Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Capital Structure and Firm Value: Evidence from U.S REITs

Chih-Hsing Hung*, Chu-Hsiung Lin, Feng-Hua Yeh, Hua-Wei Chu

Professor, Department of Money and Banking, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

Abstract: The optimal capital structure remains a key research topic, aiming to minimize the weighted average cost of capital and maximize firm value. Real Estate Investment Trusts (REITs), with their unique regulatory and tax features, offer a compelling research area. This study departs from the traditional trade-off theory based on tax shields and financial distress, instead examining REITs' target leverage from a firm value maximization perspective. Focusing on U.S. REITs from 2000 to 2021, this research applies regression analysis on leverage ratios and firm value metrics (total revenue, net income, closing price, and EV/EBIT), incorporating fixed effects to estimate optimal capital structure. Results show a positive, significant relationship between leverage and total revenue, net income, and closing price, while the squared leverage term is negative and significant—supporting the presence of a target leverage ratio. However, leverage does not significantly affect the EV/EBIT ratio. This study offers valuable insights for corporate decision-making, investors, and regulators regarding REITs' capital structures.

Keywords: Real Estate Investment Trusts (REITs) target capital structure debt ratio

Received February 07, 2026; Revised March 08, 2026; Accepted March 15, 2026

Citation: Chih-Hsing Hung, Chu-Hsiung Lin, Feng-Hua Yeh, Hua-Wei Chu, Capital Structure and Firm Value: Evidence from U.S REITs. Journal of Finance and Economics, 14 (1). 1-12. January 2026. doi:10.12691/jfe-14-1-1

1. Introduction

In recent years, Real Estate Investment Trusts (REITs) have garnered significant scholarly interest due to their rapid development and unique regulatory structure. Despite their historical exclusion from mainstream academic research on capital structure, REITs offer compelling insights into real estate securitization, where funds are raised through public stocks or shares. Managed by specialized institutions, these funds are invested in real estate to generate returns. Since the 1960 enactment of the Real Estate Investment Trust Act, REITs have become a vital, rapidly growing segment in the U.S. financial market.

REITs are categorized into Equity REITs (income from rental payments), Mortgage REITs (investments in real estate mortgage loans), and hybrids. Their stable income streams have made them an essential component of investment portfolios, enabling broader participation in real estate with lower capital requirements. The U.S. REITs market, holding assets exceeding $4.5 trillion, has flourished as the world's largest, with diverse industries including office spaces, healthcare facilities, and timberland. Oh and Verstein (2023) attribute this growth to specific legal and tax regulations [1].

A primary distinction between REITs and non-REITs lies in tax treatment: REITs are exempt from corporate income tax but are bound by unique legal requirements, including distributing 90% of earnings to shareholders and investing 75% of assets in real estate. These regulations restrict reinvestment, property sales, and development, ensuring REITs function as income-generating instruments.

Real estate’s capital-intensive nature forces REITs to rely heavily on external markets for growth, with high leverage ratios and minimal cash reserves. Access to capital markets and cost control are critical for REITs to remain competitive. Maintaining financial flexibility is also vital for navigating liquidity shocks (Riddiough and Steiner, 2020). These structural and tax characteristics highlight the need to explore REIT-specific issues like capital structure and dividend policies [2].

Barclay et al. (2013) studied the tax hypothesis, finding taxable firms' leverage ratios were not significantly higher than those of REITs [3]. Research has linked REIT leverage to asset tangibility, operational risks, market timing, and ownership structures. While these findings provide insights, consensus on capital structure determinants remains elusive.

REITs present a unique field for testing capital structure theories. Their tax-exempt status negates traditional tax shield benefits, and mandatory high dividends increase reliance on external debt. This leads REITs to adjust capital structures through debt issuance, despite minimal debt benefits, to fund acquisitions rapidly. Key capital structure theories like trade-off, pecking order, and market timing play reduced roles in REIT contexts, but decisions remain critical due to the industry's capital-intensive nature.

Additionally, REITs are closely tied to local market conditions. Boudry et al. (2010) emphasized the benefits of using REITs to enter growing global real estate markets, though research on optimal capital structure for REITs is limited [4]. This study examines the U.S. REIT market from 2000 to 2021, analyzing 146 firms to estimate target capital structures and optimal leverage ratios.

The study identifies leverage ratios as crucial for enhancing REIT value, challenging conventional trade-off theory, which is based on tax shield benefits. By focusing on weighted average cost of capital (WACC), this research explores the balance between financial distress costs and firm value maximization.

Practical insights include helping REIT management set capital structure targets and providing benchmarks for investors and policymakers. The study’s structure includes a literature review, data analysis, empirical results, and concluding remarks.

2. Literature Review

2.1. Capital Structure Theory

How do firms make decisions when raising external capital? Scholars have integrated their explanations using a vast body of financial literature, incorporating the trade-off, pecking order, and market timing theories. Existing theories suggest that financing decisions potentially impact firm value due to imperfections in capital markets. The primary objective of corporate financial policy is to optimize capital structure under these imperfect conditions. The drawbacks of capital structure influencing corporate decisions include taxation, information asymmetry, and agency conflicts.

The groundbreaking research of Modigliani and Miller (1958) posits that an increase in financial leverage directly raises the risk of shareholder cash flows, thereby increasing the required rate of return on equity (the second proposition of MM) [5]. The trade-off theory, considering market imperfections such as taxes, bankruptcy costs, and agency costs, asserts that firms must balance the costs and benefits of debt and equity financing to achieve the "optimal" capital structure. By weighing the benefits and drawbacks of debt, the theory aims to determine the proportion of debt and equity financing, with the primary benefit being the marginal tax shield effect and the primary cost being the marginal bankruptcy cost. As interest payments are tax-deductible, increasing debt reduces the taxes a firm must bear. However, when a company issues more debt, potential bankruptcy costs may increase. Therefore, the theory assumes an optimal debt and equity mix, and if an optimal mix exists, firms should adjust their financial policies to maintain the optimal long-term leverage ratio.

Hirshleifer (1966) argues that in the absence of bankruptcy costs, any tax reduction measures would increase the market value of a company [6]. Kraus and Litzenberger (1973) explores the impact on company value when both income taxes and bankruptcy costs coexist [7]. As financial leverage gradually increases, the bankruptcy costs of a company also rise. Therefore, in determining the optimal financial leverage, the trade-off relationship between taxes and bankruptcy costs should be simultaneously considered. Dogan et al. (2019) find that companies with higher levels of debt or greater volatility in cash flows and asset values incur higher costs during financial distress [8]. Specifically, REITs specializing in real estate with higher cash flow volatility, such as industrial or office properties, face higher financial distress costs. Empirical results also indicate that companies with a higher proportion of tangible assets experience lower financial distress costs. Since the majority of the value of real estate companies comes from tangible assets, namely the properties they own, their financial distress costs should be relatively lower.

