A quantile regression approach and nonlinear analysis with Archimedean copulas to explain the movements of residential real estate prices

Abstract

The primary objective of this paper is to explain the determinants of residential real estate prices in the largest real estate market in the oil-rich Gulf Cooperation Council (GCC) countries. We employ linear quantile regression analysis to investigate the impact of financial market conditions (stock market returns and volatility), key monetary policy, and macroeconomic variables (including short term interest rates, inflation rates, and crude oil prices) on the residential real estate price dynamics. The secondary objective of this paper is to investigate the nonlinear relationships among variables through the use of two different Archimedean copulas with upper and lower tail dependence. The empirical results consistently demonstrate that only the residential real estate index-inflation rate relationship is statistically significant for all quantiles. We also find a nonlinear relationship among stock market returns, crude oil prices, and the residential real estate index, where the dependence structure is asymmetric and orients toward the upper side of the distribution. This study has significant implications for the analysis of real estate markets, investors, portfolio managers and policy makers.