Household diversity and market segmentation within a single neighborhood

Abstract

C31, C51, D12, R21,

The Potential for Consumer Segmentation in the Finnish Housing Market

“…Because researchers do not know a priori how many market segments exist in a given space (Lipscomb and Farmer ), resident groups are defined endogenously in this analysis. To determine whether identifiable groups of preferred housing attributes exist among different segments of the population, we undertake a hierarchical method cluster analysis using the computed factor scores on the housing attribute preferences.…”

“…Feitelson () finds that segmentation incorporating lifestyles via K‐ means cluster analysis provides a better explanation of preferences and willingness‐to‐pay than sociodemographic segmentation. More recently, Lipscomb and Farmer () use principal components analysis to sort a combined sample of renter and owner households in one US neighborhood into three groups. Each group has distinguishable house values associated with structural characteristics and preferences concerning neighbors and neighboring uses.…”

A hedonic analysis of the demand for and benefits of urban recreation parks

“…park size) on property price. Lipscomb and Farmer (2005), however, used separate variables to segment the market through an endogenous sorting process, and to estimate the hedonic models. Our use of variables is consistent with the approach of a number of similar studies mentioned earlier (e.g., Day, 2003;Chen et al, 2009;Cho et al, 2008), and was necessary to define the submarkets that are homogenous in as many attributes as possible and to estimate the coefficient of the focus variable accurately.…”

“…Following Lipscomb and Farmer (2005), Lipscomb (2006), Day et al (2007), Cho et al (2008), we first identified the submarkets and then estimated a separate hedonic price function for each submarket. A commonly used statistical technique to identify market segmentation within a city is a k-means clustering (Bourassa et al, 1999;Day, 2003;Chen et al, 2009;Cho et al, 2008), in which the housing and neighborhood variables forming the hedonic function are used to group the houses into different clusters.…”

“…Recent studies have also used alternative methods to define the optimum number of submarkets. For example, Lipscomb and Farmer (2005) used an endogenous sorting process, which first uses principal component analysis (PCA) to extract orthogonal factors crossing a threshold Eigen value; and then employs an iterative seemingly unrelated regression approach to identify optimal clusters of households, based on maximization of a G-statistic. This technique is similar to Two Step Clustering in the sense that both of these rely on maximizing some mathematical function while classifying observations into an optimal number of homogenous groups.…”