Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki
This research addresses the limited data on environmental factors in real estate markets, providing a new dataset and insights for urban planners and property analysts, though it is incremental in applying existing methods to a new context.
The study tackled the problem of predicting housing prices by investigating the impact of noise pollution, using a newly created dataset for Thessaloniki, Greece, and found that noise significantly affects prices, with the influence varying across different areas of the city.
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.