and interfaces to ensure public and consumer trust. Integrating individual buildings and portfolios with transport systems and other city infrastructure and services also requires creative, flexible partnerships between public and private bodies. This is likely to be an incremental, gradual process but it will be fascinating to see how many of today’s complex and difficult aspirations become aspects of our built environment that we will simply take for granted in years to come.

Where does a transformative approach to data begin?

  • Start by identifying what data you already have, where it is held, and who has access to it.
  • Understand the constraints and compliance requirements that attach to that data.
  • Consider how the value in that data is protected (operational protection such as information security, but also legal protections such as securing available intellectual property rights or ensuring protections in your contractual frameworks). Make sure those protections are maximised and robust.
  • Ensure your business has a data-governance policy that shapes how all data is collected, accessed, used, shared, stored, and other overarching principles. Be aware of the additional requirements if data is being shared across numerous jurisdictions.
  • Consider next the areas of your business where you have little data. How could that be addressed?
  • Investigate whether third-party data could be sourced to boost the value of your own data.
  • In line with your wider strategy, consider how those various sources of data could together play into your objectives. What insights might it hold? What processes could it improve? What new services could it power?

The huge promise of data-driven transformation is not realised without a considerable amount of effort. Typically, this is an agile and incremental process, trying out ideas, building out the successful ones and adding to them over time. Flexibility, adaptability and interoperability are important considerations as this will be an iterative process with the underlying technology and infrastructure likely to shift over time. But data is the starting point.

Conrad Davies

Partner, Head of Urban Dynamics, Osborne Clarke
Lesley Smith

Head of Legal, Europe & Asia Pacific, Nuveen Real Estate
Tamara Quinn

Partner, IP and Data, Osborne Clarke

About Nuveen Real Estate

Nuveen Real Estate is one of the largest investment managers in the world with US$129 billion of assets under management. Managing a suite of funds and mandates, across both public and private investments, and spanning both debt and equity across diverse geographies and investment styles, we provide access to every aspect of real estate investing. With over 80 years of real estate investing experience and more than 660 employees* located across over 25 cities throughout the United States, Europe and Asia Pacific, the platform offers unparalleled geographic reach, which is married with deep sector expertise.

* Includes 310+ real estate investment professionals, supported by a further 340+ Nuveen employees.

1 ABREV/INREV Fund Manager Survey 2020. Survey illustrated rankings of 140 fund managers globally by AUM as at 31 Dec 2019
2 As of 30 Sep 2020
3 Includes 310+ real estate investment professionals, supported by a further 340+ Nuveen employees
4 Operations in Seoul through an investment partnership

Our locations around the world

  • Austria: Vienna
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  • Italy: Milan
  • Luxembourg
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  • Sweden: Stockholm
  • UK: Edinburgh, London
  • Boston, Charlotte, Chicago, Dallas, Hartford, Miami, Minneapolis, New York, Newport Beach, San Francisco, Washington D.C.
  • Hong Kong, Shanghai, Singapore, Sydney, Tokyo, Seoul⁴

Nuveen Real Estate in numbers

Top 5

real estate manager globally¹

US$129 billion AUM²

and over


working in



About Osborne Clarke

We give legal advice that is greater than the sum of its parts, combining legal expertise with sector and client understanding alongside insight into the global issues driving transformation.

Encouraging diversity and investing in our people’s wellbeing is important to us as we want everyone to be the best they can be and enjoy what they do. That way, everyone benefits, including our clients.

We’re here to help you tackle the challenges of today and tomorrow.

Osborne Clarke is the business name for an international legal practice and its associated businesses.

*Services in India are provided by a relationship firm

These materials are written and provided for general information purposes only. They are not intended and should not be used as a substitute for taking legal advice. Specific legal advice should be taken before acting on any of the topics covered.

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