Euro Area's main stock market index, the EU50, rose to 5377 points on September 2, 2025, gaining 0.16% from the previous session. Over the past month, the index has climbed 2.57% and is up 9.46% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on September of 2025.

United Kingdom Index: Month Average: Actuaries Share: FTSE Utilities data was reported at 7,259.510 30Dec1983=100 in Jul 2018. This records an increase from the previous number of 7,204.865 30Dec1983=100 for Jun 2018. United Kingdom Index: Month Average: Actuaries Share: FTSE Utilities data is updated monthly, averaging 3,873.108 30Dec1983=100 from Jan 1988 (Median) to Jul 2018, with 367 observations. The data reached an all-time high of 9,429.550 30Dec1983=100 in Jul 2016 and a record low of 1,059.314 30Dec1983=100 in Jan 1988. United Kingdom Index: Month Average: Actuaries Share: FTSE Utilities data remains active status in CEIC and is reported by Financial Times. The data is categorized under Global Database’s UK – Table UK.Z001: Financial Times Stock Exchange: Indices.

Index: TSE: 1st Section: MA: Real Estate data was reported at 1,520.779 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 1,559.857 04Jan1968=100 for May 2018. Index: TSE: 1st Section: MA: Real Estate data is updated monthly, averaging 925.960 04Jan1968=100 from Dec 1987 (Median) to Jun 2018, with 367 observations. The data reached an all-time high of 2,363.700 04Jan1968=100 in Dec 1989 and a record low of 402.363 04Jan1968=100 in Apr 2003. Index: TSE: 1st Section: MA: Real Estate data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Brazil Market Expectation: Price Indices: Producer Price Index - Internal Availability (IPA-DI): Long Term: Median data was reported at 3.750 % in 28 Jun 2019. This stayed constant from the previous number of 3.750 % for 27 Jun 2019. Brazil Market Expectation: Price Indices: Producer Price Index - Internal Availability (IPA-DI): Long Term: Median data is updated daily, averaging 4.500 % from Nov 2001 (Median) to 28 Jun 2019, with 4420 observations. The data reached an all-time high of 7.150 % in 19 Feb 2003 and a record low of 2.490 % in 09 Nov 2001. Brazil Market Expectation: Price Indices: Producer Price Index - Internal Availability (IPA-DI): Long Term: Median data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA019: Market Expectation: Price Indices: Producer Price Index - Internal Availability (IPA-DI). Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Aims to measure the evolutionary rhythm of prices practiced at the wholesale level, in intercompany transactions, that is, in wholesale trading operations, which precede retail sales. The survey is conducted from the 1st to the 30th of each month.

AI-Powered Rental Price Index Market Outlook

According to our latest research, the AI-Powered Rental Price Index market size reached USD 1.7 billion in 2024, reflecting the rapid adoption of artificial intelligence technologies in the real estate sector. The market is projected to grow at a robust CAGR of 18.9% from 2025 to 2033, with the forecasted market size anticipated to reach USD 8.5 billion by 2033. This impressive growth trajectory is driven by the increasing demand for data-driven rental pricing solutions, the proliferation of smart property management systems, and the need for real-time market intelligence among property stakeholders.

One of the key growth factors fueling the expansion of the AI-Powered Rental Price Index market is the escalating complexity and dynamism of global rental markets. Traditional pricing models often fail to capture the nuanced shifts in demand and supply, especially in urban and high-growth regions. AI-powered solutions leverage vast datasets, including historical rental data, economic indicators, neighborhood trends, and even social sentiment, to provide highly accurate and adaptive rental price indices. This enables property managers, landlords, and real estate agencies to optimize pricing strategies, reduce vacancy rates, and maximize returns. The ability to harness predictive analytics and machine learning for rental price forecasting is increasingly seen as a competitive differentiator in the industry.

Another significant driver is the digital transformation sweeping through the real estate sector. The integration of AI-powered rental price indices with property management platforms, listing services, and financial analytics tools is streamlining operations and enhancing decision-making. Cloud-based deployment models are making these advanced analytics accessible to a broader range of users, from large real estate agencies to individual landlords. The automation of rental price assessments not only reduces human error but also accelerates the leasing process, providing a seamless experience for both property owners and tenants. Furthermore, the growing emphasis on transparency and fairness in rental pricing is prompting regulatory bodies and public sector organizations to adopt AI-driven solutions for market monitoring and policy formulation.

