Most commonly used measures of housing affordability are essentially short-run indicators that compare current income with house prices or housing costs. Despite the emphasis in the literature on the importance of long-term affordability, researchers have not developed measures of lifetime income because of data constraints. Many developed countries publish annually household income by age of household heads. Using these data for Singapore, the paper presents a methodology to compute lifetime income from predicted annual household earnings over the working life for each birth cohort in the dataset. The lifetime income of Singapore households by three income quantiles sheds new light on widening income gaps. The affordability index, defined as the ratio of lifetime income to house price, reveals informative trends and cycles in housing affordability in both the public and the private sectors. The paper argues that residential property price escalations need to be avoided.

In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of studies document that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality. Contrary to the stationary case, this paper shows for the bivariate VAR(1) system that systematic sampling induces spurious bi-directional Granger causality among the variables if the uni-directional causality runs from a non-stationary series to either a stationary or a non-stationary series. An empirical exercise illustrates the relative usefulness of the results further.