Understanding the Determinants of Car Ownership : A Regression and Neural Network Study

Abstract: This thesis aims to understand the determinants of car ownership in the Swedish regions containing the largest cities: Skåne, Stockholm, and Västra Götaland. This is done by performing a fixed effects regression analysis as well as creating and comparing different predictive models. Both socioeconomic and spatial factors are looked at. The data used in the study is on a Demographic Statistic Zone level for the years 2016-2021. The data consists of approximately 90 variables that are narrowed down to 12 variables based on the level of existing multicollinearity, which are used in the final models. The results from the fixed effects regression show that variables such as population density, age, income, house owning type, and house type are the main influencers on car ownership. These results are similar for the specific regions; however, some differences are discovered, pointing out the disadvantages in creating a generalized model. The results of the predictive models shows that a Long Short-Term Memory model performs better than Random Forrest Regression and OLS, however the performance of the latter two models is considered satisfying enough making them superior as they are easier to interpret and more established within the industry. The region-specific predictive models perform equally well as the ones created from all the data. In conclusion, it can be said that the determinants of car ownership that are mentioned align well with the previous studies made and are considered reliable. Regarding which predictive model to use OLS should be considered sufficient even if more complex methods perform better.