0 DEGREE PROJECT
IN REAL ESTATE AND CONSTRUCTION MANAGEMENT
BUILDING AND REAL ESTATE ECONOMICS MASTER OF SCIENCE, 30 CREDITS, SECOND LEVEL
STOCKHOLM, SWEDEN 2021
Optimal credit rating with regard to capital structure
A mixed method study on the Swedish real estate market MARTIN BJERRING
ROYAL INSTITUTE OF TECHNOLOGY
DEPARTMENT OF REAL ESTATE AND CONSTRUCTION MANAGEMENT
Master of Science thesis
Title Optimal credit rating with regard to capital structure Authors Martin Bjerring and Ludvig Engwall
Department Real Estate and Construction Management Master Thesis number TRITA-ABE-21391
Supervisor Åke Gunnelin
Keywords Credit rating, Capital structure, Real estate, WACC, Capital Market
In Sweden, the demand for official credit ratings has historically not been as substantial as in other parts of the world. This due to the fact that Swedish banks up until recently provided the market with shadow ratings. The European Securities and Markets
Authority (ESMA) started investigating shadow ratings in August 2016 and decided that Nordic banks issuing shadow ratings were against the new directives because they were not registered as rating agencies. The Nordic corporate bond market has grown rapidly since the financial crisis and many bond issuers have avoided the organizational cost associated with obtaining and maintaining a credit rating. In 2016, more than half of Nordic bonds were issued without a credit rating, while today, the majority of Nordic bonds are issued with credit rating.
Capital structure and specifically the goal of locating the optimal capital structure has, since the breakthrough of Modigliani and Miller in 1958, been the center of attention for a lot of research and the issue is of great interest for both academicians and practitioners.
In practice, there are many factors that affect the decision of what leverage and capital structure a company decides to aim for. Among the factors are growth opportunities, firm size and profitability. With the base in corporate financial theory, the purpose of
this study is to explore what factors that influence Swedish real estate companies regarding their decisions of capital structure and credit rating. With the method of semi- structured interviews and quantitative simulations, the study aims to understand why Swedish real estate companies are divided in their strategies about credit ratings and to explore if the firms have suboptimal credit ratings with respect to their capital structure.
The quantitative part indicates that the optimal credit rating grade is A- with regard to the capital structure, for the examined Swedish real estate firms, under the current market conditions. The uncertainty of the optimal credit rating grade is displayed in a sensitivity analysis. The qualitative part of the study indicates that the rating of A- could plausibly be the optimal credit rating level and that it most likely is above the investment grade. The qualitative part further sheds light on the strategies of Swedish real estate firms and gives the market and investors insight to understand the underlying factors for why firms aim for different ratings. One can conclude that leverage and weighted average cost of capital are important factors when it comes to decisions regarding credit rating grades, but is often trumped by the quality label of credit ratings, the demand for different grades, the signals a upgrade/downgrade sends to the market and the possibility to reach the rating institutes requirements.
Titel Optimalt kreditbetyg med hänsyn till kapitalstruktur Författare Martin Bjerring and Ludvig Engwall
Avdelning Fastigheter och Byggande Masteruppsatsnummer TRITA-ABE-21391
Handledare Åke Gunnelin
Nyckelord Kreditbetyg, Kapitalstruktur, Fastigheter, WACC, Kapitalmarknad
I Sverige har efterfrågan på officiella kreditbetyg historiskt sett inte varit lika stor som i andra delar av världen. Detta på grund av att svenska banker fram till nyligen försåg marknaden med skuggratings. Europeiska värdepappers- och marknadsmyndigheten (ESMA) började undersöka skuggratings i augusti 2016 och beslutade att nordiska banker som utfärdade skuggratings gick emot de nya direktiven eftersom de inte var registrerade som kreditvärderingsinstitut. Den nordiska obligationsmarknaden har vuxit snabbt sedan finanskrisen och många fastighetsbolag har undvikit kostnader kopplade till att erhålla och underhålla ett kreditbetyg. Under 2016 emitterades mer än hälften av nordiska obligationer utan kreditbetyg, medan idag är de flesta nordiska obligationer emitterade med kreditbetyg.
Kapitalstruktur och specifikt målet att hitta den optimala kapitalstrukturen har sedan Modigliani och Millers genombrott 1958 varit centrum för mycket forskning och frågan är av stort intresse för både akademiker och utövare. I praktiken finns det många faktorer som påverkar beslutet om vilken belåningsgrad och kapitalstruktur som ett företag bestämmer sig för. Bland de påverkande faktorerna är tillväxtmöjligheter,
företagsstorlek och lönsamhet. Med utgångspunkt i företagsfinansiella teorier, är syftet
med denna studie att undersöka vilka faktorer som påverkar svenska fastighetsbolag beträffande deras beslut om kapitalstruktur och kreditbetyg. Med metod i form av semistrukturerade intervjuer och kvantitativa simuleringar syftar studien till att förstå varför svenska fastighetsbolag har olika strategier kring kreditbetyg, samt att undersöka om företagen har suboptimala kreditbetyg med hänsyn till deras kapitalstruktur.
