MIAMI—This past February, Northern Trust announced that it had deployed a production-grade distributed ledger for one of its clients, a private equity fund. Beyond signaling that the technology was ready for use in the real world, the question of widespread adoption of distributed ledger technology (DLT) became one of when, not if.
The deployment provides insight into a class of use cases—asset management—often passed over for more ambitious projects. Even when asset management is discussed in the context of DLT, the focus is often on asset registries rather than asset management functions themselves.
For a variety of reasons, explored in more detail below, implementing DLT solutions as an asset management solution not only provides a compelling value proposition, but also is suitable for early adoption. The value proposition is particularly strong for use cases involving (i) real estate asset managers and (ii) the integration of data analytics or machine learning tools, which in either case can leverage DLT to securely store the results of processed data (e.g., processed data transmitted from sensor devices embedded in mechanical systems to predict system failures before they occur) and allow for those devices to be used in a secure manner.
One of the most significant risks in any real estate transaction is informational or, more specifically, the lack of information regarding the condition of the land and the structures located thereon. With respect to the land, these issues may include the undisclosed release of hazardous materials, which could result in costly remediation once discovered, or areas subject to wetlands or conservation restrictions that restrict the owner's ability to develop the land. With respect to structures, the most common concerns surround the physical condition of the building and the mechanical systems, such as HVAC components and the elevator systems.
There are many other areas of informational risk, but these examples highlight the problem well. The approach used to mitigate against these risks today is a combination of independent due diligence and reliance on representations and warranties made by the seller of the asset.
Neither approach, however, is perfect. Due diligence is often subject to human error in searching public databases or in improper indexing by those who maintain publicly searchable databases. Many risks will not be discoverable in any database because the condition (e.g., a leaking underground tank or leaking hydraulic fluid from a mechanical system) may not have been reported to any agency that maintains records of such events.
Physical defects, such as shifting foundations or rebar not sufficiently affixed to the cement in which it was mixed, are difficult to detect without invasive testing not normally permitted in a real estate transaction. Seller representations and warranties attempt to shift this risk to the seller with respect to certain items of informational risk—and, in theory, resulting in a higher purchase price loosely correlating to the perceived reduction in risk from the buyer's perspective and corresponding increase in risk undertaking by the seller.
This relationship between risk and price is certainly very real, but the ability of transacting parties to evaluate its relationship in any objective and quantifiable manner is often limited. This leaves the parties often guessing as to the relationship between these risks and price.
These risks can be significantly reduced if buildings and the environment that makes up the land have a way to communicate reliable information, which in turn can be stored in a manner not subject to manipulation or tampering. These records can provide insight into these areas of concern with far greater accuracy. The technology to allow such communication exists all around us and is even found in our own homes today.
Once a distributed ledger infrastructure takes hold, information that was once limited to poorly maintained databases can be linked to specific properties on the ledger. This flow of information from both sensors and others with knowledge of the property can securely be associated with the property—yet giving the property owner the ultimate control over what information they choose to make available to different participants, but not being able to deceive them over the existence or non-existence of the information.
As with any industry particularly sensitive to cost, those in real estate asset management who gamble on their ability to perfectly time the adoption of transformational technology may be engaging in a winner-take-all game. Once a critical mass of cooperating asset managers is connected via DLT, there is little economic incentive for them to share any portion of the rewards derived from early adoption with those who waited.
Given the potential cost savings and competitive advantage from the elimination of informational risk, the latecomers face a dim future—if any at all. The competitive advantages provided by the combination of DLT and powerful data analytics are significant and very real, leaving little room for competitors operating with an expense structure materially higher than those leveraging DLT and data analytics.
It's important to note that it's not just about cost cutting and efficiencies, but also an improvement in the services provided by asset managers. This improvement can manifest itself in two critical areas—the level of services available to tenants in income producing properties, thereby increasing the value of the asset, and in the responsiveness and accuracy of the reporting done by the asset manager to its investors.
Like other private equity firms, real estate asset management firms often set up investment vehicles that involve intensive reporting requirements to capital providers (both equity and debt). The automation of much of these reporting responsibilities—which becomes possible with DLT, data analytics and other machine learning techniques—not only streamlines the structure of the firm, but also results in faster and more accurate reporting.
For those skeptical that such technology will exist in a mature state anytime soon, don't be—it all exists already. The only real question is which firms will usher in a new era of asset management and which will suffer a fate like those of prior companies that failed to appreciate a transformational change in their industry.
