Housing providers’ challenges can be broadly split into two areas – those relating to assets and those relating to income.
Let’s consider the income side of things first.
Income and arrears management has always been difficult, with the economic pressures felt by tenants contributing to the growing arrears and the increasing number of tenants struggling to keep up. Among the issues faced are a rental increase cap of seven per cent and overworked income officers, leading to morale issues and increasing arrears.
Solving this challenge is a perfect application for artificial intelligence. By using machine learning to analyse and organise an income team’s workload, huge efficiency gains are possible, and with predictive AI, arrears can be nipped in the bud and addressed before the debt becomes overwhelming.
Major pain points
Income teams are experiencing three major pain points: technical arrears; unmanageable caseloads; and a lack of preventative measures. The fundamental cause of these challenges lies with the underlying housing management systems and how primitive they are. Technical arrears, or cases where no actual intervention is needed, comprise a considerable portion of income officers’ caseloads, leading to repetitive administrative tasks that waste time and hinder the income officers’ ability to provide useful support.
The size of caseloads is increasing, and most housing providers aren’t increasing the number of income officers to match the rise in arrears cases. This has led to income officers being unable to complete their weekly caseloads, leading to a snowball effect.
Think like an income officer…
Out of necessity comes invention, which in this case is to teach an AI to think like an income officer so that the assessment and organisation of the caseload is done by a machine in seconds, rather than people over many hours.
The lack of viable technology in our sector combined with these overwhelming caseloads has resulted in teams becoming reactive rather than proactive. Thanks to advances in AI, there are solutions that can relieve the pressure on income officers, create more opportunities to provide timely support to tenants, deliver efficiencies and really improve the overall performance of housing providers’ income collection processes.
In short, we think AI should do all the heavy lifting, removing tedious and often inconsistent prioritisation, thereby giving income teams up to a third of their time back to really tackle the crisis. With AI-recommended caseloads, it does just that.
In other words, never give a human a machine’s job.
AI and stock condition surveys & assessments
Alongside arrears and income collection, the other equally important area is the condition of housing providers’ properties and assets, particularly the problem of damp and mould.
Not only have damp and mould complaints doubled in the last two years, but half of housing providers also don’t know which properties have damp and mould problems nor their severity. Furthermore, with the cost of living and energy prices rising, cases of damp and mould will continue to increase.
These issues point towards the immediate need for a more sophisticated, data-driven approach that predicts and prevents rather than reacts – applying AI to asset data is the key to targeting resources to where they are needed most.
Expensive and slow…
How can a large housing provider understand the condition of its assets? Conventional approaches involve surveys performed by people; this approach doesn’t scale nor will it provide the answers in a viable timeframe. And while some housing providers are installing IoT-based devices to monitor asset condition, the cost and timeframe of that approach won’t address the problem today.
In addition to the obvious health risks to tenants, damp and mould can also cause damage to the structure of the buildings as well as their fixtures and fittings, leading to expensive repairs and maintenance.
AI can know more about your housing stock than any team member ever could and it can also look at every tiny detail to analyse which properties are most at risk – once again, the heavy lifting is out of the way. AI can create an efficient alternative to resource-intensive physical surveys through stock modelling using a variety of data sources, machine learning and advanced analytics.
Prioritising damp & mould
Housing providers can then predict the likelihood of each property having damp and mould problems, and by understanding the performance of every property, you can then prioritise properties within your planned programmes.
Building on our exploratory AI collaborations with a number of forward-thinking housing providers at our data-science bootcamps, we created Occupi, an AI product empowering housing teams to efficiently manage income, arrears and improve tenants’ wellbeing. Now we are using AI to understand the condition of assets and solve damp and mould challenges.
AI is to the housing officer what a calculator is to a mathematician; a tool to help them work at their best. As Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, said, “A calculator is a tool for humans to do maths faster and more accurately than they could ever do by hand; similarly, AI is a tool for us to perform tasks too difficult or expensive for us to do on our own, such as analysing large datasets or keeping up-to-date on medical research.”
Neil Forrest is the chief commercial officer at Pivigo.