Den kvantitativa delen indikerar att det optimala kreditbetyget är A-, med hänsyn till kapitalstruktur för de undersökta svenska fastighetsföretagen, under nuvarande marknadsförhållanden. Osäkerheten kring det optimala kreditbetyget visas i en
känslighetsanalys. Den kvalitativa delen av studien indikerar att A- troligtvis kan vara den optimala kreditbetygsnivån samt att den optimala nivån högst sannolikt ligger över investment grade. Den kvalitativa delen belyser även de svenska fastighetsbolagens strategier och förser marknaden och investerare med insyn om de bakomliggande
faktorerna till varför företag strävar efter olika betyg. Slutsatsen är att belåningsgrad och kapitalkostnader är viktiga faktorer när det gäller beslut om kreditbetyg, men att det ofta priorterars efter kvalitén associerad med kreditbetyget, efterfrågan på olika kreditbetyg, signalerna en uppgradering / nedgradering sänder till marknaden och om företaget kan nå kreditvärderingsinstitutets krav.
This master thesis is written for the Department of Real Estate and Construction
Management at KTH Royal Institute of Technology. Firstly, we would like to thank our supervisors Åke Gunnelin at KTH Royal Institute of Technology for the valuable inputs and assistance during the research project. Secondly, we would like to thank all
interviewees who have contributed with knowledge and meaningful insight from the real estate sector enabling us to research this topic.
Martin Bjerring and Ludvig Engwall
Table of content
1. INTRODUCTION ... 1
1.1PURPOSE AND CONTRIBUTION ... 3
1.2RESEARCH QUESTIONS ... 3
1.3LIMITATIONS ... 4
2. LITERATURE REVIEW ... 5
3. THEORETICAL FRAMEWORK ... 13
3.1THEORIES ... 13
3.1.1 Modigliani-Miller theorem ... 13
3.1.2 Trade-off theory ... 14
3.1.3 Pecking order theory ... 16
3.2CREDIT RATING AND CREDIT SPREAD ... 18
3.2.1 Credit rating ... 18
3.2.2 Credit spread ... 23
3.3FINANCIAL MODELS ... 26
3.3.1 Asset pricing model... 26
3.3.2 CAPM ... 27
3.3.3 WACC ... 31
4. METHOD ... 33
4.1RESEARCH STRATEGY ... 33
4.2QUALITATIVE ANALYSIS ... 33
4.2.1 Qualitative method ... 33
4.2.2 Qualitative data ... 35
4.2.3 Description of the interviewees ... 36
4.2.4 Interview questions ... 39
4.3QUANTITATIVE ANALYSIS ... 40
4.3.1 Simulation method ... 40
4.3.2 Quantitative data ... 41
4.3.3 The simulation process - Step by step ... 46
4.3.4 Sensitivity analysis ... 50
4.4VALIDITY AND RELIABILITY ... 51
4.5ETHICAL CONCERNS ... 53
5. RESULTS ... 55
5.1QUALITATIVE RESULTS ... 55
5.1.1 Real estate firms ... 55
5.1.2 Banks ... 59
5.1.3 Investors ... 61
5.1.4 Rating institute ... 62
5.2QUANTITATIVE RESULTS ... 63
5.2.1 Sensitivity analysis ... 65
6. DISCUSSION ... 69
6.1QUANTITATIVE DATA ... 69
6.1.1 Credit spread ... 70
6.1.2 Corporate tax-rate ... 71
6.1.3 Loan-to-value ... 71
6.1.4 Industry asset beta ... 72
6.2QUALITATIVE DATA ... 73
7. CONCLUSION ... 85
7.1CONCLUSION FROM RESEARCH ... 85
7.2SUGGESTIONS FOR FURTHER RESEARCH ... 86
REFERENCES ... 88
WEB-BASED SOURCES: ... 93
APPENDIX ... 95
APPENDIX 1,INTERVIEW QUESTIONS FOR REAL ESTATE FIRMS ... 95
APPENDIX 2,INTERVIEW QUESTIONS FOR BANKS ... 97
APPENDIX 3,INTERVIEW QUESTIONS FOR INVESTORS ... 99
APPENDIX 4,INTERVIEW QUESTION FOR THE RATING INSTITUTE ... 100
LTV-ratio Abbreviation for Loan-to-Value ratio, which is a financial term used to express the ratio of a loan to the value of an asset.
Default risk The risk that a lender takes on indicating the probability that the borrower will be unable to make the required payments.
Refers to when one party possesses more knowledge than the other party in an economic transaction.
Short for Interest coverage ratio, which is a financial term that describes the debt and profitability ratio to help determine how easily
a company can pay interest on its outstanding debt.
Refers to the quality of a company's credit and represents a relatively low risk of default. To receive this label the credit rating needs be
BBB-/Baa3 or above.
Refers to the quality of a company's credit and represents a relatively high risk of default. To receive this label the security needs to have a
credit rating below BBB-/Baa3.
Speculative grade Same definition as “non-investment grade”.
Synthetic rating Assigning a rating to a non-rated firm based upon its financial ratios.
