Predictors of housing price cycle and housing bubbles in Malaysia: resident survey using multinomial logistic regression

Purpose This study aims to examine the predictors of housing price cycles and potential housing bubbles in Malaysia using resident survey data. Design/methodology/approach The paper uses multinomial logistic regression to understand the factors influencing housing prices based on perceptions from building industry professionals, professionals outside the building industry, and non-professionals. The factors considered include housing demand, house buyers’ opinion of the current housing market scenario, present housing market conditions, government policies, foreign buyers, economic growth outlook, and demographic factors such as property acquisition period and intention to purchase property. A total of 500 questionnaires were distributed and collected from the respondents. Findings The analysis reveals that housing demand plays a significant role in predicting housing bubble trends, with either a positive or negative influence depending on contextual factors. The findings indicate that market perceptions and macroeconomic conditions jointly shape housing cycle movements, offering empirical insights into housing market volatility in emerging economies. Originality/value The paper deepens theoretical insights into the predictors and significant conditions that can cause a downturn or upturn in the housing bubble in the residential market.