Real Estate Investment Trusts Research Articles

Analysis of the market drivers of carbon emissions for REITs in Hong Kong, China

ABSTRACT The construction industry is the world’s largest consumers of energy and the largest emitters of greenhouse gases. The potential long-term benefits created by the carbon reduction behaviors of enterprises can be perceived by the market as a means of obtaining a higher premium and economic motivation to implement the carbon reduction strategy. This paper analyzes the correlation between carbon emission performance and the financial performance of Hong Kong real estate investment trusts (REITs) to study the economic incentives for carbon emission reduction actions in the construction industry. The results show that (1) operating margin and Tobin’s Q Ratio are positively correlated with carbon emission performance indicators; (2) the greater the amount of carbon emissions, the better the financial performance − the carbon reduction of related REITs does receive the economic incentive of improving financial performance; (3) the reasons for the lack of carbon emission market drive are the different quality information disclosures of different REITs and the lack of public awareness of carbon emission; and (4) it is necessary to promote the driving force of the REIT carbon emission market through the guidance, incentive, and promotion of government and regulatory agencies and the active participation of micro subjects.

Demystifying the Indian Tax Regime for the Financial Services Sector

India has a diversified and rapidly growing financial services sector that provides an impetus to the country’s economic development. The government of India has regularly taken steps to improve the tax and regulatory environment to meet stakeholder expectations. The financial services sector in India includes, inter alia, banks and non-banking finance companies, alternative investment funds (AIFs), infrastructure investment trusts, real estate investment trusts (REITs), mutual funds, securitization trusts and insurance players. India’s focus on and commitment to the financial services sector has also been demonstrated through the launch of its project of creating a world-class financial hub (known as the International Financial Services Centre (IFSC)), which aims to provide a business, tax and regulatory environment that is comparable to other leading international financial centers. Given that any investment/transaction may be incomplete if the relevant tax laws are not taken into account, this article offers an insightful discussion on the prevalent structures in India’s financial services (FS) sector and the direct and indirect tax provisions impacting them.

Enhancing real estate investment trust return forecasts using machine learning

AbstractWe extend the emerging literature on machine learning empirical asset pricing by analyzing a comprehensive set of return prediction factors for real estate investment trusts (REITs). We show that machine learning models are superior to traditional ordinary least squares models and find that REIT investors experience significant economic gains when using machine learning forecasts. In particular, we show that REITs are more predictable than stocks and that their higher predictability is stable over time and across industries.

Financialization beyond partnerships: Real estate investment trusts in elderly care

The financial services sector plays an increasing role in the public management of social services. Yet, in contrast to well-studied arrangements such as public–private partnerships and social impact bonds, the ways in which the public sector drives financialization beyond these partnerships remain underexplored. We study the involvement of the financial industry in providing real estate for public services. Our case involves real estate investment trust (REIT) engagement with elderly care in Flanders, Belgium. We analyzed 68 annual reports by REITs and conducted 43 semi-structured interviews with key stakeholders. We show that the participation of REITs in social services is increasing significantly. Public governance mechanisms are fueling this form of financialization rather than slowing it down. The result is a form of state-led financialization with little critical scrutiny. Points for practitioners As aging populations increase demand for elderly care services and government budgets remain tight, practitioners will face critical decisions about financing and service provision. Public sector managers should recognize the growing role of financial actors, such as REITs, in social services and the influence public sector (in)action can have in driving this shift.

Does size matter? Strategies and outcomes by investor size and type in the single-family rental market in Minneapolis and St. Paul

ABSTRACT A growing body of literature has documented the entry of institutional investors into the single-family rental (SFR) market in the United States since the Great Recession. However, less research has delved into differences between investors, particularly public real estate investment trusts (REITs) and private equity (PE) firms. In this paper, we analyze the single-family rental market in the Minneapolis, Minnesota, area and compare spatial strategies and eviction outcomes of various types of SFR landlords, including national investment firms and non-institutional investor owners. We analyze and compare the spatial strategies of different types of landlords including local landlords categorized by size as well as national investors including REITs, PE, and rent-to-own firms. Through multivariate modeling, we find that even after controlling for other factors, owner type and portfolio size are correlated with eviction rates. Compared to tenants living in homes owned by micro landlords (1 property), we estimate that tenants in REIT- and PE-owned properties were 26% more likely and 17% more likely to receive an eviction filing, respectively. Residents of large, non-institutional investor-owned homes were 9% more likely to receive an eviction filing compared to micro-operator owned units. This research adds to our understanding of the SFR market and how tenant outcomes may differ across various ownership dimensions.

