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 2.580 % pa in Mar 2026. This records an increase from the previous number of 2.310 % pa for Dec 2025. New Zealand RBNZ Forecast: Offical Cash Rate: 1 Year Ahead data is updated quarterly, averaging 1.530 % pa from Sep 2017 (Median) to Mar 2026, with 35 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.

Real interest rate (%) in New Zealand was reported at --1.7229 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. New Zealand - Real interest rate - actual values, historical data, forecasts and projections were sourced from the World Bank on April of 2026.

Key information about New Zealand Long Term Interest Rate

• New Zealand Government Bond Yield: 10 Years was reported at 4.33 % pa in Feb 2026, compared with 4.61 % pa in the previous month.
• New Zealand Long Term Interest Rate data is updated monthly, available from Jan 1985 to Feb 2026.
• 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.

Mortgage Interest Rate: Flexible data was reported at 5.900 % pa in 27 Mar 2026. This stayed constant from the previous number of 5.900 % pa for 26 Mar 2026. Mortgage Interest Rate: Flexible data is updated daily, averaging 8.500 % pa from Feb 2023 (Median) to 27 Mar 2026, with 1150 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 15 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 increased to 4.25 percent in February from 4.12 percent in January of 2026. This dataset includes a chart with historical data for Deposit Interest Rate in New Zealand.

Lending interest rate (%) in New Zealand was reported at 0 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. New Zealand - Lending interest rate - actual values, historical data, forecasts and projections were sourced from the World Bank on April of 2026.

Key information about New Zealand Short Term Interest Rate

• New Zealand Short Term Interest Rate: Month End: New Zealand: Bank Accepted Bills: 90 Days was reported at 2.45 % pa in Nov 2025, compared with 2.52 % pa in the previous month.
• New Zealand Short Term Interest Rate data is updated monthly, available from Jan 1985 to Nov 2025.
• The data reached an all-time high of 27.40 % pa in Sep 1985 and a record low of 0.25 % pa in Nov 2020.
• Short Term Interest Rate is reported by reported by CEIC Data.

Bank Accepted Bills yields are 11.10 am market mid-rates.


Related information about New Zealand Short Term Interest Rate

• In the latest reports, New Zealand Government Bond Yield: 10 Years was reported at 4.36 % 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.

Deposit interest rate (%) in New Zealand was reported at 5.7308 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. New Zealand - Deposit interest rate - actual values, historical data, forecasts and projections were sourced from the World Bank on April of 2026.

Driven by mortgage lending as their largest revenue stream, New Zealand’s banks have maintained robust revenues despite cooling property prices between 2022-23 and 2023-24. The RBNZ's rate hikes from 0.25% in 2020-21 to an average of 5.48% in 2023-24 boosted mortgage interest income. While foreign investment and tight inventories keep real estate prices afloat, technology upgrades and regulations add cost pressures for major banks, squeezing profit margins. After falling over the three years through 2021-22, resulting from low interest rates and reduced mortgage revenue, banks found growth through 2024-25, with heightened interest rates raising mortgage revenue. However, a drop-off is expected in 2025-26 as rates fall again. Overall, industry revenue has risen at an annualised 9.3% over the past five years and is expected to total $37.5 billion in 2025-26, when revenue is estimated to drop by 18.4%. Beyond mortgages, banks face challenges from shifting agricultural lending and evolving regulations like the Deposit Takers Act and the CoFI legislation. Dairy price volatility, land-use changes and more rigorous oversight have subdued traditional farm loan activity. In response, major banks – ANZ, ASB, BNZ and Westpac – are modernising their operations with machine learning, enhanced cybersecurity, fintech developments and updated banking apps. These initiatives aim to elevate customer engagement and ensure compliance, but also increase costs and complexity. Looking ahead, reduced capital requirements will increase competition from smaller banks, while open banking regulations are likely to spur data-sharing and fintech collaborations. Institutions are accelerating investments in cloud systems and analytics for greater efficiency and resilience. Simultaneously, sustainability commitments grow, driving innovative loan products and improving public trust. Although margins remain tight and interest-rate volatility persists, banks that balance prudent lending with technological and sustainable strategies are poised for a moderate recovery. Industry revenue is projected to climb at an annualised 2.4% through 2030-31, reaching $42.1 billion.

Interest rate spread (lending rate minus deposit rate, %) in New Zealand was reported at --3.2617 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. New Zealand - Interest rate spread (lending rate minus deposit rate, %) - actual values, historical data, forecasts and projections were sourced from the World Bank on April of 2026.

New Zealand Business Outlook Survey: Agricultural: Interest Rates data was reported at 38.100 NA in Oct 2018. This records an increase from the previous number of 22.200 NA for Sep 2018. New Zealand Business Outlook Survey: Agricultural: Interest Rates data is updated monthly, averaging 21.900 NA from Feb 1993 (Median) to Oct 2018, with 284 observations. The data reached an all-time high of 91.900 NA in May 2014 and a record low of -87.300 NA in Dec 2008. New Zealand Business Outlook Survey: Agricultural: Interest Rates data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.S013: Business Outlook Survey.

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.

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: Moderate Growth Expected for the Future

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

Business Outlook Survey: Service: Interest Rates data was reported at 44.500 NA in Jun 2018. This records a decrease from the previous number of 45.100 NA for May 2018. Business Outlook Survey: Service: Interest Rates data is updated monthly, averaging 21.800 NA from Feb 1993 (Median) to Jun 2018, with 280 observations. The data reached an all-time high of 93.000 NA in Feb 2014 and a record low of -82.300 NA in Dec 2008. Business Outlook Survey: Service: Interest Rates data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.S013: Business Outlook Survey.

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.

One New Zealand Becomes Infratil's Next Digital Gem

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 Index Target Price Prediction

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

Business Outlook Survey: All Industries: Interest Rates data was reported at 27.700 NA in Oct 2018. This records an increase from the previous number of 17.200 NA for Sep 2018. Business Outlook Survey: All Industries: Interest Rates data is updated monthly, averaging 23.100 NA from Feb 1993 (Median) to Oct 2018, with 284 observations. The data reached an all-time high of 90.200 NA in Feb 2014 and a record low of -82.000 NA in Dec 2008. Business Outlook Survey: All Industries: Interest Rates data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.S013: Business Outlook Survey.

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.