The Asymmetric Effects of Regional House Prices in the UK: New Evidence from Panel Quantile Regression Framework

This study unfurls the non-linear behaviour of regional house prices in the United Kingdom by employing quarterly observations spanning the period 1992Q1–2017Q4. Our enquiry aims at examining UK house prices within a multivariable framework and, more specifically, by employing panel quantile regression with fixed effect. In brief, the empirical findings obtained from these methodologies indicate that the UK house prices are influenced at lower and upper quantiles, and that precisely they are influenced by variables such as income, private sector housing starts and employment. We highly support that there is a strong heterogeneity among UK regions and that asymmetry may be one of the keys of the ripple effect. Particularly, the income shows a positively significant performance at lower and higher regional house prices. Moreover, the variables private sector housing starts and employment rate are statistically significant for house prices. Leveraging for first-time panel quantile regression for the case of regional house prices in the UK, policymakers will have a profound understanding of regional house prices. JEL Classifications: C22, R21, R31

A global vector autoregression model for softwood lumber trade.

A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.

A global vector autoregression model for softwood lumber trade.

A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.

NONLINEAR RELATIONSHIP BETWEEN HOUSING SUPPLY AND HOUSE PRICE IN MALAYSIA

The novelty of this paper is to ascertain a nonlinear relationship between housing supply and house price. This study is conducted based on panel dataset of four different types of houses in Malaysia from 2002Q3 through 2016Q4. Although housing supply has been theoretically assumed to be positively and linearly related to house price, we observed that the number of new houses build in Malaysia has declined despite the increasing house prices. Hence, we posit that housing supply and house price are nonlinearly related. The results from pooled mean group estimation show the existence of inverted U-shaped housing supply curve. The threshold level of house price index is found at 186.92 where the effect of house price on housing starts will become negative after this point. We also find that the marginal effects of house price evaluated at the minimum and maximum levels are positive and negative, respectively, and statistically significant. This paper suggests that the squared term of house price should be included in estimating housing supply in Malaysia. The evidence of inverted U-shaped housing supply curve in Malaysia shows that housing authorities have taken steps to overcome the challenges of oversupply by reducing the approvals for housing development projects.

The Role of the Federal Reserve in the U.S. Housing Crisis: A VAR Analysis with Endogenous Structural Breaks

This paper reexamines the role of the Federal Reserve in triggering the recent housing crisis. Specifically, we explore if the relationship between the federal funds rate and the housing variables underwent structural changes in the wake of the housing crisis. Using quarterly data spanning 1960–2017, we estimate a VAR model involving federal funds rate, real GDP growth and a housing variable (captured by house price inflation or residential investment share or housing starts) and conduct time series analysis for the pre- and post-crisis periods. While previous studies mostly set break-dates based on events known a priori to split the full sample to subsamples, we endogenously determine structural break points occurring at multiple unknown dates. Our Granger causality analysis indicates that the federal funds rate did not cause house price inflation, although it caused residential investment share and housing starts in the pre-crisis period. In the post-crisis period, the real GDP growth caused residential investment and housing starts while house price inflation had a momentum of its own. Our impulse response and forecast error variance decomposition analysis reinforce these results. Overall, our findings suggest that housing volume fluctuates more than house prices over the business cycle.

Determinants of Growth in Multiunit Housing Demand since the Great Recession: An Age-Period-Cohort Analysis

Following the Great Recession (2007–2009), growth in multiunit housing starts has been exceptionally strong and sustained. In this study, we examine empirical evidence for three possible explanations, namely, the passage of Baby Boomers into senior years, the depressed economic conditions, and rising preference of recent birth cohorts for residing in urban cores. Applying Age-Period-Cohort analysis to census data on multiunit housing occupancy from 1970 to 2010, we find evidence to support the explanations that a sharp increase in demand from Millennials drawn to urban cores and retiring Baby Boomers are contributing to the growth in multiunit housing starts. The results provide weak evidence of a negative relationship between depressed economic conditions and demand for multiunit housing starts. Over the long term, demand for multiunit housing can be expected to moderate as a result of the projected aging of the population.