Property Benchmarking

What can we learn from our existing store network?
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We regularly read about how businesses can be benchmarked from a financial viewpoint. Many franchise systems spend considerable time and money comparing franchisees to the ‘norm’, and to use this information to try and improve performance.

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Similar can be done with regards to leases and properties you control. This information should then be used in lease negotiations.

When going into battle with the Westfields of this world, do you not think they have undertaken benchmarking of your system? Surely you realise they know what your sales figures are in each store in their centres and what you pay for rent in each store in your network. They will also have a preconceived idea of what you can afford to pay.

In many cases the negotiations may fall apart if you have not done your homework as well as they have!

Information is available for almost every shopping centre which may be of interest to you. The main points you should be looking for are:
• GLAR - Gross Leasable Area Retail (the size of the centre)
• MAT - Moving Annual Turnover (the amount the centre sells)
• Pedestrian Count – self explanatory
• Other information such as number of car parks, number of seats in the food court, who is in the centre (major tenants and their store area), number of theatre screens, etc

When you look at every store you have in your system, you should be able to benchmark the stores against the information for that centre and look at various ratios.

By comparing the rent, sales revenues and the various centre attributes, you should start to see some relationships, even if it is simply to be able to argue against rental increases.

Imagine you now have a spread sheet with all this information and you are starting to become a statistician or an analyst! You can also add many more variables along the row for each store such as:
• Population and number of households at various radius distances from the shopping centre
• Income or socio-economic information
• Family type and age profiles
• Ethnicity issues - if you feel these are positive or negative to your brand
• Household expenditure information

Eventually you can have hundreds of variables across the spread sheet.

These types of variables can give you a good insight into the area, and hopefully into the leasing and the lease negotiations. Once you understand these, there may be good arguments for dropping rents, or not increasing them as much. Some examples are:
• Revenue / rent - everyone can do this from your own records
• Rent /GLAR of the shopping centre – we expect to pay higher rents in larger shopping centres
• You start to also get comparisons amongst other centres of similar size
• Rent / MAT of the shopping centre – if the centre is not drawing in enough customers or is in a lower spending area, then there may be a good argument for lower rent
​• MAT /GLAR – this is purely dollar /sq m and we normally see the larger shopping centres having the higher benchmarks
Using the Household Expenditure Data (from the Australian Bureau of Statistics), you can estimate what is the dollar catchment of the shopping centre at a constant radius (say 3 km and 5 km in various categories).

Most shopping centres will try to determine what they call a ‘Primary, Secondary and Tertiary trade area’. At least you can determine the dollar/ household and compare like with like for various centres.

This type of information can also assist you in determining which shopping centres may be most suited to what you are trying to sell.

One variable we find very useful in assessing areas and shopping centres is SEIFA, produced by the Australian Bureau of Statistics (ABS). SEIFA stands for Socio Economic Index For Areas, and is a means for the ABS to compare one area to another, anywhere in Australia.

Instead of just looking at average income, or average unemployment, the statisticians at the ABS take into account a variety of information to come up with a descriptive number for every area in Australia.

The most ‘average’ place in Australia comes in at 1000. If you imagine a Bell Curve with 1000 as the centre, then 1100 is one standard deviation on the ‘better ‘ side. 1200 is two standard deviations, and likewise 900 and 800 are signifying poorer areas.

I like to joke that this is a scale from affluent to effluent, and everything in between.

If you want to look at a shopping centre and ask yourself what is the socio-economic status of the area, the best way we would suggest is to have calculated the SEIFA at 3 km and 5 km radius from the shopping centre. This gives you a benchmark which assists in your product decisions.

If for example, you ran a jewellery store franchise, you can start to make educated decisions on what you may stock. If you are in a very high SEIFA area, you may carry larger diamonds and a more expensive range of salt water pearls. If you are in a very low area, maybe you should carry smaller diamonds and more cubic zirconium and freshwater pearls.

Base information can be acquired from companies specialising in geodemographic and statistical analysis to allow you to attach your own corporate information, and create your own benchmarking. Imagine if you can go into lease negotiations armed with real information – and hopefully having information regarding your own network to support your arguments on why your rentals should NOT be increased exorbitantly.

​One small success in a property negotiation that you may be about to enter into will probably cover the cost of setting up this type of benchmarking 10 times over.