Factors Shaping the Net Asset Value of Saudi Real Estate Investment Trusts

This study investigates the impact of the dividend per unit, loan-to-value ratio (LTV), operating performance, and liquidity on the financial performance of Saudi Real Estate Investment Trusts (REITs), as measured by Net Asset Value (NAV). The study examines a sample of 17 listed Saudi REITs over a five-year period, 2019-2023, resulting in 85 observations. Through multiple regression analysis, the results demonstrate a significant positive relationship between NAV and dividend per unit, operating performance, liquidity, and size as a control variable. In contrast, a negative relationship is found between LTV and NAV. These findings provide important insights for investors and portfolio managers, enhancing their understanding of the key factors that drive the financial performance of Saudi REITs.

Offshore property investment and market performance of South African REITs: a regime-switching CAPM analysis

PurposeThe purpose of this study is to determine whether South African real estate investment trusts (REITs) with significant foreign real estate holdings produce better market performance metrics when compared to REITs with larger domestic holdings. The paper also provides a comprehensive overview of the market performance of South African REITs in the decade following the inception of the REIT regime in 2013.Design/methodology/approachThe authors employ the capital asset pricing model (CAPM), using different estimation techniques to determine the stability of the estimated parameters over time. In addition to the CAPM framework, several basic and advanced portfolio performance metrics are computed to assess the performance of the various REIT portfolios.FindingsThe results show that REITs with significant offshore allocations produce superior market returns than their counterparts. Across most of the risk measures analysed, the foreign-biased REIT portfolios were found to be riskier. On the whole, foreign-biased REITs performed better on a risk-adjusted basis. The results were consistent across the different sample periods and the performance metrics analysed.Practical implicationsThe results suggest that the decision to diversify internationally has implications for the pricing of REITs on stock markets. The differences in the performance metrics for the foreign- and home-biased REIT portfolios also imply an opportunity for investors to further diversify REIT portfolios by holding a mix of home-biased and foreign-biased REITs.Originality/valueThis paper is one of the few to consider the implications of international diversification on stock market performance rather than on more fundamental measures of REIT performance such as the net present value. This study also provides an emerging market (African) perspective to the literature.

Economic policy uncertainty and US sector REITs: what can we learn from spillovers?

PurposeThis study examines the impact of Economic Policy Uncertainty (EPU) on US Real Estate Investment Trust (REIT) sectors from 1994 to 2024, focusing on dynamic spillover effects across varying economic environments.Design/methodology/approachEmploying a time-varying parameter vector autoregression (TVP-VAR) model, the research analyzes sectoral interconnectedness and spillover dynamics, capturing the evolving relationships between REIT subsectors and EPU over time.FindingsThe analysis reveals that US REIT subsectors are highly interconnected, indicating substantial internal spillovers. Retail, regional malls and shopping centres are primary shock transmitters, while self-storage, manufactured homes and industrial REITs act as net receivers, demonstrating resilience. Over the study period, Economic Policy Uncertainty (EPU) generally functions as a net receiver of spillovers from REITs. However, pairwise time-varying analysis shows that EPU transmitted shocks during the economic uncertainties of the 1990s but became a net receiver during the 2008 financial crisis and the COVID-19 pandemic, suggesting that disruptions in REIT markets contributed to policy uncertainties during crises. Additionally, the sectors driving volatility shifted over time: residential and apartment REITs were predominant during the 2008 crisis, while disruptions in the office sector were significant during the pandemic.Practical implicationsPolicymakers ought to design targeted interventions to stabilize the sectors that are primary transmitters of shocks during economic downturns and monitor closely economic indicators and adopt policies to mitigate adverse spillover effects.Originality/valueThis study offers novel insights into the time-varying interconnections between REIT sectors and economic uncertainty, illustrating how these relationships evolve with economic conditions and enhancing understanding of REIT resilience under uncertainty.

The impact of futures trade on the informational efficiency of the U.S. REIT market

This study examines the impact of futures trading on market efficiency and price discovery in the U.S. real estate investment trusts (REITs) market. First, we present inconclusive evidence regarding efficiency improvement in the U.S. REIT spot market following the introduction of futures trading. To investigate the interplay between spot and futures markets, we analyze their respective roles in price discovery and find that, unlike in stock and bond markets, the spot market predominantly exhibits price leadership in the U.S. REITs market, despite the growing market size of futures. We find evidence that the limited role of futures in price discovery is associated with an increase in speculative demand, which outweighs hedging pressure. These findings suggest that policymakers should carefully monitor investor trading motives in the U.S. REITs market and consider revising market regulations to enhance liquidity, ensuring that increased liquidity does not primarily result from heightened speculative demand.

