Monthly Household Spending Indicator methodology

The Monthly Household Spending Indicator (MHSI) is derived using bank transactions, supermarket transactions and new vehicle sales data. As this data is not designed for statistical purposes, its scope varies from the Australian National Accounts concept of Household Final Consumption Expenditure (HFCE) and the Retail Trade turnover estimates for retail businesses. Coverage adjustments are subsequently made to align the MHSI with the HFCE concept.

HFCE consists of expenditure by Australian resident households on goods and services for own non-commercial use, whether they are offered within the domestic territory or abroad. It is referenced to the period where the value of the good/service is consumed, rather than when it was paid for, in cases where these differ.

The MHSI consumption concept is aligned as closely as possible with the National Accounts' HFCE concept. However, MHSI data sources do not capture spending by Australian residents on goods and services offered outside Australia. The card transactions data received from the banks excludes transactions if they are in a foreign currency and the location of the card terminal is outside Australia. Also, MHSI data sources do not capture expenditure incurred by resident non-profit institutions serving households, which is included in HFCE. 

The bank card transactions data is provided to the ABS by participating banks for statistical production purposes. The data is provided to the ABS following the end of each calendar month. Once all data is received it is validated and collated.

The data is received in different formats and classifications from the participating banks. Some banks provide data at a low level of categorisation, for example at a bank-allocated merchant category level, and others provide categorised data at the Australia and New Zealand Standard Industry Classification (ANZSIC).

Data is received at different frequencies (daily, weekly or monthly). All participating banks provide data at an identifiable state and territory level.

The aggregated bank transactions data does not contain information about individuals or households. Banks transactions data is only accessed by ABS staff required to produce relevant statistics, including the indicator. The ABS is committed to upholding the privacy, confidentiality and security of the information it collects.

The ABS receives weekly transactions data aggregates from selected supermarket chains. Transactions (scanner) data captures point-of-sale purchases from supermarkets and contains detailed information about transactions, dates, quantities, product descriptions, and values of products sold.

The data is received in different formats and classifications from the participating supermarkets and is standardised before being processed for MHSI estimation. Each supermarket provides item-level sales data by week, at varying levels of geography. 

The weekly aggregates received do not contain information about individuals purchasing the products, and are a business-side view of total product sales.

The ABS receives monthly vehicle sales data from FCAI via the VFACTS datasets, and also uses publicly available data on electric vehicle sales from the Electric Vehicle Council (EVC). These datasets capture vehicle sales within the reference month and contain detailed information about the make, class and engine type of the vehicles. FCAI sales aggregates are delivered at the state and part-of-state level of geography (e.g. Victoria metro or Victoria rural).

The monthly aggregates received do not contain information about individuals purchasing the products, and are a business-side view of total product sales.

The indicator is derived from aggregated, de-identified bank card transactions data for resident households, as well as supermarket and new vehicle sales data. The data is provided to the ABS from participating banks, selected supermarkets and the FCAI/EVC respectively. 

The source data does not cover the whole population and in the case of bank data, does not include all payment types. While cash payments, direct transfers outside the banks, cryptocurrency and BPAY transactions are all forms of household spending, these are not currently included due to lack of available data sources or ability to identify such transactions in the current datasets.

For information about benchmarking and other coverage adjustments, see the Estimation sub-section under Transformation of the data.

 Furthermore, the following assumptions have been used in producing the indicator:

1. All transactions represent final consumption expenditure for own non-commercial use. The data includes household transactions that may feed into the production process (for example, a household purchase of seeds to grow vegetables at home). As it is not possible to identify or adjust for these transactions, it is assumed any ‘intermediate consumption’ transactions in the data have no material impact on the final estimates.
2. Non-resident transactions (net expenditure overseas) captured within the bank transaction data have no material impact on the estimates. Lacking any means to identify and adjust non-resident transactions, it is assumed that expenditure patterns between resident and non-residents exhibit similar trends on consumption goods and services.

