Affordable Housing Institute : US

If economics drives housing prices, then demography drives economics.  Knowing where people will move could be of inestimable value in designing long-term investment, infrastructure, and housing prices.

 

Perhaps, in fact, we do know — and from an unlikely source — the United States Department of Agriculture.

 

 

The USDA finds the key

 

From USDA’s Rural Housing Service (formerly known as the seventy-year-old Farmers’ Home Administration) comes a fascinating study, Natural Amenities Drive Rural Population Change (full report here, 2.8 Meg pdf; summarized in Scientific American, subscription required), seeking answers to these questions.

 

If one can believe the USDA’s mountain of statistics, it boils down to just a few things — and they all have to do with the outdoor climate:

 

 

Climate, topography, and water area are highly related to rural county population change over the past 25 years.  A natural amenities index (NAI), derived and discussed here, captures much of this relationship.  Average 1970-96 population change in non-metropolitan counties was 1% among counties low on the natural amenities index and 120% among counties high on the index.  (Abstract)

 

We thus start with the evidence: the USDA’s NAI correlates beautifully with demographic growth:

 

 

The histogram (bar graph) shows a county’s score on the USDA scale (0 is neutral, -3 is awful, +3 is great).  The line shows the average percentage population change of those counties.

 

The merest tyro can see that counties with high scores showed huge population growth; those with low score were flat or declining. 

 

An even simpler, viscerally visual, and therefore compelling display is shown by comparing two maps colored with appropriate scales. 

 

First, the amenity scale (red is bad, green is good):

 

 

Then the population scale (red is loss, green is gain):

 

 

They’re very similar, aren’t they?  (Indeed, the population growth change seems even stronger than the correlation scale.)

 

Why do people move?  The six drivers

 

How did the USDA derive its nifty charts?  By identifying six elements, scoring each from -1 to +1 on an algorithmic desirability scale, and then adding the results:

 

We created the simplest type of scale and tested its ability to predict county population change against the combined predictive ability of the six separate items.  Because each item had different scales, the amenity measures were standardized so each had a mean of zero and a standard deviation of 1.  The combined scale was created by summing those standardized measures.  (Page 9)

 

The NAI score is simple, yet powerfully correlative.  This suggests the designers have struck gold: they have found the drivers.  What were they?

 

1.       Warm winter (average January temperature).

2.       Winter sun (average January days of sun).

3.       Temperate summer (low winter-summer temperature gap).

4.       Summer humidity (low average July humidity).

5.       Topographic variation (topography scale).

6.       Water area (water area as proportion of total county area).  Maximum 250 square miles.

 

(Pages 2-4)

 

Obviously there are linkages among these — winter warmth and winter sum, summer humidity, temperate summer — but the authors’ regression analysis indicates that each of these six is independently important:

 

The resulting natural amenity measures are inter-related, but not so much that any are redundant (table 2).  Often, there are tradeoffs.  Areas with more extensive water areas, for instance, tend to have less winter sun and greater summer humidity. …  Average correlations among the natural amenity measures are near zero.  No measure is consistently related, either positively or negatively, to the other measures.  The low and inconsistent correlations suggest that most counties have some negative and some positive aspects to their amenities.  (Page 5)

 

Might there be other climatic variables whose inclusion would make the scale better?

 

We initially considered land in forest and (low) elevation as measures.  Land in forest had no relationship with population change, however, either alone or in combination with other measures.  The low elevation was included in the original scale [to minimal effect].

 

Four other available climate measures — January precipitation and humidity, and July precipitation and days of sun — were less intuitive amenities than the ones selected, highly related to the measures included, and less effective in predicting population change.  (Page 4)

 

What does this tell us?

 

Six measures, all related to local climate.  What do they tell us about human motivation?

