This paper analyzed the various factors affecting housing prices in the Boston area. Given that housing prices are one of the most highly influential elements in the social economy and individual decisions, this study aimed to trace the main key determinants driving fluctuations in this market. The results from an MLR model applied on 509 observations of characteristics describing housing in the Boston area, using a dataset from Kaggle against housing prices, have been reported. The empirical results of the study reveal that crime rate, number of rooms, air quality (nitrogen oxide concentration), pupil-teacher ratio, property tax rate, distance to employment centers, and highway accessibility affect housing prices. A higher crime rate and nitrogen oxide concentration lead to a decrease in housing prices, whereas an increase in the number of rooms and better highway accessibility leads to an increase in housing prices. Other variables that the study identified as likely to have multicollinearity problems are property tax and highway accessibility. Such conclusions assist in gaining a better understanding of the dynamics associated with determinants in housing price and offer invaluable advice to policymakers, investors, and potential homebuyers in making decisions regarding real estate policy and investments within Boston's real estate market.
Read full abstract