Ryan Greenaway-McGrevy

Hot Property in New Zealand: Empirical Evidence of Housing Bubbles in the Metropolitan Centres

Using recently developed statistical methods for testing and dating exuberant behaviour in asset prices we document evidence of episodic bubbles in the New Zealand property market over the past two decades. The results show clear evidence of a broad-based New Zealand housing bubble that began in 2003 and collapsed over mid-2007 to early 2008 with the onset of the worldwide recession and the financial crisis. New methods of analysing market contagion are also developed and are used to examine spillovers from the Auckland property market to the other metropolitan centres. Evidence from the latest data reveals that the greater Auckland metropolitan area is currently experiencing a new property bubble that began in 2013. But there is no evidence yet of any contagion effect of this bubble on the other centres, in contrast to the earlier bubble over 2003–2008 for which there is evidence of transmission of the housing bubble from Auckland to the other centres. One of our primary conclusions is that the expensive nature of New Zealand real estate relative to potential earnings in rents is partly due to the sustained market exuberance that produced the broad-based bubble in house prices during the last decade and that has continued through the most recent bubble experienced in the Auckland region since 2013.

Exchange Rates as Exchange Rate Common Factors

Factor analysis performed on a panel of 23 nominal exchange rates from January 1999 to December 2010 yields three common factors. This paper identifies the euro/dollar, Swiss-franc/dollar and yen/dollar exchange rates as empirical counterparts to these common factors. These empirical factors explain a large proportion of exchange rate variation over time and have significant in-sample and out-of-sample predictive power.

The fall (and rise) of labour share in New Zealand

ABSTRACT The share of national income going to labour in New Zealand fell substantially between the 1970s and the end of the century. Approximately half of this decline was then recovered in the following decade. In this paper, we argue that the decline from the mid-1980s onwards is due to public sector reforms. Corporatisation re-orientated the public trading enterprises away from a broad range of social and trading objectives towards generating profits, while increased fiscal discipline in non-market government departments reduced payroll costs. Consistent with this hypothesis, we show that most of the decline in aggregate labour share from the mid-1980s onwards can be attributed to a significant fall in the labour share of the public sector. To more formally analyse the effects of the reforms, we build a simple model of structural transition. The model yields several predictions that are consistent with observed trends in sectoral labour share. First, there is a large and permanent decline in public sector labour share after the reforms. Second, there is a smaller, short-run decline in private sector labour share that is reversed over the long run. The model can, therefore, explain not only the decline in aggregate labour share from the mid-1980s onwards; it can also explain the partial recovery in labour share beginning in 2002.

IDENTIFYING EXCHANGE RATE COMMON FACTORS: IDENTIFYING EXCHANGE RATE COMMON FACTORS

Using recently developed model selection procedures, we determine that exchange rate returns are driven by a two-factor model. We identify them as a dollar factor and a euro factor. Exchange rates are thus driven by global, US, and Euro-zone stochastic discount factors. The identified factors can also be given a risk-based interpretation. Identification motivates multilateral models for bilateral exchange rates. Out-of-sample forecast accuracy of empirically identified multilateral models dominate the random walk and a bilateral purchasing power parity fundamentals prediction model. 24-month ahead forecast accuracy of the multilateral model dominates those of a principal components forecasting model.

Two Countries, Sixteen Cities, Five Thousand Kilometres: How Many Housing Markets?

We examine whether a single housing market exists across 16 cities covering two countries, Australia and New Zealand. Distances between almost all of these cities are vastly greater than commuting distances. For instance, Perth is over 2,000 kilometres (kms) from its nearest large city neighbour, Adelaide, and is over 5,000kms from the New Zealand cities. If there is a single housing market across these cities, then the economic forces that lead to such convergence must be other than commuting arbitrage forces that have been posited as driving convergence in densely populated countries such as the United Kingdom. We define a single housing market as one in which a single stochastic trend determines the long run path of real house prices in all cities. We adopt a strong and a weak form definition of a single housing market. The strong form occurs when an innovation to the single stochastic trend affects house prices across all cities to an equal degree. The weak form occurs when an innovation to the single stochastic trend affects house prices in all cities, but not to an equal degree. In the strong case, ratios of house prices between all city pairs stay the same in the long run, while in the weak case house price ratios between cities will tend to diverge even though they are affected by the same long run influences. We find that the sixteen housing markets are characterised by the weak form of single housing market. Thus there is a single shared long-run driver of house prices across cities that are over 5,000 kilometres apart spanning two independent countries that are themselves 2,000 kilometres apart. The dynamic structure of adjustment reveals three groups of cities. House price shocks are first reflected in the price dynamics of a leading group of cities all of which are within Australia (including the two largest cities, Melbourne and Sydney), then flow through to a group of follower cities comprising a mix of peripheral Australian and major New Zealand cities, and then to a group of laggard cities all of which are within New Zealand. Our finding of a single housing market implies that macroeconomic policies must either have been convergent across the two countries or they have been incapable of independently controlling long run real house prices, despite the existence of independent monetary and fiscal policies in each country. Our theoretical model illustrates how the weak form of single housing market may arise due to differences across cities in migration responses to house prices and/or land price responses to migration flows.

Evaluating panel data forecasts under independent realization

Independent realization is a commonly used shortcut for deriving forecast properties. It is also an unrealistic assumption in many empirical applications. In this paper we consider the effect of the assumption when deriving the properties of panel data forecasts. To do so, we derive and compare the asymptotic forecast loss of a set of vector autoregressions (VARs) under both independent and same-sample realization. We show that adopting the independent realization assumption can result in an overstatement of forecast loss when common forms of parameterized heterogeneity (such as fixed effects) are included in the VAR. Because these parameters are often used in dynamic panel forecasting applications, our results imply that the independent realization shortcut should only be used with caution when working with panel data.

Efficient Estimation and Inference for Difference-In-Difference Regressions with Persistent Errors

Abstract This paper is concerned with estimation and inference for difference-in-difference regressions with errors that exhibit high serial dependence, including near unit roots, unit roots, and linear trends. We propose a couple of solutions based on a parametric formulation of the error covariance. First stage estimates of autoregressive structures are obtained by using the Han, Phillips, and Sul (2011, 2013) X-differencing transformation. The X-differencing method is simple to implement and is unbiased in large N settings. Compared to similar parametric methods, the approach is computationally simple and requires fewer restrictions on the permissible parameter space of the error process. Simulations suggest that our methods perform well in the finite sample across a wide range of panel dimensions and dependence structures.