The goal of the research was to find a better way to predict house prices in Boulder, Colorado, without human bias. The researchers used machine learning and deep learning algorithms to analyze house features and prices. They found that random forest and artificial neural networks were more accurate than traditional methods like hedonic pricing models. This means these new methods could help predict house prices more effectively in Boulder and potentially in other cities too.