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Analyzing Parking Demand Typically, the design-day adjusted parking demands shown in Table 5 would be analyzed to determine which variables correlated most closely and would be the best predictors of parking for the Contractors' Warehouse stores. The 5th busiest day peak parking demand would be compared to daily and annual number oftransactions, square footage, and other store characteristics. In previous parking studies conducted for Home Depot, square footage typically showed the worst correlation with parking demand and annual sales (or transactions) typically showed the best correlation. However, since there are only two stores in this study, no statistical analysis can be conducted to determine which characteristics are the best predictors of parking demand. This is due to the fact that there are only two data points, between which any equation will yield a straight line, and therefore all variables will appear equally good at predicting the parking demand. Instead, this section of the report will detail the average relationships that exist between the various store characteristics and the parking demand. Square Footage The easiest method of predicting parking demand for a proposed store would be to base it on the square footage planned for the store. However, previous parking studies have shown that there is virtually no correlation between the square footage of a store and its resultant peak parking demand. While average rates can be extracted from this data that can be used to generally estimate the number of parking spaces that can adequately serve a store of a certain size regardless of customer activity, these rates would over-predict the amount of parking needed for a store with lower sales activity. The ratio of parking spaces needed to square footage varied greatly between the study stores as described above. The resultant parking ratios ranged from 957 square feet per required parking space at the Montebello store to 598 square feet per required parking space at the Pomona store. This shows that the Pomona store has about twice as much parking demand per square foot as the Montebello store (likely due to its location in a shopping center). The average rate is 777 square feet per required parking space. Using the average rate would over-estimate the parking needed for lower performing stores, which would be acceptable (though less cost-effective). However, using the average rate would under-estimate the parking needed for higher performing stores, which would be unacceptable. Therefore, the maximum observed rate would have to be used in order to provide enough parking for all store types; however, this would over-estimate parking needed for lower performing stores and be a waste of land and resources. This dilemma demonstrates why basing parking needs on square footage alone is inefficient and not cost-effective. Despite its shortcomings, if it is absolutely necessary to predict parking needs based on square footage, these general rates can be used. Specifically, a rate of one parking space per 600 square feet could be applied to new Contractors' Warehouse stores that will be located in shopping centers, and a rate of one parking space per 960 square feet could be applied to new stores that will be located in industrial areas. More store locations would need to be studied in order to refine these rates and/or calculate a more robust average rate for both store types. Barring that, the maximum parking-to- Parking Study of Two Contractors Warehouse Stores - Final Report Page 11 A-19 31A-37