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  2. Space-time machine learning models for valuation and prediction of real estate objects Space-time machine learning models for valuation and prediction of real estate objects

Space-time machine learning models for valuation and prediction of real estate objects

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.

Brief information

School:

Business

Status:

Completed

Period:

01.11.2018 - 31.10.2020

Overview

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. In particular, the focus is on developing spatially and spatio-temporally varying coefficient models that can handle large data and applying them to the task of real estate mass appraisal.

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Facts

Type of project

Forschung

Internal organisations involved
  • Institute of Financial Services Zug IFZ
External project funder
  • Innosuisse
Funding
  • Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)
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Persons involved: internal

Project manager
  • Fabio Sigrist
Member of project team
  • Jakob Dambon
  • Markus Schmidiger

Publications

  • Article, review; peer reviewed (3)

    • 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

  • Article, review; not peer reviewed (1)

    • 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.

  • Other publication formats (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

  • Presentation (conference/report/lectures) (3)

    • 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).

Brief information

School:

Business

Status:

Completed

Period:

11/01/2018 - 10/31/2020

Project Head

Prof. Dr. Fabio Sigrist

Lecturer

+41 41 757 67 61

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