Outputs are produced for 9 out of the 13 major Classification of Individual Consumption According to Purpose (COICOP) Divisions. Outputs are produced at Australia level and at state and territory level. For more information see Data release.

In addition to normal editing procedures, a number of data transformation steps and adjustments are made to derive the Monthly Household Spending Indicator. These include:

• classification and concordance mapping
• apportioning industry sales totals to individual consumer products
• estimation, including coverage adjustments and benchmarking
• seasonal adjustment.

The bank data has varying classifications for spending categories. To produce aggregate COICOP outputs, a concordance structure was developed to map the transactions data from each participating bank. This maps bank data, by merchant category or ANZSIC class, to the COICOP Division classification and creates a consistent data set for further transformation. 

For retail industry categories, bank data is mapped to the COICOP output classification using a industry-product matrix derived from the 2012/13 Retail and Wholesale Industries survey. This matrix allows industry sales to be apportioned to individual products by using survey-derived weights. 

As the new vehicle sales and supermarket transactions data already contains product data, no separate industry-product mapping is needed to produce COICOP outputs. The supermarket transactions data is used to map bank card spending at supermarkets to the ‘Food’ and ‘Cigarettes and tobacco’ categories.

There are potential areas of both undercoverage and overcoverage in the bank card transactions data. For example, not all Australian resident households are represented within the supplied bank data resulting in under coverage of the population. Additionally, not all payment types are captured in the bank data; MHSI only uses credit and debit card spending. There is also potential overcoverage as individuals may use their personal bank cards for business-related purchases. Instances of over- or undercoverage can introduce bias and variance in the estimates. 

To rectify coverage issues, data-driven adjustments are made to the estimates. A monthly adjustment is made to correct cash spending undercoverage, by combining RBA statistics on ATM withdrawals with RBA survey insights into household payment behaviour to different types of retailers. The factors that are used in the cash adjustment are smoothed using a 13-term Henderson filter to address the month-to-month volatility in the ATM withdrawals data.

An additional layer of quality assurance includes benchmarking to annual state and territory level HFCE, which is done for the primary reasons of consistency with published National Accounts’ HFCE figures and to ensure quality by using the annual HFCE as a quality control total. MHSI estimates are currently benchmarked up to 2023/24 HFCE.

When the next annual benchmark becomes available, the growth rates will be adjusted to meet the benchmark series. This will result in revisions. Revisions from the initial to the benchmarked growth rates can be used to provide an indication of the accuracy of the initial estimates of the MHSI in terms of size (variance) and direction (bias).

The indicator exhibits systematic seasonal and calendar related effects. Producing seasonally adjusted series improves the quality of the output in these ways:

• Interpretability – predictable seasonal effects will be removed to better identify short-term and long-term behavioural trends.
• Relevance – increased suite of statistics available for users.
• Coherence – comparability with existing outputs including retail trade and HFCE.

Seasonally adjusted estimates are produced for:

• Current price national and State/territory spending by COICOP category and by analytical category (goods, services, discretionary and non-discretionary).
• Chain volume measures for total national spending, total State/territory spending, and national spending by COICOP category.

Quarterly seasonally adjusted current price estimates are the sum of the monthly seasonally adjusted series. The quarterly chain volume measures are seasonally adjusted independently of their current price value and price series. 

The bank card transactions, supermarket scanner and motor vehicle sales datasets include monthly data from January 2019. For prior periods, alternative data sources and methods are utilised to produce MHSI estimates. The three approaches are described below. 

Retail Trade turnover

This method is consistent with the approach used to produce MHSI estimates from January 2019 onwards, except that Retail Trade data is used instead of bank card transactions data.

