Key takeaways
- Finder’s positivity index decreased by 6% in October 2024 to 91.7 points.
- The decrease in savings and positive economic outlook were the main contributors to the decrease in the index.
The decline in the index was primarily due to a drop in savings, with total savings down by 9% and monthly contributions falling by 12%.
Sentiment toward housing improved, with 16% of people now believing it's a good time to buy a property. However, the number of people struggling to pay their home loans rose by 15%.
The percentage of individuals who said they couldn’t manage their finances without a credit card dropped by 6%.
The number of people struggling to pay their home loans fell by 5%.
The number of people planning a holiday in the next 12 months has decreased by 14%, while the use of 'buy-now-pay-later' platforms has risen by 22% over the past six months.
What is the Finder Positivity Index?
Finder's Positivity Index is a monthly measure of Australian consumer sentiment, and a useful way to gauge the financial health of Australian consumers at a glance. It provides a snapshot of how Australians feel about their current capacity to earn a decent income; to spend enough to maintain their standard of living; and to save for the future. The index also provides an insight into where the economy as a whole is going in the short term.
The index is collated from over 50,000 responses to Finder's Consumer Sentiment Tracker since May 2019. It is produced by analysing data collected across five broad categories:
- General economic sentiment (65%)
- Housing (15%)
- Savings (10%)
- Credit card usage (5%)
- Shopping behaviour (5%)
Each category is measured with survey responses on how Australians feel about a recession; their wages; wellbeing; their ability to pay for housing; their living costs; use of buy now pay later; and how much they save per month. The final index is a synthesis of these consumer perspectives into a single index to express current and near-term consumer sentiment. This synthesis involves creating separate indexes for each data point with a baseline of 50, then calculating a weighted sum as a final index.
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