Delineating urban housing submarkets with fuzzy clustering

Abstract

It has long been argued that the housing market is spatially compartmentalized within a metropolitan area. The argument has important implications for explaining how the housing market works—should the status quo be seen as an equilibrium state? Or if no equilibrium is reached, how do loosely interlaced submarkets function both independently and interdependently? We note that the body of literature has leaned toward testing the distinctiveness of housing submarkets given a priori housing submarkets. However, there seems to be a lack of interest in developing methods for deriving housing submarkets empirically. Fuzzy clustering is well suited to this problem, given that the boundary of housing submarkets is not often sharply delineated. The study applies a fuzzy c-means (FCM) algorithm to identify housing submarkets in the Buffalo–Niagara Falls region. The study is distinct from other FCM applications in three respects. First, we reflect on issues tied to choosing parameters of fuzzy clustering. Second, we introduce overlap measures to characterize the relationship between the clusters produced. Third, we evaluate the performance of fuzzy clustering in terms of hedonic prediction accuracy. Results show that stratified hedonic models predict house price better than a market-wide hedonic model. Fuzzy clustering solutions also yield better prediction, compared with hard clustering.

Online Housing Search and the Geography of Submarkets

“…Jones et al 2012). Indeed, the data in Table 1 reinforce the idea that the spatiality of housing search can best be expressed through a multi-layered, non-nested approach to housing submarkets where there is a relationship between income constraints, property types and spatial search patterns (Watkins, 2001;Hwang and Thill, 2009). This proposition is explored in more detail in the next section.…”

Does burglary affect property prices in a nonmetropolitan municipality?

“…Research has shown that crime has an impact on housing prices (Thaler, 1978;Rizzo, 1979;Buck et al, 1991;Bowes and Ihlanfeldt, 2001;Lynch and Rasmussen, 2001;Munroe, 2007;Hwang and Thill, 2009;Wilhelmsson, 2011, 2012). Yet this evidence is based on sales of properties located in large urban areas.…”

Price Determinants of Affordable Apartments in Vietnam: Toward the Public–Private Partnerships for Sustainable Housing Development

“…The distance to central business districts (CBDs) has been critical for housing choice and prices with the "access/space trade-off" model [44,45] depicting a trade-off between the reduced land cost of suburban areas and the increasing commuting cost of travel and transportation to CBDs. Hwang and Thill (2011) found an association between job accessibility and housing price by measurement of travel-time-based job accessibility in Seattle [46]. There have been, however, contradictory debates regarding the model due to the assumption limitation, such as monocentric urban structures, the isotropic condition of terrain, and perfect competition markets [41,47,48].…”