On employing fuzzy modeling algorithms for the valuation of residential premises

In this paper, we investigate fuzzy modeling techniques for predicting the prices of residential premises, based on some main drivers such as usable area of premises, age of a building, number of rooms in a flat, floor on which a flat is located, number of storeys in a building as well as the distance from the city centre. Our proposed modeling techniques rely on two aspects: the first one (called SparseFIS) is a batch off-line modeling method and tries to out-sparse an initial dense rule population by optimizing the rule weights within an iterative optimization procedure subject to constrain the number of important rules; the second one (called FLEXFIS) is a singlepass incremental method which is able to build up fuzzy models in an on-line samplewise learning context. As such, it is able to adapt former generated prediction models with new data recordings on demand and also to cope with on-line data streams. The final obtained fuzzy models provide some interpretable insight into the relations between the various features and residential prices in form of linguistically readable rules (IF-THEN conditions). Both methods will be compared with a state-of-theart premise estimation method usually conducted by many experts and exploiting heuristic concepts such as sliding time window, nearest neighbors and averaging. Email addresses: [email protected] (Corresponding author) (Edwin Lughofer), [email protected] (Bogdan Trawiński), [email protected] (Krzysztof Trawiński), [email protected] (Olgierd Kempa), [email protected] (Tadeusz Lasota) Preprint submitted to Information Sciences June 10, 2011 The comparison is based on a two real-world data set including prices for residential premises within the years 1998 to 2008.