Methodology for estimating the luxury car tax gap

Six-step top-down method

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

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

Due to data quality issues in the registrations data, we apply a clustering approach. The clustering method is a statistical approach used to map price information from the registrations data to sales information from VFACTS. Cars are first separated into groups, or 'clusters', based on similar attributes to produce price distributions within each cluster. We then derive the proportions of the price distributions above the LCT thresholds in each cluster. We map these to the number of sales from VFACTS that fall within those clusters. The prices and volumes are subsequently combined and aggregated to produce an overall estimate of the theoretical tax liability (TTL). The more detailed steps are outlined below.

Step 1: Decode and standardise vehicle data

The Vehicle Identification Numbers (VINs) from the registrations data are decoded to obtain the correct vehicle information, such as make/model configurations and fuel consumption.

This ensures the naming conventions are consistent across vehicles and allows us to compare elements of the sales data. The format and information reported in these data sets are different, which requires significant manual review to obtain the best match possible.

Step 2: Remove LCT-exempt vehicles and LCT from vehicle prices

We remove registrations from the data associated with vehicle types not subject to LCT, such as:

• dealer registrations and transfer registrations
• emergency and commercial vehicles
• registrations older than 2 years from the time of manufacture or importation
• utility vehicles and cars part of the Toyota Landcruiser 70 series.

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

Step 3: Develop vehicle clusters and price intervals

We determine vehicle clusters based on the manufacturer, number of cylinders and body type. Our key assumption is that pricing is typically driven by vehicle performance and features. Therefore, the intent of using these separating criteria is to obtain similarly valued cars in each cluster.

Fuel-efficient and non-fuel-efficient cars have different thresholds above which LCT is payable. The LCT thresholds are also different each year. To account for all these factors, the LCT population is split by year, cluster, and whether the car is fuel-efficient or non-fuel-efficient. This allows us to appropriately determine the LCT payable for similar vehicle types.

For each cluster, we derive the representative value of vehicles exceeding the LCT thresholds. To address the issue of the representative price being skewed by high-value cars, the LCT-applicable cars in each cluster are split into 20 intervals.

The representative value of each interval is constructed from the mid-point between the mean and the maximum of the price spread. This assumes that the actual mean lies between the reported mean and the maximum of the reported values.

Step 4: Determine LCT payable for each interval

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

To obtain the appropriate values of vehicles that are subject to LCT for each interval or cluster combination, we:

1. Obtain the quantity sold in each cluster from VFACTS
2. Multiply by the proportion of cars in the cluster that meet the relevant LCT threshold (giving the number of LCT-applicable cars in the cluster)
3. Divide by 20 (the number of intervals in the cluster) to give the number of LCT-applicable cars in each interval
4. Multiply by its taxable component. Using the representative value determined from Step 3, the taxable component is the difference between the representative value and the relevant LCT threshold
5. Remove the GST component by multiplying by 10/11
6. Multiply by the LCT rate of 33% to obtain the LCT payable for all units sold in each price interval.

Step 5: Calculate total theoretical liability

The total theoretical liability is determined by aggregating the LCT payable for all price intervals, in all clusters.

Starting in the 2023 financial year, LCT refund and credit amounts are manually subtracted from the TTL. Since the model cannot distinguish between transactions that are LCT-applicable and those that are not, a manual adjustment is made.

Step 6: Calculate gross gap and net gap

The unreported amount is the difference between the theoretical LCT liability and tax reported.

The net gap is calculated by adding non-pursuable debt to the unreported amount. The gross gap is derived by adding amendments to the net gap.

Summary of the estimation process

Table 2 shows the:

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

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 estimates.

• All vehicle data is mapped by a unique VIN for each vehicle. We match VINs to the information on the specifications of the vehicles based on the first 8 or 9 digits of the VINs rather than the entire 17 digits.
• Resource-intensive data manipulation is required to
  • identify the LCT-applicable population by analysing over 2,000 models in 2022–23 to determine an estimated purchase price (or range) for each new or imported vehicle
  • determine fuel-efficient LCT vehicles by combining the VFACTS and registrations data
  • link transaction-level registrations data to the semi-aggregated VFACTS data.
• Due to some data quality issues, some vehicles may be incorrectly categorised as non-fuel-efficient (or fuel-efficient) or misclassified to a cluster.
• Overall, the estimates can be sensitive to the clustering method applied. There is an element of judgment when grouping the cars based on their likeness.
• At this stage we are uncertain on the shadow economy impacts. More work is required to isolate these amounts.

Updates 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 net gap from our current model compared to the previous estimates.

Figure 2: Comparison of previously published estimates, 2009–10 to 2022–23 – LCT gap

This data is presented in Table 3 below.