YTM Short for Yield-to-maturity, which is the total return anticipated on a bond, if held until it matures.
High Yield bonds Bonds that are rated Non-investment grade and compensates with higher yields.
Credit ratings are effectively third-party opinions about the credit risk of certain investments and have historically been of importance for transparency in the market.
Companies can acquire an official credit rating to signal their creditworthiness, which often is of crucial importance to enable alternative financing through the capital market (Moody’s, 2019). In Sweden and more specifically the Swedish real estate market, the demand for official credit ratings has historically not been as substantial as in other parts of the world. This is due to the fact that Swedish banks up until recently provided the market with their own credit ratings, also called shadow ratings, which the market accepted as an alternative to credit ratings from official credit rating agencies like Standard & Poor's, Moody's and Fitch (Fastighetsnytt, 2016). The Swedish banks still use their internal rating systems to evaluate the creditworthiness of their clients and use this to estimate the risk of their lending but the ESMA (European Securities and Markets Authority) have recently restricted them from publicly presenting their shadow ratings.
ESMA started investigating shadow ratings in August 2016 and decided that Nordic banks issuing shadow ratings were against the new directives because they were not registered as rating agencies. (ESMA, 2019)
Standard & Poor's, Moody's and Fitch are the three largest rating institutes in the world and as of 2020, they captured approximately 91% of the European credit rating market (ESMA, 2020). In addition to these three, there are smaller rating agencies and in 2018, the ESMA registered “Nordic Credit Rating AS” as an official credit rating agency in the Nordic region (ESMA, 2018).
In accordance with the trade-off-theory, a value maximizing firm will balance the benefits of leverage against the cost of financial distress, bankruptcy and other costs of debt (Agha et al. 2014). In addition to the cost-benefit interplay of leverage, credit ratings have also shown to affect company managers in their decisions of capital
structure (Graham and Harvey 2001). Capital structure and specifically the goal to locate
the optimal capital structure has, since the breakthrough of (Modigliani and Miller, 1958), been the center of attention for a lot of research and the issue is of great interest both academically and practically due to the ongoing debate of how to apply the theories to reality (Cohen, 2001). In practice, there are many factors that affect the decision of what leverage and capital structure a company decides to aim for. Among the factors are growth opportunities, firm size and profitability. The capital structure and the leverage of a firm is often compared to other firms as well, to evaluate the risk of the leverage (Frank and Goyal, 2003). The correlation between capital structure and credit ratings originates from the default risk of the company which corresponds to the leverage and the amount of debt the company possesses. The probability that the company will default on its debt is what the credit rating of the company will reflect (Cohen, 2003).
The Swedish real estate sector stands out in comparison to other countries because of the generally high loan-to-value ratios of Swedish real estate and this, in turn, leads to lower ratings from the credit rating agencies. The result of this has led to a split among
Swedish real estate managers whether to have an official credit rating or not and the companies that do aim for a credit rating are further divided around the question of what the most desirable ratings are. (Fastighetsnytt, 2014)
The Nordic corporate bond market has grown rapidly since the financial crisis and many bond issuers have avoided the organizational costs associated with obtaining and
maintaining a credit rating. In 2016, more than half of Nordic bonds were issued without a credit rating, while only 13% were unrated in the EU as a whole. Today, the majority of Nordic bonds are issued with credit ratings (Nordic credit rating, 2021).
With the different views on credit rating in mind and the effect it has on capital structure, a gap in literature appears, whether Swedish real estate companies have suboptimal credit ratings with regard to their capital structure. Furthermore, the recent restriction of shadow ratings places Swedish real estate in a new situation with lacking literature and research about the demand for official credit rating after the new
directives. In the absence of shadow ratings, one could expect an increased demand for credit ratings in the market.
1.1 Purpose and Contribution
With the base in corporate financial theory, the purpose of this study is to explore what factors that influence Swedish real estate companies regarding their decisions of capital structure and credit rating. With the method of semi-structured interviews and
quantitative simulations, the study aims to understand why listed Swedish real estate companies are divided in their strategies about credit ratings and to explore if the sector has suboptimal credit ratings with respect to their capital structure.
The relevance of this research originates from the recent regulation changes to shadow ratings and the gap in literature it has created, as well as lacking literature about optimal credit rating with respect to capital structure. Previous research, before the restrictions, has investigated the demand for official credit ratings but the literature is lacking afterwards, which is interesting since the restrictions could increase the demand.
Furthermore, the study aims to shed light on the strategies of Swedish real estate firms and by doing so, help the market and investors to understand the underlying factors for the decisions and why firms aim for different ratings.
1.2 Research Questions
- Do Swedish real estate companies have suboptimal credit ratings with regard to their capital structure?
- How do Swedish real estate companies set their aim regarding credit rating and what factors influence the decision?
The qualitative part of the research is limited to Swedish real estate companies with an official credit rating. The limitation is partly set because of the time constraint for the study but also with respect to national regulations that could affect the results between countries. Further, due to Covid-19, the interviews of the research were performed digitally, which might have limited the researchers from access to sensitive material that otherwise could have been observed in a physical meeting, like models for financial decisions.
