Bigen Africa uses @RISK to encourage funding for basic community services in South Africa

Bigen Africa works across South Africa facilitating projects that bring basic services, such as housing, water and electricity to local communities. It uses @RISK to ensure that the overall requirement is fully understood and that informed decisions are made about implementing a programme.  In particular this provides the level of detail needed to secure commercial finance.

Background
The legacy of apartheid in South Africa has left much of the country without basic services such as housing, water and electricity.  Although government initiatives, along with the UN Millennium Development Goals, are in place to tackle the issue, these resources are not enough to meet the scale of the need. As a result, commercial finance programmes will play a key role in delivering these services over the next 15 years. This requires that risk is identified, managed and mitigated if projects are to attract funding and be successful.

With 14 offices across South Africa, civil engineering company Bigen Africa describes itself as a ‘development activist’ organisation. The company acts as an intermediary between engineers that design and build infrastructure and the department of the government that delivers that infrastructure, with the aim of ensuring that each party understands each other’s exact requirements.  With project teams that include engineers (civil, electrical, township, etc), project managers, town planners and project finance specialists, Bigen Africa brings housing, electrical, roads, solid waste and water and sanitation projects to fruition.

Guarding against the risk of
‘unbankable’ projects with @RISK
Much of Bigen Africa’s work focuses on risk management with a view to mitigating the risks that impact on the financial feasibility of a project.  Bigen Africa uses Palisade’s risk analysis software @RISK to help understand what it terms ‘demand risk’, which is a measure of several key elements:

• Estimation of the demand for a service, such as water, in a target area
• Understanding whether the demand will grow and, if so, by how much
• The cost of the service
• The strategy in place to recover these costs, and the likelihood of its success
• The sustainability of the project

Failure to understand and calculate demand risk leads to inappropriate systems being implemented. For example, an engineer might design a high-specification water system.  However, the focus on design may make it over-complex and therefore expensive – with the end result that it does not meet the needs of the community, the government or the financing organisation.  Overall, poor planning without enough information makes it difficult to recoup the costs of a project, thereby making it unattractive to potential commercial financers – in other words, ‘unbankable’.

@RISK measures ‘demand risk’
Bigen Africa works on the basis that demand risk is encountered in every infrastructure programme.  If the availability of finance for a project is not to be inhibited, calculating demand risk must form a major part of the process that determines if and how to proceed.  For example, if the actual demand for a service turns out to be less than the projected demand, this threatens to push the real unit cost over budget, potentially resulting in a cash-flow deficit, to detriment of the financier.  If demand risk can be determined, it can be priced into the cost of the service thus partially mitigating this risk.  To do this, Bigen Africa has used @RISK to develop a methodology that, by quantifying this risk, is able to mitigate it as much as possible.

Determining demand for inputs to @RISK model
In recognition that demand for a service can be determined by the type of house, Bigen Africa uses the total number of households in an area, which it classifies into ‘Standard Dwelling Types’ (SDTs).  For example, it is straightforward to identify houses that have low, medium and high capacity demand for water by visiting the region and counting the number of zinc shacks and houses with swimming pools, or those that show evidence of gardening activity, and then reinforce observations using aerial photography.  In addition, because there is a correlation between income and the ability to pay the bill, this method is also a useful measure of cost recovery risk.

Each type of dwelling will use a varied amount of water over the course of each day, and this statistical distribution of the consumption will be taken into account for each group of SDTs.  For example, 6000 low-capacity homes in a region will consume a volume between X and Y each month, and the same is calculated for medium and high-capacity houses.

These figures, along with those that take the water consumption of businesses, schools, hospitals, etc, into account are used as the inputs to the @RISK model.

The growth in demand for water is also factored in by forecasting the increase in the number of houses for each SDT in the area. This requires examining current market trends for each SDT and spatial planning strategies of the relevant local authorities as well as determining practical physical development limits for the area. From this information a growth model for each SDT is developed.  These are also based on suitable statistical distributions in recognition that uncertain growth contributes to demand risk. Future increase in service levels due to improved supply and development goals (as defined by the local authority) are modelled in a similar fashion, and also contribute to the demand risk.

Determining service levels
The level of service is an additional input into the @RISK model.  For example, the water supply can be available constantly at a higher cost, or at fixed regular intervals, in which case the price is reduced. (This question is often put to the community, as its members must pay for the water they consume). The final decision will determine the type of water system that is implemented.

@RISK provides detail for potential finance partners
The @RISK model developed by Bigen Africa helps it to understand and demonstrate that it is the number and type of housing that drives the demand for services, where this demand is, what it will be in the future and who will use the services. It forms the basis of engineering / planning, the financial model, the revenue model and strategy, affordability analysis and the integration between the services (housing, roads, solid waste, water, sanitation, electricity, etc).

In doing this, the @RISK model provides the level of detail that banks require before making a decision on whether to finance a project.  At the same time, it benefits from simplicity, as it can be developed rapidly and is easily understood by a wider audience who may not be familiar with the concept of demand risk, but identify with the SDT methodology.

Simplifying risk with @RISK
“@RISK is a seamless part of Excel, offering a wide array of functions,” explains Tian Claassens, Managing Principal of development finance and advisory services at Bigen Africa. “This changes the whole approach to risk analysis.  Rather than building very complex models in the hope that a finding emerges, @RISK facilitates a simpler model, but one which enables uncertainties and their impact to be easily identified. This is far more valuable, not least because it quickly allows decisions on costing to be made. Like crossing the Rubicon, once people use @RISK, they don’t go back to their previous methods of risk analysis.”

 
Additional information:
Key @RISK software features used by Bigen Africa:
Probability percentiles, graphic representations of risk distributions and easy interface. These all allow the user of the model to quickly check that the correct and appropriate assumptions have been made.

Distributions used for the project:
Mainly normal, due to the definitions of SDTs being based on estimates of the mean. In other instances such as growths etc, uniform, triangular, Normal, Log-Normal and Trigen are used because they best represent the real life situations that Bigen Africa aims to model.

The following illustrations provide typical results for a Bigen Africa project.

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