Urban Morphology and Housing Market

Over the years, numerous conceptual, theoretical, and empirical studies have attempted to formulate, model, and quantify how the built environment is valued by people. However, studies of the valuation of urban morphology are rare, due to the lack of a powerful methodology to quantify the urban form accurately. In addition, neoclassical economic theories have emphasized location with respect to the city center as the major spatial determinant of land value, but this has become weaker or even insignificant according to the findings of some current studies of mega cities, such as Los Angeles (Heikkila et al. 1989). Urban street networks contain spatial information on the arrangement of spaces, land use, building density, and patterns of movement and therefore give each location (or street segment) in the city a value in terms of accessibility. Thus, people can be thought of as paying for certain characteristics of the accessibility of the location of their choice. Moreover, they are likely to pay different amounts of money according to the different demand levels.

The most commonly applied methods of housing price evaluation can be broadly divided into two groups: traditional and advanced methods. There are five traditional mainstream standard recognized valuation methods in the field of property valuation: comparative method (comparison), contractor’s method (cost method), residual method (development method), profits method (accounts method), and investment method (capitalization/income method). Advanced methods include techniques such as hedonic price modeling, artificial neural networks (ANN), case-based reasoning, and spatial analysis methods.

House price can be thought of as a buyer’s evaluation of a bundle of intrinsic and extrinsic attributes of a housing unit, including structural, locational, neighborhood, and environment attributes (Freeman III 1979a). Many researchers have examined the contribution to house price of specific sets of factors. Structural characteristics typically include building type, numbers of bedrooms, bathrooms, garden, heating, and swimming pool (Follain and Jimenez 1985; Sirmans et al. 2005). Locational variables attempt to model accessibility to various sources of either positive or negative externalities, such as the distance to CBD, local commercial centers, parks, and pollution sources. Environmental characteristics include air quality (Freeman III 1979) and noise pollution (Bateman and Executive 2001; Day et al. 2007), and distance to the open space (Bolitzer and Netusil 2000). Neighborhood characteristics (a subset of locational attributes) typically include quality of school (Dubin and Goodman 1982; Haurin and Brasington 1996; Downes and Zabel 2002; Debrezion et al. 2006), and socioeconomic and racial composition (King and Mieszkowski 1973; Harris 1999; Ioannides 2002).

Rosen (1974) introduced the idea of the hedonic price model in an equilibrium market with complete competition, and the marginal implicit price estimated for the characteristics of property is equal to marginal people’s willingness to pay for these housing attributes. However, the housing market is not a uniform entity; rather, it could be considered as distinctive segments across space, and the segmentation of demand and supply in geography could lead to the spatial disequilibrium. Although there is a general agreement among researchers that housing submarkets exist, there is no single and coherent definition of a housing submarket (Adair et al. 1996; Watkins 2001).

For the majority of households, housing represents the main component of wealth, and the “wealth effect” of housing on consumption is significant (Case et al. 2005). Furthermore, the housing market not only impacts the economy through wealth effects, but also has influences on other markets (e.g., the mortgage market, mortgage insurance, mortgage backed bonds, and consumer durables). Therefore, understanding the sources of housing market price volatility has great significance on economy and the welfare of society. Despite the importance of the housing market, most empirical analyses mainly focus on exploring the macrodeterminants of house price movements over time by using aggregated data (e.g., real income, real GDP, tax rates, interest rates, population, construction costs, and consumer price index) (Holly and Jones 1997; Hort 1998; Takala and Barot 1998; Meese and Wallace 2003; Harter-Dreiman 2004; Riddel 2004). At the micro-level, there are also researchers who identify the price impact of one-time changes of building characteristic, measuring the added value for housing price over time by hybrid repeat sales models and hedonic models (McMillen 2003; Case et al. 2006; McMillen and Thorsnes 2006; Tian 2006; Noonan 2007). However, hybrid repeat sales and hedonic models have been criticized for the basis of sample selection and underestimation (Polinsky and Rubinfeld 1977; Polinsky and Shavell 1982). Recently, Iacono and Levinson (2011) examined the micro-dynamic relationship between accessibility and land value over time by first difference estimation. They used property sales data of Minneapolis–St. Paul region for the years 2000 and 2005 and measured of accessibility combined with regional accessibility data for their respective years to predict changes in prices over time in a particular location. However, they failed to find any significant micro-dynamic effects of accessibility on land value over time. They also noted that measuring changes in accessibility should be described more accurately than they were able to. Although these approaches could address the problem of omitted variable, they did not consider the unobserved heterogeneity variation over time (e.g., housing policy change and construction cost change), and assume the macrofactors to be constant, which also may lead to an estimation bias.

The focus of this book is on urban morphology and property markets. In particular, it investigates the externalities of urban configuration on housing price, asking whether households are more willing to pay extra for a more generally accessible location when they purchase a property without a specific motivation and travel destination. The purpose of this final chapter is to summarize the key findings of previous chapters. It is divided into four sections. In addition, the chapter discusses policy implications of this book’s findings, including the importance of the research with respect to the hedonic price theory as well as the role of space syntax theory; general knowledge on spatial network analysis; and as a method to aid the government and private sector to plan and manage the built environment. I then comment on the limitation of the study with respect to the issue of econometric estimation and the handling of space syntax radii. The final section provides suggestions for how the research can be further developed and highlights directions for future research. Finally, some final conclusions for this entire book are offered.