Graph and download economic data for Nasdaq US Mid Cap Retail REITs Index (NASDAQNQUSM35102045) from 2011-06-06 to 2025-08-11 about mid cap, REIT, NASDAQ, market cap, retail, indexes, and USA.

Graph and download economic data for Nasdaq US Benchmark Retail REITs TR Index (NASDAQNQUSB35102045T) from 2011-06-07 to 2025-09-11 about REIT, NASDAQ, retail, indexes, and USA.

Graph and download economic data for Nasdaq US Large Cap Retail REITs Index (NASDAQNQUSL35102045) from 2012-09-24 to 2025-08-14 about large cap, REIT, market cap, NASDAQ, large, retail, indexes, and USA.

REITs in the United States saw an annual total return of **** percent in 2023, according to the FTSE Nareit All Equity REITs index. Nevertheless, in 2022, the index had a negative total return of ** percent. Performance improved for all property types, except for diversified, free standing retail, and infrastructure. FTSE Nareit All Equity REITs index is a free-float adjusted, market capitalization-weighted index of equity REITs in the U.S. In 2023, the index included were 140 constituents, with more than 50 percent of total assets in qualifying real estate assets other than mortgages secured by real property. The number of REITs has remained fairly constant in recent years, but the market cap of the REITs sector has increased notably.

The FTSE Nareit All Equity REITs index is a free-float adjusted, market capitalization-weighted index of equity real estate investment trusts (REITs) in the United States. As of December 2024, the market cap of the index was *** trillion U.S. dollars, up from *** trillion U.S. dollars in December 2021. To be included in the index, the 140 constituents have to have more than ** percent of total assets in qualifying real estate assets other than mortgages secured by real property. Infrastructure, residential, and retail real estate are the largest REIT segments: Retail real estate REITs had a market cap of *** billion U.S. dollars as of December 2024, while industrial had a market cap of almost ***** billion U.S. dollars. The number of REITs has remained fairly constant in recent years, but the market cap has increased notably.

United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Retail REITs Index data was reported at 2,264.290 NA in Apr 2025. This records a decrease from the previous number of 2,341.440 NA for Mar 2025. United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Retail REITs Index data is updated monthly, averaging 1,697.500 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 2,472.310 NA in Nov 2024 and a record low of 920.620 NA in Mar 2020. United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Retail REITs Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: NASDAQ: Total Return: Monthly.

United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Retail REITs Index data was reported at 1,907.000 NA in Apr 2025. This records a decrease from the previous number of 1,974.030 NA for Mar 2025. United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Retail REITs Index data is updated monthly, averaging 1,590.620 NA from Dec 2012 (Median) to Apr 2025, with 149 observations. The data reached an all-time high of 2,096.290 NA in Nov 2024 and a record low of 835.530 NA in Mar 2020. United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Retail REITs Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: NASDAQ: Net Total Return: Monthly.

Between April 15, 2019 and April 15, 2020, the best performing property REIT index in the United States was data center, which was up ** percent year-over-year. Meanwhile, the retail and hotel REIT indices suffered due to decreased demand during the COVID-19 pandemic and fell by ** percent and ** percent, respectively.

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.

CQR CHARTER HALL RETAIL REIT

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 Nasdaq US Small Cap Retail REITs TR Index (NASDAQNQUSS35102045T) from 2011-06-07 to 2025-08-12 about small cap, REIT, NASDAQ, market cap, retail, indexes, and USA.

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.

Phillips Edison: A Retail REIT Ready for Growth? (PECO)

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

Ebit Time Series for Link Real Estate Investment Trust. Link Real Estate Investment Trust (Link REIT) is the largest REIT in Asia by many measures including asset value. Managed by Link Asset Management Limited (Link), a leading, independent and fully-integrated real estate investor and manager focusing on the APAC region, Link REIT has been entirely owned by independent investors since its listing in November 2005 as the first REIT in Hong Kong. After initially acquiring a portfolio of shopping centres and car parks in Hong Kong valued at around HK$33 billion at the time of its IPO, Link has grown and diversified the Link REIT's property portfolio. Today, the portfolio includes retail facilities, car parks, offices, and logistics assets which span Hong Kong, Mainland China, Australia, Singapore, and the UK, with a total valuation of around HK$226 billion (As at 31 March 2025). Link aims to further grow and diversify the Link REIT portfolio to continue delivering resilient returns and growth to Unitholders. Link REIT is a constituent of the Hong Kong securities market benchmark Hang Seng Index, as well as a component of the Dow Jones Sustainability Asia Pacific Index, the FTSE4Good Index Series and the Hang Seng Corporate Sustainability Index. Asset management, portfolio management and capital management are three pillars of our management strengths. We are committed to integrating Environment, Social and Governance (ESG) considerations into our strategy and daily operations.

