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

Sebastian Scheuer* (Corresponding Author), Dagmar Haase, Nadja Kabisch, Manuel Wolff, Dagmar Haase, Annegret Haase, Nadja Kabisch, Manuel Wolff, Nina Schwarz, Katrin Großmann

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Number of pages17
JournalEnvironment and Planning B: Urban Analytics and City Science
DOIs
Publication statusE-pub ahead of print/First online - 30 May 2018

Keywords

  • data mining
  • knowledge elicitation
  • random forest
  • Residential choice
  • tacit knowledge
  • ITC-ISI-JOURNAL-ARTICLE
  • UT-Hybrid-D

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