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.

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..

In December 2024, the monthly total return index of properties owned by listed Japanese real estate investment trusts (J-REITs) stood at ******* points. The total index return is based on weighted average income returns and capital returns.

United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Diversified REITs Index data was reported at 1,608.960 NA in Apr 2025. This records a decrease from the previous number of 1,661.450 NA for Mar 2025. United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Diversified REITs Index data is updated monthly, averaging 1,488.120 NA from Dec 2012 (Median) to Apr 2025, with 149 observations. The data reached an all-time high of 1,982.860 NA in Oct 2019 and a record low of 1,084.410 NA in Dec 2012. United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Diversified 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.

Index Time Series for MUKAM MAXIS High Yield J-REIT. The frequency of the observation is daily. Moving average series are also typically included. NA

Hospitality REIT Market Outlook

According to our latest research, the global Hospitality REIT market size reached USD 97.6 billion in 2024, reflecting a robust foundation for continued expansion. The market is projected to grow at a CAGR of 7.2% from 2025 to 2033, with the forecasted market size expected to reach USD 183.2 billion by 2033. This impressive growth trajectory is driven by a resurgence in global travel, increased demand for diversified real estate investment vehicles, and the ongoing transformation of the hospitality sector through technology and evolving consumer preferences.

One of the primary growth factors propelling the Hospitality REIT market is the strong rebound in global tourism and business travel post-pandemic. As international borders reopen and travel restrictions ease, there is a marked increase in occupancy rates and average daily rates (ADR) across hotels, resorts, and serviced apartments. This resurgence is attracting both institutional and retail investors seeking stable, income-generating assets within the real estate sector. Furthermore, the hospitality industryÂ’s shift toward asset-light models, where operators focus on management and branding while REITs own the underlying assets, is further augmenting capital inflows into hospitality REITs. The growing preference for professionally managed, diversified portfolios is making Hospitality REITs an attractive choice for investors seeking exposure to the recovering travel and leisure sector.

Another significant driver for the Hospitality REIT market is the diversification of property types and geographic locations within REIT portfolios. Hospitality REITs are increasingly investing in a broad spectrum of assets, from urban business hotels to luxury resorts and extended-stay serviced apartments. This diversification strategy not only mitigates risks associated with market volatility in specific segments or regions but also enhances the potential for stable returns. The inclusion of alternative hospitality assets, such as boutique hotels and experiential resorts, is broadening the appeal of REITs to a wider investor base. Additionally, the integration of sustainability and ESG (Environmental, Social, and Governance) criteria into property selection and management practices is attracting socially conscious investors, further fueling market growth.

Technological advancements and digital transformation within the hospitality industry are also catalyzing the growth of the Hospitality REIT market. The adoption of smart building technologies, digital guest experiences, and data-driven asset management is enhancing operational efficiencies and guest satisfaction, leading to higher occupancy rates and improved profitability for REIT-owned properties. Moreover, the rise of online distribution channels and investment platforms is democratizing access to Hospitality REITs, making it easier for retail investors to participate alongside institutional players. The proliferation of fintech solutions and real estate crowdfunding is expected to further expand the investor base and drive liquidity in the market.

The concept of Hybrid Hospitality is gaining traction as a transformative trend within the hospitality industry. This innovative approach blends traditional hospitality services with flexible workspaces, catering to the evolving needs of modern travelers and remote workers. By integrating co-working spaces, meeting rooms, and leisure facilities, Hybrid Hospitality properties offer a versatile environment that supports both productivity and relaxation. This model is particularly appealing in urban centers where space is at a premium and the demand for multifunctional venues is high. As more travelers seek accommodations that support work-life balance, the Hybrid Hospitality trend is expected to drive significant investment and development opportunities within the Hospitality REIT market.

Regionally, North America continues to dominate the Hospitality REIT market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, with its mature REIT regulatory framework and high concentration of institutional investors, remains the epicenter of hospitality REIT activity. However, emerging markets in Asia Pacific and the Middle East are experiencing rapid growth, fueled by increasing tourism, infrastructure development, and

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.

United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Industrial REITs Index data was reported at 1,129.970 NA in Apr 2025. This records a decrease from the previous number of 1,243.170 NA for Mar 2025. United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Industrial REITs Index data is updated monthly, averaging 1,297.335 NA from Sep 2020 (Median) to Apr 2025, with 56 observations. The data reached an all-time high of 1,701.240 NA in Dec 2021 and a record low of 1,001.710 NA in Oct 2020. United States NASDAQ: Index: Net Total Return: NASDAQ US Benchmark Industrial 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.

