Too Much Information!

Housing associations have so much information they cannot use it to make the improvements they seek.

What we actually “know” about our each of our homes is immense, and if pushed (and with hours of time) we can usually find the answer. For decades we have collected information on the design and building process, the rectification of defects, the repairs history (3 to 4 pa),? cyclical and one off improvement works - it soon adds up to a bulging file, with pictures, floor plans, details of contracts, appointments made and cancelled, calls made and received, visits by surveyors and housing officers.

?Having the information is great, but of course having too much information can leave us paralyzed, unable to see the patterns that matter – we cannot “see the wood for the trees."

?I know artificial intelligence (AI) is all the rage, and it can seem to be just one more thing to trouble us, but for me AI could change all this. We have access to vast data sets, but we cannot identify subtle links. Which factors matter most? - the builder, the design, the unit type, year of construction, the components that it is built from, estate and property location, the investment profile, the age and number of residents, or even the weather – to the needs of the property? The complexity of the data stops us understanding the needs of the homes and the most economic and efficient way to invest in each one to prolong its useful life, to increase its value and to make it suitable and comfortable for the residents who live there.?

AI has amazing capabilities. Algorithms can analyse millions of date points and find relationships between variables that appear to be unrelated. Machine learning models, for instance, can combine structured and unstructured data—such as number of repairs with customer feedback —to provide a more holistic view of what works should be prioritised. AI can be used to summarise the most important property information on each home so that colleagues (and tenants) can get a good understanding of its history, its future and what it needs to meet the customers’ requirements. At the moment staff taking calls from residents struggle with correctly diagnosing a problem as described by the house holder, and with specifying what is needed from the DLO or contractor. Even the most experienced will rely on their hunches and guess work. The less experienced are likely to get it wrong time after time. AI could do the job a lot better, leaving staff to help people who need a personal service.

AI will also help with strategic decision making. Looking at the asset base of an association in terms of its value and liabilities should be a priority for main boards given this is “the greatest asset” any social landlord has. AI analysis on property needs could quickly flag up which require major investment, when; which home types are especially popular and why; which homes have the potential for extension; which homes are uneconomic to repair; which have had loads of repairs but are still causing dissatisfaction – suggesting there is a bigger problem to solve etc. Strategic asset management is the professional discipline that all associations should be striving for as it has the potential to improve customer satisfaction, remove health and safety hazards rapidly, dispose of unattractive and hard to let homes, create plans to achieve net zero and save a great deal of money wasted on responsive repairs.?

AI doesn’t just analyse data—it tidies it up and organises it to answer the questions boards and senior teams need to ask. By pulling out key trends and relationships, AI could allow associations to make informed decisions quickly. It eliminates the guesswork and noise, ensuring that the forest—and not just the trees—becomes startlingly clear.

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