Undertaking the Analysis

The objectives of a housing needs and market analysis might cover a wide range of issues. Again, these might be prescribed by central government or might be determined by local stakeholders. Typically, a housing needs and market analysis aims to:

  • • assess the future impact of economic, demographic and social trends upon the housing market, particularly on housing need, demand and forecast supply
  • • identify specific groups with current unmet housing needs
  • • determine the overall quantum of new housebuilding which is likely to be required over the planning period, with concomitant implications for land supply and servicing
  • • determine the proportion of the additional supply which should be ‘affordable’ as opposed to ‘market’ housing
  • • determine the ‘match’ between the existing housing stock and the demographic profile of the community (considering the location, size and other characteristics, as well as the tenure and affordability of dwellings)
  • • identify existing or emerging shortages of particular types of housing stock, such as the loss of low-cost rental dwellings, transitional accommodation or housing for the aged
  • • identify latent capacity within the existing housing stock, or other urban land uses, which could be better utilised, or sources of surplus public land which might support affordable housing projects
  • • identify issues of low housing demand, where for example, there is little or no demand for existing housing stock because of its location, design or condition in relation to market needs.

Sometimes, housing strategies must address more complex market situations where there is a mix of high and low demand in close proximity, coupled with environmental or other constraints. Overall, the key policy issues or priorities affecting a particular area will influence the kind of data that is sought and the way in which the data is assessed.

Depending on data availability, at minimum this should usually include data on:

  • • demographic trends (migration, population growth and age/sex composition, average household size and projected formation)
  • • economic drivers (income and occupation trends, industry structure, unemployment and labour force participation rates)
  • • supply characteristics and trends including: dwelling structure (composition, size); dwelling tenure (including low-cost rental and homelessness rates); new dwelling approvals/completions (by residential development type); and new residential land subdivision approvals/ planned land allocations)
  • • condition of existing housing stock (quality, adequacy/finish, disrepair and energy efficiency)
  • • specific housing needs and problems experienced by residents (lack of secure tenure, affordability/payment problem, overcrowding, concealed and sharing households); housing suitability problems (health/ disability related unsuitability problems, neighbourhood problems); preference to move
  • • market indicators (rent and sales data, including median and quartile values; numbers of households in affordability stress; changes in house prices and rents; rental vacancy rates; residential land sales and prices).

Further, we comment on the different types of data sources which may be used to inform and populate different parts of this checklist. Traditional data sources for planners include censuses, demographic projections and rates of development (permissions, completions) (McClure 2005), while for ‘housing’ departments or agencies high emphasis tends to be placed on waiting lists.

As the scope of local housing strategies has widened and the demand for evidence increased, greater interest has arisen in commissioning local housing needs surveys. While this partly reflected the new emphasis on affordability, particularly in countries such as the UK which lacked local income data, it also provided an opportunity to measure a wider range of needs as well as preferences and intentions. However, the shortcomings of needs surveys have also become more apparent, and in an era of austerity the attractions of models which rely mainly on secondary data rather than new primary data collection have become much more pressing, and reflected in official guidance. This partly reflects the greater range of secondary data now available, for example, on house prices, rents and other market indicators, as well as the richer array of national sample surveys which can be used to provide national and regional measures of key housing needs.

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