Net-Income Time Series for Link Real Estate Investment Trust. Link Real Estate Investment Trust (Link REIT) is the largest REIT in Asia by many measures including asset value. Managed by Link Asset Management Limited (Link), a leading, independent and fully-integrated real estate investor and manager focusing on the APAC region, Link REIT has been entirely owned by independent investors since its listing in November 2005 as the first REIT in Hong Kong. After initially acquiring a portfolio of shopping centres and car parks in Hong Kong valued at around HK$33 billion at the time of its IPO, Link has grown and diversified the Link REIT's property portfolio. Today, the portfolio includes retail facilities, car parks, offices, and logistics assets which span Hong Kong, Mainland China, Australia, Singapore, and the UK, with a total valuation of around HK$226 billion (As at 31 March 2025). Link aims to further grow and diversify the Link REIT portfolio to continue delivering resilient returns and growth to Unitholders. Link REIT is a constituent of the Hong Kong securities market benchmark Hang Seng Index, as well as a component of the Dow Jones Sustainability Asia Pacific Index, the FTSE4Good Index Series and the Hang Seng Corporate Sustainability Index. Asset management, portfolio management and capital management are three pillars of our management strengths. We are committed to integrating Environment, Social and Governance (ESG) considerations into our strategy and daily operations.

The real estate investment trusts (REITs) market in the United States grew slightly in 2023, after plummeting in 2022. In 2023, the market cap of all REITs, including equity, mortgage, and hybrid, reached **** trillion U.S. dollars. This was a decrease from the **** trillion U.S. dollars recorded in 2021 when the market peaked. REITs are companies which own and operate real estate to generate income. U.S. REIT sector The number of REITs operating in the U.S. has fluctuated over the past 45 years, and in 2023 measured *** firms. The number in operation has slightly fallen from its record high of *** companies in 2015. REITs often specialize in a specific property type, with industrial and retail being the most popular asset types. Global dominance of American REITs The largest ten REITs worldwide were based in the United States in 2024. Prologis, a company specializing in logistics real estate, was the largest REIT globally in terms of market capitalization in that year.

Operating-Profit-Margin Time Series for Link Real Estate Investment Trust. Link Real Estate Investment Trust (Link REIT) is the largest REIT in Asia by many measures including asset value. Managed by Link Asset Management Limited (Link), a leading, independent and fully-integrated real estate investor and manager focusing on the APAC region, Link REIT has been entirely owned by independent investors since its listing in November 2005 as the first REIT in Hong Kong. After initially acquiring a portfolio of shopping centres and car parks in Hong Kong valued at around HK$33 billion at the time of its IPO, Link has grown and diversified the Link REIT's property portfolio. Today, the portfolio includes retail facilities, car parks, offices, and logistics assets which span Hong Kong, Mainland China, Australia, Singapore, and the UK, with a total valuation of around HK$226 billion (As at 31 March 2025). Link aims to further grow and diversify the Link REIT portfolio to continue delivering resilient returns and growth to Unitholders. Link REIT is a constituent of the Hong Kong securities market benchmark Hang Seng Index, as well as a component of the Dow Jones Sustainability Asia Pacific Index, the FTSE4Good Index Series and the Hang Seng Corporate Sustainability Index. Asset management, portfolio management and capital management are three pillars of our management strengths. We are committed to integrating Environment, Social and Governance (ESG) considerations into our strategy and daily operations.

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.

RTL The Necessity Retail REIT Inc. Class A Common Stock

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

Operating-Expenses Time Series for Link Real Estate Investment Trust. Link Real Estate Investment Trust (Link REIT) is the largest REIT in Asia by many measures including asset value. Managed by Link Asset Management Limited (Link), a leading, independent and fully-integrated real estate investor and manager focusing on the APAC region, Link REIT has been entirely owned by independent investors since its listing in November 2005 as the first REIT in Hong Kong. After initially acquiring a portfolio of shopping centres and car parks in Hong Kong valued at around HK$33 billion at the time of its IPO, Link has grown and diversified the Link REIT's property portfolio. Today, the portfolio includes retail facilities, car parks, offices, and logistics assets which span Hong Kong, Mainland China, Australia, Singapore, and the UK, with a total valuation of around HK$226 billion (As at 31 March 2025). Link aims to further grow and diversify the Link REIT portfolio to continue delivering resilient returns and growth to Unitholders. Link REIT is a constituent of the Hong Kong securities market benchmark Hang Seng Index, as well as a component of the Dow Jones Sustainability Asia Pacific Index, the FTSE4Good Index Series and the Hang Seng Corporate Sustainability Index. Asset management, portfolio management and capital management are three pillars of our management strengths. We are committed to integrating Environment, Social and Governance (ESG) considerations into our strategy and daily operations.

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.

RTLPO The Necessity Retail REIT Inc. 7.375% Series C Cumulative Redeemable Preferred Stock

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.

AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment?

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.

Is iShares Core U.S. REIT ETF a Solid Real Estate Investment?

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