Original Huff Model

The Huff model is an established theory in spatial analysis. It is based on the principle that the probability of a given consumer visiting and purchasing at a given site is some function of the distance to that site, its attractiveness, and the distance and attractiveness of competing sites.

This specific model, in the area of spatial interaction research, was refined and made operational by Dr. David Huff of the University of Texas nearly 40 years ago.

In practice, census polygons (for example, block groups) are substituted for individual consumers. The calculated probability for each polygon is multiplied by some data element in the polygon database (for instance, households and dollars spent on groceries). This measure can then be summarized to give an estimate of the total. Some measure of size, such as gross leasable area (GLA), is often used as a surrogate for attractiveness.

A site has many attributes that make it attractive to consumers. Attractiveness can be computed as a function of many attributes. For a retail store, these would be its retail floor space, number of parking spaces, or product pricing. Attractiveness of a car dealership could be a function of its display area, frontage, and advertisement. The attractiveness of an office building could be a function of how many offices are within it. Attractiveness is expressed as one number that combines all the factors that make a center attractive. This number is usually referred to as an index. An index of attractiveness for a center is one number describing the factors that make it attractive to its customers. This index could also be derived by counting how many people come to that destination or by conducting a consumer survey.

You can control the distance that the Huff model extends. Type a value that will encompass all your competitors.

You can choose miles or kilometers as the distance units.

Distance-decay Function

A person's perception of how far a destination is may not be a linear function of distance. Customers are more likely to shop closer to home than far away. In other words, distance is viewed as a nonlinear deterrent to movement. This phenomenon can be modeled by using a distance-decay function. The use of a power distance-decay function is borrowed from Newton's law of gravitation from which the term gravity model is derived. A distance-decay parameter, symbolized by the Greek letter beta, can be used to exaggerate the distance to destinations. Some activities, such as grocery shopping, have a large exponent indicating that customers will travel only a short distance for such things. Other activities, such as furniture shopping, have a small exponent because customers are willing to travel farther to shop for furniture.

The exponential function is typically used for computing interactions over a small distance, such as within a city.

All Huff model inputs, exponents, trade area sizes, and results require detailed analysis by someone who is well versed in the operation of such a model. Some calibration is always required to account for other factors such as leakage (when people don't buy all their groceries at supermarkets, some of that spending leaks to other trade areas such as convenience stores, farmer's markets, and mail order services).

Learn more about the Original Huff Model.