Cluster analysis of factors influencing the valuation of real estate objects

Abstract

The current real estate market analysis reveals challenges in valuation methods, procedure adequacy, and evolving technological approaches. Uncertainty arises from using localised methods for valuing individual real estate objects. A significant concern is the reliability and completeness of valuation data. Researchers emphasise market-driven aspects as trends in real estate valuation. Features for valuation are identified through quantitative characteristics, uncovering components and their nature. The research analyses foreign and domestic practices for real estate object valuation. Challenges include understanding methodological and informational support through mathematical methods and addressing factors affecting real estate object valuation. The need for cluster analysis to identify factors affecting real estate object valuation is recognised. To implement cluster analysis of factors affecting real estate valuation, a method is proposed involving the development of classification features, optimal typological grouping, and clustering implementation technology. Six groups of factors were chosen: spatial formation, urban planning provision, environmental impact, investment indicators, infrastructure provision, and limiting characteristics. An agglomerative process calculated the distance matrix between clusters of factors. The MacQueen k-means clustering method determined final clusters, confirming the validity of the proposed factor groups. The clustering of factors affecting real estate valuation was based on obtained distance data. The result identifies a high level of factors influencing real estate object valuation. Nine coincidences justify this in their clustering with four units of factors influencing real estate object valuation.