Stratifying areas at risk of housing insecurity among families with children: a multidimensional index for the improvement of policy interventions in England

Stratifying areas at risk of housing insecurity among families with children: a multidimensional index for the improvement of policy interventions in England | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stratifying areas at risk of housing insecurity among families with children: a multidimensional index for the improvement of policy interventions in England Jamie O’Brien, Emma Coombes, Anne-Marie Burn, Hannah Fairbrother, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6856244/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background Housing insecurity resulting from multiple, involuntary residential moves is detrimental to the health and wellbeing of families with children. Policy makers seeking to mitigate these negative effects require a measure of risk of housing insecurity. Here we present the development of a novel risk index for England. Methods We undertook a literature review to select drivers of housing insecurity and identify relevant metrics. We recruited an expert panel to rank and weight these metrics using a Likert survey. The weighted metrics were summed for each small area (Lower Super Output Area) in England to produce the overall risk score. The score was then stratified into five levels, from very low to very high, linked to geographical units for data mapping. The final index (called the “Families at Risk of Housing Insecurity Index”) was made available on a public data platform. Results Eight drivers of housing insecurity were identified from the literature review as follows, (variable type and weight shown in brackets): primary school pupils eligible for free school meals (%, 0.5); income deprivation affecting children (%, 0.5); residential mobility (decile, 0.4); lone parent households (%, 0.3); pre-1919 properties (%, 0.3); households in fuel poverty (%, 0.3); households with dependent children by ethnic group (%, 0.2); mental health (Small Area Mental Health Index; decile, 0.2). Analysis of the index indicated a highly varied distribution of risk across England. Two noteworthy findings were the lower level of very high risk areas in Greater London, possibly indicating that higher living costs force households away from the capital city region. The index also suggested there were areas at higher risk in generally more affluent settings, possibly due to a greater proportion of older housing stock in these locations. Conclusion The Families at Risk of Housing Insecurity Index (FRoHII) was composed of metrics from public datasets at the small area level. The index provides a public resource to help identify areas where families with children might be at risk of housing insecurity. The index constitutes a tool and resource for professionals seeking to provide support to families within their catchment areas. Housing insecurity children and young people family health and wellbeing multidimensional risk index public data homelessness Figures Figure 1 Figure 2 Introduction Secure housing has been shown to support families’ capabilities in living well (Sen, 1997; Kimhur, 2023; Hock et al., 2024). However, housing insecurity in the UK has increased, driven by a rise in residential moves being involuntary, forced or reactive, and related to poverty, among families with dependent children (Mahony, 2020). The rise of housing insecurity is due to a range of factors, including the cost of living crisis, landlords’ electing to end tenancies (under Section 21 of the Housing Act 1988), relationship breakdowns, poor quality housing, and social factors such as discrimination (Children's Society, 2020). Insecure housing has been shown to be detrimental to residents’ physical and mental health; moreover, insecure housing itself presents a barrier to accessing key services (Shaw, 2003; Shelter, 2017; Mason et al., 2024). It has also been recognised as detrimental to children’s cognitive and social development, educational attainment, safety and physical and mental health (Hutchings et al., 2016; Children's Society, 2020). The complexity and lived experience of housing insecurity presents a challenge to researchers seeking to formulate a unifying measure of risk (Leopold et al, 2016). In this paper we present a composite index of housing insecurity that i) reflects evidenced drivers of risk as identified by a literature review, ii) is compiled using open datasets, and iii) reflects metrics that are prioritised by housing practitioners. Aims This research set out to develop a risk index of geographic areas affected by housing insecurity, and to stratify levels of risk to help locate areas that house at-risk families. We sought to highlight both areas of the highest risk, and areas with a higher risk than might otherwise be expected if markers of deprivation were to be considered alone. The purpose of developing the index was to help practitioners involved in family housing support, along with health and social workers, to identify at-risk families so they may best support them. The research sought to draw directly from the literature on housing insecurity to identify key drivers, to select small area metrics from public sources associated with specific housing, life and wellbeing pressures, and finally to engage expert ranking advice from housing practitioners. Our overall objective was to develop a stratified risk score for open publication that is available to and used by housing practitioners. Review A literature review was conducted under critical appraisal principles (CEMB, 2025) to identify the key drivers of risk of housing insecurity in England. The literature was found to be diverse, comprising peer-reviewed qualitative research and systematic review articles, detailed qualitative and quantitative reports by housing and homelessness organisations, and government statistical reports. The literature also comprised reports of separate indices of housing insecurity and were included in the review for comparison. The publications were sourced through keyword searches in academic journals, web-based repositories, and online government repositories. Inclusion criteria were: Framing of independent research in terms of the impacts of housing insecurity, or aspects of homelessness, on health and wellbeing of families with children Relevance to the context of the housing crisis in England Timeliness - research was published after the onset in 2010 of the UK austerity programme (cf. Oxfam, 2013) The literature review served to identify the following drivers of housing insecurity: cost of living, insecure tenure or potential for insecure tenure, relationship breakdown, quality of housing, quality of living environment, ethnicity, and mental health. For each indicator we also reviewed metrics that might reflect these drivers at the small area level. These are presented in the Results section, Table 1, and are reviewed in detail below. Cost of living is a major economic indicator for risk of housing insecurity (Hock et al., 2024). In the UK, as many as 15% of private rental tenants have experienced a rent rise proportionally greater than their increase in earnings (Shelter, 2021). Currently, housing costs account for up to 38% of expenditure for rented households, compared to just 19% for those in owner-occupied properties (MHCLG, 2023a). As a result, many families have been forced to cut back on essentials such as heating and food, with negative effects on family health (Shelter, 2021). Insecure tenure is strongly associated with housing insecurity. Currently in the UK, 19% of households are in the private rented sector, which is more than double the number 15 years ago, including over 1.5 million families with children, and most are supported by tenancies of less than one year (MHCLG, 2023b). Across English regions, 24%-38% of private rented sector dwellings fail to meet the Decent Homes Standard (MHCLG, 2024). Relationship breakdowns have been shown to be a preceding factor in homelessness (Forty, 2008). The UK homelessness charity Centrepoint reported that two thirds of young people who come into contact with their service do so following a relationship breakdown (Centrepoint, 2016). For families with children, divorce or separation are drivers of housing insecurity, and lone parents typically move into private rented or social housing (Mikolai and Kulu, 2017). A further measure of housing insecurity is that some regions of England have concentrations of old housing stock. More than one third of private rented properties were built prior to the Housing Act 1919, which stipulated improved building regulations. Pre-1919 properties have been associated with non-decent housing quality (MHCLG, 2017). People from ethnic minority backgrounds are more likely to experience poor quality housing and housing insecurity (Fitzpatrick, Watts & McIntyre, 2024); these households’ are more likely to remain in overcrowded accommodation so that they can remain close to their community support (ibid.). Areas with higher percentages of family households with dependent children were deemed to present greater risk of housing insecurity. Fuel poverty is associated with rising energy prices outstripping householders’ means to pay energy bills (Corlett et al, 2022). This trend results in cold, damp living conditions that are detrimental to the physical health and wellbeing of residents (cf. Marmot Review Team, 2011). Housing insecurity is detrimental to residents’ mental health; associated with anxiety, stress, depression, and poor sleep (Mason, Alexiou and Taylor-Robinson, 2024). Other housing problems, including energy poverty and short-term tenures similarly have negative impact on residents’ mental health (Carrere et al., 2022). Mental health problems also affect residents’ capabilities to deal with housing problems (cf. Diggle, at al., 2017). Other risk indices of housing and health Several studies have sought to produce indices for the inter-dependencies of housing, daily living and wellbeing (Robeyns, 2005; Jessiman et al, 2021). For example, Ndaba et al. (2024) presented a weighted score for housing insecurity based on findings of a participatory survey in the context of housing in South Africa. Whereas Boateng and Adams (2023) presented a multidimensional risk model of housing insecurity based on factor analysis from participatory surveys specific to informal settlements in Ghana, including aspects of shelter quality and tenure status. Further, a housing quality risk index for France was developed by Richard et al. (2023) from a composite, unweighted score for the impact of physical housing and service accessibility characteristics on residents’ health. The factors included in this index were derived from an independent health advisory report and tested through site visits of 27 homes of vulnerable residents. Development of the index described in this paper differs from those cited in this section. For instance, Ndaba et al. (2024) and Boateng and Adams (2023) have depended on participatory data gathering in specific locations. Whereas Richard et al. (2023) have developed an administrative tool to assess individual dwellings. Instead, the index described in this paper focused exclusively on housing insecurity at the national level in England, at the small area scale. The metrics were derived from public datasets and were weighted through an advisory process by expert practitioners. The benefit of developing an index with public datasets is that the index is uniform (based on systematic data), repeatable (i.e. as the metrics are updated periodically), and inter-operable among different administrative areas. Methods The evidence from the literature review of drivers for housing insecurity risk was used to create an unweighted dataset, featuring eight metrics for each small area (Lower Super Output Area, LSOA) in England. Metrics were either percentages or categorised into deciles. We sought to add weight to the metrics, so that those with higher impact would make a greater contribution to the overall risk score. To add correct weight to the metrics, we conducted a survey among a panel of expert practitioners to rank each indicator in order of importance to the risk index. This process of consensus-seeking from an expert panel has been highlighted for its critical importance to effective risk modelling (Alkire, 2005; Fischoff & Morgan, 2009; Aggarwal, 2016). The expert panel consisted of 29 practitioners in the field of housing, either as local authority or third-sector officers. The expert practitioners were recruited through an open invitation circulated to six local-authority partners from our wider research project. Each panel member had at least two years’ professional experience in the field, with one third having more than 15 years’ experience. We invited expert practitioners to respond to a Likert-scale survey. The survey was composed of a set of statements, which were derived from the literature review to reflect the main drivers of risk. Expert responses were collated, and the balance of responses was interpreted by the researcher to rank and weight the metrics for inclusion in the index. The statements, responses, interpretations, ranks and weights are provided in Appendix 1. The researcher then applied an established method for devising multidimensional indices (Alkire, 2005) to convert the ranked metrics to numeric weights (Table 2). Applying numeric weights in this way helps to prevent the index from clustering around the middle range of values. Instead, it allows for areas of extreme high or low risk to be highlighted. Results A complete list of drivers and data sources as contenders for the housing index is shown in Table 1. The list of metrics selected from the expert panel are shown in Table 1, including some additional notes about selection and processing the metrics. Table 1. Drivers, associated metrics, data sources and denominators used to compose the index Drivers Metric Datasets and sources Denominator Cost of living % primary school pupils eligible for free school meals HH Government Schools, pupils and their characteristics 2023/24 For LSOAs with missing data, the mean rates were imputed based on nearest neighbour; in proportion of the ratio of 0-15 to 18-64 year-olds in those areas Total school pupils per state-funded primary school Income deprivation Income Deprivation Affecting Children (IDACI 2019) Ministry of Housing, Communities & Local Government. Indices of Deprivation 2019: Income Deprivation Affecting Children Index (IDACI) Total households with dependent 0-15 year olds Insecure tenures Residential Mobility (‘churn’) Consumer Data Research Centre (CDRC) Residential Mobility Total residential moves at index year Relationship breakdown % lone parent households with dependent children Office for National Statistics (ONS): TS003 - Household composition variable: Census 2021. Single family household: Lone parent family: With dependent Total households with dependent children per LSOA Potential for insecure tenure % of private rented households ONS: TS054 – Tenure: Census 2021 All households per LSOA Quality of housing % pre-1919 properties CDRC Dwelling Ages and Prices Total dwellings per LSOA Quality of living environment % households in fuel poverty Department for Energy Security and Net Zero (DESNZ) / Data Mill North Estimated number of households per LSOA Ethnicity % Households with dependent children in which the reference person is of Asian or Asian British Black, Black British, or Caribbean ethnicity ONS England and Wales Census 2021 - RM058: Household composition by ethnic group of Household Reference Person Total households with dependent children Mental health SAMHI score PLDR: SAMHI (Daras and Barr, 2021) Total general population Notes on selection of metrics The rationale for selecting certain metrics itemised in Table 1 have been outlined as part of the literature review. Here, we provide additional details about selections of other metrics itemised in Table 1. We selected Eligibility for Free School Meals among primary-school children, an annual school data metric, as a proxy for the impact of hardship on families with children. The National Travel Survey (DoT, 2024) has revealed that most primary school children live locally to their school. For this reason, the eligibility value approximates levels of household need within the school catchment. We imputed mean values for neighbouring LSOAs in proportion to the ratio of primary school-age children to the working-age population. We selected Income Deprivation Affecting Children from 2019 population data, to reflect the proportion of 0-15 year olds living in families experiencing income deprivation. These relate to a working-age household member being in receipt of unemployment or low-income benefits. Additional details are available through MHCLG, 2019. We selected the Residential Mobility metric to reflect the frequency at which households change tenancies. This metric is released annually by the Consumer Data Research Centre and is compiled at the LSOA level electoral registers, consumer registers and land registry house sale data. Areas with higher residential mobility (‘churn’) and higher levels of socio-economic deprivation were deemed to reflect an underlying potential for housing insecurity. We selected the Small Area Mental Health Index (SAMHI; Daras and Barr, 2021) to reflect the circular relationship between housing pressures and mental health problems. The SAMHI is composed of data derived from mental health related hospital attendances, medical prescriptions, and benefits claims. While we are unable to link these data directly to housing issues, our literature review has shown that people experiencing poor mental health are also more likely to experience housing problems. Metric ranking The expert panel ranked the finalised risk metrics with an associated numerical weight to constitute the overall score. The risk score was calculated for each LSOA in England, and categorised on a range from 0-10, where 0 represented no risk and 10 represented high risk. Some areas with little to no residential housing were marked as having ‘no data’. Table 2. Risk model variables, types, and weights for score calculation. Variable Type Weight Eligibility for free school meals Percentage 0.5 Income deprivation affecting children Decile 0.5 Residential churn Decile 0.4 Pre-1919 housing stock Percentage 0.3 Households in fuel poverty Percentage 0.3 Households with lone parents Percentage 0.3 Households with dependent children in which the