The quantitative simulations are limited to publicly listed Swedish real estate companies with an official credit rating, which is a requirement in order to receive the historical data necessary for estimating the equity cost of capital and to get access to annual reports. Furthermore, the results of the simulations are only applicable to Swedish real estate companies, as the corporate tax rate has an impact on the WACC-calculations and differs between countries. A substantial part of the real estate sector is not rated and because they are not affected by the restraints that credit ratings can inflict on capital structure and financial decisions related to this, the results cannot be generalized to the entire sector. Since the quantitative part investigates optimal credit rating with regard to capital structure, the models are reliant on historical data and the result is only as good as the data inserted.
In this section, relevant research for the subject of this thesis is presented. The reviewed literature serve as a base for discussion and the conclusions of the presented research will be analyzed together with the findings of this thesis to gain depth about the subject.
The development of Sweden's non-financial corporations’ debt-financing markets is discussed in Gunnarsdottir and Lindh (2011) and the authors expect that bank regulations will increase the interest rate and decrease the supply of bank loans. The authors argue that this could lead to more efficient financial intermediation and force corporations to become more diversified with their options for financing. Achleitner, Lutz and Schraml (2009) also states that companies try to find other options for
financing when restrictions on lending occurs, and that times of recessions shows these patterns more clearly. In accordance with the previous, the Swedish central bank
confirms the trend that Gunnarsdottir and Lindh (2011) expected and one can observe, in the monetary policy report (Riksbanken, 2020), that more corporations in the capital market value the option for alternative financing. Hellström and Ängerud (2017) confirms that the more selective nature of the banks has led to increased debt
diversification in the Swedish real estate market and that financing through bonds has increased.
Several researchers have studied the effects of debt diversification and found interesting relationships. Diamond (1991) found that there is a correlation between asymmetric information and how a firm chooses to finance their debt. The findings state that firms who generally issue more public debt also have lower information asymmetries.
Faulkender and Petersen (2006), concluded in their study about levered firms, that companies with access to the bond market often have higher LTV-ratios compared to those who don't have access. Finally, empirical evidence is also found in Orlova and Harper (2016), of a positive relationship between free cash flow and debt diversification.
The result is explained with the reasoning that firms become less dependent on banks by diversifying and increasing their alternatives for financing (Orlova and Harper, 2016).
The positive relationships between alternative debt financing and debt diversification has also been found in relation to credit ratings. To many, this comes as no surprise, as credit ratings often is an underlying criterion to enable firms for debt diversification.
However, Harford and Uysal (2014) and Karampatsas et al. (2014) have shown in their research that credit ratings reduce asymmetric information and lowers the company's financial constraints. Furthermore, Hann et al. (2013) and Fee et al. (2009) found that access to external financing is significantly better for companies with investment grade ratings and that the cost of debt decreases with higher credit ratings.
Demand for credit ratings
Donner and Svensk, (2012) state that, in the Swedish real estate sector, the demand for official credit rating has historically been low. Back in 2012, none of the Swedish bond- issuing real estate companies had an official rating, except for a few government-related companies, and the authors explain that the credit rating process itself was deemed costly and difficult. Claesson (2013), states that there are two important ratios for credit risk assessment when investigating Swedish real estate companies, LTV (Loan-to-value) and the ICR (interest coverage ratio). According to Finansinspektionen (2019), Swedish real estate companies often have rather high leverage, which results in high LTV along with low ICR and this could be an important factor when analyzing why few real estate companies in Sweden have an official credit rating.
However, in a more recent market research, made by Fastighetsnytt (2016), the observed trend of increased precautions from the banks, have led to an increased demand for credit rating. Interviews show that the increased demand has its origin in a desire to strengthen one's position on the capital market and to increase the options for alternative debt financing. Researchers have also confirmed the phenomenon of increased demand for credit ratings when alternative financing increases and the author of Alp (2013) further discusses the role of rating agencies after the financial crisis 2008, which has
been highly questioned. As a result of this criticism, the author can observe that the effect on the agencies is to have more stringent ratings, due to the new regulation and directives. Bayar (2014) evaluated the new regulations on the rating agencies and states that it will increase the transparency and accountability of the agencies and also decrease the over-reliance on them. However, the author also states that the regulation probably will not increase the competition of the rating agencies, eliminate the conflict of interest completely or force the rating shopping to stop (Bayar, 2014). When evaluating the default risk of a company, thorough and stringent ratings give important information as can be viewed in (Harford and Uysal 2014; Karampatsas et al. 2014). These articles further discuss the effect credit rating has on capital structure due to differential access to debt markets
Signaling and the effects of rating changes
Kisgen and Strahan (2010) found that when a company loses its “investment grade”- rating, the company also suffers a larger increase in its cost of capital then what's appropriate for the extra risk, hence, inflicting the company with a higher probability of financial distress. For pension funds and other risk averse investors, the investment grade level is usually the benchmark and therefore, companies that don't reach this rating decrease the range of potential investors and vice versa if the company does reach
investment grade (Boot, et al. 2003). Furthermore, Kisgen (2006), found that when a company is near a change in credit rating, it issues less net debt relative to net equity.