 

Natural aspects of attractiveness can be summarized in three types of amenities: mild climate, varied topography, and proximity to surface water — ponds, lakes, and shoreline.  (Page iii)

 

For a start, they show that modern urban humanity — homo urbanus – has freed himself from the geographic restrictions that governed where our homo agricolus predecessors had to live.  Civilization began near fresh water, specifically rivers, because homo agricolus needed these to cultivate crops, grow food, and sustain life.  With irrigation and mechanized agriculture, the food we eat is trucked, trained, and flown in to homo urbanus.  Domicile is now a matter of choice, which derives from proximity to employment, which in turn from proximity to psychological recreation. 

 

We’ve also tamed the interior environment: air conditioning revolutionized the postwar South.  Offices are now comfortable … but man does not live by office alone.

 

Visible water is a source of visible comfort and ease, whether in:

 

Ocean

Lake

 

River

 

Pond

 

Varied topography means choice, again both visually and recreationally.  Hiking, biking, parasailing — look at the images and the words spring to mind. 

 

Earlier I dubbed us homo urbanus but we’re really homo recreationus, since we choose where to live based on where we can play outside. 

 

Implications for policy makers

 

Population growth drives economics — indeed, economies heat up faster than they grow their population –

 

 

Over the past 25 years employment, like population, has tended to expand more rapidly in non-metro counties with higher scores on the natural amenities scale (figure 7).  Employment growth was particularly large at the highest end of the amenities scale — three standard deviations above the norm.  (Page 14)

 

If we want to grow economies, we want to attract homo recreationus by offering choice.  Indeed, within regions, the same drivers apply, perhaps with regional variation:

 

The analysis suggests a two-tiered influence of natural amenities on population movement:

 

·         A national level of influence, affecting the movement of people across states and regions for both residence and recreation;

·         A more regional influence, affecting migration and recreation patterns within regions.

 

The qualities of attractive areas within regions appear to vary from one region to another, depending on the regional endowments.  Thus, within the Midwest, much of which is relatively flat compared with the West and parts of the Northeast and South, lake areas are the primary attraction.  (Page 19)

 

Cities compete with each other for people.  So do regions.

 

And so do nations.

 

Nothing in the USDA analysis is limited to a US context, including our definition of homo recreationus, who is evidently a global critter.  (Ironically, I am writing this in an aircraft flying to Nairobi.)  Do these criteria tell us where homo recreationus is going to bring his family, his money, his talent, and his job growth?

 

Moving out of a large developed nation brings into play new factors (’national risks’) not considered by the USDA analysts, including:

 

1. Cost of living (what does my salary buy?).
2. Personal risk (are your children safe?).
3. Property risk (is your home safe?).
4. Financial risk (is your money safe from inflation and bank failure)?

We can further subdivided homo rec into two categories: the earner and the retiree.  The earner needs to make the money; the retiree brings it with him.  So first order, the long-term retirement destinations are those places high on the amenity scale and where capital, property, and person are safe.  I give you:

 

• New Zealand
• Australia
• South Africa (post-apartheid)

 (These lists are not exhaustive; they’re places that come to mind.)

(I’m also ignoring the established snowbird flyways like Luton-to-Malaga or Berlin-to-Split.)

 

We also very swiftly identify places that, if they could address political risk, would likewise score high:

 

• Greece
• Turkey
• Israel
• Morocco

 Moving to the earner, we can also identify places where outsourcing of jobs creates a new class of homo recreationus.  Say hello to:

 

• India (many parts).
• Mexico (especially west coast).

 We can project that global trade and outsourcing will flow jobs from high-amenity areas that are also high costs to high-amenity areas that are low cost but high stability. 

 

This leads further to a core conclusion for policymakers in emerging or ‘fusion’ countries: tackle the national risks, embrace the outsourced jobs, and homo reactionus will come.

 

The twenty-first century’s job growth is informational, service-based, web-based.  Capital can be anywhere.  With an increasingly Anglospheric globe, if the people flow, the jobs will follow.  Attract and retain h. rec. and the economy will rise.

 

“If you build it, he will come.”

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