Introduction
Real estate companies are beginning to realize a hard truth: It is no longer a question of whether they need an Internet of Things (IoT) strategy, it’s a question of how and when.
With that in mind, nearly every department is scrambling to make sense of the dizzying number of options, and to create a coherent strategy. And this process is happening quickly; an estimated 242 million IoT sensors were deployed in commercial real estate properties in 2017.
For some, an IoT strategy means ensuring that an Alexa and a Nest Thermostat are included with every luxury apartment. But for those responsible for ensuring that buildings operate properly, applying the IoT may not be so straightforward.
The IoT promises to deliver something that has never existed in building operations: true transparency into the health of building systems and the indoor environment. Software fed by IoT devices can now continuously commission every piece of equipment and monitor for issues such as leaks across an entire portfolio.
And, in the same way that technology has democratized accessibility in other areas, this reality will not be confined to the Class A properties that can afford large capital investments. Unlike the 10% adoption rate of Building Management Systems (BMS) in commercial real estate, the IoT is expected to be much more ubiquitous. Still, only 9% of companies say they are currently planning a portfolio-wide adoption of IoT, 22% say they are doing so in a piecemeal fashion, and 21% say they have no plans yet.
To reach wide adoption, companies need to better understand what the IoT is, and how to implement it successfully. This article aims to help owners and operators understand the factors that go into an IoT strategy for improving building operations.
1. Edge Computing
Interestingly, some of the technologies that will enable “Smart Buildings” are conceptually familiar to building operators. Meters have long been used to record building and tenant utility consumption, and sensors have been used to trigger operations like turning the lights on. The “Things” of the IoT have existed for a long time, the only difference is that the outputs of these Things are being recorded and continuously uploaded to the Internet, or more recently, analyzed at the source.
Just as real estate professionals are beginning to wrap their heads around concepts such as the Internet of Things, machine learning, and Big Data, another innovation has emerged – edge computing.
Edge computing, simply put, is the ability for Things (meters and sensors) to analyze data at the source of collection, instead of simply transmitting that data to the Internet, or cloud.
Why is this important? Because IoT devices collect an enormous amount of data, and the process of transmitting, storing, and visualizing that data in a useful format is expensive.
Think about this: an IoT device that takes readings of equipment performance every second will have generated 86,400 data points in a single day. Multiply that by hundreds of IoT devices per building and it’s easy to see how the cost of all these data could become quite significant.
Edge computing disperses the analysis of this mountain of data too many IoT devices, instead of a centralized cloud system. This enables faster, more granular analyses, and reduces the costs involved in transferring, storing, and processing data in the cloud.
The implications of edge computing on the IoT are enormous. Not only will it bring down the costs of implementing IoT solutions, it will create the conditions for highly customized analysis. Every building is operated a little differently; instead of trying to create rules in a centralized manner that apply to a wide swath of building systems, edge computing enables IoT providers to “train” devices and allow them to “learn” the specifics of its environment.
The only catch is that there is not yet a clear consensus on how the interplay between hardware and software will be handled. Though possible, it is unlikely that hardware manufacturers will be open to everyone being able to import custom code into their devices. Alternatively, exclusive partnerships between hardware and software-focused companies may limit the extension of each aspect of the solution.
Building owners and operators evaluating IoT technologies should consider the relationship between hardware and software, as vendors that deliver both hardware and software will be better positioned to take advantage of edge computing. A successful strategy requires looking not just at current requirements, but extendibility in the future. If recent history is any indication, edge computing will develop faster than most expect.
2. Complexity
While edge computing is coming soon, there are already some ways to differentiate solutions in the current, cloud-based, environment.
Millions of data points are worse than useless to building operators. Sifting through raw data is time consuming, confusing, and worsens inertia against technological adoption. Data are only as good as the insights it generates. When vendors design analytical software to find relevant anomalies, there is some low-hanging fruit, but it is mostly complex and difficult.
For example, many Fault Detection and Diagnostics (FDD) solutions operate based on thresholds. This type of insight can be extremely useful for building operators. Some equipment is supposed to run 24/7, and so an alert that a system is no longer drawing any power can save operators from a storm of tenant complaints. In addition, thresholds are very straightforward to diagnose (i.e. if power draw is 0, then trigger an alert).