Optimal REIT Portfolio Selection Using Machine Learning

This study examines whether machine learning approaches can effectively solve the portfolio selection and optimization problems related to Real Estate Investment Trusts (REITs)-mixed portfolios. It also investigates the impact of different proportions of Equity-REIT (EREIT) and Mortgage-REIT (MREIT) on portfolio returns. This research collects daily data from 157 EREITs and MREITs, totaling 1,283,800 observations, and construct 126 portfolios in the U.S. market, covering the period from January 1, 2000, to July 31, 2022. Using a genetic algorithm (GA), we optimize REITs combined with stocks, bonds, and gold, and compare GA’s performance with the classical mean-variance (MV) method. To assess robustness, authors also perform sub-period testing to examine portfolio performance under adverse economic conditions. The results show that the GA outperforms the MV method in optimizing REIT-mixed portfolios, especially during periods such as the pre-Global Financial Crisis (GFC) and COVID-19. Portfolios with higher allocations to REITs exhibit superior returns, with EREIT outperforming MREIT in enhancing returns and controlling risk. However, MV shows better results in some low-volatility periods, such as during the GFC. This study advances existing literature by applying machine learning techniques—specifically genetic algorithms—to the optimization of REIT-mixed portfolios. The contributions include demonstrating the effectiveness of GA in achieving better risk-adjusted returns compared to traditional methods, providing practical insights for investors on optimizing their portfolios in volatile markets. Additionally, it highlights opportunities for researchers to further explore the integration of heuristic methods in portfolio management, expanding the understanding of portfolio optimization in the context of REITs. Our findings provide practical implications for investors and portfolio managers, demonstrating the benefits of GA-based optimization in the REIT sector, particularly in turbulent market conditions.

Improving Real Estate Investment Trusts (REITs) time-series prediction accuracy using machine learning and technical analysis indicators

The primary goal of investors who include Real Estate Investment Trusts (REITs) in their portfolios is to achieve better returns while reducing the overall risk of their investments. REITs are entities responsible for owning and managing real estate properties. To achieve greater returns while reducing risk, it is essential to accurately predict future REIT prices. This study explores the predictive capability of five different machine learning algorithms used to predict REIT prices. These algorithms include Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks. Additionally, historical REIT prices are supplemented with Technical Analysis indicators (TAIs) to aid in price predictions. While TA indicators are commonly used in stock market forecasting, their application in the context of REITs has remained relatively unexplored. The study applied these algorithms to predict future prices for 30 REITs from the United States, United Kingdom, and Australia, along with 30 stocks and 30 bonds. After obtaining our price predictions, we employ a Genetic Algorithm (GA) to optimise weights of a diversified portfolio. Our results reveal several key findings: (i) all machine learning algorithms demonstrated low average and standard deviation values in the error rate distributions, outperforming commonly used statistical benchmarks such as Holt’s Linear Trend Method (HLTM), Trigonometric Box-Cox Autoregressive Time Series (TBATS), and Autoregressive Integrated Moving Average (ARIMA); (ii) incorporating Technical Analysis indicators in the ML algorithms resulted in a significant reduction in prediction errors, up to 60% in some cases; and (iii) a multi-asset portfolio constructed using predictions that incorporated Technical Analysis indicators outperformed a portfolio based solely on predictions derived from past prices. Furthermore, this study employed Shapley Value-based techniques, specifically SHAP and SAGE, to analyse the importance of the features used in the analysis. These techniques provided additional evidence of the value added by Technical Analysis indicators in this context.

Analysis of the Influence of Interest Rates on Cash Flow in Real Estate Investment Funds

Interest rates play a significant role in determining financing costs, property values, and rental income, all of which have a direct impact on net cash flows derived from property investments. When interest rates rise, borrowing costs increase, reducing the net cash flow available for distribution to REIT unitholders. Conversely, a decrease in interest rates can increase net cash flow due to lower financing costs. This study aims to analyze the effect of interest rates on cash flow in Real Estate Investment Trusts (REITs) using sensitivity simulation methods, modified duration, and risk measurement using Value at Risk (VaR). The results show that changes in interest rates have a significant impact on REIT cash flow, with an increase in interest rates worsening profitability and a decrease in interest rates having a positive impact. The effect of interest rates shows that REIT investors and managers need to pay attention to dynamics in strategic planning to minimize financial risk and maximize profits.

