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Real estate markets depend on various methods to predict housing prices,including models that have been trained on datasets of residential orcommercial properties. Most studies endeavor to create more accurate machinelearning models by utilizing data such as basic property characteristics aswell as urban features like distances from amenities and road accessibility.Even though environmental factors like noise pollution can potentially affectprices, the research around this topic is limited. One of the reasons is thelack of data. In this paper, we reconstruct and make publicly available ageneral purpose noise pollution dataset based on published studies conducted bythe Hellenic Ministry of Environment and Energy for the city of Thessaloniki,Greece. Then, we train ensemble machine learning models, like XGBoost, onproperty data for different areas of Thessaloniki to investigate the way noiseinfluences prices through interpretability evaluation techniques. Our studyprovides a new noise pollution dataset that not only demonstrates the impactnoise has on housing prices, but also indicates that the influence of noise onprices significantly varies among different areas of the same city.

Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki