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  1. Proposing a global model to manage the bias-variance tradeoff in the context of hedonic house price models
    Autor*in: Granna, Julian; Brunauer, Wolfgang; Lang, Stefan
    Erschienen: [2022]
    Verlag:  Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria

    The most widely used approaches in hedonic price modelling of real estate data and price index construction are Time Dummy and Imputation methods. Both methods, however, reveal extreme approaches regarding regression modeling of real estate data. In... mehr

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 395
    Fernleihe:
    keine Fernleihe

     

    The most widely used approaches in hedonic price modelling of real estate data and price index construction are Time Dummy and Imputation methods. Both methods, however, reveal extreme approaches regarding regression modeling of real estate data. In the time dummy approach, the data are pooled and the dependence on time is solely modelled via a (nonlinear) time effect through dummies. Possible heterogeneity of effects across time, i.e. interactions with time, are completely ignored. Hence, the approach is prone to biased estimates due to underfitting. The other extreme poses the imputation method where separate regression models are estimated for each time period. Whereas the approach naturally includes interactions with time, the method tends to overfit and therefore increased variability of estimates. In this paper, we therefore propose a generalized approach such that time dummy and imputation methods are special cases. This is achieved by reexpressing the separate regression models in the imputation method as an equivalent global regression model with interactions of all available regressors with time. Our approach is applied to a large dataset on offer prices for private single as well as semi-detached houses in Germany. More specifically, we a) compute a Time Dummy Method index based on a Generalized Additive Model allowing for smooth effects of the continuous covariates on the price utilizing the pooled data set, b) construct an Imputation Approach model, where we fit a regression model separately for each time period, c) finally develop a global model that captures only relevant interactions of the covariates with time. An important methodolical aspect in developing the global model is the usage of modelbased recursive partitioning trees to define data driven and parsimonious time intervals.

     
    Export in Literaturverwaltung   RIS-Format
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/273685
    Schriftenreihe: Working papers in economics and statistics ; 2022, 12
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  2. Modeling multiplicative interaction effects in Gaussian structured additive regression models
    Autor*in: Aschersleben, Philipp; Granna, Julian; Kneib, Thomas; Lang, Stefan; Umlauf, Nikolaus; Steiner, Winfried J.
    Erschienen: [2024]
    Verlag:  Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria

    Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex... mehr

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 395
    Fernleihe:
    keine Fernleihe

     

    Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex interactions between covariates of different types. Within this framework, we present a simultaneous estimation approach for highly complex multiplicative interaction effects. In particular, a possibly nonlinear function f(z) of a covariate z may be scaled by a multiplicative effect of the form exp(˜η), where ˜η is another possibly structured additive predictor. Inference is fully Bayesian and based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach in extensive simulation experiments. Furthermore, we apply and illustrate the methodology to an analysis of asking prices for 200000 dwellings in Germany.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/286390
    Schriftenreihe: Working papers in economics and statistics ; 2024, 01
    Schlagworte: IWLS proposals; MCMC; multiplicative interaction effects; structured additive predictor
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  3. A parsimonious hedonic distributional regression model for large data with heterogeneous covariate effects
    Autor*in: Granna, Julian; Lang, Stefan; Umlauf, Nikolaus
    Erschienen: [2024]
    Verlag:  Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria

    Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 395
    Fernleihe:
    keine Fernleihe

     

    Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution into account. Thus far, the application of regression models that model the full conditional distribution of the prices, has been infeasible for large data sets, even on powerful machines. Moreover, accounting for heterogeneity of effects regarding time and location, is often achieved by naive stratification of the data rather than on a model basis. A novel batchwise backfitting algorithm is applied in the context of a structured additive distributional regression model, which enables us to efficiently model all distributional parameters of the price distribution. Using a large German dataset of apartment asking prices with over one million observations, we employ a model-based clustering algorithm to capture the heterogeneity of covariate effects on the parameters with respect to location. We thus identify clusters that are homogeneous with respect to the influence of location on price. A boosting type algorithm of the batchwise backfitting algorithm is then used to automatically determine the variables relevant for modelling the location and scale parameters in each regional cluster. This allows for a different influence of variables on the distribution of prices depending on the location and price segment of the dwelling.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/286391
    Schriftenreihe: Working papers in economics and statistics ; 2024, 02
    Schlagworte: IWLS proposals; MCMC; multiplicative interaction effects; structured additive predictor
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen