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100			_aScheuer, Sebastian _957609
245			_aCombining tacit knowledge elicitation with the SilverKnETs tool and random forests: _bthe example of residential housing choices in Leipzig/
260			_bSage, _c2020.
300			_aVol. 47, Issue 3, 2020, ( 400–416 p.)
520			_aResidential 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.
700			_aHaase, Dagmar _957610
700			_aHaase, Annegret _957611
700			_aKabisch, Nadja _957612
700			_aWolff, Manuel _957613
700			_aSchwarz, Nina _957614
700			_aGroßmann, Katrin _957615
773	0		_08876 _917104 _dLondon Pion Ltd. 2010 _tEnvironment and planning B: planning and design (Urban Analytics and City Science) _x1472-3417
856			_uhttps://doi.org/10.1177/2399808318777500
942			_2ddc _cEJR
999			_c14521 _d14521