Real Estate 3.0: Why the Future of Property Investing Depends on Data, Not Just Tokenization
Industry leaders from Blocksquare, Building Inc., Magma, and Bricken explain why the future of tokenized real estate depends on data infrastructure, digital twins, and AI-ready assets.
For years, the real estate tokenization industry has focused on a single objective: digitizing ownership.
The premise was simple and compelling. If real estate could be represented on blockchain infrastructure, ownership could become more accessible, more liquid, and more global. Investors would no longer need millions of dollars to gain exposure to high-value properties, and markets that had traditionally been fragmented by geography and regulation could become increasingly connected.
That vision has driven much of the innovation in the Real World Asset (RWA) sector over the past decade.
Yet as tokenization matures, a growing number of industry leaders are beginning to ask a different question.
What happens after ownership is digitized?
According to the executives helping build the next generation of real estate infrastructure, ownership alone does not create transparency, trust, or liquidity. Those outcomes depend on something else entirely: data.
During a recent discussion on the future of Real Estate 3.0, leaders from Blocksquare, Building Inc., Magma, and Brickken explored what they believe is the industry's next major challenge. Their message was remarkably consistent.
The future of tokenized real estate will not be defined by how many properties move on-chain.
It will be defined by how well those properties can be understood.
"Ownership Alone Doesn't Create a Market"
Denis Petrovcic has spent years helping bring real estate assets onto blockchain infrastructure. As CEO of Blocksquare, he has witnessed the industry's evolution from an experimental concept into a growing global market.
Yet despite the progress, he believes many people are still focused on the wrong problem.
The challenge is no longer whether ownership can be tokenized.
The challenge is whether investors can make informed decisions once that ownership exists.
"Valuation data usually looks at comparables and market transactions of similar assets," Petrovcic explained. "The challenge is that this is a very complex soup of information that in the end points to just a couple of data points that are really important for a potential investor."
His observation highlights a reality that every real estate investor understands. Buildings generate enormous amounts of information throughout their lifecycle. Market data, valuations, maintenance records, financial reports, occupancy rates, operational performance, and regulatory documentation all contribute to the investment picture.
The problem is that this information rarely exists in a format that is easy to analyze, compare, or trust.
As tokenized real estate grows, that challenge only becomes more visible.
Ownership records may tell investors who owns an asset, but they reveal very little about whether that asset represents an attractive investment opportunity.
For Petrovcic, the next phase of market growth depends on transforming fragmented information into usable intelligence.
Without that layer, tokenization remains incomplete.
The Industry Still Lacks a Common Language
While Petrovcic focused on the complexity of real estate information, Matthew Schneider believes the industry's larger problem is inconsistency.
The CEO of Building Inc. argues that real estate generates no shortage of data. In fact, the sector produces enormous volumes of information every day. The issue is that there is still no common framework for organizing it.
"We don't have a common language between buildings, between markets, and ultimately between tokenization platforms," Schneider said.
That lack of standardization creates challenges at every level of the investment process.
A property owner in one country may report performance differently from a property owner in another. Asset managers often use different methodologies. Valuation assumptions vary. Important operational data may be buried inside spreadsheets, PDF reports, or proprietary systems that cannot easily communicate with one another.
As a result, investors spend significant time interpreting information that should already be standardized.
Public markets solved this challenge decades ago through reporting frameworks and disclosure requirements that allow investors to compare opportunities using a common set of metrics.
Private real estate has never fully achieved that level of consistency.
According to Schneider, creating a common language for real estate data is one of the most important prerequisites for scaling tokenized markets.
But standardization alone is not enough.
Trust matters just as much.
"It's not good enough that data is circulating," he said. "We want to be able to verify the numbers that we're working with."
This is where blockchain infrastructure begins to serve a role that extends far beyond ownership records. Verification, provenance, and transparency become increasingly important as investors rely on data to make decisions.
For Schneider, the long-term opportunity is not simply moving information between systems. It is creating information that can be trusted across platforms, markets, and jurisdictions.
Looking Beyond the Ownership Record
If Schneider is focused on creating a common language for real estate, Matthieu Merchadou is focused on expanding what investors can actually know about an asset.
