Municipalities have many responsibilities. They aim to create a pleasant and functional living environment for their citizens, adequate infrastructure, city attractiveness to visitors and lower crime rates, among others. To achieve these goals, public resources need to be distributed efficiently, costs and revenues need to be balanced and budgets need to be kept in check.
However, we often note that reality can be quite different than what was planned for, especially in periods when the macro-economic environment changes. After the financial crisis in 2008, substantial distress occurred both within housing organizations as well as in municipalities that heavily invested in land purchases in the period before the crisis.
For example, the municipality of Almere (eight largest city in The Netherlands, with a large ground exploitation portfolio) had to write off 104 million on the ground investments in 2012. When markets recovered in the decade after, many of the unsold grounds could still be sold. This meant that losses could be more than recovered by subsequent gains. Still, at the time of the write off it did pose a problem for the budget in the municipality. It meant cost cuts were needed and many goals could not be met.
In such situations it is always easier to establish what went wrong post factum. However, it is not easy to predict big failures in advance and come up with appropriate control measures to be able limit the loss to manageable levels.
Data driven risk management
Following this event, the municipality of Almere decided to embark on a journey to improve its risk management process. The process included reassessment of both qualitative and quantitative methods for risk measurement by learning from practices in the financial sector. Asset Mechanics was asked to assist in the process.
The goals of this project were to ensure that risk estimates:
- become an integral part of the considerations
- will be based on statistical data for a defined negative scenario and horizon
- will be validated
The expected advantages will be:
- less surprises
- a continuous view on the risks (not only in hindsight)
Ground exploitation risk project
In this article we focus on the delivery of a solution for measuring the financial risks for ground exploitation.
The project consisted of the following main steps:
- Identification of the main risk drivers
- Creation and validation of the risk models
- Creation of business logic
- Creation of an automated risk report for each ground exploitation project
The last step combines all prior steps to create a risk report for a bird’s-eye view of what drives the risks.
The previous existing process also gave insight into the risk estimates. In this case, the negative scenarios were based on experience from employees in the ground exploitation department. While these estimates can be very useful as a first indication of the risks, it is not possible to make statements about the level of certainty that such an estimate would entail.
Advantages of data-driven approach
The new data-driven approach provides the following advantages:
- We obtain an objective and statistical estimate of the chance that a negative scenario materializes (based on historical data, context and correlations)
- The business logic becomes explicit instead of implicit in the minds of the experts
- The decision-making process becomes easier and more transparent for ultimate stakeholders
- We become better prepared and will be able to respond faster with control measures when needed
- Eventually, this will result in a lower chance of big surprises and a bigger chance of realizing the goals
During the project we learned that many aspects play a role when it comes to ground exploitation. Some key learnings include:
- ground price risk differs at different ground quotes
- volume risk is an important factor
- there is an opposite effect of deferred sales in a negative scenario for the revenue and cost side
- various risk factors on the income side (land prices and volumes on homes and businesses),
- various risk factors on the cost side (site preparation, preparation for residential use, planning costs)
- simulation aspects and sensitivity of the results
- budgets with already priced-in revenues and costs
- net present value at regulatory horizon and at end of project
- the importance of including remaining land in the calculation
This meant that we needed to have extensive and regular consultations with the ground exploitation department about the methodology and the business process. Multiple iterations were involved to obtain automated risk tooling that would:
- be easy to use
- fit in the operational processes
- lead to more efficient work processes (eg. to directly load information from the source system (TotalLink))
As ground exploitation models do not exist in the financial sector, we needed to perform extensive literature, data research, validations and backtests to be able to obtain appropriate models. We noted that especially for ground exploitation, risks are not constant over time so it was relevant to do additional research on macro-economic drivers.
We are now in a stage where we have created ground exploitation risk tooling that passed the user acceptance test and is able to mimic/simulate all important aspects of the ground exploitation practice. The ground exploitation department has committed to shadow run in 2024 alongside the previously used tooling.
It is good to know that we have succeeded to obtain a clear and user-friendly process to get objective estimates of the chance and impact of the ground exploitation risk estimates.
If you want to know how this would work practically, you can read more about this in the technical article about the ground exploitation risk tooling.