As discussed in the previous exercise, employment is the primary driver of real estate demand.  Basic employment firms (those companies that primarily export goods and services) create employment, and those employees in turn demand goods and services, which creates more employment.  Thus, basic employment has a multiplier effect that creates additional non-basic employment (goods and services for the local economy).  Economic base analysis evaluates different employment sectors in a specific city to determine what proportion of employment in each sector is basic versus non-basic.  Dividing total employment by basic employment determines the economic base multiplier (EBM).  This can be accomplished using either the Location Quotient method (which compares a city to United States averages) or the Minimum Requirements Method (which compares a city to other, similar cities).  For additional information see Economic Base Concept & Analysis.

Case Study

Suppose that your client, an equity real estate investment trust, has asked you for advice concerning the real estate market in Columbia, South Carolina.  Specifically, they would like to know about the local economy and its potential effect on the real estate market.  Your task is to analyze the Columbia general market by performing an economic base analysis (using the location quotient method) to better advise your client of the advantages and risks of investing in the multi-family residential market in Columbia.

Obtain current estimates (2000) of employment in major economic sectors for the Columbia, SC MSA and the United States.  To do so, go to the Bureau of Economic Analysis website to download the necessary (US and MSA) data from the REIS database.  First, select “Detailed county annual tables of income and employment by SIC industry.”  Then, select “CA 25 Total full-time and part-time employment by industry, Metropolitan Statistical Areas,” the year, “2000,” and click on “Display.”  Data will first come up for the entire United States: Get the data for the economic sectors noted below.  Then, from the pull-down menu, find your MSA and click on “Display” to get the MSA data.  It is easiest to just cut and paste, but you can also download the data in a comma-delimited file.  All of the sectors below are in order by their employment code.  Note that you would not select “Government and government enterprises” (code 900) if you are selecting the three subsets of Federal, Military, and State & Local employment.

Your data should cover the following 12 sectors (and corresponding codes):

100      Agricultural services, forestry, and fishing

200      Mining

300      Construction

400      Manufacturing

500      Transportation and public utilities

610      Wholesale trade

620      Retail trade

700      Finance, insurance, and real estate

800      Services

910      Government (Federal & civilian)

920      Government (Military)

930      Government (State & local)

  • Calculate the location quotient for each sector and identify what you believe is Columbia’s economic base.  (Which sector has the highest location quotient?)

Calculations for sector location quotients and basic employment are shown below:

  US (000s) E   Columbia e   LQ BE
Agricultural 2,141 0.013   3,143 0.008   0.64 0
Mining 782 0.005   462 0.001   0.26 0
Construction 9,523 0.058   21,488 0.057   0.98 0
Manufacturing 19,108 0.116   27,296 0.072   0.62 0
TCPU 8,262 0.050   17,078 0.045   0.90 0
W. Trade 7,582 0.046   17,783 0.047   1.02 365
R. Trade 27,387 0.167   61,884 0.164   0.98 0
FIRE 13,207 0.080   34,439 0.091   1.14 4,099
Services 53,441 0.326   105,218 0.279   0.86 0
Fed. Gov’t 2,892 0.018   8,403 0.022   1.26 1,759
Military 2,074 0.013   14,506 0.038   3.04 9,741
State & Local 17,774 0.108   65,458 0.174   1.60 24,626
Total 164,174     377,158       40,590
              Multiplier = 9.29

Military has the highest location quotient and can be identified as Columbia’s economic base.  Note that an area’s economic base can also be identified by the greatest amount of basic employment which would indicated that both State and Local Government and Military make up Columbia’s economic base.  Finance, insurance, and real estate are also a significant part of Columbia’s economic base.

  • Calculate the economic base multiplier (EBM) using the location quotient method.  What does this number indicate?

The location quotient approach estimates basic employment of 40,590 out of total employment of 377,158.  That results in an economic base multiplier of 9.29.  This suggests that for every basic job, 8.29 non-basic jobs are created (or a total of 9.29 jobs).

  • Familiarize yourself with a map of Columbia, SC.  Which part of town would be most vulnerable to a downturn in Columbia’s economic base?

Military is a huge component of Columbia’s economic base.  Thus, we can infer that Columbia would be vulnerable to downsizing in military employment.  Under such a scenario, real estate in the east part of Columbia (around Fort Jackson, the major military installation as shown in the map of Columbia) could be especially hard hit.  Multi-family housing would be vulnerable, as would retail and local office in that area.  On the other hand, that area should benefit the most if military employment were to increase.

  • Suppose that the Columbia MSA is expected to see an increase of 2,000 basic jobs in the near future.  How would this affect the demand for real estate in general?  What is your estimate of new demand for multi-family housing (assume that 40% of households live in multi-family and 60% in single-family housing)?

Using an EBM of 9.29, an increase of 2,000 basic jobs would result in an increase of 18,580 total jobs.  In the previous exercise, we saw an average of 1.25 jobs per housing unit.  Thus, 18,580 new jobs would create demand for 14,864 housing units.  If 40% of housing units are multi-family, the increased demand for multi-family would be about 5,945 units.  It could be argued however, that an EBM of 9.29 is abnormally high, and that it (and all related forecasts) should be revised downward.

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