Using tech and data to improve services

What have been the biggest impacts of technology on the sector so far?

How far back do you want to go? You’ve got the dawn of the internet, and then CRM systems [customer relationship management systems, which is technology that helps businesses manage and analyse customer interactions and data] and housing management systems. All that has massively overhauled how housing providers work.

More recently it has reshaped asset management and maintenance – both huge items on the sector’s to-do list – so being able to do that more effectively and efficiently is really powerful.

Then there’s compliance, record-keeping, storing digital copies instead of filing cabinets full of paper; or being able to track energy consumption in homes post-retrofit, which can be a requirement for funding streams. It boils down to being able to gather robust and accurate data, and then being able to organise and leverage it.

What’s next, in your view? Where will the next big impact be felt?

Big data. It’s absolutely vital and it’s being used already, but there is much more to be done. There’s more value to be had from having all this data in a digital format; organisations can query it and then use that to improve their processes and policies. It can give confidence that money is being spent in the right way. I haven’t seen too many examples of that data being used in that way yet, much as I think it will be in the future.

What are the barriers holding providers back from leveraging this technology?

In two words: time and money. It’s a case of having the funds available to implement these systems, but it’s also about having people dedicated to ensuring that the data is being collected in the right way.

To maximise the potential of AI, for example, you need up-to-date records for every single property – on the state of the home, on its energy bills. That takes time as well as funding, which is why I think a dedicated member of staff with the sole purpose of managing that data will help hugely.

How can housing providers accurately measure the return on investment (ROI) for these technologies?

It comes back to time and money. The lowest-hanging fruit for ROI is probably operational savings. In other areas, it’s not so easy.

Modern technology can allow for more predictive maintenance; our systems record temperature and humidity data, for example, and can indicate when the conditions are right for damp and mould so that risk can be acted on earlier and therefore more cost-effectively.

But while predictive maintenance is a major part of the ROI, it’s tricky to measure; how much would something have cost to fix if the landlord had waited longer? Predictive maintenance also improves tenants’ lives; you’re not asking them to take time off work multiple times, for example. But how do you put a price on how happy a resident is with your services? It can prove difficult.

Does the government have a role to play in enabling digital transformation in housing?

It has flirted with it. Wave 2 of the Social Housing Decarbonisation Fund included funding for digital tools to support retrofit. That really saw an uptick in the use of devices and sensors for monitoring the outcomes of retrofit interventions.

Obviously that’s aimed at energy efficiency, but once these devices are installed, they can provide a range of insights on things like damp and mould, indoor air quality and fuel poverty.

So the government has definitely helped progress digital transformation, but it’s taken a little bit of a back seat since then. It would be great to see more of that, because there are definite benefits – and because it does come down to money. 

If the government was to provide specific funding for, say, a dedicated person or service that helps landlords provide or gather data for future use, that would be really beneficial.

What can the housing sector learn from other sectors that have benefited from digital transformation?

There is a huge amount of unrealised value in data analysis, and that’s something other sectors have started to utilise more and more.

As an example: there’s a long-standing myth that your devices are listening to you all the time. You can have a conversation with your friend about umbrellas, and then half an hour later, what pops up on your phone but an advert for umbrellas? But it’s not microphones listening in – it might be that your friend Googled umbrellas, and the system used that geodata to understand that you were probably with that person, and therefore you might have been involved in that conversation. Using that sort of level of data analysis and that level of data ingenuity is bound to have value within the housing sector as well.

Say within a block of flats, three households have complained about mould. AI or data analysis software could look at the data and identify that all three flats have north-facing living rooms; maybe the cladding has failed and there are cold spots forming on internal surfaces, and that’s where the mould has been identified. That might be a detail that got missed by a human – but software can pick up the connections between all three properties and flag it.

Having the ability to understand those connections, to tie all of this data together from all these different sources and then being able to act on it is really powerful, and I think it is probably the next step for housing as well.

How can housing providers prepare for this next step?

As the old data science saying goes: garbage in, garbage out. The better the quality of the data you have coming in, the more value data analysis tools can provide.

And when AI truly does take off within housing – and it will – it can only give you as good as it gets. So it’s important to focus today on making sure that the data you’re collecting is as high quality, up to date, accurate and accessible as possible, to ensure it can remain useful in the months and years to come.

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