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.