U.S. REITs in the FTSE Nareit All Equity REITs index yielded between *** and ** percent dividend depending on the property type as of November 2023. Home financing REITs had the highest yield of ***** percent, compared to **** percent for all equity REITs. The 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 it included were *** constituents, with more than ** 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 has decreased in 2022..

As of April 2025, Premiere Island Power REIT Corp. had the highest estimated dividend yield at **** percent in the past 12 months. In contrast, AREIT had the lowest estimated dividend yield as of this trading period.

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.

Singapore FTSE: ST: DY: Month End: AS: Real Estate Investment Trusts REIT data was reported at 5.560 Index in Mar 2019. This records a decrease from the previous number of 5.750 Index for Feb 2019. Singapore FTSE: ST: DY: Month End: AS: Real Estate Investment Trusts REIT data is updated monthly, averaging 5.925 Index from Dec 2009 (Median) to Mar 2019, with 112 observations. The data reached an all-time high of 7.180 Index in Dec 2009 and a record low of 4.780 Index in Apr 2013. Singapore FTSE: ST: DY: Month End: AS: Real Estate Investment Trusts REIT data remains active status in CEIC and is reported by FTSE. The data is categorized under Global Database’s Singapore – Table SG.Z007: FTSE: Straits Times (ST): Dividend Yield.

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.

Apple Hospitality REIT: Return on Investment? (APLE)

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

The infrastructure real estate investment trust (REIT) Prologis was the largest U.S. REIT as of November 2024, with a market cap of almost *** billion U.S. dollars. In 2024, the price to funds from operation (P/FFO) ratio of Prologis was estimated at *****, with 2025 witnessing a slight decline to *****. The REITs sector has grown substantially, with the market cap reaching a record high in 2021. After a difficult year of negative returns in 2022, the year-to-date total returns for all property segments returned to positive grounds in 2023.

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.

Property Rental Solution (PRSR): A REIT Worthy of 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

In December 2023, the monthly total return index of properties owned by listed Japanese real estate investment trusts (J-REITs) stood at 3,315.5 points. The total index return is based on weighted average income returns and capital returns.

Fibra Uno Administracion SA de CV was the real estate investment trust (REIT) with the largest market cap in Mexico as of April 11, 2024. The market cap, or the aggregate value of the total outstanding shares of the company, was 6.31 billion U.S. dollars during that period. Fibra Uno Administracion also had the highest revenue and the second-highest yield among all companies in the ranking. Nevertheless, Fibra Mty SAPI de CV had the highest EBITDA margin, with earnings before interest, taxes, depreciation, and amortization amounting to 80.14 percent of the company's revenue. In terms of return on investment (ROI), CFECapital S de RL de CV ranked first.