Abstract
The development of the housing markets in different European metropolitan areas is of high interest for the urban development and the real estate markets, which are about to globalize. The Budapest housing market is well-suited for scrutiny from an institutional and evolutionary perspective due to its fragmented nature: different house types, age categories, price levels, and micro-locations, are found side by side. Applying two neural network techniques, namely the self-organizing map (the SOM) and the learning vector quantification (the LVQ), sheds some light about how physical and socio-demographic characteristics, price, and regulation are inter-related in this market.
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