reference person is of Asian or Asian British Black, Black British, or Caribbean ethnicity Percentage 0.2 Mental health index (SAMHI) Decile 0.1 Risk score aggregation: English regions We compared the proportion of LSOAs falling into risk score categories with the proportions of households with children located in these regions (Table 3). Regions in England showed different proportions of small areas with households with children in moderate and high risk of housing insecurity (Figure 1). The regions feature variation across each housing insecurity risk level. For instance, in Greater London, 38% of households with dependent children are at low risk of housing insecurity, compared to 78% in East England. Furthermore, Greater London features a higher proportion of households at moderate risk, with 44% falling within this category, compared to 16% in East England, and the greatest proportion of households at high risk, with 16% being in this category compared to 2% in East England. Regions featuring a greater proportion of households at very high risk include the North West (5%), West Midlands (4%), and Yorkshire and the Humber (7%). Table 3. Households with children at relative risk of housing insecurity, mean percent by English region (<1 = less than 1%). Region Very low Low Moderate High Very high East England 5 78 16 2 <1 East Midlands 2 62 28 6 1 Greater London <1 38 44 16 2 North East 3 61 29 6 1 North West 2 45 34 15 5 South East 8 72 17 2 <1 South West 4 66 27 2 <1 West Midlands 2 53 30 11 4 Yorkshire and the Humber 2 53 31 7 7 Risk score aggregation: multiple deprivation We compared the risk scores for each small area (LSOA) to the Index of Multiple Deprivation (IMD) quintiles, where quintile 1 represents the most deprived areas. As income deprivation is a major factor in housing insecurity, we found a linear relationship between housing insecurity and deprivation (Figure 2): as deprivation increases, generally so does the risk of housing insecurity. However, the housing insecurity index shows some useful departures from the deprivation pattern. Figure 2 shows that households in less deprived areas also experience a moderate to high risk of housing insecurity: about 20% in quintile 3 (moderately deprived), 10% in quintile 4 (less deprived), and 4% in quintile 5 (least deprived). We sought to identify any specific factors that might increase risk of housing insecurity in some areas within these IMD quintiles. Analysis of these factors is presented below. This information is important for allowing local authorities to develop more targeted approaches to reducing housing insecurity overall. Discussion The research outlined in this paper led to the successful creation of the Families at Risk of Housing Insecurity Index (FRoHII), for estimating the proportions of families with dependent children at risk of housing insecurity. The index was created at small area scale using public data, including relevant and timely datasets. Working with public datasets has served to create an index that is inter-operable between a range of housing and homelessness domains, including local authority services, charitable organisations, or academic researchers. The drivers are not dependent on domain-specific surveys and has been informed by evidence from the housing insecurity literature. The metrics were validated by an expert panel, which were ranked and weighted for the risk-score calculation. This weighting process meant that the metrics contribute to the index in proportion to their importance. The FRoHII offers an advantage over other indices of housing insecurity risk, outlined in the review section of this paper. The FRoHII is composed from public data available at the national level, and collated systematically through census or consumer index means. This index is mapped to the small area scale for England and can be searched and analysed at different geographic scales. Composing FRoHII has not relied on costly and domain-specific qualitative research. Analysis of risk of housing insecurity affecting families with children using the index has improved our understanding of the varied distributions of risk across England. Aggregating the index has also provided patterns of risk at the local authority and regional levels. For instance, Greater London features a lesser proportion of areas at very high risk (2%), compared to the North West (5%) and West Midlands (4%). One explanation is that the cost of living in and around the capital city means that households experiencing severe pressures are forced out of the region, and into regions with comparatively lower costs of living. We compared the FRoHII to the Index of Multiple Deprivation for England and found that the risk of housing insecurity increased broadly in line with the increase in deprivation (Fig. 2 ). We noted, however, how some less deprived areas also feature higher risk of housing insecurity. We conducted analysis of the underlying factors that drive housing insecurity in these areas. We compared metrics in higher risk but lower deprivation areas, to all other areas of higher risk. We found that, while most metrics had similar values when compared, there was also a far higher density (75–100%) of pre-1919 dwellings, which are more likely to be of substandard quality. This older housing stock appears to increase the risk of housing insecurity, even in less deprived areas. Time-sensitivity The FRoHII was compiled from time-sensitive metrics, derived for instance from Census 2021 data, annual schools and longitudinal data. FRoHII’s time sensitivity means that the index could be compiled for Census 2011 data, and other metrics from that year. Comparing current trends with those of previous years would allow analysis of the impact of austerity or the COVID-19 pandemic on housing insecurity. Time-sensitivity also means that FRoHII would require an updated compilation when the next census data become available (in 2031 by the current schedule). Recommendations: policy implications The Families at Risk of Housing Insecurity Index (FRoHII) is available via the Place-based Longitudinal Resource (PLDR) for any users wishing to identify the estimated level of risk for any small area in England. We envisage that the index would be particularly useful for any practitioners seeking to understand where families with children might be at risk of housing insecurity. We caution against using the index to identify individual households at risk. Instead, the index estimates the likely level of risk by area. Using the risk index will help practitioners to fulfil key aspects of their services. For instance, the Ministry of Housing, Communities and Local Government Homelessness code of guidance for local authorities (MHCLG, 2024) places emphasis on the need to mitigate housing problems and support families with children at risk of homelessness. This includes guidance on maintaining links with schools, where children transferring to the school may be experiencing the negative impacts on health and wellbeing of housing insecurity (Hutchings et al., 2016). The index may also help the local social and health care teams in providing support. For example, Integrated Neighbourhood Teams (INTs) collaborating in the United Kingdom include GP leads, health, social and wellbeing practitioners, and address the comprehensive needs of community members. Working with the index would help INTs to become aware of housing insecurity risk levels within their catchments. Future areas for research Our analysis revealed how dwelling age is a persistent factor in housing insecurity as properties built before 1919 fail to meet the Decent Homes Standard, relating to quality thresholds for facilities, insulation, and floor space. Local authorities in England are now engaged in programmes of retrofit work to bring the quality of old housing up to contemporary standards. The FRoHII could be re-compiled for the next census to compare how these retrofit interventions have affected the risk of housing insecurity. For example, we could observe how retrofit programmes might have impacted the Residential Mobility frequency in areas that otherwise remained in higher deprivation. Utilising the index in this way provides a valuable resource for longitudinal research into how drivers of housing insecurity reflect broader social patterns. Abbreviations IMD Index of Multiple Deprivation LSOA Lower Super Output Area FRoHII Families at Risk of Housing Insecurity Index SAMHI Small Area Mental Health Index Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The final index, along with the data that were compiled to produce it, are available to view and download from the Place-Based Longitudinal Resource website: https://pldr.org/dataset/2o6lg/families-at-risk-of-housing-insecurity-index-frohii Competing interests Not applicable Funding This study was funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR) (Grant Reference Number NIHR 204000). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Authors' contributions JO’B undertook the data analysis and prepared the first draft of the manuscript. 