The Asymmetric Effects of Real Estate Uncertainty Shock

Abstract

In this paper, we explore the asymmetric impact of real estate uncertainty (REU) shock on the U.S. economy and housing market in high- and low-volatility states. To do this, we apply the Markov-switching vector autoregression model. Our empirical results show that the macroeconomic and housing markets react negatively to unexpected uncertainties, and the response to the REU shock is much greater than other housing-related variables in high-volatility states. We confirm the presence of the real-options channel based on the following results: both housing starts and construction employment decrease, and housing inventory increases, especially in low-volatility states. In the short run, we observe a delayed pattern in housing starts and construction employment variables. The effect of the channel is less significant in times of high volatility but confirmed in low volatility. The forecast error variance decomposition results show that the REU shock is greater under high REU volatility and plays a more significant role in explaining most housing-related variables.

Keywords:

• Housing market
• uncertainty
• bayesian MCMC algorithm
• real-options channel

JEL classifications:

• D81
• E32
• R21
• R31

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Numerous studies have employed the REU index to evaluate uncertainty in the housing market. Cepni et al. (2020) analyze the role of REU in predicting U.S. home sale growth and find that REU reduces home sale growth or deepens a recession. Gabauer and Gupta (2020) investigate the propagation of uncertainty and spillover across REU, MU, and FU. Marfatia et al. (2020) stress that REU is a key driver of housing sentiment and might lower housing prices. Gupta et al. (2021) show that uncertainty related to the real estate market has a negative impact on the U.S. housing market and economic activities.

2 Housing starts represent changes in the annual number of new residential buildings that began construction in a particular month and are a leading indicator of the housing-sector economy. Construction employment is the number of people who work in the construction sector.

3 Specifically, the housing inventory is expressed as the number of months it takes to sell the existing housing inventory based on the monthly housing sales rate. It is calculated by the sales volume, and the longer it takes to sell a house, the more likely the housing price is to fall.

4 See Bernanke (1983), Dixit and Pindyck (1994), and Hassler (1996).

5 For a more detailed posterior sampling algorithm, see Kim and Lee (2023).

6 We also computed the dynamic conditional correlations (DCCs) between REU and the probability of a high-volatility regime. The DCC results for the two measures show high positive correlations during the identified periods of high REU.

7 Uncertainty rises significantly during the recessions triggered by economic events (Bloom, 2014; Kose & Terrones, 2012). In particular, the housing market is a leading indicator of economic recessions (Leamer, 2007; Emmons, 2018, 2019a, 2019b).

8 The reason the ΔIP response to REU shocks is greater than that of other housing variables has to do with the characteristics of ΔIP. Industrial production encompasses sectors such as manufacturing, mining, electricity, and gas utilities as well as intermediate construction goods. Because ΔIP encompasses not only the construction industry but also the broader U.S. industry, it serves as an indicator of overall economic trends.

9 When uncertainty is excessively high, to the point of being glaringly evident, it can lead to relatively short-term negative pressures on decision-making regarding investment, consumption, and employment, rather than adopting a wait-and-see attitude, because the risk becomes overly certain. However, in cases of low volatility, the negative impact of uncertainty shocks occurs with a certain time lag, depending on the real-options channel and the adoption of a wait-and-see attitude. This interpretation can be applied to three variables: ΔHOUST, ΔUSCONS, and ΔHI.

10 According to Han (2010), housing demand that is induced by housing price volatility can be explained by two effects: financial risk and hedging. Generally, under the financial risk effect, as the uncertainty of housing prices increases, the risk of future asset returns also increases, leading to a decrease in asset demand and a negative effect on housing demand. The declining response of housing prices based on this perspective aligns with results found in previous studies.

11 Previous studies have analyzed this phenomenon without distinguishing between regimes and have explained it through the real-options channel. When estimating the impulse response functions, we assign greater weight to the low-volatility regime based on the data in our analysis, which is analogous to the normal times considered in previous research. Consequently, we analyze our results by adopting the perspective of the real-options channel.

12 To closely examine the importance of ΔHOUST, ΔUSCONS, and $\Delta$HI, we conducted another variance decomposition analysis specifically for these variables. When examining the contribution of each housing variable shock to the remaining variables, we find that the contributions to ΔHOUST, ΔUSCONS, and ΔHI variables are still relatively significant, similar to REU shock.