Retail properties had the highest capitalization rates in the United States in 2023, followed by offices. The cap rate for office real estate was **** percent in the fourth quarter of the year and was forecast to rise further to **** percent in 2024. Cap rates measure the expected rate of return on investment, and show the net operating income of a property as a percentage share of the current asset value. While a higher cap rate indicates a higher rate of return, it also suggests a higher risk. Why have cap rates increased? The increase in cap rates is a consequence of a repricing in the commercial real estate sector. According to the National NCREIF Property Return Index, prices for commercial real estate declined across all property types in 2023. Rental growth was slow during the same period, resulting in a negative annual return. The increase in cap rates reflects the increased risk in the investment environment. Pricing uncertainty in the commercial real estate sector Between 2014 and 2021, commercial property prices in the U.S. enjoyed steady growth. Access to credit with low interest rates facilitated economic growth and real estate investment. As inflation surged in the following two years, lending policy tightened. That had a significant effect on the sector. First, it worsened sentiment among occupiers. Second, it led to a decline in demand for commercial spaces and commercial real estate investment volumes. Uncertainty about the future development of interest rates and occupier demand further contributed to the repricing of real estate assets.

Commercial valuation data collected and maintained by the Cook County Assessor's Office, from 2021 to present. The office uses this data primarily for valuation and reporting. This dataset consolidates the individual Excel workbooks available on the Assessor's website into a single shared format. Properties are valued using similar valuation methods within each model group, per township, per year (in the year the township is reassessed). This dataset has been cleaned minimally, only enough to fit the source Excel workbooks together - because models are updated for each township in the year it is reassessed, users should expect inconsistencies within columns across time and townships. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is property-level. Each 14-digit key PIN represents one commercial property. Commercial properties can and often do encompass multiple PINs. Additional notes: Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time. Data will be updated yearly, once the Assessor has finished mailing first pass values. If users need more up-to-date information they can access it through the Assessor's website. The Assessor's Office reassesses roughly one third of the county (a triad) each year. For commercial valuations, this means each year of data only contain the triad that was reassessed that year. Which triads and their constituent townships have been reassessed recently as well the year of their reassessment can be found in the Assessor's assessment calendar. One KeyPIN is one Commercial Entity. Each KeyPIN (entity) can be comprised of one single PIN (parcel), or multiple PINs as designated in the pins column. Additionally, each KeyPIN might have multiple rows if it is associated with different class codes or model groups. This can occur because many of Cook County's parcels have multiple class codes associated with them if they have multiple uses (such as residential and commercial). Users should not expect this data to be unique by any combination of available columns. Commercial properties are calculated by first determining a property’s use (office, retail, apartments, industrial, etc.), then the property is grouped with similar or like-kind property types. Next, income generated by the property such as rent or incidental income streams like parking or advertising signage is examined. Next, market-level vacancy based on location and property type is examined. In addition, new construction that has not yet been leased is also considered. Finally, expenses such as property taxes, insurance, repair and maintenance costs, property management fees, and service expenditures for professional services are examined. Once a snapshot of a property’s income statement is captured based on market data, a standard valuation metric called a “capitalization rate” to convert income to value is applied. This data was used to produce initial valuations mailed to property owners. It does not incorporate any subsequent changes to a property’s class, characteristics, valuation, or assessed value from appeals.Township codes can be found in the legend of this map. For more information on the sourcing of attached data and the preparation of this datase

The main stock market index of United States, the US500, fell to 6738 points on October 7, 2025, losing 0.04% from the previous session. Over the past month, the index has climbed 3.73% and is up 17.15% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

