Methodology

We use a 6-step top-down approach to estimate the luxury car tax (LCT) gap. To identify the theoretical LCT payable in any year, our estimate draws on the:

• motor vehicle registration data
• Vendor Field Analytical and Characterization Technologies System (VFACTS)
• additional internal ATO data.

Step 1: Decode and standardise vehicle data

The Vehicle Identification Numbers (VINs) from registration data are decoded to obtain the correct vehicle information, such as:

• make and model configurations
• fuel consumption.

This ensures the naming conventions are consistent across vehicles and allows us to compare elements of the sales data.

Step 2: Remove LCT-exempt vehicles and LCT from registered vehicle price

We remove registration and transaction data associated with vehicle types not subject to LCT, such as:

• dealer registrations
• emergency and commercial vehicles
• registrations older than 2 years from the time of manufacture or importation.

We then remove the LCT components from the purchase prices to obtain the values of the vehicles (inclusive of GST).

Step 3: Develop vehicle clusters and price intervals

We determine vehicle clusters based on manufacturer, number of cylinders and body type which should result in similarly priced cars. Our key assumption is that pricing is typically driven by vehicle performance and features.

Fuel-efficient and non-fuel-efficient cars have different thresholds beyond which LCT is payable. These can be different by year, so we separate them into clusters by year. This allows us to consistently determine the LCT payable of similar vehicle types.

For each cluster, we derive the probability and representative price of vehicles exceeding the LCT thresholds. To address the representative price being skewed by high-value cars, the price observations of LCT-applicable cars above the LCT thresholds are split into 20 intervals for each cluster. The probability for each price interval as a share of the total price distribution for each cluster is the same.

The representative price within each interval is constructed from the mid-point between the mean and the maximum of the price spread in each interval. Here we are assuming that the actual mean lies between the reported mean and the maximum of the reported prices.

Step 4: Determine LCT payable for each cluster

We obtain the LCT payable for each price interval within a cluster.

To obtain the values of vehicles that are subject to LCT for each interval within a cluster we:

1. Determine the price difference between the representative price in Step 3 and the LCT threshold.
2. Multiply this by its associated probability in the cluster price distribution.
3. Multiply by the quantity sold.

We then remove the GST component from this value and multiply it by the LCT rate of 33%. We get the corresponding LCT payable for each unit sold in each price interval.

Step 5: Calculate total theoretical liability

The total theoretical liability is determined by aggregating the LCT payable for each price interval for all the clusters.

Step 6: Calculate gross gap and net gap

The gross gap is the difference between the theoretical LCT liability and accrued LCT revenue excluding the compliance amounts.

The net gap is the residual gap amount after compliance amounts have been considered in the revenue base. We calculate the unreported amount by excluding non-pursuable debt from the net gap amount.

Summary of the estimation process

Table 2 shows the:

• summary of each step of the estimation process
• results for each year.

Table 2: Summary of estimation process for the luxury car tax gap, 2014–15 to 2019–20

Step	Description	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20
1-5	Theoretical tax liability ($m)	562	695	727	756	724	658
6.1	Less final tax reported ($m)	526	617	684	704	674	644
6.2	Equals final LCT liability not reported ($m)	35	79	43	51	50	14
6.3	Add non-pursuable debt ($m)	3.6	6.4	8.0	13.6	7.2	7.2
6.4	Equals net gap ($m)	39	85	51	65	57	22
6.5	Add compliance outcomes and taxpayer adjustments ($m)	7.6	5.4	8.2	21.0	12.4	6.5
6.6	Equals gross gap ($m)	46	91	59	86	70	28
6.7	Gross gap (%)	8.3	13.0	8.1	11.4	9.6	4.3
6.8	Net gap (%)	6.9	12.2	7.0	8.6	7.9	3.3

Find out more about our overall research methodology, data sources and analysis for creating our tax gap estimates.

Limitations

The following caveats and limitations apply when interpreting the LCT gap estimate:

• All vehicle data is linked by a unique VIN for each vehicle. We match VINs to the information on the specifications of the vehicles on 8 or 9 digits of the VINs rather than the entire 11 digits.
• Resource-intensive data manipulation is required to:  
  • identify the LCT-applicable population by analysing over 1,000 models and makes of cars to determine an estimated purchase price (or range) for each new or imported vehicle
  • determine fuel-efficient LCT vehicles by combining the volume of sales data from VFACTS and registration data
  • link line-by-line registration data to the semi-aggregated VFACTS data.
   
• Due to some data quality issues, some vehicles are categorised as fuel-efficient when they are not. This reduces the potential amount of LCT because fuel-efficient vehicles are subject to a higher threshold.
• Overall, the estimates can be sensitive to the clustering method applied. It contains an element of judgment by the analysts while grouping the cars based on their likeness.
• At this stage we are uncertain on the shadow economy impacts. More work needs to be done to isolate these amounts, if any.

Updates and revisions to previous estimates

Each year we refresh our estimates in line with the annual report. Changes from previously published estimates occur for a variety of reasons, including:

• improvements in methodology
• revisions to data
• additional information becoming available.

Figure 2 displays the gross gap and net gap from our current model compared to the previous estimates.

Figure 2: Comparison of previously reported estimates – LCT gap

This data is presented in Table 3 below.

Table 3: Current and previous luxury car tax gap estimates (percentage), 2009–10 to 2019–20

Year published	2009–10	2010–11	2011–12	2012–13	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20
2022	n/a	n/a	n/a	n/a	n/a	6.9	12.2	7.0	8.6	7.9	3.3
2021	n/a	n/a	n/a	n/a	8.1	3.4	10.1	5.8	7.8	9.0	n/a
2020	n/a	n/a	n/a	n/a	8.1	3.4	10.1	5.8	7.8	n/a	n/a
2016	3.9	5.8	4.6	5.1	4.7	5.2	n/a	n/a	n/a	n/a	n/a
2015	4.1	4.3	4.1	4.3	3.3	n/a	n/a	n/a	n/a	n/a	n/a
2011	4.9	5.2	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a