This behavior can be thought of as an attempt from the companies to acquire a higher rating or at least not lose the current one. Kisgen (2006) argues that the findings are consistent with managers' views of ratings and the signals a rating change sends to the market about the firm quality. Even though this study was not made in Sweden, but in the US, where credit ratings, historically, have been more common, the behavior shown in the result is argued to be generalized to most credit rated companies.
Changes of credit rating level and its effect on the company stock price have been
studied in numerous researches as in Dichev and Piotroski (2001) and Abad-Romero and Robles-Fernandez (2006). These articles show that changes in credit rating has a direct
effect on the company's stock price, because of the signals it sends to the market.
Brounen and Eichholtz (2001) also investigated the reaction of stock prices when real estate companies in Europe issue debt or equity and found that the issuance of equity was received with a significant negative price reaction. Whereas the issuance of debt was met by a significant positive price reaction from the stock market. European countries with high corporate tax policies had an even more negative price reaction to issuance of equity, than countries with lower corporate tax. Due to the credit rating agencies thorough due diligence, their decision on credit rating is received as important information and the results further show that there is a larger impact on stock prices when a company is being downgraded then upgraded. (Brounen and Eichholtz, 2001)
Credit rating and Capital structure
When it comes to non-listed real estate companies Morri and Cristanziani (2009) found that they have more leverage than listed ones which is relevant to keep in mind when evaluating capital structure in the industry, since leverage in turn affects the value of the interest tax-shield. Morri and Cristanziani, (2009) could observe in their study that managers of more levered firms, try to reduce the risk by more carefully planning their capital structure and the study also found that firms' asset size have a correlation to the amount of debt issued and that debt becomes cheaper for larger companies.
Kisgen (2006) investigates the effect credit rating has on capital structure decisions and found that it is one of the highest concerns for chief financial officers, which also is in line with the findings of Graham and Harvey (2001). The cost of financing through bonds could increase rapidly, according to Brown and Riddiough (2003), if the bonds are labeled “junk-bond” (speculative grade) and the authors explain that therefore, companies tend to cluster above “investment grade”-rating in order to optimize their capital structure. This article mostly focuses on real estate investment trusts (REIT) and not on ordinary real estate companies, but the result is interesting and supports the findings of Boot et al. (2003), regarding the increased amount of potential investors over the investment grade.
Gamba and Triantis (2008) uses the expression financial flexibility in the context that it represents the firm's ability to make changes in debt ratios at the lowest possible cost.
Furthermore, they highlight that with increased flexibility the access to funding increases which is an important factor for capital structures decisions, whilst Kisgen, (2006) make the point that credit rated companies can experience less flexibility because of the risk of being downgraded a level. This, as mentioned earlier, is especially relevant in the case of ratings around “investment grade”.
Riddiough (2004) tried to elaborate on the classical answer “use debt rather than equity, because it is cheap” to the question of “how to optimize real estate companies’ capital structure”. Riddiough explains that the answer is historically correct, because using debt results in a low weighted average cost of capital, but that the answer is more complex.
The result in this paper shows how a gap in the traditional market for external finance can appear and how convertible loan, outside equity and mezzanine debt could fill this gap. Luna (2013), when studying income producing properties, developed a
methodology for investigating the optimal capital structure. Luna (2013) stated that, even though not having a flawless model, the optimal capital structure exists and the value gain for optimally leveraged properties is in general low for a wide range of LTV- ratios. Furthermore, the study concludes that under some conditions, excess leverage could significantly reduce the levered company's value.
In order to calculate the relationship between the cost of capital (in form of the WACC) and leverage one can follow the analytical process in Cohen (2003), where a version of locating the optimal capital structure is presented. Amaya et al. (2015), state that the analytical process, Cohen uses to reach the WACC-curve, is not fully correct, because Cohen is keeping the cost of equity fixed which should change with the LTV-ratios.
Laghi and Marcantonio (2016) studies firms, unlike Cohen, without credit rating and further estimates the cost of equity through the Capital Asset Pricing Model. The results show, through regression, that this model systematically underestimates the cost of equity and argues that this speaks for the Pecking order theory, because the use of equity is not prioritized. Longstaff et al. (2005) find that, an explanation for why companies use
less debt then what the trade-off theory would predict for optimal capital structure, lies in the fact that the default component stands for a majority of the corporate spread and that liquidity affect the cost of debt. This is also in line with the result of Collin-
Dufresne et al. (2001), but one should keep in mind that both studies were made before the financial crisis 2008, which has arguably affected the calculations for corporate spread.
Bond and Scott (2006) conclude that the pecking order theory is dominated by the trade- off theory in a direct comparison of the dynamic models. Whereas, the result of Frank and Goyal (2000) concludes that the pecking order theory outperforms the conventional leverage model of the observed companies. Frank and Goyal state that leverage has a negative correlation with growth opportunities and profitability. One can observe in the above-mentioned articles that this area of finance is well discussed and Dissanaike et al.
