The basic business model of real estate development is pretty straightforward: construct a building and then lease out the space.
Presumably, the cash flow from the rent will cover the investment cost plus a profit, thus creating real monetary value for the developer. It’s interesting to note that what’s rented out is actually nothing at all—it’s just empty space. What makes that space valuable are the support systems that define it: the building enclosure and the structural, mechanical, electrical and plumbing systems. The empty space has no intrinsic value in and of itself; it’s just a void. The space is divided up into rooms of various sizes and functions, thus setting the stage for human activities to take place within. Space only becomes valuable when it’s inhabited and used for productive purposes—an empty building generates no rental income. This is how developers make something out of nothing.
That’s a simplistic way to explain the utility of buildings. However, something else is going on. The people who inhabit buildings are generating something that is far more valuable than the space itself: data. For many years, the data created by people in buildings was an unrealized asset. Nobody bothered to collect or analyze the information; it was allowed to burn off, like the natural gas from a wildcat prospector’s oil well. With the advent of sophisticated computational analytics, that is beginning to change, which in turn is transforming the fundamental value proposition of creating and inhabiting buildings.
The automobile industry serves as a useful analogy. Cars have long been regarded primarily as a means of getting from Point A to Point B. They cost money to buy, operate and maintain, and that was pretty much it. Relevant metrics were few, primarily just speed (miles per hour) and fuel consumption (miles per gallon). However, the advent of autonomous and electrically-powered vehicles has changed the game entirely. Now in addition to providing a means of transportation, automobiles have become generators of enormous amounts of data, and that data is proving to be extremely valuable. In fact, according to Brian Krzanich, CEO of Intel, “bits and bytes will replace petroleum as the primary fuel for the world’s economy.” (Source: Automobile magazine, November 2017)
Cars are now routinely equipped with cameras, GPS systems and multiple sensors that measure a variety of performance characteristics. It has been estimated that by the year 2020, 75 percent of the world’s cars will be connected to the Internet, and the autonomous vehicle market is projected to explode from $800 billion in 2035 to $7 trillion in 2050. Today, just one autonomous vehicle can generate 4,000 gigabytes of data per day; that is expected to grow to 100 gigabytes every second. What is being done with all that information? For starters, it can be used by insurance companies to incentivize safer driving habits, by vehicle repair shops to target customers whose cars need maintenance, by emergency service providers to improve response times and enhance highway safety, and by regulators to reduce emissions and automatically collect tolls. Drivers can program their cars to take the most efficient routes, factoring for real-time traffic conditions, and they can seek conveniently located gas stations, restaurants, motels and parking spaces. Indeed, the “connected driving experience” has the potential to totally transform how we think about how we get around.
“Bits and bytes will replace petroleum as the
primary fuel for the world’s economy.”
What has all this got to do with architecture? Plenty. The trick is to think of buildings as “cars without wheels.” The same basic principles of data collection and analysis can be applied just as easily to buildings as to cars, and given that people spend far more time indoors than on the road, both the amount and the value of that data is proportionately higher. The data is available for free, and it can be collected, analyzed and sold to generate considerable cash flow. This is the basic value proposition of Google and Facebook, who provide “free” services in exchange for access to data, which is then packaged and sold to advertisers at enormous profits. (It should be noted that data from individuals is essentially useless; aggregating data from many users is what creates value.)
How might this affect architectural practice? Rather than be compensated on the basis of a fixed fee or percentage of construction cost, designers might be paid a “toll” collected automatically from each user who enters the building or by a royalty pegged to the data generated (like royalties in the music industry). The data would be extremely useful in understanding the actual metrics of occupancy: how much space is really needed and how it is actually used, how much energy is expended at different times of the day and year, when preventive maintenance is required, and so forth. Because the capital cost of a building is only about 10 percent of the long-term cost of ownership and maintenance, the ability of designers to fine-tune their work with the benefit of real metrics has the potential to produce truly exceptional value for clients. (To put this in perspective, a 10 percent reduction in operations and maintenance cost over the life of the building would more than pay for the original cost of construction.) The data collected could provide an income stream that would make traditional fee structures obsolete. Imagine, for example, what would happen if Google ran an architecture firm; it could provide free professional services in exchange for the right to mine the data generated from every structure it designed. Under this scenario, data analytics could be seen as a design skill, and as a result architects would become true experts in evidence-based design. Design outcomes could easily be validated, so that architects could be paid based on actual building performance. This takes nothing away from the idea that design also deals with issues of form, materiality, and beauty; it merely adds leverage to the architect’s bag of tricks.
The idea of data-mining as an integral part of the design process may seem far out, but the business model is already in place and well tested; it’s essentially what happens when you purchase an iPhone and log on to Facebook. It’s a very short conceptual leap from self-driving cars to “self-driving buildings.” The Tesla has already fundamentally changed the driving experience; it offers breathtaking acceleration and is so thoroughly networked that it’s essentially like driving a fully equipped office. Imagine if the experience of building occupancy could be similarly transformed; what would that feel like and how much might it be worth to clients and users alike?
The concept of data as the new currency of design creates huge opportunities for architects, engineers, consultants and contractors, not to mention owners. Design which is informed by data analytics can not only enhance the way that physical objects like buildings are created and constructed, but it can also optimize the processes that make them run. This opens the door to a whole new realm of design thinking. While this may seem futuristic, the technology already exists and the business model is in place. Opportunity is knocking … the only question is who will answer.
Scott Simpson is the editor-at-large of DesignIntelligence and a Senior Fellow of the Design Futures Council.
Excerpted from DesignIntelligence Quarterly.