Despite predictions, massive adoption of driverless vehicles will take many years.
There’s a lot of buzz about driverless vehicles reshaping the commercial real estate landscape. Multiple articles predict mass adoption of driverless technology in the next two to three years.
Industry experts forecast massive shifts in road use and associated transportation related to real estate that anticipate curtailed needs and, in some cases, elimination of certain property types. Such predictions have the potential to devastate property values.
Predicted near term impacts to real estate include:
- Roads will require fewer lanes, and traffic lanes will be narrower as autonomous vehicles drive more efficiently than human-driven vehicles.
- Parking demand will go to zero as fleets of autonomous vehicles replace personal car ownership, and fleet vehicles don’t require parking.
- Suburban living may be more attractive to riders, who, without driving, can be productive on their daily commutes and don’t have to worry about parking.
- Property valuations will change as access by car or access to cheap parking become less important to real estate market participants.
For many reasons, self-driving vehicles will be slower to arrive and will have far different near-term impacts than some experts predict.
According to the U.S. Department of Transportation’s most recent calculations as of 2014, more than 250 million vehicles are registered in the U.S., and a strong correlation exists between a rising population and an increase in the number of registered vehicles.
In November 2016, IHS Markit, a leading provider of business information and analysis for the automotive industry, announced that registrations for light vehicles had grown to more than 264 million, and their average age had climbed to 11.6 years. The overall quality of today’s vehicles is a key driver for the rising age of vehicles on the road, and all of 264 million vehicles require human drivers.
Self-driving car proponents, however, forecast mass adoption timelines by 2020 or 2025. One ride hailing service executive claims that by 2025 private car ownership will be over in major U.S. cities.
Many past announcements haven’t come to pass in the timeframes predicted. Five years ago when features, such as Ford’s Traffic Jam Assist, were discussed, stories touted that if 25 percent of cars had this feature or similar capability, there would be a 37.5 percent reduction in travel time and 20 percent reduction in travel delays.
In May 2016, several articles proclaimed that GM and Lyft would be holding trials for self-driving cars in certain cities within a year. Which in March 2017, GM announced the launch would be in 2018.
Clearly, the forecasters are jumping ahead of reality. The timeframe will take longer than the industry proponents’ optimistic predictions.
The old adage that the devil is in the details applies. In computer software, exception processing, or what to do when the unexpected happens, can be the most challenging aspect of software development. With a human driver on board, when the unexpected happens, the software developers can program the car to request the human take control. With no human driver on board, the software must control the behavior of the car under all circumstances.
Basic driving is pretty straightforward. The exception processing will be the challenge. What to do when the unexpected occurs?
Examples often occur such as unexpected road or lane closures, individuals in the roadway, the approach of emergency vehicles, accidents involving the vehicle, accidents impeding the vehicle, something awry with the sensors in the car, or a flat tire. The list is endless.
For self-driving vehicles, the software developers have to work through these scenarios. The task before them will be tedious but of life-and-death importance. Overcoming these challenges will take time.
More than three decades ago, prognosticators foretold of the near term demise of commercial airline pilots as new technology emerged that commercial aircraft could fly without pilot guidance. Fast forward to today when commercial airline pilots still fly the planes.
One reason is for the exception processing. The movie “Sully” gave viewers a glimpse into the events happening on Jan. 15, 2009, when the aircraft under Captain Chesley Sullenberger’s command encountered a flock of geese shortly after takeoff. Mr. Sullenberger and his crew evaluated the unique circumstances of the aircraft and their position above New York City. Using their skill and experience, they landed on the Hudson River, everyone on board survived, and no one on the river was hurt.
Could software developers have envisioned such unique circumstances and programmed the automatic pilot to handle this situation as well? Unique circumstances aren’t limited to air travel.
Every day drivers encounter unique circumstances where judgment comes into play. For example, is the human standing in the roadway directing traffic, needing help, or meaning harm to motorists?
Another reason airline pilots remain in cockpits is to serve as a backup for component failures in the aircraft. While it is hoped the computer applications and the components of self-driving cars would be 100 percent reliable, that is tough to accomplish.
Both hardware and software components experience failures. Computer engineers advise it’s not a matter of if but when. Reliably and safely moving self-driving vehicles among other users on the roadways will be one of the hurdles the self-driving industry must overcome.
IHS Markit predicted in 2016 that the U.S. will lead early deployment of autonomous vehicles with several thousand vehicles sold in 2020, and global sales increasing to 600,000 units by year 2025, a number which is less than 1 percent of total global new vehicle sales.
According to IHS Markit, U.S. sales of autonomous vehicles will start growing rapidly beginning in 2025, reaching a volume of 4.5 million in annual sales by 2035. To put the volume of sales in context, annual sales of new automobiles in the U.S. are currently about 17.4 million.
Even if annual sales of new automobiles remained flat through 2035, the 4.5 million forecast for autonomous vehicles would represent approximately 25 percent of new vehicle sales, which means 75 percent of new vehicles will require human drivers.
