Housing and Homelessness

Why targeting older people on under-occupation is a half-baked approach

  • Published: Jun 06, 2012
  • Author: Hannah Aldridge
  • Category: Housing and Homelessness

Is there a problem with under occupied homes in Britain? Older people are much more likely to under occupy than younger households. But only half of under-occupied homes are older households, so targeting older people can only solve half problem.

Last week, JRF published the Findings version of our Market Assessment for older people report. One of the key areas that it looked at was under-occupation among older people. It comes at time of claims that older people are “hoarding homes” preventing younger people from getting on the housing ladder. Older people’s groups have opposed this rhetoric, concerned that it is causing unnecessary anxiety among vulnerable pensioners.

Part of the problem with the under-occupation discussion is the tendency to look at only one-side of the story when the data it is clearly less straight forward. This situation highlights some of the golden rules of data analysis.

Rule 1: composition vs. risk

There are two ways we can look at under-occupation and age (1) the proportion of people in each age group under-occupying, a risk approach and (2) the proportion of all under-occupiers that belong in each group a composition approach.

This is demonstrated in the two graphs below. The graph on the left shows the risk approach; clearly older households are much more likely to under-occupy their homes (57%) compared to other households (27%). But the graph on the right shows that only around half under-occupied homes contain older households.


This 2 way approach is needed regardless of the topic in question. For instance, in MPSE 2011 Indicator 43 we look at the relationship between poverty and housing tenure. There are more owner-occupied households in poverty than social rented (2.4 million compared to 2 million). But this is primarily because there are more owner-occupied households in general (17.6 million compared to 4.3 million). The proportion of social rented households that are in poverty at 44% is much higher than the proportion of owner-occupied at 13%. So there are more owner-occupied households in poverty than social rented but the risk of poverty to social rented is much greater.

So what does the age-under-occupation, risk-composition story imply for policy? Any policy that aimed to reduce under-occupation by targeting all older households would be more effective than one that targeting other households because 57% of those targeted would be under-occupiers. However, you can only expect to solve half of the problem by targeting older households because they make up only half of under-occupiers.

Rule 2: Understand the definitions

To grasp the scale of the issue, it is important to understand how ‘under-occupation’ and ‘older household’ are being defined. In the data presented above an older person household is a household containing only people aged 55 plus. Under-occupation is defined as someone who has more than 2 spare rooms according to the bedroom standard (which allows a bedroom for every single adult or couple) e.g. a single adult or a couple household living in a two bedroom home would not be under-occupying, in a three bedroom house would be under-occupying.

50% of older households are single adult households and 48% are couple households. This means almost all (98%) of older person households can only live in one bedroom or two bedroom homes to avoid under-occupying. As 63% of dwellings have three or more bedrooms and only 57% of older person households under-occupy, it appears that older person households are, to a degree, choosing homes that they think would be an appropriate size for them. The idea that older people are “hoarding housing” is incorrect.

This represents just the beginning of the understanding required for the issue of older people and under-occupation. The full report and summary findings explore the issue in more detail by looking at the impact of tenure and moving, the scale of over-crowding etc. 

Statistics plural are essential for us to grasp the size and nature of a problem and what policy makers can do to address it. But be weary of those that use a singular statistic, firstly it may not even be correctly used (see our blog on workless households) and secondly it may have chosen to wrongly present an issue as black and white when it is really riddled with grey areas. 

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