Contributing factors to apartment pricing in Stockholm Vasastan: : An analysis using multilinear regression

Abstract: This thesis uses multilinear regression analysis to identify the variables and the magnitude of the variables affecting the housing market in Vasastan, a district of Stockholm, Sweden. We then make an attempt to generalize the results to the entire Stockholm area, and reason around why certain factors may be important drivers of price. The factors identified to affect the prices are the number of rooms, living area, floor number, fee and age of the building. Some results we find are intuitive while others are less so. Some of the factors in our regression model can be changed without a massive change in construction price, which means there is a real world application of our thesis to increase the value of newly built apartments.