Common Retail Leasing Mistakes: Analysing Retail Sales Potential

Understanding how to estimate your businesses’ retail sales potential should be an essential tool in any tenant’s skill set.

The process by which business owners calculate potential revenue is called “forecasting” and the figures generated can be used to ensure:

1. Business sustainability
2. Bank finance
3. Rent sustainability

This article is the second installment of our three-part series; Common Retail Leasing Mistakes. If you have not read part one; Site Selection then we recommend starting from the beginning as key takeaways from that article will be used in this one.

Forecasting is done using two techniques which each evaluate different aspects of a site:

1. Regression Modelling – The use of data from current successful stores to predict sales for a potential site, using a ‘line of best fit’;
2. Retail Saturation Index – The use of available metrics to calculate potential sales based on competition and sales within a catchment

Regression Modelling uses a single data set or “driver” with your trade area to predict sales. To further understand how to estimate your businesses’ trade area or catchment area please read part one of the blog series; Site Selection. Some examples of potential drivers include:

• Population density
• Average age
• Growth Potential
• Foot Traffic

Ask yourself, “Which demographic your business appeals to most?” For example, if are looking to set up a jewellery business you would use the average income in your trade area in your regression modelling.

Now that you have determined your business drivers its time to model. To do so, take the yearly sales of other successful businesses in the same category as your service offer and compare them to a chosen demographic within their trade area.

In the example below, 10 Home Improvement Centres are compared to the population density within a 3 Mile radius (their estimated trade area).

These figures are then plotted on an excel graph and a line of best fit is applied:

When plotted as a graph and the slope of the line of best fit is measured ( gradient), we can see that for every 1,000 people, sales increase by $7,000 p.a.: quite a significant increase.

Regression Modelling is a simple and effective way of measuring the impact of a specific demographic on a set of stores. However, it does not account for many variables such as competition and site specifics,  so other forecasting techniques are always required in conjunction with regression modelling.

To account for competition and predict sales at the same time, valuers will use an Index of Retail Saturation (IRS). This forecasting method breaks down how much money existing businesses make /m² and use those figures to estimate your potential sales.

The key formula for to calculate IRS is: (H x RE) ÷ RF

Where:

H:            Households in the trade area

RE:          Yearly Retail Expenditure (usage specific)

RF:          Size in square metres of competition in the trade area (usage specific)

Let’s use an example. You want to open a fast-food chain in the ACT; you would look size of each of those stores and record the yearly expenditure on take away as well as households in the area. We can find these figures by using statistics from the Australian Bureau of Statistics.

In the ACT there are 110,550 households, with an average yearly expenditure, per household of $1,921.92 on fast food takeaway. The total retail space for fast food in Canberra is 28,156.22m².

Therefor IRS = (110,550 x $1921.92) ÷ 28,156.22 = $7,546.05

This means the average fast-food chain in the ACT makes $7,546.05/m².

The average becomes your baseline for comparison when comparing the strengths and weaknesses of sites. Say you wanted to see if opening up a fast-food chain in a fictional centre “Sam’s Mall” was feasible; you estimate the centre attracts a catchment area with a 7km radius. Within that area the average household expenditure on fast food is $2,000 p.a. and the number of households is 20,000. The combines retail space for fast food in the catchment area is 10,000m².

Let’s do the maths: IRS = ($2000 x 20,000) ÷ 10,000 = $4,000

From these calculations we can see that fast food chains within the Sam’s Mall Centre catchment area generate far less sales /m² than the average fast food shop in the ACT despite a large number of high density households and increased spending on fast food in the area.

This is because the area is over saturated meaning that there are already too many fast-food stores in the area making it unviable to set up a fast food business in Sam’s Mall.  In general:

• • • Higher Index = Less saturated / more $ per m²
    • Lower Index = More saturated/ less $per m²

Once your businesses’ IRS has been calculated you can use those figures to estimate the potential sales of the store by multiplying the base IRS by the size of your store. So, if your IRS is $8,000 and your store is 50m² then your estimated annual turnover will be $400,000 p.a.

Is this amount sustainable for your business? How much of your budget can you afford on rent and staffing? Is there too much competition in the area? By calculating IRS you can answer these exceedingly important business questions and make your business a success.

Author

This post was authored by Simon Fonteyn. Simon is one of Australia’s leading experts in retail, childcare and medical leasing and rental valuations. He holds a Degree in Accounting & Finance, a Diploma of Valuation, a Masters of Management and is an Associate of the Australian Property Institute. With over 25 years experience in the commercial property industry, Simon founded LeaseInfo® as a way to provide more transparency to the industry.