This data shows the credit card balances on New Zealand credit cards, as well as the average interest rates applied to those balances.

Total advances outstanding | Weighted average interest rate on personal interest bearing advances | Weighted average interest rate effective on all personal advances | |||
---|---|---|---|---|---|

Date | ($m) | (Y/Y% s.a.) | (M/M% s.a.) | (%) | (%)^{1} |

Previous years: | |||||

Dec 2020 | 6,570 | -12.2 | 0.5 | 18.1 | 10.0 |

Dec 2021 | 6,135 | -6.6 | 0.7 | 18.2 | 9.4 |

Monthly: | |||||

Jan 2022 | 5,941 | -6.2 | -0.4 | 18.3 | 9.8 |

Feb 2022 | 5,873 | -6.5 | -1.1 | 18.4 | 10.0 |

Mar 2022 | 5,747 | -6.7 | -0.4 | 18.3 | 9.8 |

Apr 2022 | 5,873 | -6.5 | 0.5 | 18.4 | 9.7 |

May 2022 | 5,930 | -5.3 | 0.9 | 18.4 | 9.7 |

Jun 2022 | 5,936 | -4.5 | 0.6 | 18.4 | 9.7 |

Jul 2022 | 5,862 | -4.4 | 0.3 | 18.4 | 9.7 |

Aug 2022 | 5,917 | 3.9 | 1.5 | 18.6 | 9.9 |

Sep 2022 | 5,973 | 7.8 | 0.9 | 18.6 | 9.8 |

Oct 2022 | 6,041 | 6.0 | 0.1 | 18.6 | 9.7 |

Nov 2022 | 6,285 | 3.7 | 0.1 | 18.6 | 9.6 |

Dec 2022 | 6,282 | 2.3 | -0.7 | - | - |

We sourced data from the Credit Card Survey. The survey covers credit and charge cards.

The survey defines credit and charge cards as those that can be used to make purchases and obtain credit at all merchants accepting branded credit and charge cards in New Zealand and overseas. They are different from store cards with brand logos (for example, GEcreditline, Qcard, Farmers Card) that can only be used at a limited number of merchants. Store cards are not in scope of the survey.

A **credit card** enables a cardholder to access a revolving credit facility. The cardholder can use the card to make transactions up to a pre-arranged limit.

A **charge card** enables a cardholder to access a non-revolving credit facility. Charge cards often do not have an explicit credit limit.

Data are published in millions of New Zealand dollars. The data are both daily averages and ‘as at’ the last day of the month, with interest rate information on both bases.

The data series begins January 1981 and includes the following credit card statistics:

- balances outstanding (table C12)
- advances outstanding
- credit limits
- overdue 90 days
- deposit balances
- interest rates.

Year on year changes are based not on the actual total $million but a monthly series corrected for seasonality and trading day effects.

Monthly.

We publish data 15 working days following the end of the reference month.

The statistics release calendar provides a long-term plan of scheduled releases. We update and release it on the first working day of the month.

View the statistics release calendar

We collect data under Section 36 of the Reserve Bank of New Zealand Act 1989 (the Act).

Read the Reserve Bank of New Zealand Act 1989

We only publish aggregated data. Individual institutional data is confidential.

Provisional data are italicised. Data are deemed provisional when a series is under review. New data, or revised data, are in bold font. This applies to the summary table only and not excel files. Revisions are generally published when the table is next due to be updated and released. Should revisions need to be made more promptly, a note is posted on the website as a ‘special note’.

We also post any major changes in methodology on the website as a special note.

Selected credit card information is published for registered banks in S32.

See Banks: Assets – Loans by product (S32)

Most data series collected in the credit card survey are affected by seasonal factors; for example, national holidays and festivities, the timing of school holidays, and the number of trading days in the month. The existence of such seasonality makes month-to-month comparisons difficult. Seasonal adjustment is the process of estimating and removing the usual seasonal effects from a time series in order to reveal the underlying non-seasonal features.

