Temporal causality between house prices and output in the US: A bootstrap rolling-window approach

Abstract

This paper examines the causal relationships between the real house price index and real GDP per capita in the U.S., using the bootstrap Granger (temporal) non-causality test and a fixed-size rolling-window estimation approach. We use quarterly time-series data on the real house price index and real GDP per capita, covering the period 1963:Q1 to 2012:Q2. The full-sample bootstrap non-Granger causality test result suggests the existence of a unidirectional causality running from the real house price index to real GDP per capita. A wide variety of tests of parameter constancy used to examine the stability of the estimated vector autoregressive (VAR) models indicate short-and long-run instability. This suggests that we cannot rely on the full-sample causality tests and, hence, this warrants a time-varying (bootstrap) rolling-window approach to examine the causal relationship between these two variables. Using a rolling window size of 28 quarters, we find that while causality from the real house price to real GDP per capita occurs frequently, significant, but less frequent, evidence of real GDP per capita causing the real house price also occurs. These results imply that while the real house price leads real GDP per capita, in general (both during expansions and recessions), significant feedbacks also exist from real GDP per capita to the real house price.

Do house prices influence stock prices? Empirical investigation from the panel of selected European Union countries

“…The results imply that, along with the increase in the house prices, there is a significant decrease in the stock prices; therefore, the desirable policy instruments are required to evaluate the impact of house prices on the stock market in a given countries contexts. The results are consistent with previous studies of Batayneh and Al-Malki (2015) and Nyakabawo et al (2015), both these studies confirmed the negative relationship between stock prices and house prices across their countries. The short-and long-run causality results are shown in Table 4.…”

“…Batayneh and Al-Malki (2015) concluded that house prices have a significant and positive association with the stock prices in Saudi Arabia financial markets that escalate a country's economic growth; on the other hand, the study established the stock led house prices in a country. Nyakabawo, Miller, Balcilar, Das, and Gupta (2015) supported the house prices led the economic growth hypothesis by using the consistent time series quarterly data of the US, which shows the soundness of the financial market to support the economic system in a country. The study formulated the third hypothesis regarding the causal relationship between stock prices and house prices, i.e.,…”

The Dynamic Relationship Between House Prices and Output: Evidence From Us Metropolitan Areas

“…Accordingly, higher income increases housing demand which raises the real estate prices. These inter-linkages are generally formalized in the housing literature by assuming a stable long-run relationship between house prices and economic fundamentals (Abraham and Hendershott, 1996;Meen, 2002;Capozza et al, 2002;Nyakabawo et al, 2013).…”

“…These include Jud and Winkler (2002), Hwang andQuigley (2006), Gallin (2006), Demary (2010), Miller et al (2011) and Nyakabawo et al (2013). Jud and Winkler (2002) and Hwang and Quigley (2006) study the dynamics of metropolitan housing prices and find that real house price appreciation is strongly driven by fundamentals including income.…”

“…Recently, Nyakabawo et al (2013) investigate the time-varying nature of the relationship 1 The reader is referred to Nyakabawo et al (2013) between house prices and GDP per capita using the bootstrap Granger (temporal) non causality test and a fixed-size rolling-window estimation approach. They find a significant and frequent unidirectional causality running from the real house prices to real GDP per capita with some evidence of temporal bidirectional causality between the two variables.…”