(2001) even states in their findings that financing cost factors are more important for the observed firms than targets of capital structure.
As observed in Luna (2013) and Riddiough (2004), real estate firms optimal capital structure isn't a simplistic matter and needs further investigation on a company-level basis. Cohen (2003) also states that it is widely believed in practice that companies should have an optimal capital structure coinciding with one specific credit rating, where the belief is spread between AA to B+.
Aswath Damodaran argues, in Damodaran, (1999 A), that credit rating could be used for measuring the country risk when calculating the equity risk premiums. This, as the rating agencies assign rating to countries with the default risk in mind. Damodaran (1999 A) also confirms some of the advantage these ratings have, such as being easily accessible, coming with default spreads and that it considers the stability of a country’s currency, its budget, trade balances and its political risks. Damodaran (2009), demonstrates that the cost of debt can be calculated by the use of a risk-free rate and the default spread of a rated company, while unrated firms' cost of debt needs be calculated through a synthetic rating. Damodaran (2009) elaborates on the relationship between the interest coverage
ratio and firms’ official ratings and shows that synthetic ratings can be calculated from firms’ financial ratios, even if it's a private business. This way of estimating the cost of debt is widely used and confirmed by Callahan and Mauboussin (2013). The authors also explain that illiquid companies, can estimate their cost of debt by determining a credit rating, similar to Damodaran (2009), and look at the average yield-to-maturity on a portfolio of bonds with the same rating.
The theoretical framework creates the lens through which the collected data and observations will be analyzed. The theoretical framework presents the chosen theories, important concepts and the financial models of the research, which all together creates the foundation upon which the method is built.
This section presents theories that serve as a base for the research of the thesis. The theories are explained, and their applicability and relevance are described with respect to the subject.
3.1.1 Modigliani-Miller theorem
In their seminal work, Modigliani and Miller (1958) states that a company's market value solely originates from its earnings and the risk of the business. Hence, the company's value is calculated as the present value of its future cash flow and its underlying assets, with no consideration to capital structure. The statements of firm value are derived under the assumptions of perfect market conditions. Implying that the market is free of taxes, free of friction, free of information asymmetry and that all parties can lend and borrow money at the same conditions. These assumptions are not
applicable to real world settings and in their later work Modigliani and Miller (1963), the authors include taxes, bankruptcy costs, and asymmetric information. The first proposition of the Modigliani and Miller theorem with taxes, states that tax-deductibility positively affects a company’s cash flows, leading to the assumption that tax shields increase the value of the levered firm in comparison to the unlevered firm. The Second proposition of the theorem with taxes assumes a direct relationship between leverage of a firm and its cost of equity. (Modigliani and Miller, 1963)
Figure 3.1: WACC-curve (weighted average cost of capital).
The lowest point on the WACC-curve indicates the optimal capital structure.
The work of Modigliani and Miller is considered as the foundation for corporate
financial theory and the following theories are made to better understand and explain the capital structure decisions of corporate firms.
3.1.2 Trade-off theory
According to the trade-off theory of capital structure, a value maximizing firm will try to find its optimal level of leverage by looking at the benefits of interest tax shield, along with other benefits of debt and balance these benefits against the cost of debt, the cost of financial distress and the cost of bankruptcy (Agha et al. 2014). An interest tax shield is a reduction in taxable income for a firm achieved through claiming allowable deductions from interest paid on mortgages. Financial leverage is the use of debt to purchase an asset with the expectation that the capital gain of the asset will exceed the cost of borrowing. The cost of financial distress increases with the level of debt, while the benefit of interest tax shields only occurs as long as the firm has positive earnings. To find the levered firm's value, the theory takes the value of the all-equity financed firm and adds the present value of the tax shield while subtracting the present value of the cost of financial distress (CFI, 2020). The optimal level is found at the threshold where the cost of debt outweighs the benefits of it. It can be illustrated using the WACC-curve, where the lowest level of the weighted average cost of capital shows the point where further debt added to the capital structure no longer favors the cost of capital, see figure 3.1.
The theory has been widely tested and since its creation, authors have found
relationships that either are supported by the theory or goes against it (Fama and French 2002; Ozkan 2001). The theory suggests that highly profitable firms should borrow more than less profitable firms to reduce tax liabilities but as an example of the contradictory findings, Fama and French (2002) found the opposite, namely that highly profitable firms borrow less. Another discussion is brought up by Myers (1984) who states that firms with high growth opportunities are more likely to not choose debt as their first financing choice, since such firms have greater agency costs and bankruptcy costs.
The trade-off theory is often divided between a static or a dynamic framework. The static version is described above, where a firm try to balance the costs of financial distress with the tax shield benefit from using debt and in this version, there exists an optimal capital structure between equity and debt. Whereas the dynamic trade off theory suggests that firms set their target leverage ratios within an optimal range, in which the ratios could vary somewhat. There is still no conclusion whether one theory could explain firms capital structure decisions the best, however study's within both the static and dynamic trade off framework, have identified firm characteristics as determinants of companies capital structure. Some of the most important found characteristics are firm size, growth, profitability, depreciation and volatility of earning (Frank and Goyal 2003;
Rajan and Zingales, 1995). Studies have also found that firms in the same industry often have similar leverage and adjust their debt towards an industry average (Hull, 1999).
