Budget super data erroneous - BGL
The Federal Government may have seriously underestimated the impacts of the Federal Budget on the Self Managed Superannuation Funds (SMSF) sector, according to an analysis by BGL Corporate Solutions.
According to the BGL analysis, released today, the Budget may affect as many as 85,000 SMSFs with 160,000 members - many more than suggested in the budget papers.
BGL managing director, Ron Lesh said his organisation had completed an analysis of the proposed budget changes using the company's new Simple Fund 360 Analytical Insights tool — something which allowed it to crunch live big data to test the assertions made by the Government.
One of the primary findings of the BGL analysis is that the proposed $1.6 million pension cap will affect at least 15 per cent of SMSFs (i.e. 85,000 SMSFs with 160,000 members at 30 Sep 15).
"This is a long way from the four cent suggested in the Budget," Lesh said but noted that the Labor Party proposal of $75,000 tax free income for pension accounts was significantly more onerous.
"Based on the average returns of SMSFs from 2010 to 2014, this equates to a cap of $1.1m and will affect at least 24 per cent of SMSFs (i.e. 136,000 SMSFs with 256,000 members at 30 Sep 15)," the BGL analysis said.
It also noted that the proposed lifetime cap of $500,000 on non- concessional contributions, apart from being virtually impossible to accurately calculate, today affected at least 10 per cent of SMSFs (ie 56,000 SMSFs with 106,000 members at 30 Sep 15).
The analysis claimed the reason the non-concessional contributions amount was virtually impossible to accurately calculate was because the data had often not been available or provided to superannuation administrators.
It said BGL was also concerned at the retrospectivity of the proposed changes in circumstances where many people had been planning their retirement for many years based on the rules that existed prior to 3 May 2016.
Lesh said the main difference between the BGL data and the Budget data was that BGL's data represented real world data.
"Like with so many things in our world today, so many people use spreadsheets to predict the effects of many things - but as we see time and time again these spreadsheets are simply wrong".
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