Flaws in fuel poverty and homelessness data show the urgent need for effective national data infrastructure

Strong data infrastructure would enable us to anticipate social needs, design services that respond to these needs and signpost people towards the best advice
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As the cost-of-living crisis grinds on, policymakers, community groups and charities alike are using data to understand the challenges better, shape public policy and uncover those areas most in need. Data can be the key to unlocking new insights and better ways of working so that limited resources can be more effectively targeted and efficiently used to relieve suffering.

At the Open Data Institute (ODI), we strongly believe in the power of data to benefit people in their daily lives, which is why we recently authored a report on how data can be used to tackle the cost-of-living crisis. We already know that people renting, single parents, and those on benefits are disproportionately suffering across all dimensions of poverty. However, after analysing 100 datasets from more than 50 sources, we encountered data silos and data gaps that not only made it difficult to identify the pockets of need that exist but also makes it likely that people in need are being missed from official statistics.

Our analysis discovered that official fuel poverty data was likely to significantly underestimate the number of people in fuel poverty by up to 3.6 million due to how it is defined. Why? Because only homes with an Energy Performance Certificate of D – or worse – are included in the official statistics despite residents in lower categories struggling to pay their bills. In 2022, 52.8% of all low-income households lived in a property with an EPC of C or above. This means around 3.6 million households may be falling into fuel poverty due to the cost of fuel without being included in fuel poverty statistics.

Our report also found issues with data about homelessness. Rough sleeping statistics show there were an estimated 3,069 homeless people in 2023. However, these official statistics may not provide an accurate overview of rough sleeping since the data is collected on a single night and only counts people who are asleep or about to “bed down”. In cold weather, people are more likely to take refuge in shelters. Women are under-reported as they often adopt safety measures to keep them off the street, such as sleeping on night buses. People living in severely overcrowded accommodation are excluded from the figures.

The way that data is collected, analysed, used and shared is often backed up by people’s real experiences.

Case study: Tom and Sarah

After Tom’s relationship with his parents broke down, he found himself sofa-surfing for three years before being provided with a room by Centrepoint. During that time, he wasn’t included in the official statistics. Like Tom, Sarah[1] was also excluded from the data. She explains, “Councils discern between homelessness and rooflessness when they decide whether to help people. After my relationship broke down, I found myself living in a mouldy and dilapidated caravan through the winter with no running water, but I couldn’t access social housing.”


Dr Tom Kerridge, homelessness charity Centrepoints’s policy and research manager, says: “There is a disconnect between official government data on youth homelessness and the reality we face. Government data on youth homelessness only represents those who are owed a prevention or relief duty after being assessed, but we know from our own research that around one-third presenting as homeless or at risk don’t reach this point. This could mean that thousands are missed, which can skew crucial support and funding away from where it’s needed most."

In addition, information about social housing based on room size isn’t collected, making it hard to generate a complete picture of availability. As private enterprises, housing associations are not subject to FOI requests, and information sharing relies on goodwill and cooperation. This makes it challenging to identify gaps or emerging trends.

Several data challenges emerged from our report’s analysis.

Data often focuses on one issue at a time. To get a more comprehensive picture, datasets need to be combined, but this can’t be done effectively without standard definitions and parameters.  For example, data can lag behind contemporary experience because it often isn’t made available until long after it is collected and can be as much as two years out of date. As the situation changes rapidly, data cannot be relied upon to make informed policy decisions. Additionally, figures are often not published at a high level of geographic granularity, making local decision-making more difficult. Several key official statistics are only published at a regional or national level. This masks sub-regional differences – for example, the biggest increases in fuel poverty are now in the southern part of the country – which means local authorities and public services often lack key intelligence to inform their decisions. There is also no uniform approach to whether datasets are available at an England, Great Britain or United Kingdom level.

These findings illustrate the urgent need for an effective national data infrastructure to improve understanding of social issues and enable better policymaking.  Real-time or near real-time data that is regularly updated would mean decisions could be based on accurate information, and taxpayers’ money could be used in more efficient and targeted ways. In a world where data works for everyone, strong data infrastructure would enable us to tackle and anticipate social needs, design policies and services that respond to these needs, and signpost people towards the best advice.

[1] Not her real name

Resham Kotecha is the global head of policy at the Open Data Institute

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