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Population Studies, 2017: 72 (1), 17–39. https://doi.org/10.1080/00324728.2017.1391955 Ndaba, Z., Dunga, SH, Mncayi-Makhanya, P. Analysing the multidimensional nature of poverty: a focus on housing insecurity in South Africa. International Journal Of Business and Development Studies, 2024: 16 (2), 269-293. DOI: 10.22111/ijbds.2024.50335.2168 Oxfam. The True Cost of Austerity and Inequality: UK Case Study. 2013. Available via: https://www-cdn.oxfam.org/s3fs-public/file_attachments/cs-true-cost-austerity-inequality-uk-120913-en_0.pdf. Accessed 21 May 2025. Richard A, Bruat C, Febvrel D, Squinazi F, Simos J, Zmirou-Navier D; members of the HCSP working group. R. BMC Public Health. 2023 May 4;23(1):815. doi: 10.1186/s12889-023-15451-y. PMID: 37143018; PMCID: PMC10157125. Robeyns, I. Selecting Capabilities for Quality of Life Measurement. Social Indicators Research, 2005: 74(1), 191–215. http://www.jstor.org/stable/27522242 Sen, AK. From Income Inequality to Economic Inequality. Southern Economic Journal. 1997: 64 (2), 384–401. https://doi.org/10.2307/1060857 Shaw M. Housing and public health. Annu Rev Public Health. 2004;25:397-418. doi: 10.1146/annurev.publhealth.25.101802.123036. PMID: 15015927. Shelter. The impact of housing problems on mental health. Shelter, 2017. Available via https//england.shelter.org.uk/professional_resources/housing_and_mental_health. Accessed 21 May 2025. Shelter. Denied The Right To A Safe Home: Report. Shelter. 2021. Available via https://england.shelter.org.uk/professional_resources/policy_and_research/policy_library . Access 21 May 2025 Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Rodgers","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"E.","lastName":"Rodgers","suffix":""}],"badges":[],"createdAt":"2025-06-09 16:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6856244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6856244/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25888-y","type":"published","date":"2026-01-29T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85068813,"identity":"d9300930-60f7-43a7-9e50-7156bd78bb9f","added_by":"auto","created_at":"2025-06-20 15:25:37","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProportions of households with children falling into each risk category for English regions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6856244/v1/de38bf460bfd6f630949dabe.jpeg"},{"id":85069953,"identity":"f83399b1-6c7d-46d6-a08e-e383b1f65cd1","added_by":"auto","created_at":"2025-06-20 15:33:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEstimated percentage of households in areas of relative risk of housing insecurity by Index of Multiple Deprivation quintiles (1=most deprived).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6856244/v1/d00e93e02734cabb1e063b05.png"},{"id":101690750,"identity":"b2ca4c3e-251c-47f4-8e84-cb59a4a76ae6","added_by":"auto","created_at":"2026-02-02 16:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1020985,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6856244/v1/78189010-5bd5-4ff4-a6a0-4550f9c88619.pdf"},{"id":85071744,"identity":"540f493b-9086-487e-8d5e-1b54b415e9e7","added_by":"auto","created_at":"2025-06-20 15:41:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15736,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6856244/v1/f4cb1fc087b45859fc11af38.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stratifying areas at risk of housing insecurity among families with children: a multidimensional index for the improvement of policy interventions in England","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSecure housing has been shown to support families\u0026rsquo; capabilities in living well (Sen, 1997; Kimhur, 2023; Hock et al., 2024). However, housing insecurity in the UK has increased, driven by a rise in residential moves being involuntary, forced or reactive, and related to poverty, among families with dependent children (Mahony, 2020). The rise of housing insecurity is due to a range of factors, including the cost of living crisis, landlords\u0026rsquo; electing to end tenancies (under Section 21 of the Housing Act 1988), relationship breakdowns, poor quality housing, and social factors such as discrimination (Children\u0026apos;s Society, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsecure housing has been shown to be detrimental to residents\u0026rsquo; physical and mental health; moreover, insecure housing itself presents a barrier to accessing key services (Shaw, 2003; Shelter, 2017; Mason et al., 2024). It has also been recognised as detrimental to children\u0026rsquo;s cognitive and social development, educational attainment, safety and physical and mental health (Hutchings et al., 2016; Children\u0026apos;s Society, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe complexity and lived experience of housing insecurity presents a challenge to researchers seeking to formulate a unifying measure of risk (Leopold et al, 2016). In this paper we present a composite index of housing insecurity that i) reflects evidenced drivers of risk as identified by a literature review, ii) is compiled using open datasets, and iii) reflects metrics that are prioritised by housing practitioners.\u003c/p\u003e\n\u003ch2\u003eAims\u003c/h2\u003e\n\u003cp\u003eThis research set out to develop a risk index of geographic areas affected by housing insecurity, and to stratify levels of risk to help locate areas that house at-risk families. We sought to highlight both areas of the highest risk, and areas with a higher risk than might otherwise be expected if markers of deprivation were to be considered alone. The purpose of developing the index was to help practitioners involved in family housing support, along with health and social workers, to identify at-risk families so they may best support them. The research sought to draw directly from the literature on housing insecurity to identify key drivers, to select small area metrics from public sources associated with specific housing, life and wellbeing pressures, and finally to engage expert ranking advice from housing practitioners. Our overall objective was to develop a stratified risk score for open publication that is available to and used by housing practitioners. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eReview\u003c/h2\u003e\n\u003cp\u003eA literature review was conducted under critical appraisal principles (CEMB, 2025) to identify the key drivers of risk of housing insecurity in England. The literature was found to be diverse, comprising peer-reviewed qualitative research and systematic review articles, detailed qualitative and quantitative reports by housing and homelessness organisations, and government statistical reports. The literature also comprised reports of separate indices of housing insecurity and were included in the review for comparison.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe publications were sourced through keyword searches in academic journals, web-based repositories, and online government repositories. Inclusion criteria were:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFraming of independent research in terms of the impacts of housing insecurity, or aspects of homelessness, on health and wellbeing of families with children\u003c/li\u003e\n \u003cli\u003eRelevance to the context of the housing crisis in England\u003c/li\u003e\n \u003cli\u003eTimeliness - research was published after the onset in 2010 of the UK austerity programme (cf. Oxfam, 2013)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe literature review served to identify the following drivers of housing insecurity: cost of living, insecure tenure or potential for insecure tenure, relationship breakdown, quality of housing, quality of living environment, ethnicity, and mental health. For each indicator we also reviewed metrics that might reflect these drivers at the small area level. These are presented in the Results section, Table 1, and are reviewed in detail below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCost of living is a major economic indicator for risk of housing insecurity (Hock et al., 2024). In the UK, as many as 15% of private rental tenants have experienced a rent rise proportionally greater than their increase in earnings (Shelter, 2021). Currently, housing costs account for up to 38% of expenditure for rented households, compared to just 19% for those in owner-occupied properties (MHCLG, 2023a). As a result, many families have been forced to cut back on essentials such as heating and food, with negative effects on family health (Shelter, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsecure tenure is strongly associated with housing insecurity. Currently in the UK, 19% of households are in the private rented sector, which is more than double the number 15 years ago, including over 1.5 million families with children, and most are supported by tenancies of less than one year (MHCLG, 2023b). Across English regions, 24%-38% of private rented sector dwellings fail to meet the Decent Homes Standard (MHCLG, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelationship breakdowns have been shown to be a preceding factor in homelessness (Forty, 2008). The UK homelessness charity Centrepoint reported that two thirds of young people who come into contact with their service do so following a relationship breakdown (Centrepoint, 2016). For families with children, divorce or separation are drivers of housing insecurity, and lone parents typically move into private rented or social housing (Mikolai and Kulu, 2017). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA further measure of housing insecurity is that some regions of England have concentrations of old housing stock. More than one third of private rented properties were built prior to the Housing Act 1919, which stipulated improved building regulations. Pre-1919 properties have been associated with non-decent housing quality (MHCLG, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePeople from ethnic minority backgrounds are more likely to experience poor quality housing and housing insecurity (Fitzpatrick, Watts \u0026amp; McIntyre, 2024); these households\u0026rsquo; are more likely to remain in overcrowded accommodation so that they can remain close to their community support (ibid.). Areas with higher percentages of family households with dependent children were deemed to present greater risk of housing insecurity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuel poverty is associated with rising energy prices outstripping householders\u0026rsquo; means to pay energy bills (Corlett et al, 2022). This trend results in cold, damp living conditions that are detrimental to the physical health and wellbeing of residents (cf. Marmot Review Team, 2011).\u003c/p\u003e\n\u003cp\u003eHousing insecurity is detrimental to residents\u0026rsquo; mental health; associated with anxiety, stress, depression, and poor sleep (Mason, Alexiou and Taylor-Robinson, 2024). Other housing problems, including energy poverty and short-term tenures similarly have negative impact on residents\u0026rsquo; mental health (Carrere et al., 2022). Mental health problems also affect residents\u0026rsquo; capabilities to deal with housing problems (cf. Diggle, at al., 2017). \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eOther risk indices of housing and health\u003c/h2\u003e\n\u003cp\u003eSeveral studies have sought to produce indices for the inter-dependencies of housing, daily living and wellbeing (Robeyns, 2005; Jessiman et al, 2021). For example, Ndaba et al. (2024) presented a weighted score for housing insecurity based on findings of a participatory survey in the context of housing in South Africa. Whereas Boateng and Adams (2023) presented a multidimensional risk model of housing insecurity based on factor analysis from participatory surveys specific to informal settlements in Ghana, including aspects of shelter quality and tenure status. \u0026nbsp;Further, a housing quality risk index for France was developed by Richard et al. (2023) from a composite, unweighted score for the impact of physical housing and service accessibility characteristics on residents\u0026rsquo; health. The factors included in this index were derived from an independent health advisory report and tested through site visits of 27 homes of vulnerable residents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDevelopment of the index described in this paper differs from those cited in this section. For instance, Ndaba et al. (2024) and Boateng and Adams (2023) have depended on participatory data gathering in specific locations. Whereas Richard et al. (2023) have developed an administrative tool to assess individual dwellings. Instead, the index described in this paper focused exclusively on housing insecurity at the national level in England, at the small area scale. The metrics were derived from public datasets and were weighted through an advisory process by expert practitioners. The benefit of developing an index with public datasets is that the index is uniform (based on systematic data), repeatable (i.e. as the metrics are updated periodically), and inter-operable among different administrative areas. \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe evidence from the literature review of drivers for housing insecurity risk was used to create an unweighted dataset, featuring eight metrics for each small area (Lower Super Output Area, LSOA) in England. Metrics were either percentages or categorised into deciles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe sought to add weight to the metrics, so that those with higher impact would make a greater contribution to the overall risk score. \u0026nbsp;To add correct weight to the metrics, we conducted a survey among a panel of expert practitioners to rank each indicator in order of importance to the risk index. This process of consensus-seeking from an expert panel has been highlighted for its critical importance to effective risk modelling (Alkire, 2005; Fischoff \u0026amp; Morgan, 2009; Aggarwal, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe expert panel consisted of 29\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epractitioners in the field of housing, either as local authority or third-sector officers. The expert practitioners were recruited through an open invitation circulated to six local-authority partners from our wider research project. Each panel member had at least two years\u0026rsquo; professional experience in the field, with one third having more than 15 years\u0026rsquo; experience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe invited expert practitioners to respond to a Likert-scale survey. The survey was composed of a set of statements, which were derived from the literature review to reflect the main drivers of risk. Expert responses were collated, and the balance of responses was interpreted by the researcher to rank and weight the metrics for inclusion in the index. The statements, responses, interpretations, ranks and weights are provided in Appendix 1. The researcher then applied an established method for devising multidimensional indices (Alkire, 2005) to convert the ranked metrics to numeric weights (Table 2). Applying numeric weights in this way helps to prevent the index from clustering around the middle range of values. Instead, it allows for areas of extreme high or low risk to be highlighted. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA complete list of drivers and data sources as contenders for the housing index is shown in Table 1. The list of metrics selected from the expert panel are shown in Table 1, including some additional notes about selection and processing the metrics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Drivers, associated metrics, data sources and denominators used to compose the index\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrivers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDatasets and sources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenominator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eCost of living\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e% primary school pupils eligible for free school meals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eHH Government Schools, pupils and their characteristics 2023/24\u003c/p\u003e\n \u003cp\u003eFor LSOAs with missing data, the mean rates were imputed based on nearest neighbour; in proportion of the ratio of 0-15 to 18-64 year-olds in those areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal school pupils per state-funded primary school\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eIncome deprivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003eIncome Deprivation Affecting Children (IDACI 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eMinistry of Housing, Communities \u0026amp; Local Government. Indices of Deprivation 2019: Income Deprivation Affecting Children Index (IDACI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal households with dependent 0-15 year olds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eInsecure tenures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003eResidential Mobility (\u0026lsquo;churn\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eConsumer Data Research Centre (CDRC) Residential Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal residential moves at index year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eRelationship breakdown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e% lone parent households with dependent children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eOffice for National Statistics (ONS): TS003 - Household composition variable: Census 2021. Single family household: Lone parent family: With dependent\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal households with dependent children per LSOA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003ePotential for insecure tenure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e% of private rented households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eONS: TS054 \u0026ndash; Tenure: Census 2021\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eAll households per LSOA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eQuality of housing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e% pre-1919 properties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eCDRC Dwelling Ages and Prices\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal dwellings per LSOA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eQuality of living environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e% households in fuel poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eDepartment for Energy Security and Net Zero (DESNZ) / Data Mill North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eEstimated number of households per LSOA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003e% Households with dependent children in which the reference person is of Asian or Asian British\u003c/p\u003e\n \u003cp\u003eBlack, Black British, or Caribbean ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003eONS England and Wales Census 2021 - RM058: Household composition by ethnic group of Household Reference Person\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal households with dependent children\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4684%;\"\u003e\n \u003cp\u003eMental health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3165%;\"\u003e\n \u003cp\u003eSAMHI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3291%;\"\u003e\n \u003cp\u003ePLDR: SAMHI (Daras and Barr, 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8861%;\"\u003e\n \u003cp\u003eTotal general population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eNotes on selection of metrics\u003c/h2\u003e\n\u003cp\u003eThe rationale for selecting certain metrics itemised in Table 1 have been outlined as part of the literature review. Here, we provide additional details about selections of other metrics itemised in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe selected Eligibility for Free School Meals among primary-school children, an annual school data metric, as a proxy for the impact of hardship on families with children. The National Travel Survey (DoT, 2024) has revealed that most primary school children live locally to their school. For this reason, the eligibility value approximates levels of household need within the school catchment. We imputed mean values for neighbouring LSOAs in proportion to the ratio of primary school-age children to the working-age population.\u003c/p\u003e\n\u003cp\u003eWe selected Income Deprivation Affecting Children from 2019 population data, to reflect the proportion of 0-15 year olds living in families experiencing income deprivation. These relate to a working-age household member being in receipt of unemployment or low-income benefits. Additional details are available through MHCLG, 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe selected the Residential Mobility metric to reflect the frequency at which households change tenancies. This metric is released annually by the Consumer Data Research Centre and is compiled at the LSOA level electoral registers, consumer registers and land registry house sale data. Areas with higher residential mobility (\u0026lsquo;churn\u0026rsquo;) and higher levels of socio-economic deprivation were deemed to reflect an underlying potential for housing insecurity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe selected the Small Area Mental Health Index (SAMHI; Daras and Barr, 2021) to reflect the circular relationship between housing pressures and mental health problems. The SAMHI is composed of data derived from mental health related hospital attendances, medical prescriptions, and benefits claims. While we are unable to link these data directly to housing issues, our literature review has shown that people experiencing poor mental health are also more likely to experience housing problems. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMetric ranking\u003c/h2\u003e\n\u003cp\u003eThe expert panel ranked the finalised risk metrics with an associated numerical\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eweight to constitute the overall score. The risk score was calculated for each LSOA in England, and categorised on a range from 0-10, where 0 represented no risk and 10 represented high risk. Some areas with little to no residential housing were marked as having \u0026lsquo;no data\u0026rsquo;. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Risk model variables, types, and weights for score calculation. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eEligibility for free school meals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eIncome deprivation affecting children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003eDecile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eResidential churn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003eDecile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003ePre-1919 housing stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eHouseholds in fuel poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eHouseholds with lone parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eHouseholds with dependent children in which the reference person is of Asian or Asian British\u003c/p\u003e\n \u003cp\u003eBlack, Black British, or Caribbean ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54.827%;\"\u003e\n \u003cp\u003eMental health index (SAMHI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003eDecile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.5865%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eRisk score aggregation: English regions\u003c/h2\u003e\n\u003cp\u003eWe compared the proportion of LSOAs falling into risk score categories with the proportions of households with children located in these regions (Table 3). Regions in England showed different proportions of small areas with households with children in moderate and high risk of housing insecurity (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe regions feature variation across each housing insecurity risk level. For instance, in Greater London, 38% of households with dependent children are at low risk of housing insecurity, compared to 78% in East England. Furthermore, Greater London features a higher proportion of households at moderate risk, with 44% falling within this category, compared to 16% in East England, and the greatest proportion of households at high risk, with 16% being in this category compared to 2% in East England. Regions featuring a greater proportion of households at very high risk include the North West (5%), West Midlands (4%), and Yorkshire and the Humber (7%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Households with children at relative risk of housing insecurity, mean percent by English region (\u0026lt;1 = less than 1%).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eEast England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eEast Midlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eGreater London\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eNorth East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eNorth West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eSouth East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eSouth West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eWest Midlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3953%;\"\u003e\n \u003cp\u003eYorkshire and the Humber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.7973%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4518%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eRisk score aggregation: multiple deprivation\u003c/h2\u003e\n\u003cp\u003eWe compared the risk scores for each small area (LSOA) to the Index of Multiple Deprivation (IMD) quintiles, where quintile 1 represents the most deprived areas. As income deprivation is a major factor in housing insecurity, we found a linear relationship between housing insecurity and deprivation (Figure 2): as deprivation increases, generally so does the risk of housing insecurity. However, the housing insecurity index shows some useful departures from the deprivation pattern. Figure 2 shows that households in less deprived areas also experience a moderate to high risk of housing insecurity: about 20% in quintile 3 (moderately deprived), 10% in quintile 4 (less deprived), and 4% in quintile 5 (least deprived).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe sought to identify any specific factors that might increase risk of housing insecurity in some areas within these IMD quintiles. Analysis of these factors is presented below. This information is important for allowing local authorities to develop more targeted approaches to reducing housing insecurity overall.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe research outlined in this paper led to the successful creation of the Families at Risk of Housing Insecurity Index (FRoHII), for estimating the proportions of families with dependent children at risk of housing insecurity. The index was created at small area scale using public data, including relevant and timely datasets. Working with public datasets has served to create an index that is inter-operable between a range of housing and homelessness domains, including local authority services, charitable organisations, or academic researchers. The drivers are not dependent on domain-specific surveys and has been informed by evidence from the housing insecurity literature. The metrics were validated by an expert panel, which were ranked and weighted for the risk-score calculation. This weighting process meant that the metrics contribute to the index in proportion to their importance.