The FFF is initially used to decompose house prices into a nonlinear long-term component and a transitory or short-run component. We use this decomposition to examine the relation between house prices and macroeconomic variables across the different time scales. We identify long-run relationships between house prices and income (real disposable income and real GDP). This was not so when both income measures were on a per capita basis. This is consistent with growth in real house prices outpacing growth in income per person. It is also consistent with a non-trivial part of disposable income and GDP growth being due to population growth.
It is worth highlighting that cointegration does not necessarily imply causation. However, our results do indicate that house prices and income (disposable or GDP) move together over the long run. There are economic forces at play that ensure that if house prices and income deviate from their long run relationship, a process of adjustment will occur to restore the long-run equilibrium. This relation, however, does not hold when considering income on a per capita basis. This raises obvious concerns regarding intergenerational wealth inequality and the difficult policy trade-offs involved.
Our results also show that the FFF decomposition reveals cointegrating relations with rent and affordability measures that are undetected when using raw house prices. Removal of short-term (possibly noisy) dynamics may therefore help to better identify long-run relationships between macroeconomic variables. Finally, we show that the short-term component of house prices displays cyclical behaviour that is strongly related to sales volumes but not income (disposable income or real GDP).
We then use the FFF to control for structural breaks in a VAR and VECM between real house prices, the cash rate, real disposable income per capita, housing sales volume, and the unemployment rate. We find that structural breaks are more important for the long-run dynamics (via a VAR in levels or VECM), than the short-run dynamics (via a VAR in first-differences). We also show that allowing for structural breaks can help better identify Granger-causality. Our models with structural breaks identified sales volumes, disposable income per capita and the unemployment rate as influential variables, with cash rates playing a lesser role. In contrast, models that failed to allow for breaks only identified sales as important. Failure to allow for structural breaks can therefore misrepresent important interrelationships between macroeconomic variables. This failure may result in poor decision making and policy outcomes.
Further research will examine the out-of-sample forecast performance of the approach. Given the FFF-VAR and FFF-VECM allow forecasts to revert to a time varying value (that evolves according to structural change), this should improve forecast performance relative to a standard VAR (where forecasts revert to the unconditional mean). Improvements may not be significant over short-term horizons but are more likely to be important for medium to long-term forecasts.
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