Therefore, from the perspective of the trade-off theory, it is predicted that REITs would have lower debt ratios because they are tax-exempt. However, empirical evidence presents a different picture. Feng et al. (2007) find that from 1991 to 2003, the debt ratio of REITs consistently remained above 50% [9]. Harrison et al. (2011) discovers that between 1990 and 2008, the average debt ratio of REITs was 48% [10]. Giacomini et al. (2015) conduct a sample study from 1990 to 2012, indicating that the average market leverage ratio of REITs was 46%, while industrial companies had an average market leverage ratio of 27% [11]. Dogan et al. (2019) study the average leverage ratio of U.S. REITs from 2002 to 2013, finding it to be 46.7% [8]. Barclay et al. (2013) demonstrate that from 1984 to 2010, the average leverage ratio of non-taxable real estate firms was 44%, significantly higher than the 18% average leverage ratio of industrial firms [3]. Breuer, Nguyen and Steininger (2019) mention that the leverage ratio of U.S. REITs is twice that of non-real estate companies in the U.S. For instance [12], between 1999 and 2015, the average industry leverage ratio in the U.S. varied from 16% for technology hardware and equipment to 50% for REITs. The results of Feng et al. (2007) neither directly support nor reject the trade-off theory [9]. Evidence from Harrison et al. (2011) suggests that borrowing costs have a significantly negative impact on leverage ratios, while cash generated from investments has a positive impact on leverage ratios, aligning with the predictions of the trade-off theory [10].

According to the pecking order theory, information asymmetry between managers and shareholders increases the cost of external financing, especially equity cost. This leads companies to prioritize retained earnings, followed by debt financing, and finally equity financing. Due to regulatory requirements for substantial income distribution in U.S. REITs, they heavily rely on external financing. Based on the pecking order theory, it is predicted that REITs would have a high debt ratio, especially in situations of high information asymmetry.

Brown and Riddiough (2003) conducted a study that found U.S. REITs primarily utilize bank loans and other public debt sources to fund investments, resorting to issuing stocks as a last resort. This empirical evidence aligns with the predicted perspective of the pecking order theory [13]. Brown and Riddiough (2003) further analyze the equity issued by publicly traded REITs, focusing on the debt structure and whether companies operate with a target long-term debt ratio [13]. The results indicated that REITs issuing equity tend to have more idle funds for investments, while proceeds from debt issuance are typically used to restructure the company's debt and those resorting to debt issuance often face capital constraints, expecting to maintain an investment-grade credit rating through leverage. Morri and Beretta (2008) demonstrated that companies with higher profitability have lower leverage ratios, while REITs with more growth opportunities have higher leverage ratios, suggesting that REITs adhere to the pecking order theory in financing decisions [14]. Dogan et al. (2019) point out that in countries where REITs must distribute most of their income but are not subject to leverage restrictions, REITs tend to have higher book leverage ratios [8]. This result indicates a preference for debt financing over equity financing for REITs, aligning with the pecking order theory.

Finally, the market timing theory posits that financing decisions are influenced by market conditions, with companies tending to issue stocks during bull markets and vice versa. Feng et al. (2007) find that REITs often exhibit sustained higher leverage ratios when their market-to-book ratio (MB ratio) is at historically high levels [9]. This suggests that REITs with high growth opportunities and high market valuations raise funds through debt issuance. Despite the lack of apparent benefits of debt financing for real estate investment trusts, this result indicates that they still engage in debt issuance. Similar explanations are provided by Harrison et al. (2011) in interpreting their research findings [10]. Ooi et al. (2010) conclude that REITs exhibit market timing behavior in the timing and types of capital issuance or reduction, attempting to capitalize on capital market conditions [15]. This market timing practice, attempting to leverage market conditions, may lead companies to deviate from their target leverage ratio. However, in the long run, most REITs do adjust their capital structure towards the target debt level.

2.2. Target (Optimal) Capital Structure

The financing decisions of a firm are crucial for its short-term and long-term sustainability, impacting the returns of owners and other stakeholders. For directors or responsible for managerial duties, carefully choosing the optimal capital or financing structure is a key financial decision that aids them in accomplishing their tasks. Capital structure plays a pivotal role in the management of every business to achieve core objectives, specifically the maximization of shareholder wealth. The static trade-off theory assumes that a firm achieves the optimal leverage when balancing the tax advantages of interest deductions from maximizing debt financing against the financial distress costs associated with increased leverage (Modigliani and Miller, 1958, 1963) [5,16], implying the existence of an optimal capital structure. Hull (1999) provides evidence that the value of a firm increases (decreases) as it approaches (deviates from) its optimal leverage ratio [17]. Kumar, Colombage and Rao (2017) suggest that the optimal capital structure involves a combination of debt and equity, ensuring the maximization of firm value through prudent investment commitments and enhancing financial and operational performance [18]. For companies, maintaining a robust capital structure is essential as it impacts investor returns and company valuation (Bajaj et al., 2021) [19]. Therefore, companies seek the best or optimal capital structure, adjusting their current leverage based on the deviation from their target leverage. Considering that the target leverage is specific to each company and depends on the value of the target leverage, the actual leverage value of a company can be used to assess whether the leverage is too high or too low.

In general, the optimal capital structure is considered to be the one that minimizes the weighted average cost of capital (WACC), thereby maximizing the company's value. Assuming the market value of debt is equivalent to its book value, the capital structure that minimizes WACC will also maximize the stock price. However, without this assumption, the minimum WACC may differ from the highest stock price. To estimate the existence of an optimal structure, Fernández (2019) assumes that the total value of the company (debt plus equity and the present value of taxes) decreases with leverage [20]. This decrease may occur due to the expected decline in free cash flows with debt levels or an increase in asset risk (either the risk of free cash flows or the possibility of bankruptcy) with an increase in leverage, or a combination of both. Fernández (2019) suggests that with increasing leverage, both debt and equity face higher risks, including the possibility of bankruptcy and higher volatility in annual returns [20]. As the debt level increases, investors demand higher returns to compensate for the increased risk. The required return is a critical assumption in optimal capital structure analysis.

Fruhan et al. (1992) illustrate how company value, stock price, and capital costs vary at different debt levels using a simple fictional company example, concluding that the optimal debt/equity ratio for the optimal capital structure is 30% [21]. Damodaran (1994) adopts a similar approach with real company data (Boeing) and arrived at the same result, considering a 30% debt/equity ratio as the target leverage ratio [22].

For managers entrusted with achieving corporate organizational goals, choosing the optimal capital structure is a key financial decision crucial to accomplishing their tasks. Lemmon, Roberts and Zender (2008) demonstrate that leverage ratios often persist over time [23]. Corporate capital structures tend to revert to target levels (Fama and French, 2002; Kayhan and Titman, 2007; Leary and Roberts, 2005; Flannery and Rangan, 2006) [24,25,26,27]. Tao et al. (2017) indicates that companies indeed have leverage targets and consider target capital structures when implementing merger and expansion strategies [28]. They utilize merger strategies to adjust leverage ratios to optimal levels, making debt costs and returns equivalent.

