House Price Prediction with Confidence: Empirical Results from the Norwegian Market

Abstract

Automated Valuation Models are statistical models used by banks and other financial institutions to estimate the price of a dwelling, typically motivated by financial risk management purposes. The preferred choice of model for this task is often tree based machine learning models such as gradient boosted trees or random forest, where uncertainty quantification is a major challenge. In this empirical contribution, we compare split conformal inference, conformalized quantile regression and Mondrian conformalized quantile regression on data from the Norwegian housing market, and use random forest as a point prediction. The data consists of N = 29 993 transactions from Oslo (Norway) from the time period 2018-2019. The results indicate that the methods using conformalized quantile regression create narrower confidence regions than split conformal inference.