What is machine learning?
Put simply, it’s about identifying patterns in historical data. Algorithms learn these patterns and then forecast future trends.
Machine learning is a type of artificial intelligence. AI is all about creating computer programmes to think and learn like humans. Machine learning is one of those computer programmes.
During this knowledge transfer partnership, I’ve designed machine learning models to help PfH analyse data. These models will power a range of new analytics-based solutions for members.
How is the KTP helping members?
The partnership has helped PfH to automate the categorisation of purchases made by PfH members, drilling right down into their spend data. Being able to classify information at such a granular level – for example telling a housing association what they spend on chrome plated bathroom taps, right across their organisation – can help landlords identify whether they are spending too much on taps compared to their peers, whether they are purchasing several types of tap unnecessarily or whether there are better value taps available.
Such low-level information can also be combined with public data sets to provide a more holistic view around packages of work. For example, is a housing association paying more than the average cost of replacing bathrooms in 1,000 three-bed social housing properties?
Machine learning has enabled us to categorise both on-catalogue and off-catalogue spend. So, if a member wants to know what proportion of their materials spend is non-compliant, PfH can tell them immediately.
Having provided these insights, PfH’s aim is to work collaboratively with members to explore the opportunities available to them, help them to save even more, boost quality further and increase overall value for money.
Wider use of machine learning
Working with PfH has given me a real insight into the social housing sector. The potential for machine learning to support providers is significant. Here are just a few examples:
- Predictive analysis could link housing providers’ repairs data to price indices to indicate the best time to buy certain products such as building materials.
- Machine learning could be used to recommend comparison products for social landlords, for instance a boiler that is less expensive, has a longer warranty, has a smaller carbon footprint.
- Data from technologies like IoT thermostats, windows sensors or smart boiler parts could recognise failure in advance and help organisations switch from reactive repairs to planned maintenance.
- Landlords could use ‘emotion AI’ to analyse social media mentions about suppliers and combine this with data on contract performance, legal disputes or redundancies to build risk profiles.
- Machine learning could help with letting decisions, using demographic and other data sets to calculate which customers are likely to leave early or create the greatest repairs demand.
- Providers could use predictive analysis to identify rent accounts likely to default, allowing them to support tenants before they run into difficulties and avoiding eviction and resettlement costs.
This month marks the end of PfH’s two-year knowledge transfer partnership with the University of Liverpool, but it’s just the beginning for PfH’s data strategy. The relationship has enabled PfH and its developers to create a powerful data warehouse which underpins new services such as price-checking and VfM reporting and adds additional insight to existing services such as spend analysis.