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Valuation Methods for the Housing Market: Evidence from Budapest

Dávid Kutasi () and Milán Csaba Badics ()
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Dávid Kutasi: Faculty of Architecture, Department of Construction Management, Budapest University of Technology and Economics, Budapest
Milán Csaba Badics: Faculty of Business Administration, Department of Finance, Corvinus University, Budapest

Acta Oeconomica, 2016, vol. 66, issue 3, 527-546

Abstract: Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.

Keywords: housing prices; hedonic method; neural networks; Budapest residential market (search for similar items in EconPapers)
JEL-codes: G12 R31 (search for similar items in EconPapers)
Date: 2016
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