Lev (1969) used a dynamic model to display at what speed a firm adjust its key ratios to more industry standard.
In relation to credit ratings, Kisgen (2006) explains that the costs associated with a change in credit rating needs to be taken into consideration when looking at a firm’s decision of capital structure, with the point that the theory might not hold for credit rated firms in situations near a rating change.
Figure 3.2: Hierarchy of the Pecking order theory
3.1.3 Pecking order theory
Myers (1984) developed the pecking order theory and it is based on the knowledge that there exists asymmetric information between the executives of a firm and its equity investors. The information gap about the firm's risk exposure as well as its opportunities lead to the final statement of the theory, namely that firms prioritize their financing according to a certain hierarchy, in order to avoid the costs of the information
asymmetry. According to the theory, the preferred financing source is retained earnings, which is followed by debt and finally equity, which is seen as a last resort (Agha et al.
2014), see figure 3.2.
The reason why debt is preferred over equity originates from the signals that issuance of new equity sends. Rational investors often interpret issuance of new equity as the firm being overvalued or that the firm finds itself in a financial situation where it can´t take on more debt.
Therefore, rational investors often demand a discount on the current market value to compensate for the information asymmetry, making external financing by equity have a negative effect on the stock price and be costly for the firm (Myers and Majluf, 1984).
According to the theory, firms will also increase their liquid assets during profitable years to be able to use them during less profitable years, to ensure that the company continues to use prioritized financial sources in the pecking order (Myers and Majluf, 1984).
Just like the case with the trade-off theory, Kisgen (2006) explains that credit rated firms do not always behave in the expected way according to the pecking order theory. Kisgen (2006) continues with an example where a firm is near a credit rating change and
chooses to issue equity instead of debt to avoid an increased loan-to-value ratio.
Signaling and adverse selection
Adverse selection may occur when two parties, buyers and sellers, have different information about, for example, the quality of a product. Typically, the sellers have better information than the buyers and because of the risk aversion of the buyers, they might not be willing to pay what the product actually is worth, because of the
information asymmetry. In a situation like this, adverse selection could push the high- quality products out of the market, because the sellers of the high-quality products can't get a reasonable price (Akerlof, 1970).
Signaling can be used by an informed party to share knowledge with a less informed party and hence, avoid adverse selection. In a customer-seller relationship, the seller might want to signal the high quality of its products, to ensure the customer that the price is reasonable. However, signaling only solves adverse selection problems if the customers view the signaling as credible (Perloff, 2018).
In relation to real estate, issuance of debt and equity are examples of signals that a firm can send to the market. While issuance of equity can be interpreted as the firm being overvalued, issuance of equity can also be a sign of a company trying to reach a better credit rating, by avoiding increased LTV-ratios. The way financial behaviors are interpreted may vary and therefore, signaling in form of disclosure for why certain decisions are taken can be of importance.
Figure 3.3: Outstanding volumes of corporate bonds issued by Swedish companies. Source: The Riksbank and Statistics Sweden (SVDB)
3.2 Credit rating and credit spread
This section elaborates on the concept of credit rating and credit spread. The concept of credit rating is a central part of the research and credit spread is a key factor for the simulations of the thesis.
3.2.1 Credit rating
Credit ratings are effectively third-party opinions about the credit risk of certain investments and have historically been of importance for transparency in the market.
Companies can acquire an official credit rating to signal their creditworthiness, which often is of importance to enable alternative financing through the capital market (Moody’s, 2019). As can be viewed in figure 3.3 the outstanding volumes of corporate bonds in the Swedish capital market has experienced a steady growth over the last decade. A trend that arguably increases the competitive advantage of having an official credit rating.
Credit rating is an evaluation of a company's ability and likelihood of paying back debt.
The credit rating evaluation consists of a numeral subset of categories that weighed into a total credit score, which is done by the credit rating institutes. Some of the categories evaluated are market positioning and asset quality, operating environment, liquidity and access to Capital, loan-to-value and interest coverage ratio. (S&P, 2020)
The risk level of the investment environment within countries is evaluated in the sovereign credit rating. The country risk is the risk considering the different factors, such as political risk, that could affect the issuers’ ability to pay back the debt. This is an important rating for countries that want access to international bond markets, and it is the three big rating institutes (S&P, Moody's and Fitch) that are most influential in deciding a country's rating. Some of the evaluated categories are income per capita, GDP growth, external debt, inflation and historical default (CPI, 2021). Sweden has a rating of AAA with a stable outlook from S&P, Moody´s and Fitch (Riksgalden, 2021).
Rating institutes often divide the ratings into short- and long-term, which indicates the likelihood that the issuer will default within a certain time. Short term is often
considered one year or less and long term refers to a time horizon over one year (Moody's, 2021).
Credit rating institutes use different rating scales when evaluating the issuers. S&P, Fitch and NCR use uppercase letters with plus/minuses to describe the credit rating.
