As I am researching predictions in the Housing Market, I watched the interview with David Laibson on behavioral economics. Behavioral economics is deeply connected to predictions in the housing market because it explores how psychological, social, and emotional factors influence the economic decisions of individuals, including buying and selling homes. Additionally, Professor Laibson touched on his experience with machine learning and how this method can predict behavior economics. Having researched many prediction methods using some form of machine learning, it was interesting to hear about this in a more academic light.
An additional source I have found is listed below. It discusses how the COVID-19 pandemic, with consumers having a total loss of control and freedom due to social distancing and other restrictions, has influenced how people shop for variety, such as in the wine market. This study uses machine learning and U.S. household data to find that people's variety-seeking behavior in wine purchases changed during 2020, initially decreasing with coupon use but later returning to normal and fluctuating with how frequently they shopped. This is just one example of how machine learning can predict behavioral economics and predict consumer preferences! Excited to further see how this affects prediction in the housing market.