We seasonally adjust data in the credit card survey using X13-ARIMA.

- We use a pre-defined adjustment model chosen on statistical merit for each series.
- We run an adjustment on the entire series as each new observation becomes available (concurrent seasonal adjustment method).
- Where a series is an aggregation of multiple series, if justified, we take seasonally adjusted values as the sum of each separately adjusted sub-series (indirect adjustment).
- Where seasonality is not significant, we set the seasonally adjusted series equal to the actual values over the indicated time span.
- We review each adjustment model annually.

Transformation: Log form

a. Dec 1993 to Dec 2000:

ARIMA (2,1,0)(0,1,1)12

Regression variables: Constant term, trading day and Easter

b. Jan 2001 onwards:

ARIMA (0,1,2)(0,1,1)12

Regression variables: Constant term, trading day and Easter; additive outlier Dec 2001, transitory change Feb 2008

c. Jan-2009 onwards:

ARIMA (0,1,1)(1,1,0)12

Regression variables: constant term, trading day, leap year and transitory change Mar-2020, additive outlier Apr-2020, additive outlier May-2020, transitory change Aug-2020

Transformation: Log form

ARIMA (2,1,2)(0,1,1)12

Regression variables: Constant term, trading day, additive outliers (Oct 1998, Sep 2003, Sep–Nov 2011), transitory changes (Sep 2003, Jun 2005)

X11 multiplicative model, seasonal 5-term and Henderson 13-term MA

Transformation: Log form

ARIMA (0,1,1)(0,1,1)12

Regression variables: Constant term, trading day, additive outlier (Jul 1997, Sep–Oct 2011)

X11 multiplicative model, seasonal 7-term and Henderson 15-term MA

The direct method of seasonal adjustment is used.

Transformation: Log form

a. Dec-1993 to Dec-2000:

ARIMA (0,1,1)(1,1,0)12

Regression variables: constant term, trading day, Easter additive outlier Mar-1996

b. Jan-2001 to Dec-2008:

ARIMA (0,1,1)(0,1,1)12

Regression variables: constant term, trading day, Easter

c. Jan-2009 onwards:

ARIMA (0,1,1)(1,1,0)12

Regression variables: constant term, trading day, leap year, level shift Mar-2020, additive outlier Apr-2020, additive outlier May-2020

The direct method of seasonal adjustment is used.

Transformation: Log forma. Jan-1981 to Nov-1993:

ARIMA (0,1,1)(0,1,1)12

Regression variables: constant term, trading day, Easter, level shift Mar-1981 and transitory change Apr-1990

b. Dec-1993 to Dec-2000:

ARIMA (0,1,1)(1,1,0)12

Regression variables: constant term, trading day, Easter and additive outlier Mar-1996

c. Jan-2001 to Dec-2008:

ARIMA (0,1,1)(0,1,1)1

Regression variables: constant term, trading day, Easter

d. Jan-2009 onwards:

ARIMA (0,1,1)(1,1,0)12

Regression variables: constant term, trading day, leap year, Easter, level shift Mar-2020, additive outlier Apr-2020, additive outlier May-2020, additive outlier Nov-2020

Transformation: Log form

a. Aug-2000 to Dec-2008:

ARIMA(0,1,0)(0,1,1)

Regression variables: constant term, trading day, leap year, additive outlier Apr-2003

b. Jan-2009 onwards:

ARIMA(0,1,1)(0,1,1)

Regression variables: constant term, Easter, transitory change Mar-2020, additive outlier Apr-2020

a. Jan-1981 to Jan-1985:

No seasonality

b. Feb-1985 to Jun-2001:

Transformation: Log form

ARIMA (2,1,2)(0,1,1)12

Regression variables: constant term, additive outlier Sep-1999, Jan-2001, transitory change Sep-1988, LC Dec-2000

c. Jul-2001 to Dec-2008:

