"Show Me the Money!"

The following text is Gil Castle's final draft of the real estate column appearing in Business Geographics, May 1998

Copyright © 1998 GIS World, Inc.

Retail trade area analysis and site selection may well be the most common uses of business geographics technology in real estate. A broad spectrum of tools exist, ranging from simple concentric rings to complicated Huff models. This is all well and good—but nonetheless insufficient.

Unless and until the outputs of these tools are site-specific forecasts of gross revenues, net profits, return on investment, or other financial performance measure, they will not be viewed as "mission critical." In real estate as in Jerry McGuire's industry, money matters. The more obvious the link between business geographics technology and higher real estate profits, the greater the likelihood the technology will be implemented and used. Show me the money!

So where are we now and how do we get to where I'm suggesting we ought to be?

The chart below applies a classic process—a "GIS data structure"—to trade area analysis:

Such a simple concept, yet so complicated to implement! The two principal obstacles to implementation arise.

The first obstacle, though complex still the easier of the two, is choosing geographic data bases for the boxes in the far left column. Just within the "Socioeconomic" box, innumerable options are offered by Claritas, CACI, Market Statistics, and other leading data vendors. The "Direct Competitors" box could contain, among the various data bases available, the longitude-latitudes and 30 attribute variables for all the shopping centers in the nation (from NRB and Trade Dimensions). Within the "Environmental Hazards" box, business geographics data bases on flood zones, earthquake fault lines, Superfund sites, etc., are available from On Target Mapping, ERIIS, Vista, EDR, to name several of the leaders. Given that software is to engines as data bases are to fuel, the choices made for the left column boxes are crucial.

The second principal obstacle is defining exactly how the left column boxes lead to the middle column boxes, and then how the middle column boxes lead to the final result in the right column box; that is, what happens within the chart's arrows. Except for the few, isolated, demand-side software packages indicated above (AnySite et al.), hardly anyone has even tried to build the requisite inter-column links. The task is far from trivial—but the potential rewards vis-a-vis truly "mission critical" applications of business geographics technology can be handsome indeed.

How might one go about building the links? The following are a few thoughts on part of the data structure.

Getting to the right column box will inevitably require the completion of a DCF or some other type of financial pro forma. A (highly simplified) financial pro forma typically encompasses these inputs for each of five to ten years:

Gross Revenues and Vacancy Adjustment will come from some combination of the "Demand-Side" and "Supply-Side" boxes in the middle-column. The specific steps will encompass a year-by-year determination of each trade area's total purchasing power for one or more products, the total number of competitors who might fulfill that demand, the spatial competitive advantage each potential site might or might not enjoy in trying to capture market share, and the corresponding rents that a retail facility could pay at each site and still make a reasonable profit.

Operating Costs typically will come from the "Operating Expenses" box. which in turn will derive from a national or regional data vendor of this type of information (e.g., "Dollars and Sense of Shopping Centers"), from local real estate professionals (brokers, appraisers, property tax assessors), or from in-house records on nearby owned properties. Operating Costs could also encompass above-average insurance costs or capital budgets for dealing with environmental hazards, etc., in the "Other Factors" box.

Again, figuring out how to build the chart's arrows is unquestionably a major challenge—but the monetary rewards could be huge. If the process leads to avoiding even one bad site investment, all the business geographics software, data, personnel, etc., will have been repaid with interest. If this type of process results in an increase of even a few basis points in the return on investment (ROI) of each retail facility, the system will have paid for itself many times over.

For everyone involved in retail trade area analysis and site selection, the bottom line objective should be to build a business geographics system along the lines delineated herein, so that the outputs are in monetary terms that the company's CEO and CFO can instantly understand and use.