The Waarborgfonds Sociale Woningbouw (Social Housing Guarantee Fund, WSW) is a unique organisation. The foundation was established by the Dutch housing corporations as a mutual benefit association. WSW’s goal is to provide participating corporations with access to the capital market, at the best possible costs of financing. The organisation guarantees the interest and repayment obligations for loans concluded with corporations. The corporations can thus borrow under more favourable lending terms, which gives them a benefit of €300 - €500 million per year. That makes for lower rental prices.
Calculating risk scores is a time-consuming process. Changes in the supplied data have a major impact on lead times and costs.
A flexible data warehouse architecture, a tailor-made risk calculator and insightful dashboards.
Shorter lead times for risk calculations. Information is auditable, reproducible and reliable. The impact of changes in the supplied data is limited.
Whether or not WSW will guarantee a corporation depends on the financial and business risks to which the corporation is exposed. Every year the guarantee fund conducts a risk analysis of each corporation. This risk analysis covers not only the past financial period but also the coming five years. ‘A rating is calculated for each corporation, based on an unequivocal risk framework. This rating determines the upper limit of the guarantees provided by WSW for the next three years, at most,’ explains Kim Schwartz, Manager Business Operations & Control. ‘When a corporation wants to borrow money, it submits a request to WSW. With the guarantee from WSW, the corporation can obtain a loan from a financial institution under more favourable lending terms.’
‘The complexity does not involve the frequency at which data is supplied, but the scope of the changes in the supplied data, often due to legislative changes.’
Kim Schwartz, Manager Business Operations & Control at WSW
Information on 340 corporations
To conduct the risk analyses, WSW collects information on 340 corporations each year. Jorre Diekerhof, Information Analyst at WSW, explains how this works: ‘Corporations upload financial and public housing information to CorpoData, an online platform of the Authority for Housing Corporations (Autoriteit Woningcorporaties) for the collection and distribution of corporation data for WSW, amongst others. This is where information is entered to substantiate the previous period and information that is required to predict risks. In addition, the WSW account managers also collect more qualitative information on management, governance and a corporation’s real estate, amongst other things.’
The information required by WSW to conduct the corporation assessments changes from year to year, due to new legislation, amongst other things. An example would be the requirement, from 2017, for corporations to provide extra information on the mandatory separation between social and commercial real estate. For WSW, this implies that the supplied data will differ each time in terms of structure and content, even though calculations and reports must always be reproducible when accountability must be provided to regulators for decisions that were made. A complex process.
Seperation between value and meaning
‘WSW’s data warehouse, in which all historical information on all corporations is stored, could not deal with this complexity. It took too long to process the data and generate risk analyses. In addition, it took WSW more and more time to verify the reliability of the data,’ explains Kim. The organisation therefore decided to develop a new data warehouse. A solution which deals much more flexibly with changes in the supplied data, but can still reproduce all historical data and is also fully auditable. ‘The new data warehouse architecture strictly separates the value and meaning of every piece of information. Structural changes hardly have any impact,’ says Jorre.
For WSW, the data warehouse is not merely a strategic instrument, it also forms the core of the primary business process. ‘Our risk calculator is the most important collector of data in the data warehouse. The calculator calculates the risk scores for all corporations, based on the risk framework,’ explains Kim. The calculations are based on several years of historical data, but also on prognoses entered into CorpoData by the corporations. Calculating the consolidated risks for several subsidiaries can become quite complex. The risk calculator was developed and tailor-made especially for WSW. The results from the calculator are stored in the data warehouse to be reused in reports and dashboards.
‘The business rules make the risk calculations complex. The consolidated risk calculation for a corporation with several subsidiaries can be incredibly complicated.’
Jorre Diekerhof, Information Analyst at WSW
The new data warehouse also forms the basis for the internal information provision. ‘Account managers have access to a client dashboard in which they view information, per corporation, on the financial and business risks, P&L, cash flow, leasable units, value under the WOZ (Real Estate Valuation Act) and residual debt. The dashboard also displays the historical development of the various risk scores,’ explains Jorre. The risk managers use this information for stress tests (what if a corporation goes bankrupt) and sector-wide analyses. Jorre gives an example: ‘an expected decline in the number of students can have a major impact on corporations with a substantial number of student housing units.’
The Information Management department at WSW does not work on any projects of its own. ‘We are a control unit, we source out these tasks, also the management of applications,’ explains Kim. The management and development aspects are outsourced to Kadenza. ‘They also fulfil the role of Scrum master,’ according to Jorre. ‘You could say that we’re utilising Scrum-as-a-service. Right now, we are working with Kadenza to set up a service organisation for the daily management of the data warehouse and the implementation of changes.’ The most important challenge after that? ‘Offering more colleagues self-service possibilities with which to analyse data in the data warehouse!’