Graham and Harvey (2001) find that 81% of companies consider a target leverage ratio or range when making capital structure decisions [29]. The optimal leverage is achieved when a firm maximizes the trade-off between the tax advantages of debt financing and the financial distress costs associated with increased leverage (Modigliani and Miller, 1958; 1963) [5,16]. The agency model of target capital structure assumes that setting a target leverage ratio should minimize conflicts between debt holders and shareholders (Jensen and Meckling, 1976; Myers, 1977; Stulz, 1990) [30,31,32]. Ross (1977) argues that managers primarily use capital structure decisions to convey the true value of the firm to market participants, considering the costs and benefits involved [33].

Similarly, Ippolito et al. (2012) find that the difference between a company's actual leverage ratio and its target leverage ratio is significantly positively correlated with expected equity returns [34]. Giacomini et al. (2015) suggest that REITs with higher leverage, relative to the target (predicted) debt ratio, exhibit better risk-adjusted performance compared to those with insufficient leverage, consistent with the positive relationship between leverage and returns [11]. Bao and Gong (2017) argue that corporate capital structure decisions are influenced by the target leverage (reference point) and observed leverage [35]. Financing costs vary depending on market conditions, where companies with excessively high leverage can benefit from leverage in a rising market with ample capital but may suffer from the negative effects of leverage when the market declines and credit tightens. Conversely, lower-leveraged companies may experience the opposite scenario.

Based on the above content, we can logically posit the following hypothesis for our research: The existence of a target leverage ratio in REITs has positive impact on the firm's value (maximizing its value).

3. Data and Methodology

3.1. Data

This study aims to investigate the target capital structure of REITs, with a primary focus on the U.S. market. The sample period spans 22 years, from 2000 to 2021, using annual data sourced from the Compustat database. Due to incomplete data for some REITs firms, these data points were excluded from the study. After these exclusions leave us with complete information for 1,063 firm-year observations, which consist of 146 equity REITs.

3.2. Variables Description

All the variables used in this study, along with their definitions, are listed in Table 1. The explanations of the variables used in this study are as follows:

Table 1. Definitions of variables

(1). Firm Value

Building on the research of Fernández (2019), Greenblatt (2010), and Ulbert et al. (2022), this study incorporates four dependent variables: Total Revenue, Net Income (NI), Closing Price, and EBIT Ratio (Enterprise value (EV) / EBIT) [20,36,37]. These four dependent variables are employed as proxy variables for maximizing company value, with the aim of estimating the target capital structure of REITs companies.

(i) Revenue

Total revenue refers to the total sales amount generated by the firm. Since firms with faster growth often have higher valuations, we anticipate a positive correlation between sales growth and firm value. A larger total revenue signifies a greater firm value, making it suitable as a proxy variable for maximizing firm value.

(ii) Net Income

Net profit is an important financial indicator that simultaneously considers the profitability of both the core and auxiliary business activities. Net profit is calculated by subtracting total operating and non-operating expenses from operating revenue and then further deducting income taxes. If the result is a positive value, it signifies that the firm is good operation. If the result is of negative value, it implies that the firm is operating at a loss. Overall, net profit reflects how much profit a firm generates for its shareholders and creditors, making it a suitable proxy for maximizing the firm's value. According to the Net Income Approach (NI) proposed by Durand (1952), this theory posits that capital structure impacts a company's value [38]. Since the cost of debt financing () is lower than the cost of equity financing (Ks), using debt financing can reduce the cost of capital, thereby increasing the firm's value. This theory suggests that when the debt ratio reaches 100%, it maximizes the firm's value, but in reality, it is challenging to achieve.

(iii) Price

Stock prices also have an impact on capital structure. Stock prices are determined by the firm's value and investors' expected returns. When a firm's profits increase, stock prices tend to rise, and shareholders' value increases as a result. Conversely, if a firm incurs losses, stock prices may decrease, leading to a decline in shareholders' value. This study uses the proxy variable of higher stock prices as an indicator of the firm's value for the optimal capital structure. Fernández (2019) conducted research on the calculation of the target capital structure, highlighting the relationship between the goal of maximizing per-share price and achieving the optimal capital structure [20]. This relationship is understood as the maximization of the company's value (combining debt and equity) and the minimization of the weighted average cost of capital (WACC).

(iv) EBIT Ratio

The calculation method is as follows: the EBIT Ratio is equal to Enterprise Value (EV) divided by Earnings Before Interest and Tax (EBIT). The concept of EV/EBIT was introduced by Greenblatt (2010) as a way to measure a firm's value, replacing the price-to-earnings ratio with EV and earnings per share with EBIT [36]. This approach allows us to assess whether a firm's value is overvalued or undervalued. Enterprise Value (EV) is the most comprehensive representation of a firm's value as it encompasses both equity and debt. Dhankar and Boora (1996) and Chadha and Sharma (2016) have also utilized this concept in their research [39,40].

(2) Leverage

The leverage ratio, also known as the debt ratio, is calculated as total debt divided by total assets and represents the proportion of a firm's total assets that are financed through debt. It is used to assess a firm's debt-paying ability and determine whether it is employing excessive or insufficient financial leverage. When the leverage ratio is higher, it indicates that the firm has more debt, which leads to higher debt repayment risk and greater financial leverage. Conversely, a lower leverage ratio signifies that the firm has less debt, resulting in less pressure for debt repayment and lower operational risk, thus implying lower financial leverage. Myers (1977) argues that the book leverage is proven to be the most useful measure since market volatility and expectations do not distort it [31]. Mackay and Phillips (2005) suggest that managers focus on book value when designing the financial structure [41]. Furthermore, expected financial distress costs in the event of bankruptcy represent the cost of borrowing, making book value of debt, not market value, a relevant metric for measuring debt. Tao et al. (2017) also find that using book value-based leverage ratio as the target leverage estimate is appropriate [28].

Brown and Riddiough (2003) find that real estate investment trusts with higher levels of secured debt are more likely to raise capital through stock issuance, whereas companies with higher levels of unsecured debt are more likely to raise capital through additional public debt offerings [13]. They further emphasized that debt capacity may vary significantly depending on the type of assets, as more stable cash flows often support higher debt levels. This study initially explores how capital structure affects company value, including total revenue, net profit, price, and the EBIT Ratio model, and calculates the target capital structure. Harrison et al. (2011) and Dogan et al. (2019) both discuss the capital structure using the leverage ratio (debt ratio) as a proxy variable [8,10].