Property Market Liquidity and Secured or Unsecured Debt

AbstractWe examine whether property market liquidity impacts the choice between secured and unsecured debt. A sample of real estate investment trusts (REITs) allows us to estimate the market liquidity of a REIT’s underlying assets and the debt secured by those assets (or unsecured). Using an instrumental variables approach, we find a positive relationship between a REIT’s property market liquidity and its use of unsecured debt relative to secured debt - when a REIT has greater exposure to more liquid underlying property markets, it is more likely to rely on unsecured debt. We investigate several aspects of this relationship including the debt level, issuances, and property loan-to-value ratio. In each case, we find support for our main result. Likewise, our results are robust to (a) using alternative instruments; (b) controlling for REITs’ unencumbered assets, as well as asset quality and redeployability; (c) controlling for credit market conditions; (d) accounting for real estate market conditions; (e) excluding firms that focus on residential real estate; and (f) adding stock market liquidity. Our study highlights the importance of property market liquidity in the debt structure of REITs.

Explaining REIT returns in emerging economies: A Fama-French approach with foreign investment and political stability

This study examines the applicability of the Fama-French 3-factor model to Real Estate Investment Trusts (REITs) in emerging economies using monthly data from January 2016 to December 2023 for 23 REITs across five emerging markets. A Generalized Method of Moments (GMM) (system) approach assesses the impact of 12 explanatory variables, including traditional factors like market, value, size, and momentum premiums, as well as emerging market-specific factors such as the Morgan Stanley Capital International (MSCI) Emerging Markets Currency Index and Bloomberg Commodity Ex-Agriculture Index. Control variables like political stability, foreign direct investment, and portfolio investment are also included. The results show that value premium, foreign direct investment, portfolio investment, and commodity prices positively influence REIT excess returns, while momentum premium and political instability negatively affect them. These findings highlight the combined importance of traditional and emerging market-specific factors, emphasizing the critical role of stable political conditions for REIT performance. This research contributes valuable insights for investors and policymakers in understanding REIT dynamics in emerging markets.

한국 리츠의 AMC 수수료 결정요인에 관한 연구 : 임대주택리츠를 중심으로

The purpose of this study is two-fold: to examine the determinants of asset management company (AMC) fees for rental housing real estate investment trusts (REITs) in Korea; and to provide basic data for establishing an AMC fee system that can improve the expertise and capacity of AMCs that invest and manage such assets for the continuous growth of the domestic REIT market. In this regard, we obtained the panel data of rental housing REITs from 2019 to 2023 (accounting for approximately half of the domestic REIT market) and conducted an analysis by using the Hausman-Taylor model. The findings showed that the higher the dividend ratio to total assets, the higher the AMC fee ratio to total assets. This indicates that the AMC fees of rental housing REITs is based on the investment and management performance of the REITs. In addition, the business history, the equity ratio, the sales revenue ratio to total assets, and the public institution share ratio of REITs were found to have a positive effect on the AMC fee ratio to total assets, whereas the return on total assets and AMC ownership ratio were found to have a negative effect on this ratio. Based on these results, we proposed policies to promote the activation of the public rental housing REIT market, including measures for ensuring the continuity of REITs and facilitating their transition into multi-asset REITs.

한국 상장 리츠의 주가급락위험 분석 : 산업 간 비교를 중심으로

For general investors, the rapid growth of Korean listed Real Estate Investment Trusts (REITs) has expanded investment opportunities in high-quality real estate assets. However, because of the information asymmetry inherent in real estate and the characteristics of sponsor structures, REITs are susceptible to agency problems, which can lead to stock price crash risk. This study measures the stock price crash risk of Korean listed REITs and empirically analyzes their relative risk level by comparing them with other industries. Korean listed REITs are found to exhibit a higher stock price crash risk than other industries, which is inferred to be related to agency problems. However, during the market expansion period after 2020, the risk gap tends to decrease, suggesting that the emergence of large-scale sponsor REITs and market maturation contribute to crash risk mitigation. Based on these findings, policy recommendations are proposed, such as strengthening internal controls and enhancing managerial transparency, to ensure the REIT industry’s sustainable development.

