Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig

Sebastian Scheuer, Dagmar Haase, Annegret Haase, Nadja Kabisch, Manuel Wolff, Nina Schwarz and Katrin Großmann
Additional contact information
Sebastian Scheuer: Humboldt-Universität zu Berlin, Geography Department, Germany
Dagmar Haase: Humboldt-Universität zu Berlin, Geography Department, Germany; Helmholtz Centre for Environmental Research–UFZ, Department of Computational Landscape Ecology, Germany
Annegret Haase: Helmholtz Centre for Environmental Research–UFZ, Department of Urban and Environmental Sociology, Germany
Nadja Kabisch: Humboldt-Universität zu Berlin, Geography Department, Germany; Helmholtz Centre for Environmental Research–UFZ, Department of Urban and Environmental Sociology, Germany
Manuel Wolff: Humboldt-Universität zu Berlin, Geography Department, Germany; Helmholtz Centre for Environmental Research--UFZ, Department of Urban and Environmental Sociology, Germany
Nina Schwarz: Helmholtz Centre for Environmental Research–UFZ, Department of Computational Landscape Ecology, Germany

Environment and Planning B, 2020, vol. 47, issue 3, 400-416

Abstract: Residential choice behaviour is a complex process underpinned by both housing market restrictions and individual preferences, which are partly conscious and partly tacit knowledge. Due to several limitations, common survey methods cannot sufficiently tap into such tacit knowledge. Thus, this paper introduces an advanced knowledge elicitation process called SilverKnETs and combines it with data mining using random forests to elicit and operationalize this type of knowledge. For the application case of the city of Leipzig, Germany, our findings indicate that rent, location and type of housing form the three predictors strongly influencing the decision making in residential choices. Other explanatory variables appear to have a much lower influence. Random forests have proven to be a promising tool for the prediction of residential choices, although the design and scope of the study govern the explanatory power of these models.

Keywords: Residential choice; random forest; tacit knowledge; knowledge elicitation; data mining (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/2399808318777500 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:47:y:2020:i:3:p:400-416

DOI: 10.1177/2399808318777500

Access Statistics for this article

More articles in Environment and Planning B
Bibliographic data for series maintained by SAGE Publications ().