Chapter 8 | Analyzing Metropolitan Long-Term Growth Patterns

Overview

Real estate developers and operators are in the business of servicing demand from population and job growth for places to live, work, play, and shop.  From 2017 to 2030, the U.S. is poised to grow its population by some 33 million people, due in part to continued strong immigration.  However, there is no such thing as a homogenous “national” real estate market – the dynamics of individual sub-markets are unique, and growth does not occur uniformly from place to place.  Responsible analysis of a sub-market involves its study from the bottom up, rather than a top-down interpolation from national trends.

Summary

Population and job growth fills space and supports new development, thus it is of high interest to financial stakeholders in real estate.  Vigilant commercial real estate stakeholders are constantly refining their theses for demand for their real estate products, focusing on the most local levels possible.

Two studies in particular provide insights into past and future U.S. metropolitan area population and job growth.  The first forecasts Metropolitan Statistical Area (MSA) growth as a percent of the total U.S. population, determined by local factors such as: share of foreign-born residents, educational attainment, race and age share percentages, taxes per capita, voting trends, political party representation, weather characteristics, age of housing stock, proximity to an ocean or Great Lake, and geographic region.  These dependent variables capture attributes of an area that cause it to grow economically and attract employers and employees.

This 2006 Peter Linneman and Albert Saiz regression analysis-based study, updated by Linneman Associates in 2014, found that about 75% of all variation in county population growth is statistically identifiable.  In other words, there will always be “wild card” factors that cannot be foreseen, such as where Sam Walton decided to locate Walmart’s operations, or where Amazon selects for its second headquarters.  The analysis reveals that the single most important factor in determining future population growth is past growth (a conclusion confirmed by prior studies).  Other significant predictors included economic diversity, immigrant presence, a skilled labor force, weather, and the vintage of the existing housing stock.

Generally, population and job growth occurs where: people want to live and play; necessary building approvals are relatively easy to obtain; potential growth can be accommodated; and firms find it efficient to produce.  The latter relates to the notion of agglomeration economy benefits –  that is, firms becoming more productive if they locate closer to similar firms, enabling them to share information, infrastructures, and a pool of relevant workers, and to reduce the transportation costs of their common inputs and outputs.

The second study, performed by Linneman Associates, attempted to answer the question: as the U.S. economy moves through cycles, which MSAs will over-react around trend growth on the up-cycle (but disproportionately suffer during down-cycles), and which will under-react, growing more steadily around their trends over the cycle?  The analysis provides a metric with which to manage risk expectations around generally smoothly growing pro forma analyses of local demographics.

The models produced by the study are simple indicators of how coincident each MSA’s economy is with movements of the national economy, providing insight into the demand volatility around the local trend during unusual boom or bust times (which occur, but are never modeled in pro formas).

The interaction between an MSA’s intrinsic growth (its “alpha”) and its “beta” (a multiplier applied to the national percent change in employment) is summarized by the U.S. employment growth rate required to generate positive job growth in the MSA.  Specifically, what minimum employment growth rate at the national level is associated with positive job growth for each MSA?  The more negative this “breakeven” U.S. job growth rate, the stronger are the MSA’s underlying growth fundamentals.  The study found that the most predictable MSAs with respect to coincident job growth when there is national job growth are Chicago, Charlotte, Atlanta, Cleveland, and Minneapolis, while the least predictable are Honolulu (by far), Knoxville, Albany, D.C., and Houston.

These analyses have important consequences for investors.  Specifically, when the national economy is in a strong expansion phase, targeting office development in high employment beta MSAs provides the greatest space-demand upside.  When national employment grows, Fort Lauderdale, West Palm Beach, Detroit, Austin, and Boston exhibit the highest employment growth betas and, thus, will experience the greatest regional percentage growth above that of the nation.  During a national recession, on the other hand, low employment beta MSAs, such as New York, Philadelphia, Houston, St. Louis, and Washington, D.C., provide greater downside demand-risk protection.

Questions

These are the types of questions you will be able to answer after studying the full chapter.

1. What did the 2006 Linneman/Saiz study conclude was the number one predictor of future metropolitan area growth? What are other significant predictors?

2. What are agglomeration economy benefits?

3. Why should real estate investors care about the extent to which a particular MSA’s employment growth moves on a muted or magnified basis relative to national employment growth?

Audio Interviews

A Nation of Constant Positive Population Growth (2:45)

 

Technological Impact on Agglomeration Benefits (2:36)

 

Regional Employment Growth Variability (1:57)

Key Term

Agglomeration Economies – the benefits that come when firms and people locate near one another together in cities and industrial clusters.

Chapter Headings

  • A Nation of Constant Positive Population Growth
  • Metropolitan County Population Growth
  • 2030 Forecast
  • Local Population Growth Insights
  • Past Growth
  • Economic Diversity
  • Immigrant Presence
  • Biology and Age Distribution
  • Weather
  • Vintage of Existing Housing Stock
  • Coastal Adjacency and Zoning
  • Educational Achievement
  • Local Income and Sales Tax Rates
  • Skilled Labor Force
  • Population Density
  • Regional Growth Variability
  • Methodology
  • Insights

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