Valuation Methods for the Housing Market: Evidence from Budapest

Dávid Kutasi () and Milán Csaba Badics ()
Additional contact information
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
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.akademiai.com/doi/pdf/10.1556/032.2016.66.3.8 (application/pdf)
subscription

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aka:aoecon:v:66:y:2016:i:3:p:527-546

Ordering information: This journal article can be ordered from
Akadémiai Kiadó Zrt., P. O. Box 245, H-1519 Budapest, Hungary
https://akjournals.com/

Access Statistics for this article

Acta Oeconomica is currently edited by Mihályi, Péter

More articles in Acta Oeconomica from Akadémiai Kiadó, Hungary
Bibliographic data for series maintained by Kriston, Orsolya ().