The CEO of Magma believes one of the industry's biggest misconceptions is that ownership information alone is sufficient.
In reality, ownership is only a small part of the story.
"Ownership can only carry so much information as the title," Merchadou explained. "It doesn't give a real view of what the asset is, what the risk of buying this asset could be, what is the governance about this asset, what will be my capital expenditure on this building."
Those are precisely the questions sophisticated investors care about most.
Understanding a building requires more than knowing who owns it. It requires visibility into how the asset operates, how it is maintained, how it performs over time, and what future costs or opportunities may emerge.
This is why Merchadou advocates for creating digital representations of buildings throughout their lifecycle.
Rather than viewing a property as a static asset, he envisions a future where buildings continuously generate structured information about their equipment, materials, maintenance history, energy performance, renovations, and operational activities.
"By creating a digital representation of the building during its lifecycle, you lower the risk for investors," he said.
The significance of this approach extends well beyond property management.
A digital representation of a building creates a richer understanding of the asset itself. It provides investors with greater visibility into risk, allows operators to identify inefficiencies, and creates datasets that can eventually support more advanced forms of analysis.
In many ways, this is where the concept of Real Estate 3.0 begins to emerge.
The goal is no longer simply digitizing ownership.
It is digitizing understanding.
Why AI Makes the Data Problem Impossible to Ignore
As the discussion shifted toward artificial intelligence, the panelists largely agreed that AI could become one of the most significant beneficiaries of a standardized real estate data layer.
At the same time, they were quick to point out that AI cannot solve a problem that the industry itself has not yet solved.
When asked about the role of AI agents in investment decision-making, Petrovcic responded with a simple analogy.
"Can an AI agent buy you a pair of shoes if it doesn't know what your size is?"
The point was difficult to argue with.
Artificial intelligence depends entirely on the quality of the information it receives. If data is incomplete, inconsistent, or inaccurate, AI systems are forced to make assumptions.
Those assumptions can produce dramatically different conclusions.
"If data is incomplete, agents will have hallucinated opinions that can differ widely," Petrovcic warned.
The implication is clear.
Before AI can improve investment decisions, the industry must first improve the quality of the underlying information.
Schneider sees AI as a powerful accelerator rather than a replacement for investors. Advances in compute power and machine learning are making it possible to process large volumes of information in ways that were previously impractical. Data can flow between systems, support valuation models, improve reporting, and help investors navigate increasingly complex markets.
But the foundation remains unchanged.
Better AI starts with better data.
The Next Users of Blockchain May Not Be Human
Ludovico Rossi, CRO of Brickken, offered perhaps the most forward-looking perspective of the discussion.
While much of the blockchain industry has spent years trying to simplify adoption for human users, Rossi believes the rise of AI agents may fundamentally change the equation.
"We saw blockchain technology getting adopted, but really slowly," he said. "But now with agents, everything is changing."
His argument is not that AI will replace investors.
Rather, it is that autonomous systems will increasingly become participants in digital economies, helping analyze information, automate workflows, and interact with digital assets.
In that environment, the importance of structured information becomes even greater.
"Only with readable and clear data and information will the agent be able to take the right decisions," Rossi explained.
Like the other panelists, Rossi ultimately returned to the same conclusion.
The future depends on data.
Whether information is being consumed by an investor, an institution, or an AI system, the requirement remains the same. Data must be structured, verifiable, and understandable.
Without that foundation, the promise of automation becomes difficult to realize.
The Real Meaning of Real Estate 3.0
What emerged from the discussion was not a vision of buildings trading like stocks or AI replacing investment professionals.
Instead, it was something more fundamental.
The leaders building the infrastructure for tokenized real estate increasingly believe that ownership is becoming a solved problem. The industry's next challenge is creating a framework that allows assets to be understood, compared, verified, and analyzed at scale.
That framework is the data layer.
It is the layer that sits between ownership and decision-making. It is the layer that transforms information into intelligence. And it is the layer that may ultimately determine whether tokenized real estate reaches institutional scale.
Because while tokenization tells investors what they own, the data layer tells them whether they should own it.
The companies building Real Estate 3.0 are no longer focused solely on digitizing assets.
They are focused on making those assets understandable.
And that may prove to be the more important innovation.
