Returns Predictability in Emerging Housing Markets

This essay searches for a link between house prices, broad money, private credit and the macro-economy among 19 emerging markets. We also explain which variables predict the emerging markets house price index returns. The analysis consists of data for 19 emerging markets spanning the period 19662012. To investigate the relationship among our variables as well as the direction of the relationship, we used Fixed-Effects Panel Data Approach, Vector Auto-Regression, Granger Causality, and Variance Decomposition analysis. Using the fixed effects panel data approach, this paper analyzes the variation in 19 emerging markets house price index returns. Our results show that money market rate, growth in GDP and CPI as well as log of private credit (D) and money supply (M3) have significant predictive power on growth in house price indices a quarter ahead. There is a strong negative correlation between the growth in house price indices and the lagged money market rates and private credit. There is a strong positive correlation between the growth in house price indices and the lagged growth in GDP, CPI and log of M3. When we look at different regions, we show that in Eastern Europe only GDP has a predictive power whereas in Asia MMR, D as well as M3 has statistically significant predictive power. Granger causality tests for each country reveals strong evidence of multi-directional link between house prices, GDP, CPI, interest rates, money, and credit. House prices significantly affect the future broad money, private credit and macro variables. When we look at the variance decomposition, in almost every country we investigated, forecast errors to GDP, CPI and money market rate play an important role in explaining innovations to the house price index returns.