Transformation: Log form

ARIMA (0,1,0)(0,1,1)12

Regression variables: constant term, trading day

d. Jan-2009 onwards:

Transformation: Log form

ARIMA (1,1,1)(0,1,1)12

Regression variables: constant term, trading day, leap year, additive outlier Mar-2020, additive outlier Apr-2020, additive outlier May-2020, additive outlier Jun-2020, additive outlier Jul-2020, additive outlier Aug-2020, level shift Sep-2020

Transformation: Log form

a. Jul-2000 to Dec-2008:

ARIMA (0,1,1)(0,1,1)12

b. Jan-2009 onwards:

ARIMA (0,1,0)(0,1,1)12

Regression variables: constant term, trading day, leap year, additive outlier Jan-2020 level shift Apr-2020

a. Jul-2000 to Dec-2005:

No seasonality

b. Jan-2006 onwards:

Transformation: Log form

ARIMA (0,1,1)(1,1,1)12

Regression variables: constant term, level shift Apr-2020, additive outlier May-2020

The direct method of seasonal adjustment is used.

Transformation: Log form

a. Jul-2000 to Dec-2008:

ARIMA (0,1,1)(0,1,1)12

Regression variables: constant term, additive outlier Dec-2007

b. Jan-2009 onwards:

ARIMA (0,2,1)(1,1,0)12

Regression variables: constant term, level shift Apr-2020, additive outlier May-2020, transitory change Sep-2020

This is the value of advances outstanding (or credit card debt) that is older than 90 days. This time series enables analysis of the ability or willingness of credit card users to repay debt.

This is the amount of money, either on deposit for, or owed in total by, all credit card holders as at the end of each month. This includes both active and inactive accounts, but no closed accounts.

This is the total value of all cardholders' credit limits at the end of the month for credit cards able to be used for spending (‘unblocked' cards). A credit limit is authorised by the issuer for total credit a customer may have outstanding on a card without being in breach of the agreement with the issuer. For some cards this will be zero.

Credit limit utilisation is calculated as the ratio of total advances outstanding (or credit card debt) to total allowable credit limits.

All borrowing outstanding on personal cards, calculated as a daily average for the month, is included here.

Interest-bearing advances outstanding is the amount of credit outstanding on which interest is being charged to the customer, calculated as a daily average.

Non-interest-bearing advances outstanding is the amount of credit outstanding that does not have interest being charged to the customer, calculated as a daily average. This amount corresponds to the `free float' that exists as a result of the `interest free days' arrangements made by issuers for most cards. It is estimated using a formula agreed with issuers. This section also includes any 0% balance transfers offered to customers.

The interest rate charged on the standard, or `classic' card, usually the most commonly held, is reported as a simple average among the nine issuers with such cards.

Where interest is being charged to the customer, the daily weighted average of such rates is reported here.

The calculation of this rate takes as denominator both the daily average balances of credit card balances paying interest and the `free float' on credit and charge cards, with revenue from interest payments as the numerator. The resulting interest rate is called the `effective' interest rate.

These comprise spending by credit card holders and cash advances received during the month concerned, both on cards issued in New Zealand and issued overseas. `Billings' means spending on a card, or `amounts financed' - the monthly flow of total card debits (excluding interest), either by direct purchase or via cash advances. The rates of growth of billings are based on seasonally-adjusted data, which allows for trading day effects.

This is the spending within New Zealand by holders of cards issued in New Zealand and overseas-issued cards.

This total adds New Zealanders' domestic card spending to their card spending overseas (billings overseas).

Symbol or convention | Definition |
---|---|

0 | Zero or value rounded to zero |

- | Not applicable |

.. | Not available |

bold |
Revised/new |

italics |
Provisional |

Light grey background | Historical |

- Individual figures may not sum to the totals due to rounding
- Percentage changes are calculated on unrounded numbers
- You are free to copy, distribute and adapt these statistics subject to the conditions listed on our copyright page.

View other data in the Monetary and lending series.