(3) Growth Opportunity

Growth prospects entail an analysis of a firm's future development trends, measured by dividing the firm's market value by total assets. The trade-off theory predicts a negative correlation between growth opportunities and leverage, with high-growth firms often avoiding debt. In cases of high growth prospects, REITs firms tend to prefer utilizing shareholder funds for investments rather than debt funds. This preference arises because real estate investments involve substantial capital, and using equity funds for investment is considered safer than employing debt funds. Baker and Wurgler (2002) find that when the price-to-book value ratio is relatively high, firms tend to attempt equity issuance [42]. This behavior indicates a negative relationship between the level of leverage used for financing and the price-to-book value ratio. Fama and French (2002) also find a negative relationship between a company's growth prospects and financial leverage [24]. Harrison et al. (2011) discovers a negative correlation between growth prospects and financial leverage in REITs [10], while Morri and Cristanziani (2009) examine the determinants of capital structure choice in European real estate companies, showing that non-REITs firms have significantly higher financial leverage ratios than REITs firms, and there is a negative relationship between growth opportunities and leverage. Bradley et al. (1984), Titman and Wessels (1988), Barclay, Morellec and Smith (2006), and Chikolwa (2009) all align with the pecking order theory's perspective [43,44,45,46].

However, the pecking-order theory predicts a positive relationship between growth prospects and financial leverage because high-growth companies require capital for investments that exceed their internally retained earnings. Due to regulatory requirements for high dividend payouts, REITs tend to maintain lower levels of internal capital. In such circumstances, managers are more inclined to use debt financing as the next best option after retained earnings. Feng et al. (2007) argue that REITs with higher market-to-book ratios exhibit "sustained higher leverage ratios" and attribute these increased growth choices to companies funding their capital expansion through debt rather than equity [9]. Deesomsak, Spieler and Tsang (2004), Morri and Beretta (2008), Giambona et al. (2008), and Dogan et al. (2019) all support the arguments of the financing hierarchy theory [47,8,48,14].

(4) Profitability

High profitability implies financial stability, making debt issuance easier and less costly, which suggests a positive impact of profitability on leverage. Some scholars argue that profitability affects a company's debt-servicing capacity (Morri and Artegiani, 2015) [49]. Profitability is defined as operating cash flow (FFO) divided by total assets. Seok et al. (2020) point out that FFO is a unique profitability measure for REITs that more accurately reflects their actual operational performance [50]. However, it can be influenced by scale factors and may not fully explain large REITs' leverage.

The trade-off theory predicts a positive relationship between profitability and leverage because higher profitability increases the value of interest tax shields. Furthermore, higher profits reduce the likelihood of financial distress and associated costs, which should result in higher leverage. Companies with stronger profitability generate more income, making debt issuance advantageous. Barclay et al. (2006) and MacKay and Phillips (2005) both find a positive relationship between profitability and financial leverage [41]. However, the pecking-order theory suggests a negative relationship between profitability and leverage (Titman and Wessels, 1988; Rajan and Zingales, 1995; Fama and French, 2002) [24,51,46]. If a firm is profitable, it means there is more internal funding available for investments. To avoid costs arising from information asymmetry, companies prioritize using retained earnings for investments. Rajan and Zingales (1995) analyze financing decisions in seven industrialized countries (the United States, Japan, Germany, France, Italy, the United Kingdom, and Canada) and find a negative relationship between profitability and financial leverage [51]. Larger firms tend to issue less equity, and as firm size increases, the negative impact of profitability on financial leverage becomes stronger. It is believed that if a firm has weak market power, managers will avoid debt financing and reduce financial leverage as profitability increases. Baker and Wurgler (2002) argue that profitability primarily affects the debt ratio through retained earnings [42]. Morri and Cristanziani (2009) and Harrison et al. (2011) support these views [10,52]. However, Danis, Rettl and Whited (2014) find that profitability is positively related to financial leverage only when a firm is at or near its optimal financial leverage [53], while it is negatively related at other times. Merika et al. (2015) study 117 internationally listed shipping companies, dividing them into expansion, peak, trough, and lateral movement phases [54]. The results showed a positive relationship between profitability and financial leverage during peak periods but a negative relationship at other times, aligning with previous scholars' perspectives.

(5) Tangibility of Assets

Tangible assets serve as collateral for lenders and reduce agency costs, as they can be liquidated more quickly, thus lowering the cost of financial distress. While REITs hold a substantial amount of tangible assets in the form of real estate properties, concerns may arise regarding the liquidity of these assets. Myers (1977, 1984) argues that tangible assets affect have an impact on a firm's financial leverage [31,55]. Baker and Wurgler (2002), Feng et al. (2007), and Dogan et al. (2019) all find a correlation between the use of tangible assets and debt capacity [42,8,9], while Hall (2012) and Chikolwa (2011) find a positive correlation between tangible assets and financial leverage [56,57]. Chikolwa (2011) conducts a study on the determinants of capital structure for Australian REITs (A-REITs) and find that tangible assets and firm size are positively correlated with financial leverage [56]. Moradi and Paulet (2019) examine publicly listed companies in six European countries - Austria, Belgium, France, Germany, Luxembourg, and the Netherlands - to investigate the impact of firm-specific factors on capital structure decisions [58]. Their research reveals a significant positive correlation between tangible assets and financial leverage. Giacomini et al. (2015) argue that the collateral assets provided by real estate investment trusts (REITs) help mitigate concerns about bankruptcy costs associated with leveraged financing strategies [11]. Harrison et al. (2011) note that the tangibility of assets appears to be positively related to a company's use of financial leverage. In summary, it can be inferred that tangible assets are positively correlated with a firm's financial leverage, and this study incorporates them as a variable [10].

3.3. Methodology

To commence our analysis, we first evaluate whether the capital structure of REITs is correlated with their value. Specifically, while controlling for REITs' characteristics, we regress proxy variables for REITs' value on each indicator. We employ an Ordinary Least Squares (OLS) model with heteroscedasticity-robust standard errors and incorporate fixed effects for firm types (refer to Baltagi (2005) for an introduction to panel data analysis and these estimators.) [59].

3.3.1. Target Capital Structure Model

This study investigates how capital structure affects total revenue, net profit, closing price, and EBIT ratio, utilizing the concept of four regression models representing firm value proposed by Ulbert, Takács and Csapi (2022) [37]. We constructed regression equations for estimating the target leverage ratio of REITs in this study. Here, "value" refers to the firm's value, defined as the total market value of equity and debt. The models are expressed as follows:

Revenue model:

[picture] | (1)

Net Income model:

[picture] | (2)

Price model:

EBIT Ratio model：

[picture] | (4)

Where "i" represents the i-thfirm, and "t" denotes the t-th year. "Leverage" stands for the debt ratio, and "β" represents the vector within the regression model.) Includes growth, the square of growth, tangible assets, and profitability. This study posits a diminishing effect of growth and, therefore, includes the squared term. "" represents the error term for company i in year t. In order to determine the target leverage ratio for REITs companies, this research also considers

Based on the target capital structure derived from four models, including the total revenue, net profit, price, and EBIT ratio, these models are utilized as proxy variables for maximizing firm value (Fernández 2001; Greenblatt 2010; Ulbert et al., 2022) [20,36,37]. The objective is to determine the target capital structure. The target leverage ratios for each model are calculated using the following formulas:

[picture] | (5)

Let (Leverage) be represented byX，where Y corresponds to Revenue、NI、

Price and EBIT Ratio (EV/EBIT)。

Differentiating equation (5) and setting it equal to zero, we obtain:

After rearranging equation (6), we obtain the simplified equation for estimating the target leverage ratio:

[picture] | (7)

4. Result

4.1. Descriptive Statistics

The optimal capital structure model aims to minimize the cost of working capital and maximize the firm's value. This research investigates how capital structure affects firm value on REITs, using total revenue, net income, closing price, and EBIT ratio as proxies. In order to determine the target leverage(debt) ratio for REITs firms, this study incorporates the square of the debt ratio. The sample includes 146 US REITs from 2000 to 2021, and after excluding firms with incomplete data, a total of 1063 samples were obtained.

