Anders Hjort

Doctoral Research Fellow
Image of Anders Hjort
Norwegian version of this page
Room 806
Visiting address Moltke Moes vei 35 Niels Henrik Abels hus 0851 Oslo
Postal address Postboks 1053 Blindern 0316 Oslo


  • August 2022: I just presented a poster titled House Price Prediction with Confidence: Empirical Evidence from the Norwegian Market at the annual COPA symposium in Brighton (UK). The poster is here and the extended abstract is here (to be published in PMLR). 
  • July 2022: Here is a work-in-progress titled Interpretable House Price Prediction Using a Collection of Local Machine Learning Models I recently presented at the WEAI 97th Annual Conference in Portland (Oregon) this summer. The corresponding slides are here
  • April 2022: I recently gave a short introduction to conformal inference to my supervisors. The introduction is my attempt at summarizing some parts of this book, this article and this article. My presentation can be found here
  • April 2022: Happy to announce that the paper House Price Prediction With Gradient Boosted Trees Under Different Loss Functions has been accepted to the Journal of Property Research. The paper is joint work with Johan Pensar, Ida Scheel and Dag Einar Sommervoll. Full paper can be found here
  • July 2021: I just presented a working paper with the title House Price Prediction:
    Classical Methods vs. Machine Learning Methods
    on the (virtual) WEAI 96th Annual Conference. The paper is joint work with Johan Pensar, Ida Scheel and Dag Einar Sommervoll. See the presentation here

Academic interests:

  • Gradient boosted trees (XGBoost etc.)
  • Conformal inference for uncertainty quantification
  • Statistical methods for house price prediction 
  • Computational statistics


I am an industrial PhD candidate at the Statistics and Data Science group at Department of Mathematics. 

My PhD project (Uncertainty in house price prediction, 2021-2024) aims to study methods for house price prediction, with special interest in the uncertainty associated with a prediction. We are especially interested in uncertainty in tree based methods for supervised machine learning, such as random forest and boosted trees. 

The project is supervised by associate professor Johan Pensar, associate professor Ida Scheel and professor Dag Einar Sommervoll (NMBU). The project is conducted in collaboration with Eiendomsverdi, a Norwegian property technology company. 

I completed my MSc in Industrial Mathematics from NTNU in Trondheim in 2019, where I also spent one academic year at TU Berlin in Germany. You can find my master thesis here. I have previously worked as a consultant for Deloitte.  

Tags: Boosted trees, house price prediction
Published May 26, 2021 11:30 AM - Last modified Sep. 12, 2022 5:27 PM

Research groups