Using big (synthetic) data to identify local housing market attributes

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

Recent advances in data disaggregation and the generation of synthetic spatial microdata are exploited for identifying local housing market attributes. Traditional public sector ‘small’ data is disaggregated to yield big (synthetic) spatial microdata using a three-stage approach. First, an allocation algorithm is used to attach synthetic socio-economic attributes to residential buildings. Second, inconsistencies between housing values and the ascribed socio-economic attributes of the resident population are identified. These indicate incipient clusters of housing market change. Third, clusters are typologized using the synthetic socio-economic microdata coupled with building attributes data. This yields information on housing market attributes such as segmentation and dynamics such as gentrification. The approach is operationalized for the entire stock of residential units and households in Israel and can be easily reproduced in other national contexts.

Original languageAmerican English
Title of host publicationBig Data for Regional Science
PublisherTaylor and Francis
Pages109-120
Number of pages12
ISBN (Electronic)9781351983266
ISBN (Print)9781138282186
DOIs
StatePublished - 1 Jan 2017

Bibliographical note

Publisher Copyright:
© 2018 selection and editorial matter, Laurie A. Schintler and Zhenhua Chen.

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