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The goal of this project is to develop novel machine learning and statistical models for both hedonic mass appraisal of real estate objects and for scenario-based real estate price prediction.
Wirtschaft
Abgeschlossen
01.11.2018 - 31.10.2020
Forschung
Dambon, Jakob; Fahrländer, Stefan; Karlen, Saira; Lehner, Manuel; Schlesinger, Jaron; Sigrist, Fabio & Zimmermann, Anna (2022). Examining the Vintage Effect in Hedonic Pricing using Spatially Varying Coefficients Models: A Case Study of Single-Family Houses in the Canton of Zurich. Swiss journal of economics and statistics / ed. by the Swiss Society of Economics and Statistics / hrsg. von der Schweiz. Gesellschaft für Volkswirtschaft und Statistik / publ. par la Société suisse d'économie et de statistique, 1.
Sigrist, Fabio (2022). Gaussian Process Boosting. Journal of Machine Learning Research (JMLR), 2022(23), 1-46.
Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (2021). Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction. Spatial Statistics, 1. doi: 10.1016/j.spasta.2020.100470
Schliessen
Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (2021). Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models. arXiv, 1.
Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (11.10.2019). varycoef: Modeling Spatially Varying Coefficients [Softwareprogramm]. https://cran.r-project.org/web/packages/varycoef/index.html
Dambon, Jakob (19.06.2020). varycoef: Modeling Spatially Varying Coefficients. eRum 2020, virtual.
Dambon, Jakob (25.10.2019). varycoef:An R Package to Model Spatially Varying Coefficients. Swiss Statistics Seminar, Bern (Switzerland).
Dambon, Jakob (09.11.2018). Spatially Varying Coefficients Models: A Comparison of Maximum Likelihood Estimators with other Estimators. Swiss Statistics Seminar, Bern (Switzerland).
