Key information about New Zealand Long Term Interest Rate

• New Zealand Government Bond Yield: 10 Years was reported at 4.36 % pa in Nov 2025, compared with 4.06 % pa in the previous month.
• New Zealand Long Term Interest Rate data is updated monthly, available from Jan 1985 to Nov 2025.
• The data reached an all-time high of 18.90 % pa in Mar 1986 and a record low of 0.51 % pa in Sep 2020.
• Long Term Interest Rate is reported by reported by Reserve Bank of New Zealand.

CEIC calculates monthly Long Term Interest Rate from daily 10 year Government Bond Yield. The Reserve Bank of New Zealand provides 10 year Treasury Bond Yield. Long Term Interest Rate prior to March 1985 is sourced directly from Reserve Bank of New Zealand.. The ten-year benchmark for New Zealand Government bonds with a fixed interest coupon paid semi-annually in arrears. The bonds are redeemable at par on maturity.


Related information about New Zealand Long Term Interest Rate

• In the latest reports, New Zealand Short Term Interest Rate: Month End: New Zealand: Bank Accepted Bills: 90 Days was reported at 2.45 % pa in Nov 2025.
• The cash rate (Policy Rate: Month End: New Zealand: Official Cash Rate) was set at 2.25 % pa in Nov 2025.
• New Zealand Exchange Rate against USD averaged 1.73 (USD/NZD) in Oct 2025.

The benchmark interest rate in New Zealand was last recorded at 2.25 percent. This dataset provides - New Zealand Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

New Zealand RBNZ Forecast: Offical Cash Rate: 1 Year Ahead data was reported at 3.230 % pa in Mar 2025. This records a decrease from the previous number of 3.330 % pa for Dec 2024. New Zealand RBNZ Forecast: Offical Cash Rate: 1 Year Ahead data is updated quarterly, averaging 1.870 % pa from Sep 2017 (Median) to Mar 2025, with 31 observations. The data reached an all-time high of 5.160 % pa in Sep 2023 and a record low of -0.160 % pa in Dec 2020. New Zealand RBNZ Forecast: Offical Cash Rate: 1 Year Ahead data remains active status in CEIC and is reported by Reserve Bank of New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.M004: Cash Rate: Forecast: Reserve Bank of New Zealand.

Actual value and historical data chart for New Zealand Real Interest Rate Percent

Mortgage Interest Rate: Flexible data was reported at 5.800 % pa in 03 Dec 2025. This stayed constant from the previous number of 5.800 % pa for 02 Dec 2025. Mortgage Interest Rate: Flexible data is updated daily, averaging 8.750 % pa from Feb 2023 (Median) to 03 Dec 2025, with 1036 observations. The data reached an all-time high of 8.750 % pa in 31 Jul 2024 and a record low of 5.800 % pa in 03 Dec 2025. Mortgage Interest Rate: Flexible data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under High Frequency Database’s Lending Rates – Table NZ.DL: Mortgage Interest Rate.

Deposit Interest Rate in New Zealand decreased to 3.96 percent in October from 4.14 percent in September of 2025. This dataset includes a chart with historical data for Deposit Interest Rate in New Zealand.

Monthly and long-term New Zealand Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Non-banks and other financial institutions' assets have grown relatively steadily over the past few years, but revenue has fluctuated considerably. Despite the Reserve Bank of New Zealand (RBNZ), or Te Putea Matua, easing loan-to-value ratio (LVR) lending restrictions from June 2023, major banks still grappled with high LVR lending restrictions and tight lending standards. For this reason, households are turning to non-bank lenders for finance. Previously, official cash rates (OCR) were kept low, which curbed non-banks' expansion. Yet, to combat inflation, the RBNZ raised the OCR to a rate not seen since October 2008. Consequently, non-bank lenders were able to expand their loan portfolios by increasing their interest expenses and capitalising on higher net interest margins. Revenue is expected to rise at an annualised 14.8% to $1.76 billion over the five years through 2025-26. As interest rates have started to drop since August 2024, non-bank lenders have faced renewed pressure on their profit margins, as lower rates tend to compress the spread between lending and funding costs. For this reason, revenue is expected to drop by 1.8% in 2025-26. Additional competition in the industry, brought on by the arrival of fintech powerhouses like Revolut, has constrained profit margins. Larger non-banks and financiers have used acquisitions as a means to grow their market shares. For example, UDC Finance agreed to purchase the Bank of Queensland's New Zealand assets and loan book in February 2024, and MTF acquired Lending People in January 2023. As interest rates drop, technology will become increasingly vital in maintaining non-bank financial institutions' profitability and competitive edge. Integrating advanced technologies can streamline services, enhance efficiency, increase scalability and improve the precision of financial procedures, proving essential in preserving robust profit margins. Heightened regulatory capital requirements, which are set to continue, will impact registered banks and will provide non-bank lenders with more opportunities to garner a larger slice of the mortgage market. Overall, revenue is forecast to rise at an annualised 2.8% over the five years through 2030-31 to $2.02 billion.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones New Zealand Index: A Bullish Journey or Bearish Plunge?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NZ Dow Jones Forecast: Gains Projected Amidst Global Uncertainty for Local Market Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ANZ AUSTRALIA AND NEW ZEALAND BANKING GROUP LIMITED

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones New Zealand: A Market on the Rise?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Bank Lending Rate in New Zealand decreased to 9.87 percent in October from 10.18 percent in September of 2025. This dataset provides - New Zealand Base Lending Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interbank Rate in New Zealand increased to 2.46 percent on Monday December 1 from 2.45 in the previous day. This dataset provides - New Zealand Three Month Interbank Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in New Zealand increased to 3 percent in the third quarter of 2025 from 2.70 percent in the second quarter of 2025. This dataset provides - New Zealand Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The yield on New Zealand 10Y Bond Yield rose to 4.38% on December 2, 2025, marking a 0.05 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.27 points and is 0.06 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. New Zealand 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on December of 2025.