A new generation of internet information services that enable house hunters to select their 'ideal' neighbourhood have the potential to widen the divide between the richest and poorest places in Britain.
A report for the Joseph Rowntree Foundation warns that sophisticated new Internet-based Neighbourhood Information Systems (IBNIS) could lead to a more segregated society by not only guiding buyers to the best schools or lowest crime figures, but also helping them choose areas with the kind of existing residents they would most want as neighbours.
In the United States, IBNIS already enable users to search for neighbourhoods that match their prioritised criteria, using extensive, zip-coded data sets compiled by market research companies. Equivalent websites in the UK do not yet offer neighbourhood searches by ranked characteristics, but a number of commercial sites feature information collected by postcode; while the Government's own Neighbourhood Statistics website provides statistical, demographic and environmental information on neighbourhoods. 'Joke' sites, such as those listing 'crap' or 'chav' towns, also claim to capture the social characteristics of different areas, most often in negative terms.
Professor Roger Burrows, who led the research team from the Universities of York and Durham, said: "We already have a 'digital divide' in Britain between those whose internet access makes them information-rich and those whose inability to afford computers or fast web connections makes them information-poor. But it seems only a matter of time before the kind of powerful neighbourhood search sites available in the United States start to reinforce the divide between the more and less prosperous locations in the UK. This is potentially worrying. Given what we know about the benefits of mixed-income communities in promoting social cohesion, it is important that greater public access to the 'social sorting' technology used by market research does not pull in the opposite direction and lead to even greater segregation between communities."
The research, based on an analysis of existing website services and interviews with providers, users and other stakeholders, found four main types of IBNIS site:
Professor Burrows said: "The technology available can not only sort people according to basic data such as their incomes, but also according to individual tastes, consumer preferences, lifestyle habits and so on. Until recently these 'segmentation' processes have been largely invisible to the public, but with the emergence of IBNIS it is entirely possible that people will start using them to 'sort themselves out' into neighbourhoods where their neighbours are less diverse and more like themselves."
He added: "While no one would want to prevent public access to neighbourhood information, we should recognise the potential implications for disadvantaged neighbourhoods and the people who live in them. At a minimum it would be sensible to insist that IBNIS websites specify their sources and make it clear how their information was compiled. We also recommend that local people are given opportunities to challenge the way their neighbourhoods are being portrayed, if necessary."