  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.
