Delineation of residential housing submarkets using spatially constrained multivariate clustering

Abstract

Every housing market is made up of unique submarkets. Submarkets are areas or neighborhoods where houses have similar features, such as the age of the houses or price. Segmenting housing markets into submarkets is recommended for better understanding and more effective interventions in the housing market. While different submarket delineation approaches exist, many do not impose spatial constraints, overlooking the spatial relationships between houses. This oversight results in submarkets with poorly defined boundaries that do not match the urban layout, making accurate spatial inferences difficult and limiting stakeholders' ability to establish policy zones. To address these limitations, this study uses the SKATER clustering algorithm, which demarcates submarkets by taking into account the location of houses and ensuing spatial relationships alongside the structural attributes of the houses. The proposed method is implemented in a case study of King County, using house sale data from May 2014 to May 2015. It identifies four submarkets with boundaries closely aligned with the landscape, marking improvement over previous research. The analysis reveals a notable housing market imbalance whereby northern cities like Bellevue feature high-priced, spacious, high-quality houses on large lots. At the same time, the southern region, including SeaTac and Federal Way, offers older, smaller houses at relatively lower prices. These findings help stakeholders and investors make accurate spatial inferences for addressing housing challenges, particularly market imbalances.

Keywords: housing market, spatial constraints, submarkets