Table 2 presents a descriptive statistics table for various variables. The mean (median) of the dependent variable, total revenue, is 880.278 (470.165), with a standard deviation of 1,189.564. The maximum value is 9,356.000, and the minimum value is 1.743, indicating a significant variation in total revenue among REIT firms. Profitability is mixed, with a mean (median) net income of 171.292 (60.020), a standard deviation of 349.307, a maximum value of 3,147.937, and a minimum value of -732.000. Regarding the independent variables, the average debt ratio for REITs is 55.1% (with a median of 53.2%), with a standard deviation of 0.17. The maximum debt ratio is 153.7%, and the minimum is 0.9%. Harrison et al. (2011) found that the average debt ratio for REITs during the period from 1990 to 2008 was 48% [10]. Dogan et al. (2019) reported an average leverage ratio of 46.7% for U.S. REITs from 2002 to 2013 [8]. Barclay et al. (2013) found that the average leverage ratio for non-taxable real estate firms from 1984 to 2010 was 44% [3]. Giacomini et al. (2015) analyzed a sample from 1990 to 2012 and found that the average market leverage ratio for REITs was 46% [11]. The empirical results from these studies indicate that the leverage ratio is below but close to 50%. Feng et al. (2007) provide evidence that REITs maintain a debt ratio of over 50% during the period from 1991 to 2003 [9]. Breuer et al. (2019) mention that the leverage ratio of U.S. REITs is twice that of non-real estate s in the U.S. For example, from 1999 to 2015, the average leverage ratio for U.S. REITs was 50% [12]. The empirical results of this study are consistent with those of Feng et al. (2007) and Breuer et al. (2019), indicating leverage ratios above 50% [9,12].

Table 2. Descriptive statistics

Furthermore, due to the nature of the REITs industry, firms invest in a large amount of related real estate assets, resulting in an average tangible asset proportion of 82% and a median of 95%.

Table 3 presents the correlation analysis of various key variables, aimed at assessing the multicollinearity between explanatory variables and the dependent variable. The results indicate that the correlations between the variables are relatively low, with only a high correlation coefficient of 0.657 observed between total revenue and net income. Therefore, there should be no concerns regarding multicollinearity in the target capital structure model.

Table 3. Correlation statistics for main variables

The financing choices for Real Estate Investment Trusts (REITs) may vary depending on their property types. Table 4 provides descriptive statistics for various subsectors of REITs. When looking at REITs subsectors, Mortgage, Infrastructure, Residential, and Diversified REITs tend to have relatively higher average leverage ratios. Among these, Mortgage REITs exhibit the highest average financial leverage at 77.7%. This is because Mortgage REITs can invest in real estate-related loans to earn interest income, requiring more capital, and thus, utilizing higher financial leverage. On the other hand, Self-Storage REITs have the lowest average financial leverage (44.3%). Dogan et al. (2019) note that REITs specializing in self-storage properties in the United States typically have lower leverage, which aligns with our research findings [8]. Dogan et al. (2019) also suggest that REITs' investment choices are subject to strict regulation, limiting the risk of bad investments [8]. The primary agency cost of highly leveraged REITs is the cost of forgoing profitable investments, meaning REITs operating in rapidly growing areas or high-demand property types often avoid high leverage.

Table 4. Descriptive statistics of REITs sub-sectors

Our study data indicates that Industrial (51.9%) and Office (49.3%) sectors have lower average leverage ratios compared to Residential (61.6%). Harrison et al. (2011) empirically finds that REITs specializing in industrial, or office properties tend to use less debt than those specializing in residential properties due to the less stable cash flows and asset values of industrial and office properties [10]. Giacomini et al. (2015) also make similar observations. The leverage ratios for most REITs subsectors typically fall within the range of 50% to 60% [11]. Industry type is a crucial determinant in capital structure decisions (Remmers et al., 1974; Bajaj et al., 2018) [60,61]. Companies within the same industry tend to have similar debt ratios due to similarities in technology, liquidity demands, collateral compositions, returns, and growth. Giambona et al. (2008) report empirical evidence that REITs specializing in the most liquid property types use higher leverage and longer-term debt [48]. Ertugrul and Giambona (2011) demonstrate that the volatility of REITs' operating performance relative to others in the same industry is a significant determinant of their leverage ratios [62].

4.2. Estimate Target Leverage

This study employs panel regression techniques to estimate target leverage ratios. We examine variations among different firms and estimate target leverage ratios using their estimated coefficients. In Table 5, we provide Ordinary Least Squares (OLS) estimates with firm fixed effects, controlling for factors influencing capital structure such as growth, profitability, and tangible assets. The regression results for the impact of leverage on four firm values are presented in Table 5, with four models respectively named the Revenue model, Net Income model, Price model, and EBIT Ratio model.

According to Table 5, the empirical results for model (1) to model (3) reveal that the coefficient for Leverage in Column (1) is 1176.748 (p < 0.05), in Column (2) it is 631.643 (p < 0.01), and in Column (3) it is 101.177 (p < 0.01). These results indicate that the coefficient of financial leverage has a positive and significant impact on the firm’s value. This suggests that as a firm takes on more debt, its value increases, and the magnitude of the increase in firm value tends to rise with higher levels of debt. However, there comes a point where the firm's value stabilizes or begins to decline, which is why the coefficient for the square term of financial leverage, Leverage^2, becomes negative but remains statistically significant. Specifically, the Leverage^2 coefficients are as follows: in Column (1), it is -836.691 (p < 0.10); in Column (2), it is -530.438 (p < 0.01); and in Column (3), it is -89.137 (p < 0.01). These results suggest that the models for total revenue (Column 1), net income (Column 2), and closing price (Column 3) are all suitable for estimating target leverage ratios using the leverage coefficient.

Modigliani and Miller's (1963) research demonstrates that firms benefit from tax shields by saving on interest payments through debt financing, which could lead to the maximization of the firm's value [16]. Debt can provide tax shield benefits to firms, but various costs associated with debt increase as the debt ratio rises. When the debt ratio reaches a certain level, EBIT (Earnings Before Interest and Taxes) starts to decline, and agency costs and bankruptcy costs increase, ultimately leading to a decrease in the firm's value. Therefore, a firm's financing choices should aim to strike the optimal balance point between debt value maximization and the costs of bankruptcy and agency.