\u003c/p\u003e \u003cp\u003eThe FRoHII offers an advantage over other indices of housing insecurity risk, outlined in the review section of this paper. The FRoHII is composed from public data available at the national level, and collated systematically through census or consumer index means. This index is mapped to the small area scale for England and can be searched and analysed at different geographic scales. Composing FRoHII has not relied on costly and domain-specific qualitative research. Analysis of risk of housing insecurity affecting families with children using the index has improved our understanding of the varied distributions of risk across England.\u003c/p\u003e \u003cp\u003eAggregating the index has also provided patterns of risk at the local authority and regional levels. For instance, Greater London features a lesser proportion of areas at very high risk (2%), compared to the North West (5%) and West Midlands (4%). One explanation is that the cost of living in and around the capital city means that households experiencing severe pressures are forced out of the region, and into regions with comparatively lower costs of living.\u003c/p\u003e \u003cp\u003eWe compared the FRoHII to the Index of Multiple Deprivation for England and found that the risk of housing insecurity increased broadly in line with the increase in deprivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We noted, however, how some less deprived areas also feature higher risk of housing insecurity. We conducted analysis of the underlying factors that drive housing insecurity in these areas. We compared metrics in higher risk but lower deprivation areas, to all other areas of higher risk. We found that, while most metrics had similar values when compared, there was also a far higher density (75\u0026ndash;100%) of pre-1919 dwellings, which are more likely to be of substandard quality. This older housing stock appears to increase the risk of housing insecurity, even in less deprived areas.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTime-sensitivity\u003c/h2\u003e \u003cp\u003eThe FRoHII was compiled from time-sensitive metrics, derived for instance from Census 2021 data, annual schools and longitudinal data. FRoHII\u0026rsquo;s time sensitivity means that the index could be compiled for Census 2011 data, and other metrics from that year. Comparing current trends with those of previous years would allow analysis of the impact of austerity or the COVID-19 pandemic on housing insecurity. Time-sensitivity also means that FRoHII would require an updated compilation when the next census data become available (in 2031 by the current schedule).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations: policy implications\u003c/h2\u003e \u003cp\u003eThe Families at Risk of Housing Insecurity Index (FRoHII) is available via the Place-based Longitudinal Resource (PLDR) for any users wishing to identify the estimated level of risk for any small area in England. We envisage that the index would be particularly useful for any practitioners seeking to understand where families with children might be at risk of housing insecurity. We caution against using the index to identify individual households at risk. Instead, the index estimates the likely level of risk by area.\u003c/p\u003e \u003cp\u003eUsing the risk index will help practitioners to fulfil key aspects of their services. For instance, the Ministry of Housing, Communities and Local Government Homelessness code of guidance for local authorities (MHCLG, 2024) places emphasis on the need to mitigate housing problems and support families with children at risk of homelessness. This includes guidance on maintaining links with schools, where children transferring to the school may be experiencing the negative impacts on health and wellbeing of housing insecurity (Hutchings et al., 2016). The index may also help the local social and health care teams in providing support. For example, Integrated Neighbourhood Teams (INTs) collaborating in the United Kingdom include GP leads, health, social and wellbeing practitioners, and address the comprehensive needs of community members. Working with the index would help INTs to become aware of housing insecurity risk levels within their catchments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFuture areas for research\u003c/h2\u003e \u003cp\u003eOur analysis revealed how dwelling age is a persistent factor in housing insecurity as properties built before 1919 fail to meet the Decent Homes Standard, relating to quality thresholds for facilities, insulation, and floor space. Local authorities in England are now engaged in programmes of retrofit work to bring the quality of old housing up to contemporary standards.\u003c/p\u003e \u003cp\u003eThe FRoHII could be re-compiled for the next census to compare how these retrofit interventions have affected the risk of housing insecurity. For example, we could observe how retrofit programmes might have impacted the Residential Mobility frequency in areas that otherwise remained in higher deprivation. Utilising the index in this way provides a valuable resource for longitudinal research into how drivers of housing insecurity reflect broader social patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIMD Index of Multiple Deprivation\u003c/p\u003e\n\u003cp\u003eLSOA Lower Super Output Area\u003c/p\u003e\n\u003cp\u003eFRoHII Families at Risk of Housing Insecurity Index\u003c/p\u003e\n\u003cp\u003eSAMHI Small Area Mental Health Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe final index, along with the data that were compiled to produce it, are available to view and download from the Place-Based Longitudinal Resource website: https://pldr.org/dataset/2o6lg/families-at-risk-of-housing-insecurity-index-frohii\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR) (Grant Reference Number NIHR 204000). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eJO\u0026rsquo;B undertook the data analysis\u0026nbsp;and prepared the first draft of the manuscript. EC provided critical feedback on the research methods and results. EH and NW recruited the local authority advisory group from which the expert panel was recruited. HF, SR, A-MB conceived and led the study design. All authors read, edited, and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors express their gratitude to the expert practitioners who, as part of a panel, ranked the metrics used in the index.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlkire S. Choosing Dimensions: The Capability Approach and Multidimensional Poverty. In: Kakwani, N, Silber, J. (eds) The Many Dimensions of Poverty. Palgrave Macmillan, London. 2013. doi:10.1057/9780230592407_6\u003c/li\u003e\n \u003cli\u003eBoateng G, Adams, E. A multilevel, multidimensional scale for measuring housing insecurity in slums and informal settlements. Cities, 2023:132 (1). DOI:http://doi.org/10.1016/j.cities.2022.104059.\u003c/li\u003e\n \u003cli\u003eCarrere J, V\u0026aacute;squez-Vera H, P\u0026eacute;rez-Luna A. Novoa AM, Borrell C. Housing Insecurity and Mental Health: the Effect of Housing Tenure and the Coexistence of Life Insecurities. J Urban Health, 2022: 99, 268\u0026ndash;276. https://doi.org/10.1007/s11524-022-00619-5\u003c/li\u003e\n \u003cli\u003eCDRC. Dwelling Age Band Counts (to 2021) [open dataset]. Consumer Data Research Centre. 2021. Available via: https://data.cdrc.ac.uk/dataset/dwelling-ages-and-prices/resource/data-dwelling-age-band-counts-2021. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eCEMB. 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Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eHock ES, Blank L, Fairbrother H, \u003cem\u003eet al.\u003c/em\u003e Exploring the impact of housing insecurity on the health and wellbeing of children and young people in the United Kingdom: a qualitative systematic review. BMC Public Health. 2024:24, 2453. https://doi.org/10.1186/s12889-024-19735-9\u003c/li\u003e\n \u003cli\u003eHutchings HA, Evans A, Barnes P, Demmler JC, Heaven M, Healy MA, James-Ellison M, Lyons RA, Maddocks A, Paranjothy S, Rodgers SE, Dunstan F. Residential Moving and Preventable Hospitalizations. Pediatrics. 2016 Jul;138(1):e20152836. doi: 10.1542/peds.2015-2836. Epub 2016 Jun 3. PMID: 27260695.\u003c/li\u003e\n \u003cli\u003eJessiman PE, Powell K, Williams P, Fairbrother H, Crowder M, Williams JG, Kipping R. A systems map of the determinants of child health inequalities in England at the local level. PLoS One. 2021 Feb 12;16(2):e0245577. doi: 10.1371/journal.pone.0245577. PMID: 33577596; PMCID: PMC7880458.\u003c/li\u003e\n \u003cli\u003eKimhur, B. How to apply the capability approach to housing policy? Concepts, theories and challenges. Housing, Theory and Society. 2020:\u003cem\u003e37\u003c/em\u003e(3):257-277.\u003c/li\u003e\n \u003cli\u003eLeopold J, Cunningham M, Posey L, Manuel T. Improving Measures of Housing Insecurity: A Path Forward. 2016. Urban Institute. Available via: https://www.urban.org/sites/default/files/publication/101608/improving_measures_of_housing_insecurity_2.pdf. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eMahony, S. Moving Always Moving Report: The normalisation of housing insecurity among children in low income households in England. The Children\u0026rsquo;s Society. DOI: 10.13140/RG.2.2.19121.20325. Available via: https://www.childrenssociety.org.uk/information/professionals/resources/moving-always-moving. