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Expectations - Managed by Images and Algebra Combined

One of the feasibility studies I prepared in 2015 was for a new exterior express wash in Arizona. While discussing the project with one of the principals, I was told, “You know, Bob, out here we are seeing capture rates of between 1.5 percent and 2 percent.

By Robert Roman

09/01/16

One of the feasibility studies I prepared in 2015 was for a new exterior express wash in Arizona. While discussing the project with one of the principals, I was told, “You know, Bob, out here we are seeing capture rates of between 1.5 percent and 2 percent.

Of course, optimism is to be expected because financial experts contend that setting one’s expectations prior to investing is one of the most crucial aspects for investing success.

For instance, let’s assume the decision is to invest in either an actively managed mutual fund or a low-cost passively managed fund. Here, an investor can form and track expectations by looking at both funds’ performance over time. If the fund chosen is consistently above the median in performance, it may meet the investor’s expectations. If not, it may be time to reevaluate the reasons for holding it.

Arguably, investment expectations are not about predicting the market, but rather about expectations as to how individual holdings will perform against some benchmark.

Benchmarking is a way to judge the quality or level of other similar things as in a standardized problem or test that serves as a basis for evaluation or comparison.

For example, the information contained in annual operator survey reports published by Auto Laundry News can be used as a performance benchmark.

Here, the express exterior benchmark captures 0.82 percent of average daily traffic of 34,000 vehicles or 279 cars per day or 87,000 cars per year based on 312 days.

There is also financial benchmarking where one performs a financial analysis and compares the results in an effort to assess overall competitiveness and productivity.

The survey results for the express exterior format shows average gross revenue per car of $8.69 and operating costs as a percentage of total revenue as 57 percent.

So, one expectation of net operating income (benchmark) would be $325,000 = 87,000 cars per year X $8.69 average revenue X 0.43 gross net (1 – 0.57).

There is also technical benchmarking that can be used to compare existing conditions with an aim to achieve the best possible performance in new situations. An example would be a financial engineering study of business specifications based on accepted car wash industry standards and guidelines.

The natural mathematical apparatus for managing and manipulating business specifications is a combination of sketches (e.g., drawings, pictures) and logically derived algebra.

The core idea in algebra is using letters (or names, symbols, etc.) to represent relationships between numbers without specifying what those numbers are. For instance, cost is a composition of chemical, utilities, labor, insurance, rent, etc., whereas location is an aggregation of area, lot position, competition, access, visibility and so on.

Consider the checklist method used in car wash site selection and store turnover forecasting. The checklist includes several major site factors, each one divided into several attributes, which appear to directly affect the performance of a car wash location.

Figure 1 – Checklist Model

As shown in Figure 1, if all of thesite-specific factors are set to ideal conditions, the turnover fraction is 1.5 percent (0.015).

Sensitivity can be examined by changing the rating of factors. For example, if we changed the area profile from shopping to business, turnover fraction (capture rate) drops from 1.5 to 1.45.

Some modelers will couple a demographic component that includes several major factors, which appear to directly affect car wash performance.

 

As shown in Figure 2, after coupling this component and using the target values, capture rate increases from 1.5 to 1.97.

Testing the sensitivity of the demographic component, we changed households with income greater than $35,000 from 50 percent to 40 percent and the capture rate dropped from 1.97 to 1.94.

Of course, for outcomes to make sense, they must be plausible. For instance, if the base price is set to zero, the capture rate increases from 1.97 to 3.21 (a percentage change of 69) and the estimate would be 802.5 cars daily or 250,380 cars annually.

Conversely, if we increase the base price from $5 to $18 (a percentage change of 260), the capture rate drops from 1.97 to 1.5 (down 24 percent).

However, neither of these scenarios appears to make sense from a practical point of view. No doubt, a base price of zero would overwhelm the wash and quite possibly result in average price falling below variable cost per unit. When a firm’s average price is lower than variable costs per unit, break-even is not possible. Profit at break-even is zero.

Likewise, increasing the base price by a factor of 2.6 is implausible given contemporary merchandising and pricing schemes. This is where sketches (imagery) come into play when managing and manipulating business specifications. 

Figure 3 – Retail Site

For example, Figure 3 shows a retail property with ideal site characteristics. The site offers a multiplicity of products and services. The property is a corner location. It’s easy to get in and out. There is ample space to navigate within the site and for vehicle stacking and parking. Visibility is great. There is access to traffic in both directions, and so forth.

In comparison, the property shown in Figure 4 has basically the same site characteristics as the one shown in Figure 3 with the exception that it’s an inside lot — an out parcel in the parking lot of a shopping center.

Figure 4 – Inside Lot Location 

So, while this site may be adequate for fast food, it would be much less so for a convenience store, gas station, or car wash because there is no direct access to principal roads, and it’s not easy to get in and out.

Figure 5 – Paradise Bay Car Wash

This is not the case for Paradise Bay Car Wash shown in Figure 5. Paradise Bay is a former convenience store and gas site repositioned as a car wash with discounted gasoline. The site has all the right stuff with respect to the site-specific factors previously mentioned. Of course, it takes more than good property characteristics to drive traffic to the site.

Figure 6 – Retail Composition

As shown in Figure 6, the area surrounding Paradise Bay contains high density and good variety of retail stores that are tightly clustered. Residential areas predominate nearby development. These characteristics are crucial in reallocating the demand inherent in a stream of traffic.

Figure 7 – Proposed Car Wash

By comparison, Figure 7 provides an aerial view of the surrounding area for a proposed car wash development in Florida. The outcome of the study for this project was a capture rate of 1.95 or 146,000 cars annually based on an average daily traffic of 25,000. Here, checklist outcome was 1.33 (out of a possible 1.5), and the score was attributable to a less than ideal lot position and travel speed.

However, when we examine the surrounding area, it brings into question the reasonableness of the estimate. For example, Walmart, WAWA, and Culver’s (fast food) are great stores but there are simply not enough of them.

Figure 8 – Street Views

Figure 8 shows street views near the subject property. The southbound view (top photo) shows a divided highway and complicated intersection near the property, and WAWA is an inside lot. Thus,getting to the wash would be difficult. The northbound view (bottom photo) shows an inconspicuous entrance for Walmart as well ascommercial and light-industrial uses near the site.

Clearly, this location does not exhibit the site-specific characteristics and location dynamics that Paradise Bay does.

Arguably, this underscores the importance of combining sketching and algebra for more precise management and manipulation of business specifications as well as expectations.

 

 



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