Generally speaking, the impact of leverage on earnings is positive in the profit region and negative in the loss region. Additionally, firms exhibit risk aversion in the profit region but risk-seeking behavior in the loss region. The trade-off theory, developed by Myers (1977), aims to identify the determinants of optimal capital structure decisions to achieve value maximization by balancing the benefits and costs of debt [31]. Similarly, Ippolito et al. (2012) find a significant positive correlation between the difference between a firm's current leverage ratio and its target leverage ratio and expected equity returns [34]. Ulbert et al. (2022) discover that having a target capital structure can enhance a firm's performance [37]. Chathoth and Michael (2007) find that performance is influenced by financial leverage [63]. Giacomini et al. (2015) find that highly leveraged real estate investment trusts (REITs) have higher average return rates and return variances than low-leverage REITs [11]. Moreover, REITs that exceed their target leverage ratios actually perform better than those with insufficient leverage, consistent with the positive correlation between leverage and returns.

However, during the specific period of the financial crisis, evidence from Sun, Titman and Twite (2015) and Ling and Naranjo (2015) suggest that REITs with higher debt ratios and shorter debt maturities experienced more significant price declines during the 2007-2008 U.S. REITs price slump [64,65]. Scholars such as Antoniou et al. (2008), Rajan and Zingales (1995), and Feidakis and Rovolis (2007) have indicated a mixed relationship between leverage and firm value. We believe that understanding this relationship is of utmost importance [51,66].

The growth opportunities of a firm can potentially impact its performance, which may have a positive effect on financial performance (Jermias, 2008) [67]. Additionally, tangible assets are considered a significant determinant of performance. Maury and Pajuste (2005) argue that companies with lower levels of tangible assets may possess a higher proportion of intangible assets (such as human capital) that generate cash flows [68]. Consequently, there exists a negative relationship between asset tangibility and company value. Akintoye (2009) investigates the importance of asset tangibility in company operations and suggests that if a firm makes substantial investments in tangible assets, its financial distress costs would be relatively lower. Holding other conditions constant, a higher level of tangible assets held by a firm increases its ability to manufacture products and generate more revenue from sales [69]. Therefore, there is a positive correlation between asset tangibility and financial performance. In the real estate industry, having more tangible assets allows a firm to offer more collateral, thereby gaining access to more funds for investment in additional projects. Higher profitability can lead to more retained earnings for a firm. As profitability increases, the value of the firm also rises.

Table 5. Statistical test of variables in the estimation model of target leverage

In Column (4) of Table 5, the EBIT Ratio model reveals that both financial leverage and its squared term are negatively correlated with EBIT Ratio, but the correlation is not statistically significant. Consequently, it can be inferred that the debt ratio does not significantly impact the firm's value. Hence, EBIT ratio is not a suitable proxy variable for maximizing company value.

According to Equation (7), we can estimate the target leverage ratios for the following models:

Revenue model： 70.3% (A1)

Net Income model： 59.5% (A2)

Price model： 56.8% (A3)

However, since the leverage ratio is not significantly correlated with the EBIT ratio and does not influence the capital structure, it cannot be considered a proxy variable for maximizing firm’s value. In this paper, we compile the estimated target leverage ratios for the three models of REITs companies and also include the actual average leverage ratios obtained from previous literature in Table 6.

The estimated target capital structure in this study are as follows: the estimated target leverage ratio for the total revenue model is 70.3%, for the net income model, it is 59.5%, and for the closing price model, it is 56.8%. The actual average leverage ratio from this statistical analysis is 55.1%. As per empirical evidence from the literature, it is known that REITs, relative to other industries, typically have higher leverage ratios, often exceedingly twice that of other sectors.

From Table 5, it can be observed that the average leverage ratio obtained from the REITs data selected for the years 2000 to 2021 is generally higher than the results from previous empirical studies in various stages (ranging from 43.1% to 52.0%) (Feng et al.,(2007; Morri and Beretta 2008; Harrison et al., 2011; Barclay et al., 2013; Giacomini et al., 2015; Dogan et al., 2019; Breuer et al., 2019) [3,12,8,9,11,10,14]. In Table 6, you can observe a gradual upward trend in REITs' leverage ratios over the years. This trend might be attributed to stricter financial regulatory mechanisms, making it easier for investors to participate in real estate investments and obtain stable cash flow returns from real estate. As a result, REITs' management teams are likely to use leverage cautiously, aiming for higher returns and value while moving closer to their target leverage ratios.

Hull (1999) provides evidence that when companies move away from (towards) their optimal leverage ratios, their value decreases (increases) [17]. The level of debt financing significantly affects the performance of REITs (Sun et al., 2015). Bao and Gong (2017) find that reference points (or target leverage) play a crucial role in the leverage-earnings relationship [35,65]. Capital structure decisions of firms are influenced by the target leverage (reference point) and the observed leverage. Riddiough and Steiner (2020) emphasize the importance of maintaining financial flexibility for REITs as capital-intensive companies, given the need to address future liquidity shocks, often resulting in high leverage ratios [2].

Table 6. Summary of the estimated target leverage and the empirical actual average leverage on REITs

5. Conclusion

Due to the unique regulatory and tax environment that REITs operate in, individual studies on the capital structure of REITs are interesting and necessary. This study explores how capital structure affects firm value and estimates the target leverage ratio. Dogan et al., (2019) show that real estate investment trusts (REITs) have the highest book leverage ratio [8]. This suggests that REITs prefer debt financing over equity financing. Most empirical evidence also indicates that REITs actually use a significant amount of debt (Feng et al., 2007; Boudry et al., 2010; Harrison et al., 2011; Barclay et al., 2013) [3,4,9,10].

Empirical evidence from Leary and Roberts (2005) and Flannery and Rangan (2006) support the notion that companies actively rebalance their capital structure to stay within optimal ranges [25,27]. Bao and Gong (2017) argue that a company's capital structure decisions are influenced by the target leverage (reference point) and the observed leverage [35]. Riddiough and Steiner (2020) emphasize the importance of maintaining financial flexibility for REITs as capital-intensive companies, given their typically high leverage ratios [2]. Hull (1999) also provides evidence indicating that when companies move away from (towards) their optimal leverage ratios, their value decreases (increases) [17].

This study focuses on data from U.S. REITs sampled from 2000 to 2021 and conducts regressions using four firm value proxies: total revenue, net income, closing price, and EBIT ratio. Firm fixed effects were employed in the analysis to estimate the target leverage ratios of REITs during this period. The results of this study indicate that different firm value proxy variables are associated with different target leverage ratios.

In descending order, for total revenue, the estimated target leverage ratio is 70.3%; for net income, it is 59.5%; and for the price model, it is 56.8%. We infer that the total revenue model may imply high revenue but also high operational risks, which necessitate a higher required rate of return. In the case of the price model, market forces react swiftly to increased leverage, leading to a lower target leverage ratio. Consequently, under market-based conditions, the target capital structure is the lowest and closely aligns with the actual average leverage ratio of 55.1%. The net income model, based on accounting profits, falls in the middle among the three estimation models, but the difference between its target leverage ratio and the one from the price model, as well as the actual average leverage ratio, is minimal.

