This project is about how the relationship between house prices and various variables that describe the house and its surroundings can be modeled. From this house price model, the traffic noise is examined to see if noise has any impact on the house price and if so how big is this impact on the house price.

The hedonic price method was used as a method to describe the house price. For the house price model data from BBR, SVUR, and noise data from the Danish Environmental Protection Agency were used. The variables that are used in the model describes the structural characteristics, the location of the house, the surroundings, and the environmental characteristics.
Through the project of modeling the best-fitted model that could describe the relationship between the house price and the variables, several different models have been tested. Models that have been tested are simple linear and logarithmic. OLS regression and Machine Learning have also been used to construct a model.

The best-fitted model was found when using Machine Learning using Linear Regression. This model had an accuracy of 73% which was significantly higher than the other models that have been tested. Based on this model, it is concluded that traffic noise does not have any significant effect on the house price compared to the effect of the other variables.