Running Before You Can Walk

Imagine a world where software could analyze what's happening in a building and  predict the needs of both tenants and equipment, dynamically adjusting settings and directing maintenance technicians on the fly.

Reality check

Everyday, as millions of hours of equipment data from across the industry are compiled, we inch closer to this reality.

On the ground however, many commercial real estate portfolios, even the most sophisticated in the industry, still operate the same way they have for decades. 

Warranties collect dust in filing cabinets, vendor contact information is taped to equipment, maintenance schedules are printed out onto clipboards, and a wealth of asset-specific knowledge is known only to the chief engineer (who is likely retiring soon).

The truth is, despite the waste and inefficiency, it hasn't mattered as long as the job got done. Tenants have continued to sign leases with higher rents and asset values have increased. 

But that equation is starting to change.

The job is getting more difficult as building operations and systems become more complex, portfolios expand, tenant expectations continue to rise, and economic pressures mount from new regulations and investors looking for alpha in a frothy market. 

Meanwhile, owners have less to work with as the engineers they have relied on  for the past 30-40 years are retiring and the skills gap is widening.

What about all those management systems?

It's not entirely fair to say that commercial real estate hasn't adopted technology at all.

The market for building management systems is expected to top $19 billion by 2023. Owners are already spending $7 billion a year on energy management systems and $1.5 billion on both maintenance rounds and submetering technology.

Each one of these have tackled an aspect of building operations and saved time for operators. Instead of manually programming the schedule and temperature set point for large HVAC equipment, operators using a BMS can do the same from their office. 

The problem is, these systems haven't fundamentally altered how portfolios are operated. Ultimately, the knowledge of how to use these systems remain confined to the building and the chief engineer.

Worse, the data produced by these various systems is siloed. Each one must be accessed (sometimes physically on site) to get a sense of how the asset is doing operationally.

And that's the best-case scenario. 

More common is a Frankenstein combination of legacy systems, newer software pilots, and large gaps where things are still done with pen and paper. 

"Alexa, who maintains my elevators?"

Today, an owner of commercial real estate could ask Google or Alexa for answers to millions of questions about the world. But who are they supposed to ask to know about how their money is being spent?

"I heard we're having problems with our chiller, when did we buy that? From who? What about the critical parts? When was the last time it was maintained? Do we have a maintenance contract? Is it still covered by the warranty?"

Sure, those answers are likely one or two phone calls away, but what's the cost to the portfolio of all the combined hours spent relaying answers that should be accessible to everyone?

More fundamentally, isn't it strange to talk about optimization when we can't answer relatively straightforward questions?

Take stock trading as a parallel. Back in the day, before Bloomberg Terminals provided live quotes and analyses to traders, people used to read bid-ask spreads off of chalkboards and historical data off of miles of ticker tape.

Predictive maintenance and machine learning in building operations is like offering these pre-Bloomberg traders the chance to shave a fraction of a second on their trades with the newest fiber wires. Sounds great, but one thing at a time.

Horse, meet cart

Data is king in real estate, it always has been. Having an informational advantage is how the best portfolios consistently out compete the market.

But data can mean a lot of different things. As IoT sensors have become less expensive and more powerful, "data" has become synonymous with real-time readings about temperature, electric demand, air quality, etc. 

The sheer amount of this type of data already being produced from commercial assets is staggering. It's often said that the trick is translating this data into actionable insights, and that's true. 

But there is much lower hanging fruit to be picked. First, owners and operators should take a step back and ask themselves what information already exists in their organization but simply isn't accessible to them.

This is data too, and deserves more attention.