Further inferences suggest that U.S. REITs, operating under unique legal regulations and tax mechanisms, have achieved effective risk management and value pursuit in their financing decisions. This has resulted in relatively small deviations in their target leverage ratios.

References

[1]   | Oh, J. and Verstein, A. (2023), “A Theory of the REIT”, Yale Law Journal, Forthcoming, Available at SSRN: https:// ssrn.com/ abstract=4365127.
[2]   | Riddiough, T. and Steiner, E. (2020), “Financial Flexibility and Manager-Shareholde Confict: Evidence from REITs,” Real Estate Economics, Vol. 48, No. 1, pp. 200-239.
[3]   | Barclay, M. J., Heitzman, S.M. and Smith, C.W. (2013), “Debt and taxes: evidence from the real estate industry”, Journal of Corporate Finance, Vol. 20, pp. 74–93.
[4]   | Boudry, W. I., Kallberg, J. G. and Liu, C. H. (2010), “An analysis of REIT security issuance decisions”, Real Estate Economics, Vol. 38, No. 1, pp. 91-120.
[5]   | Modigliani, F. and Miller, M. H. (1958), “The cost of capital, corporation finance, and the theory of investment”, American Economic Review, Vol. 48, No. 3, pp. 261–297.
[6]   | Hirshleifer, J. (1966), “Investment decisions under uncertainty: Applications of the state-preference approach”, Quarterly Journal of Economics, Vol. 80, No. 2, pp. 252-277.
[7]   | Kraus, A. and Litzenberger, R. H. (1973), “A state-preference model of optimal financial leverage”, Journal of Finance, Vol. 28, No. 4, pp. 911-922.
[8]   | Dogan, Y. Y., Ghosh, C. and Petrova, M. (2019), “On the determinants of REIT capital structure: Evidence from around the world”, Journal of Real Estate Finance and Economics, Vol. 59, pp. 295-328.
[9]   | Feng, Z., Ghosh, C. and Sirmans, C. F. (2007). “On the capital structure of real estate investment trusts (REITs)”, Journal of Real Estate Finance and Economics, Vol. 34, No. 1, pp. 81–105.
[10]   | Harrison, D. M., Panasian, C. A. and Seiler, M. J. (2011), “Further evidence on the capital structure of REITs”, Real Estate Economics, Vol. 39, No. 1, pp. 133–166.
[11]   | Giacomini, E., Ling, D. C. and Naranjo, A. (2015), “Optimal capital structure and the effects of deviations from target leverage on REIT return performance”, RERI Journal, pp. 930-978.
[12]   | Breuer, W., Nguyen, L. D. and Steininger, B. I. (2019), “Decomposing Industry Leverage in the US: The Real Estate Investment Trust (REIT) Debt Puzzle”, Capital Structure of Real Estate Investment Trusts: New Insights, Vol. 21.
[13]   | Brown, D. T., and Riddiough, T. J. (2003), “Financing choice and liability structure of real estate investment trusts”, Real Estate Economics, Vol. 31, No. 3, pp. 313-346.
[14]   | Morri, G. and Beretta, C. (2008), “The capital structure determinants of REITs: Is it a peculiar industry?”, Journal of European Real Estate Research, Vol. 1, No. 1, pp. 6-57.
[15]   | Ooi, J. T. L., Ong, S.-E. and Li, L. (2010), “An analysis of the financing decision of REITs: the role of market timing and target leverage”, Journal of Real Estate Finance and Economics, Vol. 40, No. 2, pp. 130–160.
[16]   | Modigliani, F. and M. H. Miller (1963). “Corporate income taxes and the cost of capital: A correction”, American Economic Review, Vol. 53, No. 3, pp. 433–443.
[17]   | Hull, Robert M. (1999), “Leverage ratios, industry norms, and stock price reaction: An empirical investigation of stock-for-debt transactions”, Financial Management, Vol. 28, pp. 32-45.
[18]   | Kumar, S., Colombage, S. and Rao, P. (2017), “Research on capital structure determinants: A review and future directions”, International Journal of Managerial Finance, Vol. 13, No. 2, pp. 106–132.
[19]   | Bajaj, Y., Kashiramka, S. and Singh, S. (2021), “Application of capital structure theories: A systematic review”, Journal of Advances in Management Research, Vol. 18, No. 2, pp. 173–199.
[20]   | Fernández, P. (2019), “Optimal capital structure: Problems with the Harvard and Damodaran Approaches”.
[21]   | Fruhan, W. E., Kester, W. C., Mason, S. P., Piper, T. R. and Ruback, R. S. (1992), “Case Problems in Finance”, Richard D. Irwin. Inc.
[22]   | Damodaran, A., (1994), “Damodaran on Valuation”, John Wiley & Sons, New York.
[23]   | Lemmon, M., Roberts, M. and Zender, J. (2008), “Back to the beginning: Persistence and the cross-section of corporate capital structure”, Journal of Finance, Vol. 60, pp. 2575–2619.
[24]   | Fama, Eugene F. and French, Kenneth R. (2002), “Testing Trade-Off and Pecking Order Predictions about Dividends and Debt”, The Review of Financial Studies, Vol. 15, No. 1, pp. 1-33.
[25]   | Flannery, M. and Rangan, K., (2006), “Partial adjustment toward target capital structures”, Journal of Financial Economics, Vol. 79, pp. 469–506.
[26]   | Kayhan, A. and Titman, S. (2007), “Firms’ histories and their capital structure”, Journal of Financial Economics, Vol. 83, pp. 1-32.
[27]   | Leary, M. and Roberts, M. R., (2005). “Do firms rebalance their capital structures?”, Journal of Finance, Vol. 60, pp. 2575–2619.
[28]   | Tao, Q., Sun, W., Zhu, Y. and Zhang, T. (2017), “Do firms have leverage targets? New evidence from mergers and acquisitions in China”, The North American Journal of Economics and Finance, Vol. 40, pp. 41-54.
[29]   | Graham, J. R. and Harvey, C. R. (2001), “The theory and practice of corporate finance: Evidence from the field”, Journal of financial economics, Vol. 60, No. 2-3, pp. 187-243.
[30]   | Jensen, M. and Meckling, W. (1976). “Theory of the firm: managerial behavior, agency costs and ownership structure”, Journal of Financial Economics, Vol. 3, pp. 305–360.
[31]   | Myers, S. C. (1977), “Determinants of corporate borrowing”, Journal of Financial Economics, Vol. 5, No. 2, pp. 147–175.
[32]   | Stulz, R.M., (1990), “Managerial discretion and optimal financing policies”, Journal of Financial Economics, Vol. 26, pp. 3-27.
[33]   | Ross, S.A., (1977), “The determination of financial structure: The incentive-signaling approach”, Bell Journal of Economics, Vol. 8, pp. 23-40.
[34]   | Ippolito, F., Steri, R. and Tebaldi, C. (2012), “The relative leverage premium”, In AFA 2012 Chicago Meetings Paper.
