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At the time they occurred, the savings and loan insolvencies were considered the worst financial crisis since the Great Depression. Contrary to what was then believed, and in sharp contrast with 2007–2009, they in fact had little macroeconomic significance. Savings and Loan (S&L) remediation cost between 2 percent and 3 percent of Gross Domestic Product (GDP), whereas the Troubled Asset Relief Program (TARP) and the conservatorships of Fannie and Freddie actually made money for the US Treasury. But the direct cost of government remediation is largely irrelevant in judging macro significance. What matters is the cumulative output loss associated with and plausibly caused by failing financial institutions. I estimate output losses for 1981–1984, 1991–1998, and 2007–2026 (the latter utilizing forecasts and projections along with actual data through 2015) and, for a final comparison, 1929–1941. The losses associated with 2007–2009 have been truly disastrous – in the same order of magnitude as the Great Depression. The S&L failures were, in contrast, inconsequential. Macroeconomists and policy makers should reserve the word crisis for financial disturbances that threaten substantial damage to the real economy, and continue efforts to identify in advance financial institutions which are systemically important (SIFI), and those which are not.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.