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

Journal article

Publication Details

Author list: Scheuer S., Haase D., Haase A., Kabisch N., Wolff M., Schwarz N., Großmann K.

Journal: Environment and Planning B: Urban Analytics and City Science

Publication year: 2020

Volume number: 47

Issue number: 3

Pages: 400-416

ISSN: 2399-8083

eISSN: 2399-8091

DOI: 10.1177/2399808318777500


Languages: English-Great Britain


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.


Last updated on 2020-03-08 at 12:45