House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

CEIC calculates House Prices Growth from quarterly House Price Index. Federal Housing Finance Agency provides House Price Index with base January 1991=100.

Key information about House Prices Growth

• Mexico house prices grew 8.8% YoY in Dec 2024, following an increase of 9.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Dec 2024, with an average growth rate of 7.4%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

CEIC calculates House Price Growth from quarterly House Price Index. Federal Mortgage Society provides House Price Index with base 2017=100.

Key information about House Prices Growth

• India house prices grew 2.6% YoY in Sep 2024, following an increase of 6.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2011 to Sep 2024, with an average growth rate of 5.1%.
• House price data reached an all-time high of 30.6% in Mar 2011 and a record low of -11.4% in Sep 2020.

CEIC calculates House Prices Growth from quarterly House Price Index. National Housing Bank provides House Price Index with base 2017-2018=100. House Prices Growth covers Mumbai only. House Prices Growth prior to Q2 2014 is calculated from House Price Index with base 2007=100.

30 Year Mortgage Rate in the United States decreased to 6.35 percent in September 11 from 6.50 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

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.

Interest Rates, High Prices, and Inventory Shortage to Slow Down Housing Market

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

Graph and download economic data for All-Transactions House Price Index for California (CASTHPI) from Q1 1975 to Q2 2025 about appraisers, CA, HPI, housing, price index, indexes, price, and USA.

Fixed 30-year mortgage rates in the United States averaged 6.49 percent in the week ending September 5 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Existing Home Sales in the United States increased to 4010 Thousand in July from 3930 Thousand in June of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Property Valuation Data Listing offered by BatchData delivers an extensive and detailed dataset designed to provide unparalleled insight into real estate market trends, property values, and investment opportunities. This dataset includes over 9 critical data points that offer a comprehensive view of property valuations across various geographic regions and market conditions. Below is an in-depth description of the data points and their implications for users in the real estate industry.

The Property Valuation Data Listing by BatchData is categorized into four primary sections, each offering detailed insights into different aspects of property valuation. Here’s an in-depth look at each category:

1. Current Valuation AVM Value as of Specific Date: The Automated Valuation Model (AVM) estimate of the property’s current market value, calculated as of a specified date. This value reflects the most recent assessment based on available data. Use Case: Provides an up-to-date valuation, essential for making current investment decisions, setting sale prices, or conducting market analysis. Valuation Confidence Score: A measure indicating the confidence level of the AVM value. This score reflects the reliability of the valuation based on data quality, volume, and model accuracy. Use Case: Helps users gauge the reliability of the valuation estimate. Higher confidence scores suggest more reliable values, while lower scores may indicate uncertainty or data limitations.

2. Valuation Range Price Range Minimum: The lowest estimated market value for the property within the given range. This figure represents the lower bound of the valuation spectrum. Use Case: Useful for understanding the potential minimum value of the property, helping in scenarios like setting a reserve price in auctions or evaluating downside risk. Price Range Maximum: The highest estimated market value for the property within the given range. This figure represents the upper bound of the valuation spectrum. Use Case: Provides insight into the potential maximum value, aiding in price setting, investment analysis, and comparative market assessments. AVM Value Standard Deviation: A statistical measure of the variability or dispersion of the AVM value estimates. It indicates how much the estimated values deviate from the average AVM value. Use Case: Assists in understanding the variability of the valuation and assessing the stability of the estimated value. A higher standard deviation suggests more variability and potential uncertainty.

3. LTV (Loan to Value Ratio) Current Loan to Value Ratio: The ratio of the outstanding loan balance to the current market value of the property, expressed as a percentage. This ratio helps assess the risk associated with the loan relative to the property’s value. Use Case: Crucial for lenders and investors to evaluate the financial risk of a property. A higher LTV ratio indicates higher risk, as the property value is lower compared to the loan amount.

4. Valuation Equity Calculated Total Equity: based upon estimate amortized balances for all open liens and AVM value Use Case: Provides insight into the net worth of the property for the owner. Useful for evaluating the financial health of the property, planning for refinancing, or understanding the owner’s potential gain or loss in case of sale.

This structured breakdown of data points offers a comprehensive view of property valuations, allowing users to make well-informed decisions based on current market conditions, valuation accuracy, financial risk, and equity potential.

This information can be particularly useful for: - Automated Valuation Models (AVMs) - Fuel Risk Management Solutions - Property Valuation Tools - ARV, rental data, building condition and more - Listing/offer Price Determination

The Federal Housing Finance Agency (FHFA) is an independent regulatory agency that is not part of the Department of Housing and Urban Development (HUD).

The FHFA was established by the Housing and Economic Recovery Act of 2008 (HERA) and is responsible for the effective supervision, regulation, and housing mission oversight of Fannie Mae, Freddie Mac (the Enterprises), Common Securitization Solutions, LLC (CSS), and the Federal Home Loan Bank System, which includes the 11 Federal Home Loan Banks (FHLBanks) and the Office of Finance. Since 2008, FHFA has also served as conservator of Fannie Mae and Freddie Mac.

Conforming Loan Limits are mortgage limits set annually (as required by HERA) by the FHFA. In order for a mortgage loan to be eligible to be insured by Freddie Mac or Fannie Mae, the loan amount must be less than the loan limit. Mortgage exceeding the Conforming Loan Limit are referred to as "non-conforming loans" or "jumbo loans." While most counties use a single set of Conforming Loan Limits based on the number of units, high cost of living counties use higher Conforming Loan Limits. The FHFA analyzes year-over-year change in average home prices in October of each year using the Monthly Interest Rate Survey (MIRS) to adjust the Conforming Loan Limits for the upcoming year.

Geospatial data in this feature service uses the Census 2010 County geographies.

To learn more about about the FHFA, please visit:https://www.fhfa.gov/AboutUs



For more information about FHFA Conforming Loan Limits, please visit:https://www.fhfa.gov/DataTools/Downloads/Pages/Conforming-Loan-Limits.aspx, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Date of Coverage: 2022 Data Dictionary:DD_FHFA Conforming Loan Limits

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

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.

Machine Learning stock prediction: HD Stock 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

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.

PEAK Options & Futures 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

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.

Will Walmart (WMT) Stock Rocket?

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.

Will the S&P Bitcoin Index Revolutionize Cryptocurrency?

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 benchmark interest rate in Sweden was last recorded at 2 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.

Case Shiller Home Price Index YoY in the United States decreased to 2.10 percent in June from 2.80 percent in May of 2025. This dataset includes a chart with historical data for the United States Case Shiller Home Price Index YoY.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Starts in the United States decreased to 1307 Thousand units in August from 1429 Thousand units in July of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.