Teralytics recently looked at data from 500,000 smartphone users to determine how, when, and where Puerto Ricans moved between August 2017 and February 2018 during and following Hurricane Maria – generally considered to be the worst natural disaster on record for the area.
CityLab published the data here and along with the following maps:
It shows the locations and the top 10 counties that received Puerto Rican population during the above time period. Florida and the Northeast are at the top of list, which isn’t all that surprising. Privacy concerns aside, it is once again an example of the kind of granular data that we now have access to. Prior to this data being available, all we apparently had was estimates.
This is a fascinating study by Issi Romem about the characteristics of cross-metropolitan migration in the United States. The key findings are that in-migrants to expensive coastal cities tend to have higher incomes and more education than the out-migrants, and that the opposite is true for the less expensive cities in the US. “Expensive” means expensive housing.
Here is the income chart:
Let’s use San Francisco as the example since it’s the most expensive metro (all the way to the right on the x-axis). The way to read this is that on average, from 2005 to 2016, in-migrants to the San Francisco metro area earned $12,640 a year more per household (y-axis) after they arrived compared to out-migrants before they left. This chart shows the difference between in and out incomes.
One of the great things about social media is that it gives us access to data that previously didn’t exist or was difficult to collect.
Take, for example, LinkedIn’s monthly report on employment trends called the Workforce Report. They look at which industries are hiring, where people are moving for jobs, and so on. Click here for the June 2017 edition.
Note that architecture/engineering hiring appears to be up nationally, which is usually a positive leading indicator.
I’ll leave you all to go through the report, but I did want to pull out a few of their maps and one of their takeaways. Below are maps of the cities that lost the most workers and gained the most workers over the last 12 months.
Teralytics recently looked at data from 500,000 smartphone users to determine how, when, and where Puerto Ricans moved between August 2017 and February 2018 during and following Hurricane Maria – generally considered to be the worst natural disaster on record for the area.
CityLab published the data here and along with the following maps:
It shows the locations and the top 10 counties that received Puerto Rican population during the above time period. Florida and the Northeast are at the top of list, which isn’t all that surprising. Privacy concerns aside, it is once again an example of the kind of granular data that we now have access to. Prior to this data being available, all we apparently had was estimates.
This is a fascinating study by Issi Romem about the characteristics of cross-metropolitan migration in the United States. The key findings are that in-migrants to expensive coastal cities tend to have higher incomes and more education than the out-migrants, and that the opposite is true for the less expensive cities in the US. “Expensive” means expensive housing.
Here is the income chart:
Let’s use San Francisco as the example since it’s the most expensive metro (all the way to the right on the x-axis). The way to read this is that on average, from 2005 to 2016, in-migrants to the San Francisco metro area earned $12,640 a year more per household (y-axis) after they arrived compared to out-migrants before they left. This chart shows the difference between in and out incomes.
One of the great things about social media is that it gives us access to data that previously didn’t exist or was difficult to collect.
Take, for example, LinkedIn’s monthly report on employment trends called the Workforce Report. They look at which industries are hiring, where people are moving for jobs, and so on. Click here for the June 2017 edition.
Note that architecture/engineering hiring appears to be up nationally, which is usually a positive leading indicator.
I’ll leave you all to go through the report, but I did want to pull out a few of their maps and one of their takeaways. Below are maps of the cities that lost the most workers and gained the most workers over the last 12 months.
Take note of Miami which is sitting at a similar place to New York and Los Angeles on the horizontal income line, but has home values similar to Phoenix, Chicago, and Philadelphia.
Now here’s the education chart:
Similarly, it is showing the difference in educational attainment between in and out migrants.
So what does all of this tell us?
Well, it tells us, among other things, that US metros are continuing to sort based on income and that this process of polarization is probably contributing to home price appreciation. Because even if the incomes of current residents aren’t growing, these “expensive cities” are effectively swapping out poorer residents for richer ones. That, alone, would mean more money for expensive homes.
For Issi Romem’s full article, click here.
The established trend of people moving from colder northern cities to warmer amenity-rich cities seem to play out here.
That said, one of their “key insights” is that fewer workers today are moving to the San Francisco Bay Area. Since February 2017, there has been a 17% decline in the net number of workers.
They blame housing affordability (ahem, lack of supply). People are simply turning to other great cities like Seattle, Portland, Denver, and Austin. They’re growing and cheaper.
One of the other cool things about the report is that you can drill down into individual cities to see where people are moving from. I looked up Miami and Chicago just to do a quick comparison.
Not surprisingly, Miami is seeing a significant contingent from South America. What’s interesting about this random comparison is how international Miami is and how regional Chicago is in terms of their draws.
I would love to see similar data for Canada. This is valuable stuff.
Take note of Miami which is sitting at a similar place to New York and Los Angeles on the horizontal income line, but has home values similar to Phoenix, Chicago, and Philadelphia.
Now here’s the education chart:
Similarly, it is showing the difference in educational attainment between in and out migrants.
So what does all of this tell us?
Well, it tells us, among other things, that US metros are continuing to sort based on income and that this process of polarization is probably contributing to home price appreciation. Because even if the incomes of current residents aren’t growing, these “expensive cities” are effectively swapping out poorer residents for richer ones. That, alone, would mean more money for expensive homes.
For Issi Romem’s full article, click here.
The established trend of people moving from colder northern cities to warmer amenity-rich cities seem to play out here.
That said, one of their “key insights” is that fewer workers today are moving to the San Francisco Bay Area. Since February 2017, there has been a 17% decline in the net number of workers.
They blame housing affordability (ahem, lack of supply). People are simply turning to other great cities like Seattle, Portland, Denver, and Austin. They’re growing and cheaper.
One of the other cool things about the report is that you can drill down into individual cities to see where people are moving from. I looked up Miami and Chicago just to do a quick comparison.
Not surprisingly, Miami is seeing a significant contingent from South America. What’s interesting about this random comparison is how international Miami is and how regional Chicago is in terms of their draws.
I would love to see similar data for Canada. This is valuable stuff.
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