Abstract
This chapter reviews recent developments in hedonic modeling of house prices based on structured additive regression (STAR) models. In STAR models, continuous covariates are modeled as P(enalized)-splines. Furthermore, random effects for spatial indexes, smooth functions of two-dimensional surfaces, and (spatially) varying coefficient terms may also be estimated using this methodology. Based on hierarchical STAR models, we discuss a number of useful extensions. With respect to value-at-risk concepts, financial institutions are often not only interested in the expected value but also in different quantiles of the distribution of real estate prices. To meet these requirements, we apply multilevel STAR models for location scale and shape (GAMLSS type regression) and a Bayesian version of quantile regression. As another extension, we sketch multiplicative region-specific scaling factors for nonlinear covariates in order to permit spatial variation in the nonlinear price gradients.
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Acknowledgements
This work was supported by funds of the Oesterreichische Nationalbank (Oesterreichische Nationalbank, Anniversary Fund, project number: 15309).
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Razen, A., Brunauer, W., Klein, N., Lang, S., Umlauf, N. (2015). Hedonic House Price Modeling Based on Multilevel Structured Additive Regression. In: Helbich, M., Jokar Arsanjani, J., Leitner, M. (eds) Computational Approaches for Urban Environments. Geotechnologies and the Environment, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-11469-9_5
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DOI: https://doi.org/10.1007/978-3-319-11469-9_5
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