Comparing Machine Learning Techniques for House Price Prediction

Fourkiotis, Konstantinos Panagiotis; Tsadiras, Athanasios

doi:10.1007/978-3-031-34107-6_23

Cited by 4 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Gu et al (2011) (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). Further meta-regressors that combined the predictions of different ML models can be found in Fan et al (2018), Lu et al (2017), and Fourkiotis & Tsadiras (2023.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for greater transparency in housing price estimation

Mueller-Kett

2024

AGILE GIScience Ser.

View full text Add to dashboard Cite

Abstract. This paper investigates the use of machine learning (ML) models to predict housing prices. A well-performing housing price model was trained, which can seamlessly be integrated into public sector processes to increase market transparency and is based on modern ML and feature engineering methods. For these models, particular consideration was given to the spatial component. The research uses the Design Science Research approach, with a case study carried out in the city of Duisburg, Germany. The ML models developed showed better performance than traditional models. The models were embedded in official processes using Shapley values to increase interpretability. The study concludes that ML models can contribute to increased market transparency in the real estate sector.

show abstract

Section: Introductionmentioning

confidence: 99%