Commercial property: Vulnerability of retail incomes to disruptions in footfall

The value of commercial property portfolios depends on an "ecosystem". The key driver of this ecosystem is the business turnover of the tenants, which is itself dependent on footfall past the shop. 

We developed a system dynamics model to demonstrate how different type of "threats" could be prioritised in order to assist the decision making within commercial property companies on where they choose properties and what measures they should take to limit any fall in rental values.

Building the model

The model was based on a shop on Oxford Street in London with the main material flow consisting of people flowing between "stocks" representing different locations (in front of the shop, to the east and to the west).

The rates at which people arrive from the different directions is driven by the rate at which different modes of transport "deliver" them into these areas; we simply went to the nearby bus & tube stops counted the number of people arriving per hour. We also counted the proportion of people who went into the shop and used the annual accounts to estimate the average spend and running costs of the shop. These figures enabled us to complete the key information flows.

Running the model

Using the inputs regarding movement of people and spending, we ran the model with a number of "shocks" which represented different types of disruptions to the number of people passing the shop; terrorist attacks, public protests, tube derailments etc. The two main outputs of the model were the impact on the number of people entering the shop (footfall) and cashflow (profit). Of note is that whereas footfall recovers quite quickly, profit takes a much longer time to recover. Tthis is because all the time that footfall is reduced the shop is making a loss; although revenue is down costs remain the same (rental & staff costs being the most signicant and non-variable).

By running the model for the different scenarios it is possible to compare the different profits (or losses) due to different events and thus prioritise which disruptions should be mitigated against.