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The importance of search behaviour has long been recognised in the study of housing markets, but research in this area has frequently been hampered by lack of data. In many nations, the vast majority of initial housing search queries are now conducted online and the data this generates could, in theory, provide us with better insights into how housing market search operates spatially, in addition to generating new knowledge on the geography of local housing submarkets. This paper seeks to explore these propositions by discussing existing conceptions of search before developing a framework for understanding housing search in the digital age. A large, user-generated housing market search data-set is then introduced and analysed with respect to area definition, submarket geography and search pressure locations. The results indicate that this kind of ‘big data’ approach to housing research could generate important new insights for housing market analysts.
Alasdair Rae (Mon,) studied this question.