Analysing the Residential Market Using Self-Organizing Map

Olgun Aydin and Krystian Zieliński ()
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Krystian Zieliński: PwC Polska

A chapter in Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, 2022, pp 465-478 from Springer

Abstract: Abstract Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood’s key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto Area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

Keywords: Self-organizing map; Real estate market; Clustering; Residential property prices (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-030-85254-2_28

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DOI: 10.1007/978-3-030-85254-2_28

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