![]() Firstly, the Canton of Zurich is sufficiently large and contains urban as well as rural areas. We select the Canton of Zurich as our area of analysis for several reasons. In this paper, we model spatially varying vintage effects for single-family houses (SFHs) in the Canton of Zurich (ZH, Switzerland). ( 2021a) who found pronounced spatially varying effects on the rents and the prices of apartments, respectively. More recent evidence for such behavior is presented in Brunauer et al. They compared individual hedonic models for 59 metropolitan areas in the United States and concluded that “everal metropolitan areas exhibited significant deviations from the average depreciation patterns.” (Malpezzi et al., 1987, p. For instance, one of the first observations of spatial differences in the age effects can be found in Malpezzi et al. Our study is motivated by previous work on spatially varying relationships between house prices and age. In particular, we want to examine if such non-stationary vintage effect exists and, in a second step, see if we can improve the quality of hedonic models in price prediction. The goal of this paper is to unify both frameworks, i.e., vintage effects and SVC modeling, to investigate a possible spatially varying vintage effect. ![]() Applications of these methods consistently show the existence of non-stationary coefficients, e.g., Baton Rouge (LA, United States, see Gelfand et al., 2003), in Toronto (ON, Canada, see Wheeler et al., 2014), Singapore (Cao et al., 2019 van Eggermond et al., 2011), and Shenzhen (China, see Geng et al., 2011). ( 2010) find “substantial spatial variation” of covariate effects between the districts of Vienna.Įxisting methods to model such spatially varying coefficients (SVC) are Bayesian processes (Gelfand et al., 2003) and geographically weighted regression (Fotheringham et al., 2002). For instance, when applying additive mixed regression models on rents in Vienna (Austria), Brunauer et al. There are numerous publications which show a clear indication of spatially varying covariate effects within hedonic pricing models. ![]() Over the last two decades, there emerged a special focus on location specific effects due to newly available modeling methodologies. Studies investigating this particular type of behavior, i.e., a deviation from a pure depreciative effect once a particular age has been reached, are referencing to it as a vintage effect (Clapp & Giaccotto, 1998 Goodman & Thibodeau, 1995 Rubin, 1993). The quadratic appearance of the age effect has also been observed by Fahrländer ( 2006) and linked to the building material and architectural style. This behavior is a result of two main features of the age as an independent variable: (1) In general, older buildings depreciate due to deterioration (2) “however, beyond some point, only those houses with the best locations and the highest construction quality survive.” (Case et al., 2004, p. ( 2004) report a “plausible quadratic form” for the building age. It has been found that the age effect is nonlinear (Clapp & Giaccotto, 1998 Goodman & Thibodeau, 1995). The marginal effect of the building age on house prices has been well-studied. Hedonic real estate models contain several predictor variables, and age is a key explanatory variable.
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