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:

• The boxes in the far left column are "raw" geographic data sets. These are usually purchased from vendors, sometimes are acquired from government agencies, and occasionally are developed in-house. Typical examples in the "Socioeconomic" box are historic, current and future estimates of population, income, and age distribution at the block group level; in the "Lifestyles" box, cluster data bases such as PRIZM or MicroVision; and so on.
  
  
• The boxes in the middle column are maps derived from the boxes on the left, using various GIS analytic functions, models, methods, procedures. The boxes in middle column equate to the standard inputs into financial models—demand, supply, etc. Most business geographics software packages that are tailored to trade area analysis (e.g., TrendMap from Spatial Insights, AnySite from Integrated Technologies) focus on only one box, "Demand-Side" analysis; such packages will determine, for example, the total number of households, aggregate household incomes, and corresponding retail buying power within a one-mile radius of a site.
  
  
• The box in the far right column is again a derived map, this time from the boxes in the middle column. The map combines demand, supply, and other financial factors to depict all trade areas in terms of how much money can be made by investing in each. Each trade area might, for example, be ranked according to the present value of ten years of net operating income (NOI) determined from a discounted cash flow (DCF) model. The map might simply have each trade area labeled with a dollar figure, be thematic (e.g., with the trade areas colored Hot/Warm/Cool), be in three dimensions (with the most lucrative trade areas having the highest peaks), or follow some other presentation format. The map can refer to one investment, or can fit that investment into a portfolio of investments. This is the map that responds to "Show me the money!" This is where we want to be.

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—calculated (more or less) by multiplying the leasable square footage of an existing or planned retail facility by prevailing market rents;
  
  
• Vacancy Adjustment—the Gross Revenues not realized from one year to the next due to non-rented (vacant) leasable space;
  
  
• Operating Costs—all the non-capital costs for keeping the retail facility functioning, including utilities, landscaping, security, facility administration (e.g., overall management, accounting, leasing), and so on; and
  
  
• Net Operating Income (NOI)—Gross Revenues minus Vacancy Adjustment minus Operating Costs.

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.