Predictive Analytics and Macroeconomic Influence: A Detailed Exploration of the Toronto Housing Market Dynamics

The housing market, serving as a pivotal entity in economic matrices, inherently exhibits a particular complexity. This research sets foot into a comprehensive investigation of the Toronto housing market, unraveling its intertwining associations with macroeconomic variables while attempting to predict future trends based on such exploration. Backed by a robust data set from January 2011 to July 2023 of the Canadian economy, this study employs correlation models (Spearman and Pearson) and ARIMA to generalize and perform housing price predictions. Preliminary findings via correlation analysis signal a substantial linkage between housing prices and macroeconomic indexes of GDP, employment, and exchange rates. The ARIMA model application, underscored by a p-value of approximately 0.042 from the Ljung-Box test, provides a valid, future price estimation. In the intrinsic puzzle of the housing market, this research offers an alternative understanding from a macroscopic lens. This research, while showcasing predictive prowess, also stands as a testament to the multifaceted nature of the housing market and advocates for the ongoing refinement and diversification of predictive models in navigating its complexities.