Driving Innovation

Invest Where Innovation Is Creating, Not Constraining, Jobs

While it is interesting to dig into the whiz-bang capabilities and technical aspects of new technologies, its impact on jobs is most profound. Additionally, while it is difficult to know the exact impacts of the technologies on where and how we live, work, and play, we do know that markets populated with the companies creating technology will see employment gains, while those populated with industries disrupted by the technologies will lose employment. Accounting for this variation in impact on employment across markets should be a primary consideration in shaping investment strategy.

The Bay Area and Seattle are established tech hubs that will continue to flourish and maintain their status as innovation centers. The Bay Area is the dominant cluster, bar none, for companies pioneering disruptive self-driving technology. Uber, Alphabet's Waymo unit, Tesla, Apple, and GM's Cruise are all based in San Jose or San Francisco. 

As these technologies reach high levels of adoption, companies will grow as their hiring needs become more substantial and availability of tech talent becomes more difficult to secure in these established hubs. Major markets with huge labor pools such as New York, Los Angeles, Washington, D.C., and Chicago have been able to attract the presence of larger tech firms like Google, Amazon, Microsoft, and Apple, along with smaller markets such as Austin, Portland, and Denver.

The flipside to this technology-driven employment growth is employment destruction. Self-driving vehicles could revolutionize the transportation industry, potentially eliminating broad swaths of jobs. Many traditional distribution markets have higher concentrations of trucking jobs, making these the most vulnerable in broad industry adoption of self-driving trucks. This likely wouldn't materially impact warehouse demand in these markets, given that many are distribution hubs serving broad geographic areas. The net effect on local employment and wages could impact other property types within these markets; a loss in transportation jobs means a loss in local services jobs. 

Additionally, increased warehouse automation and markets with elevated trucking employment concentration would at best face a shift in employment composition and at worst, outright reduction. Markets having positive exposure to self-driving cars via programming and engineering jobs are also the least exposed to the types of transportation jobs that are potentially at risk of automation.

There are two broad categories of potential commercial real estate disruptors. The first is the group of startups that are actively vying to disrupt the commercial real estate industry itself. The second is the group of innovations occurring outside of the industry that has the potential to disrupt other industries, the economy overall, and, as a result, the commercial real estate space demand - self-driving cars being the chief example. 

The Road Ahead

The market potential for self-driving car technology is enormous. There are 260 million cars, motorcycles, and buses in the U.S. In 2016, $2.25 trillion was spent on car ownership, public transportation, rental cars, taxis, limousines, and black cars. When considering the adoption timeline for the technology, it is important to understand that most predictions from industry experts and global automakers are light on acknowledging the significant potential for disparity between assumptions and eventual reality. Despite the potential of self-driving technology, challenges exist such as legislation hurdles, cybersecurity risks, and pushback from incumbents like insurance providers.

Many assume autonomous self-driving cars will improve efficiencies related to commutes. It's also argued that self-driving cars will increase worker productivity during commutes, because passengers could catch up on emails and perform other tasks. Because of these efficiency and productivity gains, it is assumed that people may relocate farther away from a city center and into the suburbs, increasing commute times while productivity remains steady.

These posited efficiency gains may be quickly mitigated for the same reason lane widening on freeways doesn't lead to improved traffic times. In economics, the concept of induced demand, also known as Jevons paradox, occurs when technological progress increases the efficiency with which something is used (reducing the amount necessary for any one task), but the rate of consumption rises because of increasing demand.

This dynamic is omnipresent in transportation and has even been referred to as the Iron Law of Congestion. Thus, while Marchetti's constant would tell us self-driving cars will cause sprawl, Jevons paradox tells us this sprawl will be mitigated due to more people commuting on roadways.

Whether commuters are willing to locate further from their work environment, the key question is: While productivity could increase, do workers really want to spend more time away from home? Said another way, while productivity could increase, the fact remains that people still won't be able to engage in non-work activities during the commute, such as spending time with family and friends or engaging in outdoor activities. Will workers really want to spend more time away from home to be working in the car?

Property Sector Impacts

For office space, it's theorized that much less parking space will be required as cars will zip around shuttling people elsewhere instead of sitting idle while employees work. Optimists point out that this will free up previously underutilized land and space, while pessimists claim the potential loss of NOI for office landlords as income from parking could be significant.

