Understanding spatial housing choice and demand: A comparative study of London, Liverpool, and Sheffield

Online search behaviour has become increasingly utilised in the study of housing markets. Currently, the majority of research uses Google Trends data, available from 2006, which is in aggregate form, to ‘nowcast’ rather than predict the future in a quantitative manner. This brings about a clear lack of raw data availability. This project uses recent, fine-grained data, encompassing Internet searches conducted for housing via Rightmove.co.uk from 2012 - 2018. The analysis reveals the geographical patterns associated with housing market search across England and Wales, and in the case study areas (London, Liverpool, and Sheffield). There are already data available for the ‘revealed demand’ in the form of sold property prices, but there are not any data for the ‘latent demand’. The aim of the research was to assess the extent to which user-generated online housing search data can help us identify, spatially, housing market and submarket areas. This research therefore adds knowledge to the geography of housing search in relation to the geography of choice and demand. Novel geographical information systems methods have then been designed to uncover the latent demand of online housing searches through four sub-methods: (1) search intensity, (2) correlation between search and sales, (3) most drawn shape boundaries, and (4) the intricateness of drawn shapes. It demonstrates that there is significant value in taking a ‘big data’ approach to housing search and that in doing so we can build upon earlier theories and concepts about the operation of the housing market from a geographical perspective. It concludes that technology has helped prospective buyers be successful when looking for housing online. This research then has real world utility. It informs house builders, local authorities and mortgage lenders, as well as academics, who for too long have suffered from a data deficit.