This study investigates the effects of key economic factors on the median list price and median selling price in the U.S.housing market.Key economic factors such as interest rates,unemployment rates,inflation rates,re...This study investigates the effects of key economic factors on the median list price and median selling price in the U.S.housing market.Key economic factors such as interest rates,unemployment rates,inflation rates,real gross domestic product,money supply,mortgage rate,Standard&Poor’s(S&P)500,and government expenditure are investigated to understand their relationships with housing prices.Conventional econometric models are typically used for housing market analysis;however,advancements in data science and machine learning allow these relationships to be examined more accurately.This study employs a decision tree regressor,k-nearest neighbors,random forest,and gradient boosting to enhance analysis accuracy and feature selection,thus enriching literature pertaining to machine learning in the housing market domain.The significance of housing market data as an indicator of economic growth is emphasized,and its effect on the overall economy,consumer spending,investment patterns,and financial stability is discussed.By utilizing a robust dataset and performing rigorous preprocessing,this study aims to provide valuable insights for policymakers,investors,and individuals involved in the housing sector.展开更多
文摘This study investigates the effects of key economic factors on the median list price and median selling price in the U.S.housing market.Key economic factors such as interest rates,unemployment rates,inflation rates,real gross domestic product,money supply,mortgage rate,Standard&Poor’s(S&P)500,and government expenditure are investigated to understand their relationships with housing prices.Conventional econometric models are typically used for housing market analysis;however,advancements in data science and machine learning allow these relationships to be examined more accurately.This study employs a decision tree regressor,k-nearest neighbors,random forest,and gradient boosting to enhance analysis accuracy and feature selection,thus enriching literature pertaining to machine learning in the housing market domain.The significance of housing market data as an indicator of economic growth is emphasized,and its effect on the overall economy,consumer spending,investment patterns,and financial stability is discussed.By utilizing a robust dataset and performing rigorous preprocessing,this study aims to provide valuable insights for policymakers,investors,and individuals involved in the housing sector.
