Extend the user research with housing officers
Firstly, to understand better how Rapid is used to record information on visits or calls with tenants (for sentiment analysis).
Secondly, to understand their perspectives of the factors that lead someone to be at increased risk of going into arrears – what have they noticed are some factors which seem to contribute to people who fall into arrears frequently?
Thirdly, to understand the interventions they’ve used before to help someone deal with those risk factors. Some of these came out during the initial research, but it was important that we could map these against the risk factors – and understand which risk factors were in the remit of the council and which weren’t.
Extend research with tenants
We hadn’t been able to speak to any tenants as part of this short project, but it’s a crucial step to engage with tenants to really be human-centred in our approach. Not only to explore their experiences of rent arrears and what impacts their ability to pay rent, but also to look at their interaction with the council and housing officers to understand how support might be improved.
Incorporate that knowledge into a modelling approach
Our focus so far has been on problem understanding, data identification and refining scope to ensure appropriate application of data science to support housing officers and tenants, steering away from negative unintended consequences. The lessons learned so far can guide us in constructing and implementing a model.
Given the focus on understanding risk factors, the modelling approach should reflect the need for an explainable, flexible and transparent way of understanding the factors behind arrears.
The model should incorporate information at different levels (e.g. individual factors, population level socioeconomic factors, factors related to the location of the property with respect to the wider environment or other properties etc.).
The model should also be flexible enough to account for different levels of uncertainty associated with each variable.
These requirements make the use of hierarchical modelling frameworks appealing. At the bottom of the hierarchy, the observation model should reflect any modelling assumptions related to the process of being in arrears at a point in time (e.g. using Generalised Linear Models). Framing the problem through a Bayesian approach would allow for incorporating different levels of prior information in the model’s hierarchy. For example, the degree of belief in the influence of socioeconomic status of an individual household to rent arrears could be informed by the levels of deprivation in the area the property is located. Likewise, the effect of the location of a property with respect to arrears levels of households in the vicinity can be represented using a neighboring structure, allowing clustering effects to emerge. Iteration and reevaluation of outputs should be included in the pipeline and should be scrutinised by domain experts (such as housing officers).
Continuing consequence scanning
The doteveryone consequence scanning activity has been picked up by Camden on other data projects, and proved a really useful tool. To be useful, this needs to be iterated on and reviewed, with an eye on mitigating negative consequences and giving everyone involved in the project opportunities to raise any emerging unintended consequences they saw.
The combination of data science and human centred design support on this data science fellowship proved invaluable – we believe this combined expertise is particularly important in shaping opportunities for getting value out of data, especially on tricky council problems where ultimately the aim is to support residents making best use of council resources.