The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In June 2025, global inflation rates and central bank interest rates showed significant variation across major economies. Most economies initiated interest rate cuts from mid-2024 due to declining inflationary pressures. The U.S., UK, and EU central banks followed a consistent pattern of regular rate reductions throughout late 2024. In the first half of 2025, Russia maintained the highest interest rate at 20 percent, while Japan retained the lowest at 0.5 percent. Varied inflation rates across major economies The inflation landscape varies considerably among major economies. China had the lowest inflation rate at 0.1 percent in June 2025. In contrast, Russia maintained a high inflation rate of 9.4 percent. These figures align with broader trends observed in early 2025, where China had the lowest inflation rate among major developed and emerging economies, while Russia's rate remained the highest. Central bank responses and economic indicators Central banks globally implemented aggressive rate hikes throughout 2022-23 to combat inflation. The European Central Bank exemplified this trend, raising rates from 0 percent in January 2022 to 4.5 percent by September 2023. A coordinated shift among major central banks began in mid-2024, with the ECB, Bank of England, and Federal Reserve initiating rate cuts, with forecasts suggesting further cuts through 2025 and 2026.

Graph and download economic data for 10-Year Real Interest Rate (REAINTRATREARAT10Y) from Jan 1982 to Aug 2025 about 10-year, interest rate, interest, real, rate, and USA.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset shows the average interest rates for U.S. Treasury securities for the most recent month compared with the same month of the previous year. The data is broken down by the various marketable and non-marketable securities. The summary page for the data provides links for monthly reports from 2001 through the current year. Average Interest Rates are calculated on the total unmatured interest-bearing debt. The average interest rates for total marketable, total non-marketable and total interest-bearing debt do not include the U.S. Treasury Inflation-Protected Securities.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Average Interest Rates on U.S. Treasury Securities dataset provides average interest rates on U.S. Treasury securities on a monthly basis. Its primary purpose is to show the average interest rate on a variety of marketable and non-marketable Treasury securities. Marketable securities consist of Treasury Bills, Notes, Bonds, Treasury Inflation-Protected Securities (TIPS), Floating Rate Notes (FRNs), and Federal Financing Bank (FFB) securities. Non-marketable securities consist of Domestic Series, Foreign Series, State and Local Government Series (SLGS), U.S. Savings Securities, and Government Account Series (GAS) securities. Marketable securities are negotiable and transferable and may be sold on the secondary market. Non-marketable securities are not negotiable or transferrable and are not sold on the secondary market. This is a useful dataset for investors and bond holders to compare how interest rates on Treasury securities have changed over time.

This table contains 13 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

About the Dataset

The breakeven inflation rate represents a measure of expected inflation derived from 10-Year Treasury Constant Maturity Securities (BC_10YEAR) and 10-Year Treasury Inflation-Indexed Constant Maturity Securities (TC_10YEAR). The latest value implies what market participants expect inflation to be in the next 10 years, on average. Starting with the update on June 21, 2019, the Treasury bond data used in calculating interest rate spreads is obtained directly from the U.S. Treasury Department.

This paper investigates both the effects of domestic monetary policy and external shocks on fundamental macroeconomic variables in six fast growing emerging economies: Brazil, Russia, India, China, South Africa and Turkey—denoted hereafter as BRICS_T. The authors adopt a structural VAR model with a block exogeneity procedure to identify domestic monetary policy shocks and external shocks. Their research reveals that a contractionary monetary policy in most countries appreciates the domestic currency, increases interest rates, effectively controls inflation rates and reduces output. They do not find any evidence of the price, output, exchange rates and trade puzzles that are usually found in VAR studies. Their findings imply that the exchange rate is the main transmission mechanism in BRICS_T economies. The authors also find that that there are inverse J-curves in five of the six fast growing emerging economies and there are deviations from UIP (Uncovered Interest Parity) in response to a contractionary monetary policy in those countries. Moreover, world output shocks are not a dominant source of fluctuations in those economies.

Zimbabwe ZW: Real Interest Rate data was reported at 5.728 % pa in 2016. This records a decrease from the previous number of 7.576 % pa for 2015. Zimbabwe ZW: Real Interest Rate data is updated yearly, averaging 34.675 % pa from Dec 1980 (Median) to 2016, with 32 observations. The data reached an all-time high of 572.936 % pa in 2007 and a record low of 4.257 % pa in 1980. Zimbabwe ZW: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

Inflation Rate in Turkey decreased to 33.52 percent in July from 35.05 percent in June of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

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.

S&P 500: A Bull or a Bear?

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

Nepal NP: Real Interest Rate data was reported at -6.207 % pa in 2010. This records an increase from the previous number of -6.823 % pa for 2009. Nepal NP: Real Interest Rate data is updated yearly, averaging 3.657 % pa from Dec 1975 (Median) to 2010, with 29 observations. The data reached an all-time high of 18.214 % pa in 1977 and a record low of -12.173 % pa in 1975. Nepal NP: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

Analysis of ‘USA Key Economic Indicators’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/calven22/usa-key-macroeconomic-indicators on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Domino’s Pizza, like many other restaurant chains, is getting pinched by higher food costs. The company’s chief executive, Richard Allison, anticipates “unprecedented increases” in the company’s food costs, which could jump by 8-10%. He said that is three to four times what the pizza chain would normally expect in a year.

This leads to the paramount issue of inflation which affects every aspects of the economy, from consumer spending, business investment and employment rates to government programs, tax policies, and interest rates. The recent release of consumer inflation data showed prices rose at the fastest pace since 1982. Inflation forecasting is key in the conduct of monetary policy and can be used in many other ways such as preserving asset values. This dataset is a consolidated macroeconomic official statistics from 1981 to 2021, containing data available in month and quarterly format.

Content

The Core Consumer Price Index (ccpi) measures the changes in the price of goods and services, excluding food and energy due to their volatility. It measures price change from the perspective of the consumer. It is a often used to measure changes in purchasing trends and inflation.

Do note there are some null values in the dataset.

Acknowledgements

All data belongs to the U.S. Bureau of Economic Analysis official release, and are retrieved from FRED, Federal Reserve Bank of St. Louis.

Inspiration

What are some noticeable patterns or seasonality of the economy? What are the current trends of the economy? Which indicators has an effect on Core CPI or vice-versa based on predictive power or influence?

Quarterly data and monthly data can be merged with forward-fill or interpolation methods.

What is the forecast of Core CPI in 2022?

--- Original source retains full ownership of the source dataset ---

Inflation Rate in Japan decreased to 3.10 percent in July from 3.30 percent in June of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}