Other-Current-Liabilities Time Series for PennantPark Floating Rate Capital Ltd. PennantPark Floating Rate Capital Ltd. is a business development company. It seeks to make secondary direct, debt, equity, and loan investments. The fund seeks to invest through floating rate loans in private or thinly traded or small market-cap, public middle market companies. It primarily invests in the United States and to a limited extent non-U.S. companies. The fund typically invests between $2 million and $20 million. The fund also invests in equity securities, such as preferred stock, common stock, warrants or options received in connection with debt investments or through direct investments. It primarily invests between $10 million and $50 million in investments in senior secured loans and mezzanine debt. It seeks to invest in companies not rated by national rating agencies. The companies if rated would be between BB and CCC under the Standard & Poor's system. The fund invests 30% is invested in non-qualifying assets like investments in public companies whose securities are not thinly traded or do not have a market capitalization of less than $250 million, securities of middle-market companies located outside of the United States, high-yield bonds, distressed debt, private equity, securities of public companies that are not thinly traded, and investment companies as defined in the 1940 Act. Under normal conditions, the fund expects atleast 80 percent of its net assets plus any borrowings for investment purposes to be invested in Floating Rate Loans and investments with similar economic characteristics, including cash equivalents invested in money market funds. It expects to represent 65 percent of its portfolio through senior secured loans. In case of floating rate loans, it holds investments for a period of three to ten years.

This is a preliminary version of a new open data asset and will be updated later this year once the Assessor's Office has finished reassessing commercial properties, then once annually. Use accordingly.

Commercial valuation data collected and maintained by the Cook County Assessor's Office, from 2021 to present. The office uses this data primarily for valuation and reporting. This dataset consolidates the individual Excel workbooks available on the Assessor's website into a single shared format. Properties are valued using similar valuation methods within each modelgroup, per township, per year (in the year the township is reassessed). This dataset has been cleaned minimally, only enough to fit the source Excel workbooks together - because models are updated for each township in the year it is reassessed, users should expect inconsistencies within columns across time and townships.

When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded.

This data is property-level. Each 14-digit key PIN represents one commercial property. Commercial properties can and often do encompass multiple PINs. Additional notes: Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time.

Data will be updated yearly, once the Assessor has finished mailing first pass values. If users need more up-to-date information they can access it through the Assessor's website.

The Assessor's Office reassesses roughly one third of the county (a triad) each year. For commercial valuations, this means each year of data only contain the triad that was reassessed that year. Which triads and their constituent townships have been reassessed recently as well the year of their reassessment can be found in the Assessor's assessment calendar.

Commercial properties are calculated by first determining a property’s use (office, retail, apartments, industrial, etc.), then the property is grouped with similar or like-kind property types. Next, income generated by the property such as rent or incidental income streams like parking or advertising signage is examined. Next, market-level vacancy based on location and property type is examined. In addition, new construction that has not yet been leased is also considered. Finally, expenses such as property taxes, insurance, repair and maintenance costs, property management fees, and service expenditures for professional services are examined. Once a snapshot of a property’s income statement is captured based on market data, a standard valuation metric called a “capitalization rate” to convert income to value is applied.

This data was used to produce initial valuations mailed to property owners. It does not incorporate any subsequent changes to a property’s class, characteristics, valuation, or assessed value from appeals.Township codes can be found in the legend of this map.

For more information on the sourcing of attached data and the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub.

Read about the Assessor's 2023 Open Data Refresh.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This table contains consumer prices for electricity and gas. Weighted average monthly prices are published broken down into transport rate, delivery rates and taxes, both including and excluding VAT. These prices are published on a monthly basis. The prices presented in this table were used to compile the CPI up to May 2023. Prices for newly offered contracts were collected. Contract types that are no longer offered, but have been in previous reporting periods, are imputed. The average can therefore diverge from the prices paid for energy contracts by Dutch households. Data available from January 2018 up to May 2023. Status of the figures: The figures are definitive. Changes as of 17 July 2023: This table will no longer be updated. Due to a change in the underlying data and accompanying method for calculcating average energy prices, a new table was created. See paragraph 3. Changes as of 13 February: Average delivery rates are not shown in this table from January 2023 up to May 2023. With the introduction of the price cap, the average energy rates (delivery rates) of fixed and variable energy contracts together remained useful for calculating a development for the CPI. However, as a pricelevel, they are less useful. Average energy prices from January 2023 up to May 2023 are published in a customized table. In this publication, only data concerning new variable contracts are taken into account When will new figures be published? Does not apply.