The surge in urbanization and the proliferation of rental properties, especially in emerging economies, are also contributing to market growth. As cities expand and rental housing becomes a primary option for a growing segment of the population, the need for accurate, real-time rental price indices becomes critical. AI-powered platforms are uniquely positioned to capture hyper-local trends, adjust for seasonality, and factor in external events such as economic shocks or policy changes. This level of granularity and agility is essential for navigating the increasingly competitive and fragmented rental market landscape. Additionally, the COVID-19 pandemic has accelerated the adoption of digital solutions in real estate, further boosting the demand for AI-powered rental price indices.

Regionally, North America currently dominates the AI-Powered Rental Price Index market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, has witnessed widespread adoption of AI-driven property management tools, supported by a mature real estate ecosystem and high digital literacy. Europe is rapidly catching up, driven by regulatory initiatives and a strong focus on data-driven urban planning. The Asia Pacific region is expected to exhibit the highest CAGR over the forecast period, fueled by rapid urbanization, rising investments in proptech startups, and the digitalization of real estate services in countries like China, India, and Australia. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as local governments and private players recognize the value of AI in addressing housing market inefficiencies.

Component Analysis

The AI-Powered Rental Price Index market is segmented by component into Software and Services, each playing a pivotal role in the ecosystem. The software segment comprises AI algorithms, analytics engines, and user interfaces that enable stakeholders to access, interpret, and act on rental price data. These platforms are increasingly incorporating advanced features such as n

The Standardized Precipitation Index (SPI) was generated for certain Environment Canada long-term climate stations in Ontario.

The SPI quantifies the precipitation deficit and surplus for multiple time scales, including:

one month three months six months nine months 12 months 24 months

You can use the SPI to study the impact of dry and wet weather conditions to create comprehensive water management approaches.

The SPI data package is distributed as a Microsoft Access Geodatabase.

This is a legacy dataset that we no longer maintain or support.

The documents referenced in this record may contain URLs (links) that were valid when published, but now link to sites or pages that no longer exist.

Additional Documentation

Standardized Precipitation Index - User Guide (PDF)

Status Completed: production of the data has been completed

Maintenance and Update Frequency
Not planned: there are no plans to update the data

Contact
Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Brazil Market Expectation: Price Indices: National Consumer Price Index (INPC): 1 Year Ahead: Average data was reported at 3.810 % in 28 Jun 2019. This stayed constant from the previous number of 3.810 % for 27 Jun 2019. Brazil Market Expectation: Price Indices: National Consumer Price Index (INPC): 1 Year Ahead: Average data is updated daily, averaging 4.570 % from Aug 2000 (Median) to 28 Jun 2019, with 4736 observations. The data reached an all-time high of 7.400 % in 08 Jan 2003 and a record low of 2.330 % in 05 Jan 2001. Brazil Market Expectation: Price Indices: National Consumer Price Index (INPC): 1 Year Ahead: Average data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA015: Market Expectation: Price Indices: National Consumer Price Index (INPC). Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Is calculated by the Brazilian Institute of Geography and Statistics (IBGE). Consider the price variation in 11 regions: Rio, São Paulo, Belo Horizonte, Brasilia, Porto Alegre, Curitiba, Belém, Fortaleza, Salvador, Recife and Goiânia. It is based on the budget of families with monthly income between one and eight minimum wages.

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.

Kelly Stock Forecast: Time to Tap into a Talent Pool of Growth (KELYA)

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 Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

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.

Cotton Index: The Future of Textile Trade?

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

Japan Index: TSE 1st Section Composite data was reported at 1,667.450 04Jan1968=100 in Nov 2018. This records an increase from the previous number of 1,646.120 04Jan1968=100 for Oct 2018. Japan Index: TSE 1st Section Composite data is updated monthly, averaging 1,133.125 04Jan1968=100 from Feb 1970 (Median) to Nov 2018, with 586 observations. The data reached an all-time high of 2,881.370 04Jan1968=100 in Dec 1989 and a record low of 148.350 04Jan1968=100 in Dec 1970. Japan Index: TSE 1st Section Composite data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

TagX is your trusted partner for stock market and financial data solutions. We specialize in delivering real-time and end-of-day data feeds that power software, trading algorithms, and risk management systems globally. Whether you're a financial institution, hedge fund, or individual investor, our reliable datasets provide essential insights into market trends, historical pricing, and key financial metrics.

TagX is committed to precision and reliability in stock market data. Our comprehensive datasets include critical information such as date, open/close/high/low prices, trading volume, EPS, P/E ratio, dividend yield, and more. Tailor your dataset to match your specific requirements, choosing from a wide range of parameters and coverage options across primary listings on NASDAQ, AMEX, NYSE, and ARCA exchanges.

Key Features of TagX Stock Market Data:

Custom Dataset Requests: Customize your data feed to focus on specific metrics and parameters crucial to your trading strategy.

Extensive Coverage: Access data from reputable exchanges and market participants, ensuring accuracy and completeness in your analyses.

Flexible Pricing Models: Choose pricing structures based on your selected parameters, offering cost-effective solutions tailored to your needs.

Why Choose TagX? Partner with TagX for precise, dependable, and customizable stock market data solutions. Whether you require real-time updates or end-of-day valuations, our datasets are designed to support informed decision-making and enhance your competitive edge in the financial markets. Trust TagX to deliver the data integrity and accuracy essential for maximizing your trading potential.

Brazil Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI): 2 Years Ahead: Median data was reported at 3.800 % in 28 Jun 2019. This stayed constant from the previous number of 3.800 % for 27 Jun 2019. Brazil Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI): 2 Years Ahead: Median data is updated daily, averaging 4.500 % from Nov 2001 (Median) to 28 Jun 2019, with 4426 observations. The data reached an all-time high of 7.000 % in 09 Jan 2003 and a record low of 3.500 % in 08 Apr 2002. Brazil Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI): 2 Years Ahead: Median data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA013: Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI). Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Reflects the price changes of the entire reference month. That is, from the 1st to the 30th of each month. It is made up of the IPA (Wholesale Price Index), Consumer Price Index (IPC) and INCC (National Construction Cost Index), with weights of 60%, 30% and 10%, respectively. The indicator analyzes the price changes of agricultural and industrial raw materials in wholesale and of final goods and services in consumption.

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.

Tarsus (TARS) Eyeing Growth: Is This the Time to Invest?

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

Based on the homogenized daily temperature and precipitation time series in Greece during 1960-2010, 26 climate change indices time series at 56 stations defined by ETCCDI were obtained. All datasets are completed by cooperation between Greece (Laboratory of Atmospheric Physics, Department of Physics, University of Patras) and China (Key Laboratory of Regional Climate-Environment in Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences).The information are as following:Climate change indices time series in Greece during 1960-2010.rar (1.2MB) There is a .xlsx file named ‘Information at 56 stations in Greece’, including four columns (No. Station No. Longitude, and Latitude) There are 1456 climate indexes .csv files(56 stations×26 climate indexes), in each .csv file, there are 2 columns (date and index records).

Graph and download economic data for CBOE NASDAQ 100 Volatility Index (VXNCLS) from 2001-02-02 to 2025-08-28 about VIX, volatility, stock market, and USA.

Climate indices were interpolated from observations and transformed time series at automatic weather stations and precipitation stations in the Netherlands for the period 1991-2020 and for the KNMI'23 scenarios around 2050 and 2100. The interpolated data is projected onto a 1 km by 1 km grid, without corrections for local land cover features such as cities or forests. However, large-scale climatic patterns, such as distance from the sea and elevation, are accounted for in the interpolation. The climate indices include annual and seasonal precipitation, the number of days per year with at least 15 mm or 25 mm of precipitation, and the maximum precipitation deficit, including median values and estimates for a 10-year recurrence of precipitation deficit. Temperature-related climate indices include average minimum and maximum temperatures by season and year, the number of ice days, frost days, warm days, summer days, tropical days, and tropical nights, as well as Cooling Degree Days and Heating Degree Days. Data was compiled by interpolating observations from stations that had a nearly complete set of measurements for the period 1991-2020.

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MYD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.Improvments/Changes from Previous Version The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).

Explore LSEG's Euronext Market Data, including full access to benchmarks and indices, and corporate action and dividend data.