1. Aggregate Retail Trade turnover data to various industry categories.
2. Map the aggregate spending from industry to COICOP via a concordance. For spending at supermarkets, the mapping is based on supermarket scanner ratios that have been previously calculated and applied in the compilation of HFCE. For all other industries, the mapping is based on data collected from the 2012/13 RISWIS.
3. Splice this series onto MHSI levels (which start at January 2019 and are derived from bank transactions), using the MHSI levels prior to the annual benchmarking step.
4. Annually benchmark the resulting series to HFCE state x COICOP levels.

Turnover from Business Activity Statements

This method is fairly consistent with the approach used to produce MHSI estimates from January 2019 onwards, with a couple of notable departures. Firstly, Business Activity Statement (BAS) turnover data is used instead of the bank card transactions data. Secondly, the series are benchmarked to quarterly HFCE in an intermediate step to maintain a consistent narrative between the MHSI and HFCE. 

1. Aggregate ATO monthly national Business Activity Statement (BAS) turnover data by ANZSIC class, and map to COICOP via a concordance.
2. Apply adjustments for outliers.
3. Benchmark the series to quarterly state-level HFCE via Denton-Cholette benchmarking.
4. Splice this series onto MHSI levels (which start at January 2019), using the MHSI levels prior to the annual benchmarking step.
5. Annually benchmark the resulting series to HFCE state x COICOP levels.

Modelling using seasonal factors

This method is used for categories where Retail Trade data is not available, and BAS turnover is not an accurate indicator of household spending.

1. Calculate the 2019 calendar year seasonal factors for the bank-derived MHSI series at the State/territory level.
2. Produce a monthly series from July 2012 to December 2018 comprising solely of these seasonal factors, copied into the respective months across the back series years.
3. Benchmark the series to quarterly HFCE via Denton-Cholette benchmarking.
4. Splice this series onto MHSI levels (which start at January 2019), using the MHSI levels prior to the annual benchmarking step.
5. Annually benchmark the resulting series to HFCE state x COICOP levels.

Current price estimates reflect both price and volume changes, and are published on a monthly basis. However, chain volume estimates, which are published on a quarterly basis (March, June, September and December publications), measure changes in value after the direct effects of price changes have been eliminated, and hence only reflect volume changes. The chain volume measures of the MHSI are annually reweighted chain Laspeyres indexes referenced to current price values in the chosen reference year. The indexes are linked using the one-quarter overlap method, and the reference year is currently 2023–24. Each financial year’s quarterly data in the MHSI chain volume series is based on the prices of the previous financial year, except for those quarters of the current financial year, which are based on the prices of the reference year. Comparability with previous years is achieved by linking (or chaining) the series together to form a continuous time series. 

With each release of the September quarter issue of the MHSI publication, a new base year is introduced, and the reference year is advanced by one year. This means that with the release of the September quarter 2026 issue of the MHSI publication, the chain volume measures for 2025–26 will have 2024–25 (the previous financial year) as their base year rather than 2023–24, and the reference year will be 2024–25. A change in the reference year changes levels but not growth rates for all periods. A change in the base year can result in revisions to growth rates for the last financial year.

Chain volume measures are not generally additive. In other words, component chain volume measures do not, in general, sum to a total in the way original current price components do. This means, for example, that the chain volume estimates for COICOP Divisions will not add to the total for Australia. To minimise the impact of this, the ABS uses the latest base year as the reference year. By adopting this approach, additivity does exist for the quarters following the reference year and non-additivity is relatively small for the quarters in the reference year and those immediately preceding it. Further information on the nature and concepts of chain volume measures is contained in the ABS publication Information Paper: Introduction of Chain Volume Measures in the Australian National Accounts (cat. no. 5248.0).

A deflator seeks to measure price change between two time periods on a like-for-like product basis. A deflator is often a price index, and is used to compile chain volume estimates.

Price indexes sourced from the Consumer Price Index (CPI) are mapped to relevant products at the Input-Output Product Classification (IOPC) level. These price indexes are aggregated to COICOP categories by using current price expenditure data from the Input-Output tables as weights to form MHSI deflators.

