"Real Estate Investors Use Business Geographics to Fine Tune Investment Strategies"
The following text is Gil Castle's final draft of the real estate column appearing in Business Geographics, July/August 1993
Copyright © 1993 GIS World, Inc.
Capital flowing annually into real estate generally originates from two groups. The first group, accounting for approximately three-fourths of the capital, consists of organizations whose principal businesses are not real estate related — for example, manufacturing firms that need space for assembly lines, warehouses, executive offices, and so on. The second group, covering the remaining one-fourth of the capital, is comprised of institutional investors (such as pension funds and insurance companies); lending institutions (in the form of construction loans, long term mortgages, etc.); various Wall Street securities (e.g., real estate investment trusts [REITs], collateralized mortgage obligations [CMOs], real estate limited partnerships [RELPs]); and miscellaneous other sources.
Players in both groups, but especially the second group, more often than not have real estate assets distributed throughout the nation. Their balance sheets can change substantially from one year to the next as the values of their real estate portfolios increase or decrease. Accordingly, these organizations are motivated to keep track of which real estate markets are likely to experience significant appreciation or depreciation in property values in each of the next five to ten years. Based on these "market rankings," each organization decides whether to dispose of holdings in cities with unfavorable outlooks and/or to acquire properties in promising cities.
Applying the "80-20" rule to investment-grade real estate, the ranking of markets can be limited to the largest 100 Metropolitan Statistical Areas (MSAs). My previous real estate columns in Business Geographics have discussed how well suited GIS technology is for analyzing and visualizing real estate opportunities. Not surprisingly, then, various real estate organizations — especially many of the nation's leading investment management firms — have established GIS-based market ranking systems focusing on (usually) the top 50 to 100 MSAs. Examples include: The RREEF Funds and Mellon/McMahan Real Estate Advisors (both in San Francisco, CA); Metric Partners (Foster City, CA); Karsten Realty Advisors (Los Angeles, CA); Equitable Real Estate Investment Management (Atlanta, GA); and Homart Development Corporation (Chicago, IL).
Market ranking systems usually reside on personal computers. They typically employ software from Strategic Mapping Inc., MapInfo, Tactician, and similar firms offering low-cost, quick-to-implement solutions. As it happens, real estate organizations buy the systems based as much on presentation capabilities as on analytic functions; that is, to provide a competitive advantage when attracting new clients (as in the case of brokers and investment managers), to ameliorate the concerns of regulators (e.g., banks whose mortgage portfolios are of concern to the OCC), to obtain financing (e.g., a development company pitching a new construction project to a lender), and so on.
A market ranking system has two principal components: data inputs and proprietary ranking formulas. Concerning data inputs, since real estate is essentially a game of information arbitrage, a considerable number of variables can be included for each MSA, city, county, or other market definition. Some firms focus mainly on demand-side data, arguing that no real estate investment will yield adequate financial returns if demand is weak; typical demand-side variables include historic and projected growth rates of total population, households, household income, various categories of employment (office, wholesale trade, retail trade), and even quality-of-life scores. Other firms recognize that the balance between supply and demand is crucial; in addition to the above demand-side variables, these firms also seek information on total inventory, new construction, net absorption, asking rents, vacancy rates, etc.
Turning to the proprietary formulas, many take the form of weighted overlay mapping — long used by GIS professionals in urban planning and natural resource management. A highly simplistic formula for ranking the shopping center potential for numerous markets, for example, could be the following:
S = (a*H) + (b*I) - (c*V)
S = The overall score, used when ranking the markets from highest to lowest
H = The projected percentage change in the number of households over five years
I = The projected percentage change in median household income over five years
V = The projected percentage change in the vacancy rate over five years
a, b and c = Coefficients (weights) established statistically or judgmentally
Actual formulas are usually much more complex. For example, one of my preferred methods is to combine various demand-side and supply-side variables into a model that generates a five year financial pro forma for the average building in each market; the markets are then ranked on the basis of the internal rates of return of their pro formas. Other financial-based rankings that produce more meaningful results than an abstract score include: the present value of five to ten years of net operating income (NOI); the sum of the pre-tax cash flows over that period; the payback period (time required to recover the up-front investment), and so on.
GIS is used to depict input variables, e.g. a map of the nation showing the population growth rates of major markets, or median householdhold incomes, or employment rates, or other key factors. GIS is then used to show the rankings of the markets, e.g., a map of the United States with each market identified by a color, symbol, and/or proportional graphic representing its relative score. GIS can also be used to show the current distribution of the GIS user's assets in comparison to the market rankings.
As is true of any GIS, a market ranking system is only as good as the data being analyzed and displayed. Many organizations rely largely or completely on internal, proprietary data bases, such as market intelligence gathered by their asset managers around the nation. Other organizations turn to a limited number of firms that collect, synthesize and sell data in a uniform and credible manner for markets across the nation; examples include REIS Reports, Inc. (New York, NY) and DRI/F.W. Dodge (Lexington, MA). Selected periodicals publish comparative market statistics; for example, the April 1993 issue of the widely read National Real Estate Investor reports market rankings by the real estate consulting division of Ernst & Young, the Big Six accounting firm. Numerous regional and local data sources of varying reliability also abound. (For a more complete discussion of data sources, see "Chapter 5: Real Estate" in Profiting from a Geographic Information System, published by GIS World, Inc.)
Next time we will examine a related topic: the use of GIS by lenders for risk management in their mortgage portfolios.