https://doi.org/10.1140/epjds/s13688-023-00424-3
Regular Article
Does noise affect housing prices? A case study in the urban area of Thessaloniki
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
Received:
5
February
2023
Accepted:
3
October
2023
Published online:
17
October
2023
Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.
Key words: Housing prices prediction / Noise pollution / Ensemble models / Interpretability
© The Author(s) 2023
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