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.

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

When going into battle with the Scentre / Westfield’s of this world, do you not think they have undertaken benchmarking of your system? Surely you realise they know what your sales in each store in their Centres, what is the rent you pay in each store in your network, and they have a preconceived idea of what you can afford to pay.

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

Information is available about every shopping centre that may be of interest to you. The main points you should be looking for are:

  • GLAR _ Gross Leasable Area Retail (how big is the Centre)
  • MAT – Moving Annual Turnover (how much does that Centre sell)
  • 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 and a lot more is all available.


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 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


Eventually you can have hundreds of variables across the spread sheet.
You can even buy this data around every main shopping centre in Australia at various radius.

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 like Chadstone and Castle Hill. You start to also get comparisons amongst others 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 less rent that a stronger Centre
  • MAT / GLAR – this is purely $ / 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 $ 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 $ / 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. SEIFA stands for Socio Economic Index For Areas, and is the ABS’s way of comparing 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 1,000. If you imagine a Bell Curve with 1,000 as the centre, then 1,100 is one Standard deviation on the “better “ side, 1,200 is 2 standard deviations, and likewise 900 and 800 is signifying poorer areas. I like to joke that this is a scale from Affluent to Effluent, and everything in between….

If I want to look at a shopping centre and ask myself what is the socio economics 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 us a benchmark which assists in our product decisions.

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

Base information can be acquired from companies like ours 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 of my roles in a past life was Property Manager for a major oil company, and we used to try and have the best information from our existing service station network to support our case in whatever negotiations we were entering in to.

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.

Peter Buckingham is the Managing Director of Spectrum Analysis Australia Pty Ltd, a Melbourne based Geodemographic and statistical consultancy. Spectrum specializes in assisting clients with decisions relating to store and site location using various scientific and statistical techniques.