Co-movement seems to be an inherent property of business cycle data. Aggregate co-movement refers to strongly positively correlated macroeconomic aggregates such as output, consumption, investment, hours worked and real wages. On the other hand, business cycle data also flags strong sectoral co-movement, meaning that variables such as output, employment and investment tend to rise and fall together in different sectors of the economy.
It would be natural to argue that these co-movement properties reflect the central role that aggregate shocks play in driving business fluctuations. However, it has proven to be surprisingly difficult to generate both aggregate and sectoral co-movement, even in models where aggregate shocks are the sole drivers of fluctuations. For example, Barro and King (1984) are able to generate aggregate co-movement in their one-sector growth model only when the model economy is hit by contemporaneous shocks to total factor productivity (TFP). Other shocks produce a negative correlation between consumption and hours worked. On the other hand, Christiano and Fitzgerald (1998) show that a two-sector version of the neoclassical model driven by aggregate, contemporaneous TFP shocks lacks the mechanism to generate sectoral co-movement of investment and hours worked.
Underlying the difficulty in generating co-movement between consumption and hours worked is the strong wealth effect in labour supply. Such strong wealth effects are also a feature of news (or expectations) shocks, which typically refer to shifts in agents' beliefs concerning future productivity developments in the economy. In standard neoclassical models of aggregate fluctuations, for example, an increase in perceived future productivity growth induces households to increase their current consumption, including leisure, since they act on an increase in their perceived wealth. A rapidly growing literature has emerged on the role of news shocks in business cycle fluctuations in general and on mechanisms, with or without frictions, that help overcome the strong wealth effect associated with news shocks in particular. The relevant research has produced a number of ideas on how to generate co-movement from news shocks: complementarities between durable and non-durable goods, variable capacity utilization, investment adjustment costs to investment and preferences giving rise to weak wealth effects on labour supply, habit persistence.
A particularly interesting line of thought in this context seeks an answer to the question of the role of news shocks in generating boom-bust cycles in asset prices, and perhaps also in real economic activity, or, if not a boom-bust cycle in real activity, at least a hump-shaped co-movement in output, consumption, investment, hours worked and housing investment as observed during the boom-bust housing price cycles in e.g. the US. However, as also noted by Luisa Lambertini, Caterina Mendecino and Maria Punzi (henceforth, LMP) in their forthcoming Bank of Finland discussion paper ‘Expectations-Driven Cycles in the Housing Market’, modelling endogenous boom-bust cycles in macroeconomics is a major challenge. It is difficult to generate extended periods of sustained house price growth followed by reversals through unanticipated shocks, which generate the strongest responses in the short run and eventually die out.
To motivate their analysis, LMP refer to a potential link between households’ optimism about favourable future house price developments and housing market booms. More generally, recent research has come to emphasize the role of households’ beliefs and various behavioural biases, optimism being one of them, in economic boom-bust cycles. Empirical research has provided evidence that some of these biases have the potential of amplifying asset price and business fluctuations, although it may still be difficult to generate amplified cycles from these behavioural biases in models of business cycle fluctuations of the neoclassical growth model variety, as shown by Jaimovich and Rebelo (2006). LMP further argue that survey evidence gives a significant role not only to news on business conditions and optimistic expectations of future house price developments, but also to future credit conditions, in affecting house price developments.
LMP seek to show that news on a variety of shocks can generate optimism about future house price developments. On the theoretical side, the authors extend the DSGE model of Iacoviello and Neri (2010), which explicitly models the price and quantity side of the housing market, to allow for news shocks originating in different sectors of the economy. Essentially, the model constructed by Iacoviello and Neri combines four elements: i) a multi-sector structure with housing and non-housing goods, ii) nominal rigidities, iii) financial frictions in the household sector (with impatient and patient consumers or households that do and don't accumulate capital) and iv) a rich set of shocks. The ex ante heterogeneity among the households, with the impatient household wanting to borrow against future income to increase current consumption, induces credit flows across the households. Impatient households can borrow only up to a fraction (= Loan-To-Value, LTV) of their housing wealth. The fourth feature of the model is needed to take the model to the data.
LMP show that the news shocks they incorporate in the model have the potential to generate empirically plausible housing market fluctuations accompanied by hump-shaped dynamics in key macroeconomic variables, which, in addition, display co-movement. For the more specific issues of a boom to emerge from news shocks, the authors show that the necessary condition in this context is that agents expect rising house prices, which in turn fuels current housing demand and raises house prices on impact. Because the impatient households borrow against the collateral value of their houses, inflated house prices are coupled with an endogenous increase in household indebtedness. If expectations are not fulfilled, the housing market collapses, with a dramatic drop in both quantities and prices.
LMP also show that anticipated shocks originating in different sectors of the economy can generate housing market boom-bust cycles characterized by co-movement in GDP, consumption, investment, hours worked and real wages. In particular, perceived future changes in both productivity and monetary policy can be a source of empirically plausible swings in house prices. However, the dynamic simulations indicate, interestingly, but perhaps somewhat surprisingly, that expectations only on shocks related to some of the nominal variables (such as the monetary policy rate and (central bank target) inflation) that are not met are likely to have adverse subsequent macroeconomic effects. LMP furthermore investigate the role of credit conditions as a source of housing market fluctuations as well as their role in the transmission of boom-bust cycles generated by expectations of different shocks. A representative household tends to think it is a good time to buy a house when current credit conditions are favourable and when they perceive credit tightening in the future. On the other hand, a lower LTV ratio dampens the boom-bust cycle in household debt, consumption and GDP. The more general proposition that emerges indicates that lower LTV ratios tend to reduce the volatility of these variables in the presence of both innovations and news shocks.
Hence, constraints on leverage seem to be the key to reducing excessive macroeconomic volatility that threatens to increase the fragility of the economy and to expose it to welfare-reducing boom-bust cycles. Important as these results derived by LMP are, further research is needed to gain further understanding on the precise nature of e.g. optimal collateral policy. Also, what is the nature of optimal monetary policy in the face of news shocks? For example, does monetary policy depend on whether or not a perceived shock is realized in the future?