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eMarmot Review Team. The health impacts of cold homes and fuel poverty. Marmot Review Team and Friends of the Earth. 2011. Available via: http://www.instituteofhealthequity.org/projects/the-health-impacts-of-cold-homes-and-fuel-poverty. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eMason, K, Alexiou A, Li, A, Taylor-Robinson, D. The impact of housing insecurity on mental health, sleep and hypertension: Analysis of the UK Household Longitudinal Study and linked data, 2009\u0026ndash;2019, Social Science \u0026amp; Medicine. 2024: 351, 116939, ISSN 0277-9536, https://doi.org/10.1016/j.socscimed.2024.116939.\u003c/li\u003e\n \u003cli\u003eMHCLG. English Housing Survey: Stock Profile and Condition. Ministry of Housing, Communities and Local Government. 2017. Available via: https://assets.publishing.service.gov.uk/media/5d2d9cf140f0b64a7b5d411f/EHS_2017_Stock_Condition_Report.pdf. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eMHCLG. English indices of deprivation 2019: technical report. Ministry of Housing, Communities and Local Government. 2019. Available via: https://www.gov.uk/government/publications/english-indices-of-deprivation-2019-technical-report. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eMHCLG. English Housing Survey. [data series]. \u003cem\u003e4th Release\u003c/em\u003e. 2019. Ministry of Housing, Communities and Local Government. 2019. UK Data Service. SN: 200010, DOI: http://doi.org/10.5255/UKDA-Series-200010\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eMHCLG. English Housing Survey: Chapter 2 Housing Costs and Affordability. Ministry of Housing, Communities and Local Government. 2023a. https://www.gov.uk/government/statistics/chapters-for-english-housing-survey-2022-to-2023-headline-report/chapter-2-housing-costs-and-affordability. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eMHCLG. English Housing Survey 2021 to 2022: private rented sector. Department for Levelling Up, Housing and Communities. 2023b, http://www.gov.uk/government/statistics/english-housing-survey-2021-to-2022-private-rented-sector/english-housing-survey-2021-to-2022-private-rented-sector. Accessed 21 May 2025\u003c/li\u003e\n \u003cli\u003eMHCLG. Homelessness code of guidance for local authorities. HM Government, Ministry of Housing, Communities and Local Government, 2018, 2024 update. Available via http://www.gov.uk/guidance/homelessness-code-of-guidance-for-local-authorities/updates . Accessed 21 May 2025/\u003c/li\u003e\n \u003cli\u003eMikolai J, Kulu, H. Short- and long-term effects of divorce and separation on housing tenure in England and Wales. Population Studies, 2017:\u003cem\u003e72\u003c/em\u003e(1), 17\u0026ndash;39. https://doi.org/10.1080/00324728.2017.1391955\u003c/li\u003e\n \u003cli\u003eNdaba, Z., Dunga, SH, Mncayi-Makhanya, P. Analysing the multidimensional nature of poverty: a focus on housing insecurity in South Africa. International Journal Of Business and Development Studies, 2024: 16 (2), 269-293. DOI: 10.22111/ijbds.2024.50335.2168\u003c/li\u003e\n \u003cli\u003eOxfam. The True Cost of Austerity and Inequality: UK Case Study. 2013. Available via: https://www-cdn.oxfam.org/s3fs-public/file_attachments/cs-true-cost-austerity-inequality-uk-120913-en_0.pdf. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eRichard A, Bruat C, Febvrel D, Squinazi F, Simos J, Zmirou-Navier D; members of the HCSP working group. R. BMC Public Health. 2023 May 4;23(1):815. doi: 10.1186/s12889-023-15451-y. PMID: 37143018; PMCID: PMC10157125.\u003c/li\u003e\n \u003cli\u003eRobeyns, I. Selecting Capabilities for Quality of Life Measurement. Social Indicators Research, 2005: 74(1), 191\u0026ndash;215. http://www.jstor.org/stable/27522242\u003c/li\u003e\n \u003cli\u003eSen, AK. From Income Inequality to Economic Inequality. Southern Economic Journal. 1997: \u003cem\u003e64\u003c/em\u003e(2), 384\u0026ndash;401. https://doi.org/10.2307/1060857\u003c/li\u003e\n \u003cli\u003eShaw M. Housing and public health. Annu Rev Public Health. 2004;25:397-418. doi: 10.1146/annurev.publhealth.25.101802.123036. PMID: 15015927.\u003c/li\u003e\n \u003cli\u003eShelter. The impact of housing problems on mental health. Shelter, 2017. Available via https//england.shelter.org.uk/professional_resources/housing_and_mental_health. Accessed 21 May 2025.\u003c/li\u003e\n \u003cli\u003eShelter. Denied The Right To A Safe Home: Report. Shelter. 2021. Available via https://england.shelter.org.uk/professional_resources/policy_and_research/policy_library . Access 21 May 2025\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Housing insecurity, children and young people, family health and wellbeing, multidimensional risk index, public data, homelessness ","lastPublishedDoi":"10.21203/rs.3.rs-6856244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6856244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHousing insecurity resulting from multiple, involuntary residential moves is detrimental to the health and wellbeing of families with children. Policy makers seeking to mitigate these negative effects require a measure of risk of housing insecurity. Here we present the development of a novel risk index for England.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe undertook a literature review to select drivers of housing insecurity and identify relevant metrics. We recruited an expert panel to rank and weight these metrics using a Likert survey. The weighted metrics were summed for each small area (Lower Super Output Area) in England to produce the overall risk score. The score was then stratified into five levels, from very low to very high, linked to geographical units for data mapping. The final index (called the “Families at Risk of Housing Insecurity Index”) was made available on a public data platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEight drivers of housing insecurity were identified from the literature review as follows, (variable type and weight shown in brackets): primary school pupils eligible for free school meals (%, 0.5); income deprivation affecting children (%, 0.5); residential mobility (decile, 0.4); lone parent households (%, 0.3); pre-1919 properties (%, 0.3); households in fuel poverty (%, 0.3); households with dependent children by ethnic group (%, 0.2); mental health (Small Area Mental Health Index; decile, 0.2).\u003c/p\u003e\n\u003cp\u003eAnalysis of the index indicated a highly varied distribution of risk across England. Two noteworthy findings were the lower level of very high risk areas in Greater London, possibly indicating that higher living costs force households away from the capital city region. The index also suggested there were areas at higher risk in generally more affluent settings, possibly due to a greater proportion of older housing stock in these locations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Families at Risk of Housing Insecurity Index (FRoHII) was composed of metrics from public datasets at the small area level. The index provides a public resource to help identify areas where families with children might be at risk of housing insecurity. The index constitutes a tool and resource for professionals seeking to provide support to families within their catchment areas.\u003c/p\u003e","manuscriptTitle":"Stratifying areas at risk of housing insecurity among families with children: a multidimensional index for the improvement of policy interventions in England","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 15:25:32","doi":"10.21203/rs.3.rs-6856244/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T05:13:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T17:53:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215784133142651721838013625446038768803","date":"2025-07-29T17:48:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T17:23:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46594965126682867969582624545618015171","date":"2025-07-29T14:59:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280185997064163622400481327639544734329","date":"2025-07-02T14:53:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T16:10:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92439169079450958779127449206146512717","date":"2025-06-13T10:17:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T07:26:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T03:17:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T03:15:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-06-09T16:18:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8abed07f-8890-4679-b6ee-f7eab19a991c","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:05:01+00:00","versionOfRecord":{"articleIdentity":"rs-6856244","link":"https://doi.org/10.1186/s12889-025-25888-y","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-01-29 15:57:54","publishedOnDateReadable":"January 29th, 2026"},"versionCreatedAt":"2025-06-20 15:25:32","video":"","vorDoi":"10.1186/s12889-025-25888-y","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25888-y","workflowStages":[]},"version":"v1","identity":"rs-6856244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6856244","identity":"rs-6856244","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}