THE HYPOTHESIS

This rests on a hypothesis being true: That AI access to data will become more democratised and AI models will be able to generate insights from open, and synthetic, sources.

The old, and existing, value equation relies on exclusive access to proprietary data as a moat. Firms can extract value by controlling rare or difficult-to-source information.

The new value equation will likely be based on how effectively firms leverage AI to generate unique, high-value insights and actions from data—regardless of whether that data is proprietary or widely available.

Wider access to data has been much debated, and called for, in CRE for decades. With limited results. Why might it ‘be different this time?’

DATA OPENS UP

There are five reasons:

1. AI is capable of extracting market insights from non-traditional sources (e.g., satellite imagery, IoT sensors, public filings, foot traffic analysis). For example, with pervasive building data, we’ll understand ‘the building’ and occupancy trends far more granularly than any ‘Lease Comp’. Furthermore the rise of publicly accessible alternative datasets will weaken the monopoly of proprietary CRE data providers.
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2. Decentralised & Crowdsourced Data Networks Will Grow. Increasingly companies with limited data will start to pool what they have with their peers. A current example, WIN, the Workspace Intelligence Network describes its mission as being to ‘Contribute to the growth and sustainability of the flexible workspace sector through data collaboration’. There will be others.
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3. Regulations May Force More Transparency. The EU’s Data Act and similar regulations worldwide are moving toward mandated data-sharing requirements. Cities might require real estate firms to publish anonymised rent rolls for transparency in pricing & valuation.
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4. Investor & Tenant Demand for Real-Time Insights Will Grow. Institutional investors want real-time data feeds rather than delayed quarterly reports. Tenants will expect more lease transparency and performance benchmarking, pushing landlords toward more open data ecosystems.
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5. Causal AI and the increasing use of synthetic data generated by AI models. Causal AI models (which seeks to understand cause-and-effect relationships within data) need a lot of data, but Generative AI can create synthetic data when real-world sources are low. Which removes the need for proprietary datasets. Meaning AI-powered simulations will replace reliance on historical lease comps & transaction data.

And added to this is the simple fact that the multi-modal processing of data (text, imagery, video and audio) means that AI can ‘see’ a lot more than we are historically used to. Data is becoming hard to hide.

THIS WILL TAKE TIME

This isn’t going to happen quickly. Most likely it will happen à la the 'boiling frog, with no-one noticing much difference until it turns out everything has changed.

Incumbents, obviously, will fight to protect the value of proprietary data, but over the medium term, perhaps 3-7 years, AI will seep out through an increasing number of gaps and traditional data barriers will rot from within. After this, whilst full data openness is unlikely, we’ll be in a world where AI-driven decision execution is their new value proposition.

The world does not belong to those who can hoard data.

‍And that will be a good thing. The Finance industry is so much more dynamic than Real Estate BECAUSE data is open. How many poor people do you know working in the public stock markets?

As in software, open-source wins. Eventually.

THE OLD MODEL

So, we know the old, and current, real estate model derives a lot of its advantage from information asymmetry—having data that others do not.

Which is valuable because data is hard to gather, expensive to acquire, difficult to analyse and insights take time to propagate.

And this in turn allows for high fees for advisory and brokerage services, provides an edge in underwriting and investment decisions, and enables a power play with tenants due to superior knowledge of occupancy trends.

In this model, owning data = owning value. But …..

THE COMING MODEL

The new value equation will have none of this, and instead power and business will be acquired by those with the greatest insight and ability to execute.

In this world competitive advantage won’t derive from "who owns the most data?” Rather the key questions will be:

• Who can generate the most actionable insights from data?
• Who can execute those insights faster and better than competitors?
• Who has the most AI-optimised decision-making frameworks?

This means the new value equation looks like this:

Value = (Data x AI Processing) + Execution Speed + Contextual Intelligence

In other words, CRE firms that succeed in the AI era will be those that:

• Develop the best proprietary AI decision models that make the smartest use of freely available (and some proprietary) data.
• Act on insights faster than competitors (e.g., instant underwriting, predictive leasing strategies).
• Demonstrate the strongest ‘Human+AI’ Judgement.
• Optimise workflows to make real-time data actionable, reducing lag in decision-making.

How well you use data in an AI-powered decision system is what will matter. Not merely access to that data.

Even if competitors have the same raw data, the firm with the better AI-powered decision system will win because they can act faster and with more confidence. Developing that ‘AI Synergy’ is going to be the super skill.

SIDEBAR

We've already seen this model succeed spectacularly in other industries. Renaissance Technologies revolutionised quantitative investing by focusing not on acquiring proprietary data, but on developing superior mathematical models to extract unique insights from public market data. With returns of approximately 66% annually over decades, they demonstrated that how you process data matters far more than exclusive ownership of it.

NEW DIFFERENTIATION STRATEGIES

To adapt to the new value equation, smart CRE firms will need to shift their strategies to:

Developing proprietary AI systems that leverage their unique capabilities and insights, and internal, operational data.

Removing friction from all transactions. Focussing on real-time data, and continuous processing, transactions should be as close to automated as possible.

Creating exceptional user experiences for clients, tenants and stakeholders.

Developing exclusive AI-powered insight networks where they share data with complementary companies. As in ‘WIN’ above.

CONCLUSION

The old paradigm of "who owns the most data wins" is eroding. And exactly in accordance with Clayton Christensen's 'Law of Conservation of Attractive Profits' (which states that when one part of a value chain becomes commoditised, another typically becomes more valuable) we will see the increasing availability of proprietary asset data likely shift profits to new integration points or value-added services.

Profits will concentrate where firms integrate open data into proprietary systems—whether through AI-driven insights, IoT-enabled buildings, or hyper-personalised advisory services.

It’s not that profits will disappear. They will move. To those who can position themselves as the new integrators.

The #FutureProof message is to not rely on data as a protector, and to start ‘skating to where the puck is going’.

OVER TO YOU

Do you agree CRE data will become more open? What’s your timescale? Who do you think wins, or loses?

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