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.

Dollar stores with single tenant net leases in the United States had an average cap rate of **** percent as of the fourth quarter of 2024. That made them the property type with the second-highest cap rate. Conversely, convenience stores had a cap rate of *** percent, the lowest among the property types observed. Triple net leases (NNN) are single tenant leases, where in addition to rent and utilities, the tenant is responsible for the additional property expenses, including taxes, insurance, and maintenance. These leases are common for office, retail, industrial, and logistics properties.

Cap rates in the U.S. multifamily real estate sector have increased significantly since 2021, reflecting a rise in borrowing costs. In 2023, the average multifamily cap rate was **** percent, up **** percent in 2021, when it was at its low. By 2026, the average multifamily cap rate is forecast to decline slightly, to **** percent.

The cap rate for industrial and logistics real estate in the United States grew in 2023, after hitting a record-low in 2023. In the fourth quarter of 2023, the cap rate was **** percent and by the end of 2026, it was forecast to decline to **** percent. Cap rates measure the expected rate of return on investment properties and are calculated by dividing the net operating income of the property by the current asset value. While a higher cap rate indicates a higher rate of return, it is also associated with higher risk, such as declining property values.

In the first quarter of 2022, retail centers in the United States had a higher capitalization rate than shops. Capitalization rate, also referred to as cap rate, shows the ratio of the net operating income towards the property asset value. In the first quarter of 2022, the average cap rate of retail real estate in the U.S. was **** percent.

Vancouver had the lowest cap rate for industrial and logistics real estate across different Canadian markets in 2023. The capitalization rate measures the rate of return on commercial properties and is calculated by dividing the net operating income of a property by its asset value. While a higher rate might promise higher return, it is also an indication of a riskier asset. In the first quarter of the year, the average cap rate for class A and B industrial and logistics properties in Vancouver was 4.4 percent, while in Toronto, it stood at 5.06 percent.

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

As of March 2025, grade A offices in core locations in Bengaluru, India, had a median cap rate of around *** percent. In comparison, the median cap rate of grade A offices in core locations in Taipei, Taiwan, was around *** percent as of March 2025.

Multifamily buildings had some of the lowest cap rates in Canada in the first quarter of 2023. For class A multifamily high rise buildings, investors could expect a capitalization rate of **** percent, while for class AA downtown offices, the cap rate was **** percent. The capitalization rate measures the rate of return on commercial properties and is calculated by dividing the net operating income of a property by its asset value. While a higher rate might promise higher return, it is also an indication of a riskier asset.

The average cap rate for quick service restaurants (QSR) in the U.S. increased between 2022 and 2024. Cap rates show the rate of return investors expect from the investment property. In March 2024, the cap rate was **** percent, up from **** percent in April 2022.

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.

Omega Healthcare's (OHI) Earnings Outlook: Hope or Despair?

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

In 2024, the average cap rate for quick service restaurants (QSR) in the U.S. was **** percentage points lower than the cap rate for single tenant net leases. In the first quarter of the year, Chipotle was the chain with the lowest cap rate, at *** percent.

RLX Technology market cap as of June 27, 2025 is $2.69B. RLX Technology market cap history and chart from 2020 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.

Key information about United States Market Capitalization: % of GDP

• United States Market Capitalization accounted for 155.0 % of its Nominal GDP in Dec 2022, compared with a percentage of 205.0 % in the previous year
• US Market Capitalization: % Nominal GDP is updated yearly, available from Dec 1975 to Dec 2022
• The data reached an all-time high of 205.0 % in Dec 2021 and a record low of 36.7 % in Dec 1978

The average cap rate for single-tenant net lease (STNL) in the U.S. increased between 2022 and 2024. Cap rates show the rate of return investors expect from the investment property. In March 2024, the cap rate was **** percent, up from *** percent in April 2022.

Pacific Current reported AUD570.41M in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Pacific Current | PAC - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Solventum market cap as of June 28, 2025 is $11.46B. Solventum market cap history and chart from 2023 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.

Key information about India Market Capitalization

• India Market Capitalization accounted for 4,388.733 USD bn in Feb 2025, compared with a percentage of 4,899.710 USD bn in the previous month
• India Market Capitalization is updated monthly, available from Jan 1993 to Feb 2025
• The data reached an all-time high of 5,663.221 USD bn in Sep 2024 and a record low of 55.322 USD bn in Apr 1993

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.

Is Small Cap 2000 Set to Soar?

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.

Netcapital (NCPL): Are You Capitalized on the Crowdfunding Craze?

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