On the other hand, equally damaging issues with building systems cannot be identified with such simple logic. One example is equipment short cycling, which is when equipment shuts down and starts up in rapid succession. This can be very detrimental to equipment life and wastes energy-related costs. As the length of each cycle can vary wildly, there is no simple logic for identifying this issue; it requires a much more sophisticated analysis.
For building owners and operators evaluating IoT technologies, it can be difficult to differentiate which vendors provide sophisticated analyses, and which rely on simple logic.
There’s no direct answer to this question, but there are a few dimensions that owners and operators can use to judge sophistication.
These dimensions include the level of granularity of the data (every second vs. every minute or 15-minutes), the focus of use-types (a pump in a manufacturing plant does not operate the same as a pump in an office building), and the length of data lifespan.
IoT solutions that take readings every second, are designed for a specific property type, and maintain data indefinitely will be much better positioned to identify complex operational issues and deliver the most value.
3. Conversions
Another factor to consider when creating an IoT strategy is how the data generated by meters and sensors will be converted into meaningful information for operators.
Unfortunately, many on-site operators do not have any context for the units of measurement that IoT devices are capturing. For example, equipment performance is usually measured with the power demand unit kilowatts (kW), but very few operators could tell you what the baseline demand of any piece of equipment is.
Understanding this, some vendors have implemented algorithms to convert data into units that are used daily by their end users. While kilowatt hours might not mean anything to an operator, knowing the runtime hours of a piece of equipment is a very valuable piece of information that can inform maintenance schedules, operating schedules, and set point adjustments.
The evaluation of technology usually takes place in the C-suite, but the execution of technology happens in the field. Owners and operators would be wise to evaluate technology from the perspective of who will use it, with an eye on their existing knowledge base.
4. Range of Applicability
One of the most exciting elements of the IoT is the range of applications of the technology. While equipment performance should be focused on a specific property type, other types of IoT devices can be deployed widely and at a low cost.
For example, leaks from Packaged Terminal Air Conditioners (PTACs) are a common and expensive problem for many real estate companies. At the same time, the space temperature of tenant areas is a critical responsibility for operators. There’s no reason that real estate companies should need to engage one vendor for monitoring leaks and another for monitoring space temperature.
Historically, landlords deployed an IoT Solutions for a specific function. When outlining a new strategy specifically for the IoT, owners and operators should preference platforms that can utilize a wide range of data inputs.
5. Network Effect
Google was not the first search engine. However, because they developed a superior algorithm, people flocked to the service. Each new query adds another data point, which has been used to further refine the algorithm and attract more users. At this point, the company has created an insurmountable data advantage over other internet search providers.
IoT platforms for commercial real estate are likely to play out in the same way. The solutions that implement the important factors mentioned in this article will attract more owners and buildings. Each new building will add to the aggregate data set, which can be used to further refine the analytical capabilities of the platform, attracting more users, and eventually creating an insurmountable data advantage.
While it is difficult to predict the future, there are a few indicators to help owners and operators make educated guesses. There is generally a sweet spot for technology providers where they have achieved success in larger portfolios, have received investment from technology experts, and still have incentive to innovate.
In addition, to have a network effect, a massive data set needs to be maintained. For the same reasons mentioned earlier, storing massive amounts of data is expensive, and some vendors may opt to avoid this expense. The problem with avoiding this cost is that it eliminates the possibility of analyzing historic data, which is essential to the network effect.
Conclusion
There is a wide spectrum of use cases, budgets, requirements, and internal resources available for the IoT in commercial real estate companies.
Fortunately, the IoT is affordable, targeted, modular, and can provide immense value at any scale.
But this is only true with a strategic and successful IoT strategy. Certainly, that strategy covers more than building operations, but this article can be a useful guide for developing that aspect of the strategy.
If building owners and operators can find solutions that can take advantage of edge computing, focuses on uncovering complex insights, converts data into meaningful metrics, extends to a wide range of applications, and is poised to take advantage of the network effect, the likelihood of a successful rollout will be greatly increased.
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