The benchmark interest rate in the United States was last recorded at 4 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.

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.

United States CSI: Expected Interest Rates: Next Yr: Go Down data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 6.000 % for Apr 2018. United States CSI: Expected Interest Rates: Next Yr: Go Down data is updated monthly, averaging 11.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 54.000 % in Jun 1980 and a record low of 3.000 % in May 2014. United States CSI: Expected Interest Rates: Next Yr: Go Down data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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

United States US: Real Interest Rate data was reported at 2.208 % pa in 2016. This records an increase from the previous number of 2.152 % pa for 2015. United States US: Real Interest Rate data is updated yearly, averaging 3.162 % pa from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 8.720 % pa in 1981 and a record low of -1.280 % pa in 1975. United States US: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.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.; ;

30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

United States Interest Rates: 12 Months Expectation: Higher data was reported at 56.100 % in Apr 2025. This records an increase from the previous number of 53.400 % for Mar 2025. United States Interest Rates: 12 Months Expectation: Higher data is updated monthly, averaging 55.200 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 79.900 % in Mar 1989 and a record low of 23.400 % in Oct 2001. United States Interest Rates: 12 Months Expectation: Higher data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H051: Consumer Confidence Index: Interest Rate Expectation. [COVID-19-IMPACT]

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

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

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

CSI: Expected Interest Rates: Next Yr: Don’t Know data was reported at 2.000 % in May 2018. This records an increase from the previous number of 1.000 % for Apr 2018. CSI: Expected Interest Rates: Next Yr: Don’t Know data is updated monthly, averaging 2.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 14.000 % in Feb 1978 and a record low of 0.000 % in Nov 2017. CSI: Expected Interest Rates: Next Yr: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

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

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

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates?

The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set.

You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others.

Acknowledgements

https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-for-banking/True/#ProblemStatement

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Hong Kong HK: Real Interest Rate data was reported at 3.288 % pa in 2016. This records an increase from the previous number of 1.309 % pa for 2015. Hong Kong HK: Real Interest Rate data is updated yearly, averaging 3.551 % pa from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 13.347 % pa in 2000 and a record low of -3.093 % pa in 1992. Hong Kong HK: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.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.; ;

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