MHSI and HFCE deflators comparison

MHSI and HFCE deflators use the same source data – predominantly CPI (for COICOPs in scope for both indicators). However, there are two notable differences in methodology and scope:

• Methodology: MHSI deflators are created by mapping price indices at the IOPC level, while HFCE deflators are mapped at varying COICOP levels.
  • The IOPC system is a lower and more detailed classification system, and multiple products constitute each COICOP division.
• Scope: MHSI deflators exclude expenditure by Australian resident households on goods and services offered overseas. However, this spending is in scope for HFCE deflators.

Coverage

The data from participating banks does not cover all domestic households or all spending across the 9 in-scope COICOP Divisions. While bank card transactions are common for most COICOP Divisions, they do not capture all spending due to the absence of cash and other payment modes. Benchmarking adjusts levels to improve coverage and fill data gaps, while growth rate adjustments correct for changes in cash and card spending behaviour.

Household spending as a measure of economic activity

Due to the varying payment modes, high concentration of non-card bank transaction payments for some COICOP Divisions, and lack of available monthly data sources, a household spending indicator could not be produced for all major COICOP Divisions.

The appropriateness of using available data sources to measure household spending across each COICOP Division was assessed. This resulted in some Divisions being excluded from the Monthly Household Spending Indicator.

COICOPs that are currently excluded from the indicator are:

• Rent and other dwelling services
• Electricity, gas and other fuels
• Communication Services
• Education Services
• Insurance and other financial services.

Revisions

Revisions are a change in the values of published data and may arise due to a variety of reasons.

Revisions may be applied to the Monthly Household Spending Indicator due to:

• revisions to the source data
• refinements to decisions made around the treatment of data anomalies in the series
• implementation of methodological and process improvements
• application of the annual benchmark
• revisions as a standard feature of seasonal adjustment and trend estimation
• application of a new chain volume reference year.

Methodological enhancements

The methods and data sources used will be subject to ongoing review to improve outputs and maintain the relevance of this indicator.

Interpreting monthly changes

Indicator estimates are produced in: 

• current price original, seasonally adjusted and trend terms
• chain volume original and seasonally adjusted terms.

Seasonally adjusted estimates account for trading day impacts and length of month, and remove the time-of-year related regular variation in consumption, so that movements represent meaningful deviations from seasonal patterns.

Rounding

Published dollar levels are converted to millions and rounded to one decimal place. Published percentage changes are calculated on the underlying dollar levels and rounded to one decimal place. Any discrepancies between percentage changes published and percentage changes derived from published dollar levels are due to rounding.

Monthly and quarterly series are produced for total household spending and 9 of the major COICOP Divisions:

• Food
• Alcoholic beverages and tobacco
• Clothing and footwear
• Furnishing and household equipment
• Health
• Transport
• Recreation and culture
• Hotels, cafes and restaurants
• Miscellaneous goods and services (excluding Insurance and Other Financial Services).

Published outputs also include analytical series such as total consumption of goods/services, and discretionary/non-discretionary spending. The table below shows how the COICOP categories are mapped to these particular groups.

Legislative requirements to ensure privacy and secrecy of this data have been adhered to. The source data for MHSI has been aggregated and de-identified by the data providers. Therefore, in accordance with the Census and Statistics Act 1905, the outputs are unlikely to enable the identification of a particular person or organisation.

All personal information is handled in accordance with the Australia Privacy Principles contained in the Privacy Act 1988. For more information, see ABS privacy.

In March 2025 the ABS released an information paper, which provides guidance on using MHSI data: Interpreting the Monthly Household Spending Indicator.

The ABS ceased the Retail Trade publication on 31 July 2025. We will continue to work with partners and stakeholders to further enhance the MHSI and provide users with suitable timely data to support their needs. More Information about this transition can be found on the Cessation of the Retail Business Survey and Retail Trade Publication webpage or through contacting us at household.spending@abs.gov.au.