How to prepare for the housing market of the future | The case of Vesteda

Recently we were contacted by Vesteda, a Dutch residential investor focusing on the mid-rental segment. They believe that data can help them in achieving their goals:

• To be sustainable.
• To match the right house to the right customer, maximising customer satisfaction.
• To ensure their portfolio remains financially stable in the changing environment and economy.

They asked us to train several groups in the organization on working with data and data science, how to organize around data and what it can and cannot deliver, thus enabling their next step in becoming more data-driven.

The data strategy framework

First off, we prepared a half-day workshop for members of the board. The goal: introducing a framework for thinking about a data strategy, and then apply the framework to the organisation. Our framework takes the value that an organization is striving for as the ultimate goal. Then we explored five important areas that all need to come together to create this value. In Vesteda’s case the goal is to invest sustainably for the future, simplify the application process for prospective clients and automate part of the internal processes. These things all require data products, like analyses, dashboards or machine learning models. The participants of our workshop left with a good idea of where they are in each of these areas of the framework, and some strategic questions that have to be answered to get to the next level.

How to identify and select the right use case

Secondly, a few rounds of day-long workshops took place for different groups of managers from around the organization. In this workshop we started out by sharing the same framework so everyone had a common understanding of data-related concepts.
After that, we brainstormed about interesting use cases to help reach the goals of sustainable investments, smoother customer processes and partial automation. These were then prioritised and for a small selection of them we worked out in more detail in the next part of the workshop.

Refining use cases

To prioritize the different ideas we used the criteria of feasibility and impact. For each use case idea we estimated the expected impact on the goals, as well as the effort required and risks that the idea would not work out. Then we could select the ideas with highest feasibility and expected input. For example an idea to create a matching score between a prospective client and an available house could have a risk of failing because of data availability concerns, if not enough data about satisfaction of previous renters was available. However, expected value can be quite high if the probability of people being happy with their house is higher and people move out less quickly. These are the types of things we considered in the workshop for each idea.

Selecting use cases

For a selection of the ideas we discussed in more detail what would be required to execute them. Which data would be required and is it available? Who would be the user of the data product, and how would they use it? What type of risks are involved? What is the monetary value of the end result? For the previous example the user could be the employees in charge of showing houses to the people interested. They could make a better selection of what to show to which clients, saving time and providing the customers with a better experience. The result was that participants walked away with some good use cases, and had learned how to prioritize and start working out an approach.

Data Product Lifecycle

The last part of the day we used to go over the lifecycle of a data product. We discussed what the different phases are and what is needed at each of them. Moving from an idea, to a proof of concept and then to a fully implemented solution takes many different tasks and decisions. When moving from phase to phase, there is a moment to evaluate whether the idea should even be taken forward. Does it bring the right amount of value? Are all necessary requirements in place? If the answer is yes, then all important technical and business aspects need to be addressed to make the project successful.
Armed with this information Vesteda knows how to prepare for and successfully create the plans for the chosen use cases.

What makes a successful data strategy

As was mentioned above, in our vision of a data strategy, everything is about the value that an organization wants to create with data. From that starting point, all other pillars of the strategy need to align and come together to make this value a reality. Every part of the organization is needed for creating and implementing a successful strategy, not just the data team. Having people from all parts of the organization participate in a workshop enables them to have a joint language and to know what to ask and expect of each other. Over the course of the training period, Vesteda’s data lead created and shared a data strategy for the coming years - a great foundation for a data-driven transformation.

With all this effort combined, the organization is now ready to embark on the next stages of the journey to become data-driven. The challenges for an organization managing and renting out property are numerous and we trust our efforts will help to face these successfully.

At BigData Republic we offer workshops and trainings like the ones described here on request. We work closely with the client to ensure their needs are met and the level is tailored to where they are and where they want to go. In case you are interested to learn more, contact our commercial manager Irene.