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 September of 2025.

Key information about New Zealand Long Term Interest Rate

• New Zealand Government Bond Yield: 10 Years was reported at 4.58 % pa in Feb 2025, compared with 4.55 % pa in the previous month.
• New Zealand Long Term Interest Rate data is updated monthly, available from Jan 1985 to Feb 2025.
• The data reached an all-time high of 18.90 % pa in Mar 1986 and a record low of 0.46 % 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 5.71 % pa in Jun 2023.
• The cash rate (Policy Rate: Month End: New Zealand: Official Cash Rate) was set at 5.50 % pa in Jun 2023.
• New Zealand Exchange Rate against USD averaged 1.63 (USD/NZD) in Jun 2023.

The benchmark interest rate in New Zealand was last recorded at 3 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.

Deposit Interest Rate in New Zealand increased to 4.15 percent in August from 4.14 percent in July of 2025. This dataset includes a chart with historical data for Deposit Interest Rate in New Zealand.

Mortgage Interest Rate: Flexible data was reported at 6.800 % pa in 18 May 2025. This stayed constant from the previous number of 6.800 % pa for 17 May 2025. Mortgage Interest Rate: Flexible data is updated daily, averaging 8.600 % pa from Feb 2023 (Median) to 18 May 2025, with 837 observations. The data reached an all-time high of 8.750 % pa in 31 Jul 2024 and a record low of 6.800 % pa in 18 May 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.DL001: Mortgage Interest Rate.

Bank Lending Rate in New Zealand decreased to 10.92 percent in August from 10.97 percent in July of 2025. This dataset provides - New Zealand Base Lending Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Driven by mortgage lending as their largest revenue stream, New Zealand’s banks maintain robust revenues despite cooling property prices. The RBNZ's rate hikes from 0.25% in 2020 to 4.25% in 2024 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 slow revenue from 2020 to 2022 resulting from low interest rates and reduced mortgage revenue, banks found growth in 2023 and 2024, with heightened interest rates raising mortgage revenue. However, a drop-off is expected in 2025 as rate hikes make themselves felt, the actual volumes of mortgages lower and defaults rise with cost-of-living pressures. Overall, industry revenue has risen at an annualised 1.1% over the past five years and is expected to total $31.1 billion in 2024-25, when revenue will drop by an estimated 29.9%. 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, heightened capital requirements will push large banks to raise equity, while open banking regulations 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 balancing prudent lending with technological and sustainable strategies are poised for a moderate recovery. Industry revenue is projected to climb at an annualised 2.5% through 2029-30, reaching $35.2 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: 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

Prices in New Zealand rose by 1.72 percent in 2020. Central bankers at the Reserve Bank of New Zealand were surely relieved to see the rebound from the dangerously low .29 percent inflation in 2015.

What is inflation?

Inflation is the rise in price levels in an economy. 2 percent inflation means 100 New Zealand dollars will be worth 98 dollars in one year. While the precise inflation target varies, most economists agree that inflation between 2 to 3 percent is optimal for an economy. High inflation can lead to higher unemployment because firms would rather wait and higher workers at the same price using future dollars, making the labor relatively cheaper. However, it affects the trade balance because of the relatively higher purchasing power of foreign currencies.

Other risks of inflation and deflation

Inflation helps a country with higher national debt when the debt is in the local currency, because the country can repay with the future dollars which are relatively cheaper. Deflation, then, helps when debts are in a foreign currency. The main problem with deflation is that investors prefer to hold their money, waiting to invest until it is worth more. This is particularly true of countries like New Zealand, where the lion’s share of employment is in the services sector.

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

Interbank Rate in New Zealand remained unchanged at 2.96 percent on Tuesday September 16. This dataset provides - New Zealand Three Month Interbank Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

MCY MERCURY NZ 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

Inflation Rate in New Zealand increased to 2.70 percent in the second quarter of 2025 from 2.50 percent in the first quarter of 2025. This dataset provides - New Zealand Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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: What's In Store?

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

The yield on New Zealand 10Y Bond Yield eased to 4.28% on September 17, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.17 points, though it remains 0.14 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 September of 2025.