As of late, there has been much discussion about migration from place-to-place as a result of COVID-19 and pointing to various data points as evidence. It is critical for real estate professionals, retailers and analysts to be able to rely on such data as it fuels their models and strategies to navigate through the new post-pandemic world and take advantage of evolving opportunities.
Unfortunately, much of what’s out there for discussion is based on qualitative extrapolations from datasets that don’t have anything to do with human mobility. In other words, it’s unreliable.
The number of hits Zillow gets, or the number of houses up for sale in a given area, is not a reliable sign of human migration. It may just mean that people are listing their houses to see what they can get. People could also just be researching an area, comparing it to a half-dozen other cities. Do those simple searches indicate human migration as well? Of course not.
Changing one’s location details on LinkedIn also does not constitute proof of migration, it just shows that someone updated their profile. I’ve also heard people referring to lines outside of U-Haul in certain cities as an indicator of migration. This is not proof that people are migrating to different cities, just that people may be moving, and they do that all the time.
So, while these observations are convenient vanity metrics, they don’t really tell us how many people are migrating place-to-place. Simply stated: there is no way to mitigate a hard knowledge gap through soft vanity metrics.
Vanity metrics also tell us nothing about who the people migrating are, let alone what their loss or gain may mean for a given city, neighborhood, commercial office tower, brand, or local store. Big location datasets that are synthesized, standardized and hardened over time can tell us those things.
When we use location data from smartphones in concert with vanity metrics, reasonable new explanations for seemingly migratory behaviors quickly arise -- there’s no on-site visit or move to accompany that real estate search, or the ‘migration’ is actually a trip of indeterminate length to the cabin in order to escape a more densely-populated city dwelling.
But it is when we combine location data with hard data from other industries, such as commercial real estate or retail, that meaningful insights emerge and a base level of mobility for measuring migration can be established.
Establishing a base level for measuring migration
The discussion about migration patterns is interesting because we don't know to what extent the purported migration will last. Yes, we see that people are changing their location, but why and for how long?
To produce a real, updated and accurate view of human mobility and migration in the post-pandemic era, you need to reference data going back at least a year previous to the COVID-19 outbreak in North America. There is simply no other way to establish an accurate baseline of activity in a given region.
Further, in order to answer the much more significant questions of who the people migrating are, and what the impact of their migration will be for the brands and real estate markets they are leaving behind, we need to go beyond mass accountancy - e.g. 10,000 people have left City X - and understand who those 10,000 people are.
If these are People With Money, perhaps they have decided to find a vacation property away from the city. If they were Workers, perhaps they were also commuters, meaning they haven’t migrated as much as more firmly localized in their neighborhood outside the business district they used to commute to. If they had only moved to the area recently - for work or school or something else - perhaps a shift to a work-from-home policy is the source of the ‘migration.’
The point is, if you fail to account for the mobility history and core characteristics of migrating persons and correlate that with other key data, no reliable baseline of activity can be established, and no insights gleaned as to the resulting economic impact.
Understanding the full impact of evolving migration patterns post-pandemic will take time. As months and quarters go by, our data becomes more meaningful and important for investment decisions of all kinds.