Josias N. Dewey is a financial services and real estate attorney at Holland & Knight. He is considered one of the foremost thought leaders on distributed ledger technology. The views expressed here are the author's own.
MIAMI—This past February,
The deployment provides insight into a class of use cases—asset management—often passed over for more ambitious projects. Even when asset management is discussed in the context of DLT, the focus is often on asset registries rather than asset management functions themselves.
For a variety of reasons, explored in more detail below, implementing DLT solutions as an asset management solution not only provides a compelling value proposition, but also is suitable for early adoption. The value proposition is particularly strong for use cases involving (i) real estate asset managers and (ii) the integration of data analytics or machine learning tools, which in either case can leverage DLT to securely store the results of processed data (e.g., processed data transmitted from sensor devices embedded in mechanical systems to predict system failures before they occur) and allow for those devices to be used in a secure manner.
One of the most significant risks in any real estate transaction is informational or, more specifically, the lack of information regarding the condition of the land and the structures located thereon. With respect to the land, these issues may include the undisclosed release of hazardous materials, which could result in costly remediation once discovered, or areas subject to wetlands or conservation restrictions that restrict the owner's ability to develop the land. With respect to structures, the most common concerns surround the physical condition of the building and the mechanical systems, such as HVAC components and the elevator systems.
There are many other areas of informational risk, but these examples highlight the problem well. The approach used to mitigate against these risks today is a combination of independent due diligence and reliance on representations and warranties made by the seller of the asset.
Neither approach, however, is perfect. Due diligence is often subject to human error in searching public databases or in improper indexing by those who maintain publicly searchable databases. Many risks will not be discoverable in any database because the condition (e.g., a leaking underground tank or leaking hydraulic fluid from a mechanical system) may not have been reported to any agency that maintains records of such events.
Physical defects, such as shifting foundations or rebar not sufficiently affixed to the cement in which it was mixed, are difficult to detect without invasive testing not normally permitted in a real estate transaction. Seller representations and warranties attempt to shift this risk to the seller with respect to certain items of informational risk—and, in theory, resulting in a higher purchase price loosely correlating to the perceived reduction in risk from the buyer's perspective and corresponding increase in risk undertaking by the seller.
This relationship between risk and price is certainly very real, but the ability of transacting parties to evaluate its relationship in any objective and quantifiable manner is often limited. This leaves the parties often guessing as to the relationship between these risks and price.
These risks can be significantly reduced if buildings and the environment that makes up the land have a way to communicate reliable information, which in turn can be stored in a manner not subject to manipulation or tampering. These records can provide insight into these areas of concern with far greater accuracy. The technology to allow such communication exists all around us and is even found in our own homes today.
Once a distributed ledger infrastructure takes hold, information that was once limited to poorly maintained databases can be linked to specific properties on the ledger. This flow of information from both sensors and others with knowledge of the property can securely be associated with the property—yet giving the property owner the ultimate control over what information they choose to make available to different participants, but not being able to deceive them over the existence or non-existence of the information.
As with any industry particularly sensitive to cost, those in real estate asset management who gamble on their ability to perfectly time the adoption of transformational technology may be engaging in a winner-take-all game. Once a critical mass of cooperating asset managers is connected via DLT, there is little economic incentive for them to share any portion of the rewards derived from early adoption with those who waited.
Given the potential cost savings and competitive advantage from the elimination of informational risk, the latecomers face a dim future—if any at all. The competitive advantages provided by the combination of DLT and powerful data analytics are significant and very real, leaving little room for competitors operating with an expense structure materially higher than those leveraging DLT and data analytics.
It's important to note that it's not just about cost cutting and efficiencies, but also an improvement in the services provided by asset managers. This improvement can manifest itself in two critical areas—the level of services available to tenants in income producing properties, thereby increasing the value of the asset, and in the responsiveness and accuracy of the reporting done by the asset manager to its investors.
Like other private equity firms, real estate asset management firms often set up investment vehicles that involve intensive reporting requirements to capital providers (both equity and debt). The automation of much of these reporting responsibilities—which becomes possible with DLT, data analytics and other machine learning techniques—not only streamlines the structure of the firm, but also results in faster and more accurate reporting.
For those skeptical that such technology will exist in a mature state anytime soon, don't be—it all exists already. The only real question is which firms will usher in a new era of asset management and which will suffer a fate like those of prior companies that failed to appreciate a transformational change in their industry.
Josias N. Dewey is a financial services and real estate attorney at
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