Palynological, geochemical, and physical records were used to document Holocene paleoceanographic changes in marine sediment core from Dease Strait in the western part of the main axis of the Northwest Passage (core 2005-804-006 PC latitude 68°59.552'N, longitude 106°34.413'W). Quantitative estimates of past sea surface conditions were inferred from the modern analog technique applied to dinoflagellate cyst assemblages. The chronology of core 2005-804-006 PC is based on a combined use of the paleomagnetic secular variation records and the CALS7K.2 time-varying spherical harmonic model of the geomagnetic field. The age-depth model indicates that the core spans the last ~7700 cal years B.P., with a sedimentation rate of 61 cm/ka. The reconstructed sea surface parameters were compared with those from Barrow Strait and Lancaster Sound (cores 2005-804-004 PC and 2004-804-009 PC, respectively), which allowed us to draw a millennial-scale Holocene sea ice history along the main axis of the Northwest Passage (MANWP). Overall, our data are in good agreement with previous studies based on bowhead whale remains. However, dinoflagellate sea surface based reconstructions suggest several new features. The presence of dinoflagellate cysts in the three cores for most of the Holocene indicates that the MANWP was partially ice-free over the last 10,000 years. This suggests that the recent warming observed in the MANWP could be part of the natural climate variability at the millennial time scale, whereas anthropogenic forcing could have accelerated the warming over the past decades. We associate Holocene climate variability in the MANWP with a large-scale atmospheric pattern, such as the Arctic Oscillation, which may have operated since the early Holocene. In addition to a large-scale pattern, more local conditions such as coastal current, tidal effects, or ice cap proximity may have played a role on the regional sea ice cover. These findings highlight the need to further develop regional investigations in the Arctic to provide realistic boundary conditions for climatic simulations.

**🏏 IPL 2025 - Complete Match Dataset (With Ball-by-Ball, Orange Cap & Purple Cap Stats)

UPVOTE would be appreciated!** Got any questions or special requests? Feel free to reach out! 🙌

📌 Context

The Indian Premier League (IPL) has been a festival of cricket ever since its inception in 2008. This professional Twenty20 cricket league, governed by the Board of Control for Cricket in India (BCCI), brings together the best cricketing talent from around the world into ten competitive franchise teams. With its unmatched popularity, IPL is the richest and most-watched cricket league globally, usually held between March and May every year.

The 2025 edition marked the 18th season of the IPL. Starting from March 22, 2025, the tournament concluded with a thrilling final on June 3, 2025, where RCB (Royal Challengers Bangalore) finally lifted their first-ever IPL trophy, defeating PBKS (Punjab Kings) in an exciting clash.

📂 About the Dataset

This dataset provides comprehensive information on IPL 2025, including match details, ball-by-ball action, and top performances. It can be used for analysis, visualisation, fantasy cricket predictions, and more. ## 📁 Files Included ✅ matches.csv Match-level information: teams, toss, result, venue, player of the match, and more. ✅ deliveries.csv Ball-by-ball breakdown: batsman, bowler, runs, extras, wickets, over-by-over progression. ✅ orange_cap.csv Top batting performances: runs, strike rate, matches played, team details. ✅ purple_cap.csv Top bowling performances: wickets taken, economy rate, bowling average, matches.

🔍 What You Can Do With This Dataset 📊 Analyze team & player performances

🧠 Build predictive models for future match outcomes

🎯 Create fantasy cricket insights

📈 Develop data visualizations and dashboards

📚 Explore trends like toss impact, powerplay performance, or death over stats

### 📎 Acknowledgements Data Sources: Google Search, ESPNcricinfo official website

Special thanks to the creators and fans who continue to fuel IPL data exploration!

## 💡 Inspiration for Projects - Predict the winner of a match using machine learning 🧠 - Rank teams based on net run rate progression 📈 - Create a dashboard using Power BI or Tableau 📊 - Analyse Orange & Purple Cap trends 📌

Simulate matches based on past performance 📅

Feel free to fork, explore, and build something exciting with this IPL 2025 dataset. Enjoy the data journey — and don’t forget to upvote if you found it helpful! 🌟

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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.

China A50 - A Window into the Future of Chinese Equities?

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

Canada's main stock market index, the TSX, fell to 30319 points on October 7, 2025, losing 0.70% from the previous session. Over the past month, the index has climbed 4.45% and is up 25.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on October of 2025.