For multifamily, pessimists posit that self-driving cars will induce urban sprawl, negatively impacting the relative value of urban properties and punishing investors who over-allocated investments to urban cores. Optimists argue that reduced parking requirements could boost the potential for housing density and commercial property. This will create more vibrant environments, and future development will be less costly thanks to reduced parking requirements.

For retail and industrial properties, shuttered suburban retail assets will be reborn, either as retail or last-mile distribution centers, thanks to increased sprawl. Investors who bet heavy on urban infill will miss a wave of industrial demand catering to such sprawl. Self-driving trucks will be a compounding factor, making it feasible for companies to stretch their logistics networks by taking advantage of cheaper, more plentiful land for warehouses.

More broadly, transit-oriented assets will suffer as people rely less on public transit. The automated nature of self-driving technology and its widespread application (that is, trucks, ships, and rail) will eliminate or transform millions of jobs, disrupting the entire U.S. labor market.

At least that is the conventional wisdom. But how sure are we that any of these scenarios are likely to play out? It's important to note that these are all just estimations and forecasts. There is still a great deal of dispute over the timeline of self-driving rollout, regulatory adoption, and consumer adoption. 

The prevailing sentiment in the industry and among futurists is that it will change the values and best uses of real estate across the board. That may be the case, but the timing is highly uncertain.

Ride-Sharing

A lot of conflicting information exists on the impacts of ride-sharing on the demand for public transit. Much prevailing analysis calls for a future scenario where increased ride-sharing dramatically reduces the need for public transit, in turn reducing the value premium enjoyed today by transit-oriented developments (TODs). Two reasons are fueling much of the negative sentiment. First, at a high level, public transit ridership is falling in the U.S.

While the fear is that ride-sharing has been cannibalizing public transit ridership, this narrative doesn't add up when you take a closer look at ridership growth. Ridership declines in the early and mid-2000s went along with waning public transit participation through the last decade. Uber didn't launch until 2009 and Lyft came in 2012, while meaningful adoption occurred even later. 

More recently, a 2017 paper from the University of California Davis Institute of Transportation Studies made headlines for identifying a relationship between increased ride-sharing use and decreased transit ridership. The paper reported that ride-sharing increases were associated with major declines in transit ridership, but the results were more modest and mixed. The study found that when survey respondents increased their use of ride-sharing, they decreased their use of bus and light rail services by 6 and 3 percent respectively, while commuter rail usage actually increased 3 percent.

The study also acknowledged two other key points. The first is that 49 to 61 percent of ride-hailing trips would not have been made at all by any other means of transportation, and that ride-hailing will likely contribute to more vehicle miles traveled in the large cities surveyed. This means more overall trips and more traffic in major cities, which would increase the relative appeal of public transit as riders seek to avoid worsening congestion. Other similarly designed survey studies found ride-sharing and public transit to be complementary, with an increase in use in one leading to an increase in use in the other.

The most recent and cutting-edge research, published in June 2018 by researchers at McGill University in Montreal, offers further explanation for declining ridership: Declines resulted from reductions in bus and train routes as well as deferred maintenance across transit modes, making adoption less compelling for riders. The presence of ride-sharing or bike-sharing was insignificant. It's no surprise that two of the cities where public ridership grew in 2015 to 2016 were Houston and Seattle, both of which have undergone bus network overhauls. No matter how automated cars become, they will still take up a lot more space per passenger than a bus or a train. This scale and the public nature of buses and trains lower the cost per rider significantly, making them an ideal choice for lower-income workers. If planners can maintain and modernize existing public infrastructure, public transit will continue to be viable into the future.

Uber and Lyft aren't eliminating the viability of public transit and, therefore, transit-oriented development. A well-designed and well-located TOD, especially in larger metros, derives much of its value from factors that might have nothing to do with proximity to transit. A 2017 research note from CBRE Econometric Advisors analyzing the factors driving multifamily rent premiums in the Denver market found that proximity to light rail had no statistical significance on rent levels and that TOD assets tended to derive their rent premium because they were newer, were more proximate to the central business district, and tended to have more retail density within a half-mile radius.

Conclusion

Promising new technologies often create bold proclamations. Both pessimistic and optimistic prognostications can create irrational exuberance or unfounded fear. The same is true with the current crop of promising new technologies. 

Technology's greatest impact is and will be on the composition of jobs. Rather than fretting over doomsday headlines or fairy tale outlooks related to the next big thing, CRE professionals must understand the employment composition of the markets. In the end, areas with companies creating the disruptive technologies will thrive, while those subjected to negative employment effects will not.