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.

The infrastructure real estate investment trust (REIT) Prologis was the largest U.S. REIT as of November 2023, with a market cap of almost ** million U.S. dollars. The funds from operation (FFO) of American Tower Corp are estimated to decrease by *** percent. Nevertheless, the specialty REIT Americold Realty Trust Inc. had the highest FFO growth estimate at almost ** percent.

Property unit trust revenue is expected to contract at a compound annual rate of 5.6% over the five years through 2025-26 to £315 million, including an estimated decline of 2% in 2025-26. Regulations under the Markets in Financial Instruments Directive II have inflated costs because of additional tax now charged on research, which has seen the average industry profit drop to 2% in 2025-26. Over recent years, the UK’s property and unit trust sector has seen hefty withdrawals due to the unfavourable regulatory environment and general economic uncertainty. This has prompted many leading firms to wind down their property unit trusts and other open-ended investment funds directly investing in property. Amid the inflationary environment, the base rate environment grew over the two years through 2023-24, prompting prompting investors to shift their demand to alternative investments that offer higher returns — this included cash savings. REITs also became an attractive venture, trading at attractive discounts and weighing on demand for PUTs. Remote working continues to weigh on PUT returns, as softer demand for office space eats away at rental yields. However, PUTs with heavy exposure to the residential market are likely to have outperformed the industry, which has seen rental costs skyrocket amid fierce demand and limited supply. Property unit trust revenue is expected to shrink at a compound annual rate of 1.5% to £308.2 million over the five years through 2030-31. In the short term, economic uncertainty driven by sticky inflation and uncertain trade policy will curb investment and revenue in the property unit trust sector. Yet, adapting investment strategies to include mixed-use developments could cushion this impact by aligning with the evolving demand for hybrid work environments, which has revived demand for efficient and versatile spaces. To strengthen their position, especially against REITs, property unit trusts are moving towards Property Authorised Investment Funds (PAIFs) for better tax efficiency. Managers are also seeking to capitalise on healthy tenant demand for green properties. These spaces ask for higher rental costs and support yields, stemming the drop in revenue over the coming years.

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

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.

CUSTODIAN REIT PLC Stock Forecast, Price & Rating (LON:CREI)

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.

Target Healthcare REIT (THRL): Navigating the Healthcare Real Estate Landscape

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

United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Infrastructure REITs Index data was reported at 937.510 NA in Apr 2025. This records an increase from the previous number of 897.490 NA for Mar 2025. United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Infrastructure REITs Index data is updated monthly, averaging 922.380 NA from Sep 2020 (Median) to Apr 2025, with 56 observations. The data reached an all-time high of 1,276.380 NA in Dec 2021 and a record low of 685.222 NA in Sep 2023. United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Infrastructure 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.

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.

NewRiver REIT (NRRstock) - A Property Portfolio Poised for Growth?

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.

Iron Mountain (IRM) REIT Stock Outlook Shifts Amid Growth Projections

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

Return on capital employed (RoCE) is a measure of profitability and efficiency of a company and is calculated as the ratio of earnings before interest and tax to capital employed. A higher the ratio indicates a more profitable company. Among the real estate investment trusts (REITs) trading on the London Stock Exchange (LSE) with highest market cap as of January 31, 2020, Tritax Big Box Reit PLC ranked first by RoCE. In 2018, the REIT had RoCE of **** percent. A REIT is a company that manages property on behalf of shareholders. Rather than developing new properties or reselling, REITs usually specialize in leasing space, collecting rent and then distributing dividends to shareholders. Mortgage REITs focus on lending money to real estate owners and operators.

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: Total Return: NASDAQ US Benchmark Other Specialty REITs Index data was reported at 4,302.120 NA in Apr 2025. This records an increase from the previous number of 4,111.940 NA for Mar 2025. United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Other Specialty REITs Index data is updated monthly, averaging 2,219.965 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 5,050.890 NA in Nov 2024 and a record low of 1,078.500 NA in Feb 2012. United States NASDAQ: Index: Total Return: NASDAQ US Benchmark Other Specialty 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.