[35]   | Bao, H. X. and Gong, C. M. (2017), “Reference-dependent analysis of capital structure and REIT performance”. Journal of behavioral and experimental economics, Vol. 69, pp. 38-49.
[36]   | Greenblatt, J. (2010). “The little book that still beats the market”, John Wiley & Sons, Vol. 29.
[37]   | Ulbert, J., Takács, A. and Csapi, V. (2022), “Golden ratio-based capital structure as a tool for boosting firm's financial performance and market acceptance”, Heliyon, Vol. 8, No. 6, e09671.
[38]   | Durand, D. (1952), “Costs of debt and equity funds for business: trends and problems of measurement”, Conference on research in business finance, pp. 215-262.
[39]   | Chadha, S. and Sharma, A. (2016), “An Empirical Study on Capital Structure in Indian Manufacturing Sector”, Global Business Review, Vol. 17, pp. 411-424.
[40]   | Dhankar, R. S. and Boora, A. S. (1996), “Cost of Capital, Optimal Capital Structure, and Value of Firm: An Empirical Study of Indian Companies”, Vikalpa, Vol. 21, pp. 29-36.
[41]   | MacKay, P. and Phillips, G. M. (2005), “How does industry affect firm financial structure?.”, The review of financial studies, Vol. 18, No. 4, pp. 1433-1466.
[42]   | Baker, M. and Wurgler, J. (2002). “Market timing and capital structure”, Journal of Finance, Vol. 57, No. 1, pp. 1–32.
[43]   | Barclay, M. J., Morellec, E. and Smith, Jr, C.W. (2006), “On the debt capacity of growth options”, Journal of Business, Vol. 79, No. 1, pp. 37–60.
[44]   | Bradley, M., Jarrell, G. A. and Kim, E. H. (1984), “On the existence of an optimal capital structure: Theory and evidence”, The Journal of Finance, Vol. 39, No. 3, pp. 857-878.
[45]   | Chikolwa, B. (2009), “Determinants of capital structure for A-REITs”, In 15th Annual Conference of Pacific Rim Real Estate Society.
[46]   | Titman, S. and Wessels, R. (1988), “The determinants of capital structure choice,” The Journal of Finance, Vol. 43, No. 1, pp. 1–19.
[47]   | Deesomsak, R., Paudyal, K. and Pescetto, G. (2004), “The determinants of capital structure: Evidence from the Asia Pacific region”, Journal of Multinational Financial Management, Vol. 14, No. 4-5, pp. 387-405.
[48]   | Giambona, E., Harding, J. P. and Sirmans, C. F. (2008), “Explaining the variation in REIT capital structure: The role of asset liquidation value”, Real Estate Economics, Vol. 36, No. 1, pp. 111-137.
[49]   | Morri, G. and Artegiani, A. (2015), “The effects of the Global Financial Crisis on the capital structure of EPRA/NAREIT Europe index companies”, Journal of European Real Estate Research, Vol. 8, No. 1, pp. 3-23.
[50]   | Seok, S. I., Cho, H. and Ryu, D. (2020), “The information content of funds from operations and net income in real estate investment trusts,” The North American Journal of Economics and Finance, Vol. 51.
[51]   | Rajan, R. G. and Zingales, L. (1995), “What do we know about capital structure? Some evidence from international data,” Journal of Finance, Vol. 50, No. 5, pp. 1421–1460.
[52]   | Morri, G. and Cristanziani, F. (2009), “What determines the capital structure of real estate companies? An analysis of the EPRA/NAREIT Europe index”, Journal of Property Investment and Finance, Vol. 27, No. 4, pp. 318–372.
[53]   | Danis, A., Rettl, D.A. and Whited, T. M. (2014), “Refinancing, profitability, and capital structure”, Journal of Financial Economics, Vol. 114, No. 3, pp. 424-443.
[54]   | Merika, A., Theodoropoulou, S., Triantafyllou, A., and Laios, A. (2015), “The relationship between business cycles and capital structure choice: The case of the international shipping industry”, The Journal of Economic Asymmetries, Vol. 12, No. 2, pp. 92-99.
[55]   | Myers, S. C. (1984), “The capital structure puzzle”, Journal of Finance, Vol. 39, No. 3, pp. 575–592.
[56]   | Chikolwa, B. (2011), “Investigating the capital structure of A-REITs”, Journal of Real Estate Literature, Vol. 19, No. 2, pp. 391–411.
[57]   | Hall, T. W. (2012), “The collateral channel: Evidence on leverage and asset tangibility”, Journal of Corporate Finance, Vol. 18, No. 3, pp. 570-583.
[58]   | Moradi, A. and Paulet, E. (2019), “The firm-specific determinants of capital structure – An empirical analysis of firms before and during the Euro Crisis,” Research in International Business and Finance, Vol. 47, pp. 150-161.
[59]   | Baltagi, B. H. (2005), “Econometric Analysis of Panel Data”, 3rd ed., Wiley, Chichester.
[60]   | Bajaj, Y., Kashiramka, S. and Singh, S. (2018), “Dynamics of capital structure: evidence from Indian manufacturing firms”, Journal for Global Business Advancement, Vol. 11 No. 6, pp. 667-686.
[61]   | Remmers, L., Stonehill, A., Wright, R. and Beekhuisen, T. (1974), “Industry and size as debt ratio determinants in manufacturing internationally”, Financial Management, Vol. 3 No. 2, pp. 24-32.
[62]   | Ertugrul, M. and Giambona, E. (2011), “Property segment and REIT capital structure”, Journal of Real Estate Finance and Economics, Vol. 43, pp. 505-526.
[63]   | Chathoth, P. K. and Michael M. D. (2007), “The effect of environment risk, corporate strategy, and capital structure on firm performance: An empirical investigation of restaurant firms”, International Journal of Hospitality Management, Vol. 26, No. 3, pp. 502-516.
[64]   | Ling, D. C. and Naranjo A. (2015). “Returns and Information Transmission Dynamics in Public and Private Real Estate Markets”, Real Estate Economics, Vol. 43, No. 1, pp. 163–208.
[65]   | Sun, L., Titman S. D. and Twite G. J. (2015). “REIT and Commercial Real Estate Returns: A Postmortem of the Financial Crisis”, Real Estate Economics, Vol. 43, No. 1, pp. 8–36.
[66]   | Feidakis, A. and Rovolis, A. (2007), “Capital Structure Choice in European Union: Evidence from Construction Industry”, Applied Financial Economics, Vol. 17, pp. 989-1002.
[67]   | Jermias, J. (2008). “The relative influence of competitive intensity and business strategy on the relationship between financial leverage and performance”, British Accounting Review, Vol. 40, pp. 71–86.
[68]   | Maury, B. and Pajuste, A. (2005), “Multiple large shareholders and firm value”, Journal of Banking & Finance, Vol. 29, No. 7, pp. 1813-1834.
[69]   | Akintoye, I. R. (2009), “Sensitivity of Performance to Capital Structure”, Banking and Finance Letters, Vol. 1, pp. 29–35.