Abstract
This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred to as vintage effects. We estimate a spatially varying coefficients (SVC) model to investigate the spatial structures of vintage effects on SFH pricing. For SFHs in the Canton of Zurich, Switzerland, we find substantial spatial variation in the age effect. In particular, we find a local, strong vintage effect primarily in urban areas compared to pure depreciative age effects in rural locations. Using cross validation, we assess the potential improvement in predictive performance by incorporating spatially varying vintage effects in hedonic models. We find a substantial improvement in out-of-sample predictive performance of SVC models over classical spatial hedonic models.
Highlights
Hedonic real estate models contain several predictor variables, and age is a key explanatory variable
We model spatially varying vintage effects for single-family houses (SFHs) in the Canton of Zurich (ZH, Switzerland)
The in-sample performance of the geostatistical model is improved by 7.7% by using the spatially varying coefficients (SVC) model
Summary
Hedonic real estate models contain several predictor variables, and age is a key explanatory variable. The marginal effect of the building age on house prices has been well-studied. Case et al (2004) report a “plausible quadratic form” for the building age. 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) “beyond some point, only those houses with the best locations and the highest. Over the last two decades, there emerged a special focus on location specific effects due to newly available modeling methodologies. There are numerous publications which show a clear indication of spatially varying covariate effects within hedonic pricing models. When applying additive mixed regression models on rents in Vienna (Austria), Brunauer et al
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