Understanding both consumer demand and the existing retail landscape are vital to successful retail network planning. The AGS Consumer Expenditures database helps retailers estimate demand for any given product in any given area. The Consumer Expenditure database covers most major household expenditures in a multi-level hierarchical classification. Expenditures can be expressed either as aggregate expenditure or per household expenditure for any geographic level from the block group to national. Data is available for current year and five-year projections, and the Consumer Expenditures database forms the basis of our retail potential database and the demand side of our retail gap database as well.

The Consumer Expenditure database consists of a multi-level hierarchical classification of household expenditures, which covers the majority of annual household expenditures. It is derived from an extensive modeling effort using the latest available data from the Bureau of Labor Statistics.

The major categories represented are:

  • Total Expenditure
  • Food and Beverages
  • Shelter
  • Utilities
  • Household Operations
  • Household Furnishings/Equipment
  • Apparel
  • Transportation
  • Health Care
  • Entertainment
  • Personal Care
  • Reading
  • Education
  • Tobacco Products
  • Miscellaneous Expenses
  • Cash Contributions
  • Personal Insurance
  • Gifts

Most of these categories include two or three levels of sub-category detail. For example, a typical classification for an item in the food group is:

  • Total Expenditure
  • FB Food and Beverage
  • FB1 Food At Home
  • FB106 Dairy Products
  • FB10604 Cheese

This structure permits ready analysis of expenditures at any level of detail and between levels of detail. It is possible to analyze any individual category within the context of its parent category (e.g. cheese expenditures as a share of total dairy product expenditures or total food at home expenditures).

The database consists of a total of 396 base variables, which are aggregated in up to four levels of detail. A hierarchical structure is utilized throughout, so that it is possible to aggregate or disaggregate categories as required for analysis.

When combined with Retail Potential and Retail Gap, the marriage of demand and supply results in the ability to map retail over and undersupply by category. For retailers, gap (or void) analysis is a useful tool for evaluating potential store locations. For shopping centers, the database provides a unique opportunity to consider the tenant mix in order to attract retail chains to maximize traffic.