Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method

Abstract

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

“…Previous studies that reveal such non-stationary relationships have dealt with spatial heterogeneity by delineating the cities into distinct geographic areas or different city tiers and estimating the global regression separately. However, city classification is often problematic in practice and hinders researchers from making generalizations about the uncertainty of a broader and dynamic housing market [17]. In this context, we use a better regression model, GWR, which enables us to estimate local coefficients to deal effectively with spatial non-stationarity.…”

Modelling hedonic residential rents for land use and transport simulation while considering spatial effects

“…ere are examples in the elds of climatology , ecology (Kimsey et al 2008;Zhang and Shi 2004), education (Fotheringham et al 2001), marketing research (Mittal et al 2004), regional science (Huang and Leung 2002), political science (Calvo and Escolar 2003), and transport research (Chow et al 2006;Clark 2007;Hadayeghi et al 2003;Lloyd and Shuttleworth 2005;Nakaya 2001). In the housing eld, there are studies by Bitter et al (2007), Farber and Yeates (2006), Fotheringham et al (2002), Kestens et al (2006), Páez et al (2007), and Yu et al (2007). To our knowledge, there have been no studies on residential rents employing GWR so far, as all of the authors mentioned above focus on apartments or single-family house transaction prices.…”

Modelling transport and real-estate values interactions in urban systems

“…Spatial dependence or autocorrelation which is defined as the existence of a functional relationship between what occurs at a point in space and what occurs at nearby or neighbouring points, and spatial heterogeneity (or spatial non-stationarity), that is the lack of structural stability in the parameters or of spatial errors in a model. In the context of real estate markets both effects could be present due to various factors: lack of equilibrium between housing supply and demand in different sectors of an urban area (Bitter et al, 2007), diffusion effects of market prices for housing in nearby areas or, simply, the omission of relevant variables that were not included in the model because of the lack of or poor quality available data. Therefore, it would be necessary to use spatial econometric models in order to avoid biased or inefficient parameters in case studies in which these effects play a significant role (LeSage and Pace, 2009).…”