Since the average age of cars on the road is more than 11 years, it is easy to argue that human driven vehicles will be the majority of automobile on U.S. roads through at least 2050.
Reality on the Road
Now with that timeline in our rear view mirror, let’s shift gears and focus on impacts to real estate. Some self-driving industry proponents assert that without human drivers, traffic congestion will be eliminated. Cars will be able to travel in relatively close proximity at high speeds. In addition, with computers in charge of steering vehicles, traffic lanes can become narrower, diminishing the need for real estate dedicated to vehicle traffic.
Self-driving cars will be an improvement but will fall short of such lofty expectations. Such forecasts overlook other users of the roadway.
Self-driving cars will share the road with ambulances, firetrucks, garbage trucks, delivery trucks, and construction vehicles. Lanes must be wide enough to safely accommodate these vehicles.
As for vehicles traveling much closer together, stopping distance is a matter of reaction time plus braking distance. With computers in control, reaction time will be improved, but braking distance is a matter of physics. The speed and weight of the vehicle in combination with the brakes, tires, and road conditions are the key variables, and these factors don’t change when a computer is in control.
Another assertion is that the use of personal vehicles in cities will be replaced by a monumental shift to using transportation network companies, such as ride hailing services like Uber and Lyft, which will have fleets of self-driving, electric cars.
According to this outlook, no parking structures will be needed because the TNCs’ vehicles will drive from passenger-to-passenger throughout the day. This view ignores that electric cars, self-driving or not, must be parked to charge batteries, to be cleaned, and to be maintained.
This perspective overlooks that ride hailing services use the roads more intensively than personally owned cars. Consider that for a personally owned vehicle, if the owner goes on a trip, then the car goes from the originating point to its destination.
For a ride hailing passenger, the car must travel from its location to the pickup point, take the passenger to their destination, then leave the destination for the next ride. The result is more trips and more road miles than if passengers had taken their personal cars.
Currently, cities encounter this paradox as they realize the increased use of TNCs actually increases road congestion.
In a recent report by Schaller Consulting entitled “UNSUSTAINABLE? The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City,” the author, Bruce Schaller, noted that TNCs in New York City added nearly 50,000 vehicles to the city’s roads since 2013, with more than 600 million additional miles driven. The study found that miles traveled without passengers exceeded the miles driven with passengers. If these trends continue, or increase with TNCs using self-driving cars, then the potential exists for much more congestion on the roads, not less.
As cities grapple with fleets of self-driving TNC vehicles, one outcome is the development of policies to reduce the number of self-driving TNC vehicles on the roads during nonpeak hours. For example, if TNCs collectively have 50,000 fleet vehicles in a city, officials will not want most of these 50,000 vehicles driving around empty during nonpeak times of the day.
For the TNC, empty cars driving on the road represent expenses incurred without incoming revenues. Off-peak times, such as before the morning rush hour, midday, and after the evening rush hour, are the time for TNCs to charge electric batteries, clean vehicles, and perform maintenance.
Some speculate that TNC operators will send their cars out of the cities to remote service facilities. However, this practice seems impractical.
More likely, TNC operators will want facilities located nearby their riders where the vehicles will be ready when the demand picks up. Rather than build specialized facilities for storing, charging, and maintaining TNC fleets, a natural place for TNCs to find these facilities will be existing parking structures.
Self-driving cars won’t be limited to fleet vehicles; they will also be sold to individuals. Predictions abound that owners of self-driving cars will ride their cars to work, be dropped off, and then send their empty cars back home. Again, two trips will occur instead of one. Before the end of the work day, the self-driving cars will drive empty back to pick up their owners and drive them home.
In some circumstances, owners may choose to do this, but for many it introduces considerably more cost and inconvenience. Maintenance and insurance costs increase with the additional mileage. Any road or bridge tolls would be incurred as the car travels without a rider. And if the owner’s schedule changes, then the car must be requested, and the owner waits while the car makes its journey to the pick-up location.
A more likely scenario is that owners, who can afford the premium of buying a self-driving car, will want their car parked nearby and ready for their use.
To sum up the dynamics of today’s U.S. cities, the words to Bob Dylan song “The Times They Are A-Changin” come to mind. The nation’s population surpassed 325 million earlier this year, and according to the U.S. Census Bureau projections, the U.S. population will grow to 370 million by 2035 and reach 398 million by 2050.
Much of the growth will be in U.S. cities. Municipal governments will have to manage the growth and provide the quality of life that residents expect.
Whether self-driving cars will dominate our cities sooner or later, the management of automotive vehicles on city streets and the storage of vehicles not being used will continue to be one of the many matters discussed in city halls nationwide.
In closing, this is the dawn of an era of autonomous driving that has captured the imaginations of both the public and private sectors. However, the technology will take longer to implement than many predict.
Each U.S. city is different for its weather, terrain, demographics, and transportation capacity and capability. Through policy, municipal governments may accelerate or decelerate self-driving adoption.
More than ever, real estate professionals need to stay informed of transportation developments affecting their local markets.
By: Ted Anglyn and Alan Anglyn (CCIM)
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