Housing market volatility in the OECD area: Evidence from VAR based return decompositions

Abstract

Vector-autoregressive models are used to decompose housing returns in 18 OECD countries into cash ‡ow (rent) news and discount rate (return) news. Only for two countries -Germany and Ireland -do changing expectations of future rents play a dominating role in explaining housing return volatility. For the majority of countries news about future returns is the main driver, and both real interest rates and risk premia play an important role in accounting for housing market volatility. Bivariate cross-country correlations and principal components analyses indicate that part of the return movements have a common factor among the majority of countries. However, in a minority of countries (Germany, Japan, and the Netherlands) return movements have been basically unrelated to return movements in other countries.JEL Classi…cation: C32, G12, R31

The effect of negative information on the volatility of real estate residential prices in Abuja, Nigeria

“…Despite the importance of the real estate market, the residential price volatility has received limited attention in literature (Kaulihowa and Kamati, 2019). Among the few studies that addressed house price volatility include that of Begiazi and Katsiampa, 2019;Bloch and Bloch, 2006;Cook and Watson, 2017;Engsted and Pedersen, 2014;Kaulihowa and Kamati, 2019;Lin Lee, 2009;Lee and Reed, 2014;and Reen and Razali, 2016). All the studies revealed evidence of real estate price volatility and justified that volatility was extensively overwhelmed by negative and positive price appreciations.…”

“…Therefore, understanding the underlying causes of volatility in real estate prices is significant for real estate analysts and investors. This is due to real estate wealth which comprises of a significant portion of a household's total wealth and has a major impact on household consumption (Engsted and Pedersen, 2014). Similarly, in many countries information about future returns plays a vital role in accounting for real estate market volatility (Haven et al, 2012).…”

High-Frequency Volatility Forecasting of US Housing Markets

“…Given this, a growing number of studies have attempted to model and predict volatility (using univariate models and also with econometric frameworks including wide array of factors) at the aggregate and regional (state and metropolitan statistical areas (MSAs)-levels) of the US (see for example, Dolde and Tirtiroglu (2002), Crawford and Fratantoni (2003), Miller and Peng (2006), Miles (2008a), Miles (2008b), Miles (2011), Zhou and Haurin (2010), Elder and Villupuram (2012), Li (2012), Ajmi et al (2014), Engsted and Pedersen (2014), Barros et al (2015), Bork and Møller (2015), Fairchild et al (2015), André et al (2017), Chen (2017), Nyakabawo et al (2018)). All these studies find the existence of Autoregressive Conditional Heteroskedasticity (ARCH) and long memory effects in housing price returns volatility, and a relatively good forecasting performance of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-type models.…”

Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment

“…In light of this, a growing number of studies have attempted to model and predict volatility (using univariate models and also with econometric frameworks including wide array of factors) at the aggregate and regional (state and metropolitan statistical areas (MSAs)-levels) of the US (see for example, Dolde and Tirtiroglu (2002), Miller and Peng (2006), Miles (2008), Zhou and Haurin (2010), Li (2012), Barros et al, (2015), Ajmi et al, (2014), Engsted and Pedersen (2014), Bork and Møller (2015), Fairchild et al, (2015), André et al, (2017), Chen (2017), Nyakabawo et al, (forthcoming)). In general, these studies highlight the role of information in macroeconomic, financial, and economic uncertainty related variables in predicting US housing market volatility.…”