What are the benefits of AI in social housing?

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Woman using technology at home - ai in social housing

Civica’s Alex Oldman explores how AI in social housing is making a big difference – but there’s still some way to go

Rapid advances in tech, such as AI in social housing, are proven to deliver benefits and protect residents from harm using pattern cognition and augmenting reality.

While the UK government announces plans to introduce “Awaab’s Law” into the forthcoming Regulation of Social Housing Bill, technology already exists to protect vulnerable households from hazards such as dampness and mould.

Why has AI in social housing not already been widely deployed?

There are examples of early adopters taking action.

Flagship Housing is two years into its programme to deploy 20,000 Switchee devices to homes across the east of England by 2030. These devices monitor the home environment, harvesting data and looking for patterns.

The AI software then adjusts the heating system, saving the household money and protecting the building and residents against the risk of dampness and mould.

Pre-emptive boiler breakdown alerting also improves Flagship’s repair service and customer satisfaction.

AI learning can be used to determine deployment priorities based on occupant needs, built form and other human-determined factors.

How can AI make a difference in social housing?

Kingdom Housing has implemented Visual AI in the form of augmented reality (AR), which overlays digital information with the real world.

The organisation deployed an AR solution during the Covid pandemic to provide remote guidance for responsive repairs.

The solution allows repair operatives to provide a hands-overlay in an app to show how to resolve a problem.

Visual AI gives the software the skill to scan, identify and classify objects from video or still image sources. This allows solutions that can recognise and understand an image they are being presented with.

For example, using Visual AI to inspect photos provided by residents for evidence of dampness can provide protection or drone surveys to identify problems with roofing that might be invisible to the naked eye. And a drone can survey hundreds of houses per day from the air, saving time and efficiency.

Meanwhile, text-based AI enriches the customer digital experience by allowing searches based on semantic analysis and natural language processing to improve interaction with searches and chatbots.

Functional AI can detect a problem by comparing an actual working pattern with a standard one. A good example of this plant room monitoring, where the acoustic signature is first learned and then anomalies are found by algorithms to find faults in bearings.

Preventative maintenance can then be scheduled to prevent downtime from unplanned breakdowns.

95% of housing leaders believe connected devices can benefit the housing sector

In our Perspectives report looking at Connected Devices, we reported that 95% of housing leaders believed connected devices have the potential to benefit their sector.

Using AI in social housing can keep landlords informed, predict which services will be needed most in the future and help control properties through smart technology.

For example, to support vulnerable residents, sensors around a property can send information about patterns of activity or medication adherence, helping older adults to live independently and feel more secure.

Alerts can be set when unusual behaviour is detected.

So much added value will come from connecting wider networks of devices from the individual, their home and the wider community to create an “internet of us”.

Organisations must be clear on policies before deploying AI

Much of this will come from explaining how this will help and building citizen trust in sharing their data for their and our community’s benefit.

But a word of warning: organisations must be very clear on their policies before deploying any type of AI. As Terry Pratchett once said: “Real stupidity beats artificial intelligence every time.”

Huge data capture and processing must be balanced with data privacy. Automated decision-making must be aware of the bias that might not consider race, age or gender. But with all these considerations taken into account, it’s clear that change is coming – and we need to be ready for it.

AI in social housing
Alex Oldman

Alex Oldman
Client relationship manager, housing
Civica
www.civica.com/housing
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