Investigating the Effects of Climate Policy Uncertainty on the Volatility of the U.S. Real Estate Markets

Abstract

As climate change worsens, dangerous weather events are becoming more frequent or serve: Even the world's wealthiest nations could not put out large-scale fires, which are raging in the world. The rise of sea levels, the deadly floods, the imbalances between the temperatures, and the uncertainty in climate policy has raised many eyebrows in the last few years. Although climate change influences many sectors including real estate markets, information regarding the impacts of climate policy uncertainty on these markets remains poor. In order to analyze the impacts of climate change uncertainty on the real estate markets in the USA. We use the Climate Policy Uncertainty (CPU) index and the Volatility of the Real Estate Markets (REMV) index based on monthly data which starts in January-2000 and ends in March-2021. This study utilized the VAR model to analyze the collected data. Surprisingly, the results of the Granger causality test show no G-causality between the CPU index and the REMV index. This means that there is no statistically significant causal relationship between these two variables in the dataset used. Further, according to the results of the Impulse response test, the variables react to the shocks which come to themself positively and provide a meaningless result to the reactions between the variables. In other words, the shocks or disturbances within the variables do not lead to predictable or significant effects on the other variable. Lastly, the Variance decomposition test results show that the variables lagged by 99% of their dynamics and lagged by 1% of the other variables' dynamics. Generally, no negative connection can be observed between the two variables in the dataset used.