Analysis of The Financial Performances of Real Estate Investment Trusts Critic Aras and Copras Methods: The Case of Bist

Purpose –It was aimed to analyze the financial performances of Real Estate Investment Trust companies whose stocks have been traded in Borsa Istanbul over the years 2017-2021.Design/methodology/approach –In the research study; the criteria weights of financial data were determined in the analysis employing the CRITIC Method, one of the Multi-Criteria Decision-Making Techniques. ARAS and COPRAS Methods were employed to rank the financial performances of theReal Estate Investment Trust companies.Findings –In the analysis; while the criterion with the highest weight in determining the criterion weights of financial ratios with the CRITIC method was the Gross Operating Profit Margin in 2017, 2018, 2020, and 2021, it was determined as the Net Sales Growth for 2019. While the lowest weighted criterion was the Return on Assets for 2017 and 2021, it was determined as the Return on Equity for 2018, 2019, and 2020. In the ARAS method, the companies with the best financial performance year by year were PAGYO, PAGYO, AVGYO, AVGYO, and TDGYO, respectively; whereas the companies with the worst financial performance were PEGYO, NUGYO, NUGYO, PEGYO, and DGGYO, respectively. In the evaluation of the financial performances of the Real Estate Investment Trust companies employing the COPRAS method, it was determined that the companies with the best financial performance were PAGYO, PAGYO, AVGYO, AVGYO, and TDGYO, respectively; whereas the companies with the worst financial performance were PEGYO, NUGYO, PEGYO, AKFGY,and DGGYO, respectively.Discussion –The results of the study supported the use of Multiple Criteria Decision-Making techniques by financial information users to calculate the financial performance of businesses. Additionally, the research found that the Real Estate Investment Trust companieswith high profitability and low debt ratios also had high financial performance rankings.

Financial analysts’ use of industry specific stock valuation models

PurposeThe purpose of this paper is to explore the industry-specific preferred valuation model utilised by analysts’ in determining a stock’s target price. By understanding analysts’ use of industry-specific valuation models, we can enhance our comprehension of important aspects of value creation in these sectors. Therefore, understanding the industry context is crucial for accurately assessing the value of companies within that industry and selecting the most suitable valuation model.Design/methodology/approachThe method employed in this study is content analysis, examining the output of analysts’ valuation models within 25 Global Industry Classification Standard (GICS) industry groups. I hand-collected 806 equity reports from Capital IQ, selecting the four companies with the largest market capitalization from each of the 25 industry groups.FindingsPrice/Earnings (P/E) emerges as the preferred valuation model in 20 out of the 25 industry groups based on the GICS, with some exceptions. Notably, EV/EBITDA is favoured in the telecom, energy and materials sectors, while the capital goods industry primarily relies on Price/Cash flow (P/CF). In the Real Estate Investment Trusts (REITs) sector, P/AFFO (adjusted funds from operations) is the most commonly employed model. While earnings multiples remain the favoured valuation model for financial analysts, a noticeable shift away from multiperiod valuation models is evident after the first decade of the 21st century.Research limitations/implicationsThe findings can increase our comprehension of the interplay between valuation methodologies, industry characteristics and investment decision-making.Practical implicationsIt establishes a foundation for future research in this field and is anticipated to be of interest to analysts, fund managers and investors. The findings can increase our comprehension of the interplay between valuation methodologies, industry characteristics and investment decision-making.Originality/valueThis paper represents the first systematic and comprehensive examination of analysts’ utilisation of industry-specific stock valuation methods across all 25 GICS industry groups.

Game Theoretical REIT Acquisitions

This study conceptualized and created the three-person cooperative game. To date, the existing cooperative game is the two-person cooperative game by [Nash, J (1953). Two-person cooperative games. Econometrica, 21(1), 128-140]. The three-person cooperative game leads to cross-subsidization. Therefore, in the context of real estate investment trust (REIT) acquisitions, friendly deals are more beneficial than hostile mergers. The latter finding is consistent with prior studies such as [Womack, KS (2012). Real estate mergers: Corporate control & shareholder wealth. The Journal of Real Estate Finance and Economics, 44(4), 446–471].

The Price Dynamism during REIT Acquisitions

This study creates, models and calibrates the prices of real estate investment trusts (REITs) within an option pricing framework. Unlike Barraclough et al. [(2013). Using option prices overpayments and synergies in M&A transactions. The Review of Financial Studies, 26(3), 695-772] and Marcato and Sebehela [(2022). The paradoxical prices of options. Review of Pacific Basin Financial Markets & Policies, 25(2), 2250009], this study went further and incorporated new information in the form of news. The results show that for a target (acquiring) firm, the exponential risk premium is made up of the cost of carry plus (less) new news. The same illustration applies equally in a merged entity.