The canary in the city: indicator groups as predictors of local rent increases

As cities grow, certain neighborhoods experience a particularly high demand for housing, resulting in escalating rents. Despite far-reaching socioeconomic consequences, it remains difficult to predict when and where urban neighborhoods will face such changes. To tackle this challenge, we adapt the c...

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Bibliographic Details
Published in:	EPJ data science Vol. 7; no. 1; pp. 21 - 15
Main Authors:	Steentoft, Aike A., Poorthuis, Ate, Lee, Bu-Sung, Schläpfer, Markus
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2018 Springer Nature B.V SpringerOpen
Subjects:	Bioindicators Cities Complexity Computer Appl. in Social and Behavioral Sciences Computer Science Data-driven Science Housing prices Indicator group Indicator species Individual and Collective Human Mobility: Description LBSN Modeling and Theory Building Modelling Neighborhoods Prediction Regular Article Rents Social sensing Socioeconomics Urban environments Social sensing LBSN Indicator group Housing prices
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Summary:	As cities grow, certain neighborhoods experience a particularly high demand for housing, resulting in escalating rents. Despite far-reaching socioeconomic consequences, it remains difficult to predict when and where urban neighborhoods will face such changes. To tackle this challenge, we adapt the concept of ‘bioindicators’, borrowed from ecology, to the urban context. The objective is to use an ‘indicator group’ of people to assess the quality of a complex environment and its changes over time. Specifically, we analyze 92 million geolocated Twitter records across five US cities, allowing us to derive socio-economic user profiles based on individual movement patterns. As a proof-of-concept, we define users with a ‘high-income-profile’ as an indicator group and show that their visitation patterns are a suitable indicator for expected future rent increases in different neighborhoods. The concept of indicator groups highlights the potential of closely monitoring only a specific subset of the population, rather than the population as a whole. If the indicator group is defined appropriately for the phenomenon of interest, this approach can yield early predictions while simultaneously reducing the amount of data that needs to be collected and analyzed.
ISSN:	2193-1127 2193-1127
DOI:	10.1140/epjds/s13688-018-0151-y