While Moody´s uses a combination of uppercase letters and of lowercase letters with an added number to describe their rating. In table 3.1 one can observe the rating scales and how it translates to other rating institutes scales. The scale rank from excellent/prime to poor/default, from AAA/Aaa to D. All grades over BBB-/Baa3 is considered as
Investment grade and all grades under are referred to as Non -Investment grade, Speculative grade and Junk grade.
Table 3.1: International credit rating agencies scores
In the European market, the most common credit rating for corporate bonds, during August 2020, was BBB+. In figure 3.4, one can see the distribution between the rating categories. Note that all corporate bonds with a rating below investment grade are combined into the category “High Yield”. In comparison to the European market, figure 3.5, illustrates the credit rating distribution of outstanding Swedish corporate bonds, at the same point of time. Note that the combined group of non-rated corporate bonds in Sweden stood for about 30% of the outstanding bond volumes. The same category in Europe stood for about 8%. However, the most common rating category among the rated bonds in Sweden was BBB.
Figure 3.4: Credit rating for outstanding corporate in the euro area. Source: The Riksbank and Statistics Sweden (SVDB)
Figure 3.5: Credit rating for outstanding corporate bonds issued by Swedish companies in SEK. Source: The Riksbank and Statistics Sweden (SVDB)
Figure 3.6: Monthly issues of corporate bonds by Swedish companies in SEK by credit rating. Source: The Riksbank
As can be viewed in figure 3.5, most credit rated Swedish companies lie above the investment grade benchmark. In figure 3.6, one can more clearly see the distribution between Swedish outstanding investment grade bonds and high yield bonds, from January 2019 to September 2020.
Figure 3.7: Sector distribution for outstanding corporate bonds issued by Swedish companies in SEK. Source: The Riksbank and Statistics Sweden (SVDB)
Figure 3.8: Monthly volumes of corporate bonds issued in SEK by Swedish companies. Source: The Riksbank and Statistics Sweden (SVDB)
Regarding the sector distribution of the outstanding bonds in Sweden, the Swedish Riksbank shows that, during August 2020, the real estate sector stood for approximately 50% of the corporate bonds issued in SEK. The Riksbank further declares that the real estate sector is the sector with the largest volumes both in the investment grade- and the high yield segment. As a consequence of the real estate sector's influence, the Riksbank argues that the entire Swedish market is affected by how investors assess the health of the real estate sector (Riksbank, 2020).
In figure 3.7, one can view the sector distribution for outstanding corporate bonds, issued by Swedish companies in SEK (August 2020). Figure 3.8 shows the monthly volumes of corporate bonds, issued in SEK by the real estate sector in comparison to the other sectors combined. Figure 3.8 also illustrates the average monthly volume issued by Swedish real estate sector during 2019, which was approximately 7 billion SEK per month.
Figure 3.9: Swedish real estate rating distribution
The majority of the companies in the Swedish real estate market are rated within the BBB-segment and no company is rated above AA. This is illustrated in Figure 3.9.
3.2.2 Credit spread
The word “spread” is used in different scenarios and refers to a variation of phenomena.
In finance, a spread can be referred to as the difference between two prices, such as the gap between the bid from a buyer and the asking price from the seller of a security.
Furthermore, it can also refer to a gap in trading positions, where the difference in a short position (selling) or a long position (buying) is inferred. In the context of yields, bond and credit rating, one often refers to the credit spread or “yield spread”. The credit spread shows the difference between the return (yield) of two different debt instruments with the same maturity and illustrates the different qualities of the debt instruments.
Common among investors is to refer to the spread as the “credit spread of X over Y”.
Meaning the annual percentage return on investment (yield to maturity, YTM) of financial instrument X minus the YTM of financial instrument Y. The credit spread reflects the additional yield required for a buyer to take on the extra credit risk. Credit
spread is in practice commonly derived from the gap in yields between a treasury bond, which is considered “risk free”, and a corporate bond, as can be viewed in equation 3.1.
𝐶𝑟𝑒𝑑𝑖𝑡 𝑠𝑝𝑟𝑒𝑎𝑑 = 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝐵𝑜𝑛𝑑 𝑌𝑖𝑒𝑙𝑑 − 𝑇𝑟𝑒𝑎𝑠𝑢𝑟𝑦 𝐵𝑜𝑛𝑑 𝑌𝑖𝑒𝑙𝑑 [3.1]
When subtracting the risk-free rate (the Treasury bond yield) from the corporate bond yield, the riskiness of the bond is determined and the higher the spread, the riskier the bond is. Investors can also substitute the Treasury bond yield for a benchmark bond yield, which could be an AAA-rated corporate bond yield (CFI, 2021), see equation 3.2.
𝐶𝑟𝑒𝑑𝑖𝑡 𝑠𝑝𝑟𝑒𝑎𝑑 = 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝐵𝑜𝑛𝑑 𝑌𝑖𝑒𝑙𝑑 − 𝐵𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘 𝐵𝑜𝑛𝑑 𝑌𝑖𝑒𝑙𝑑 [3.2]
The credit spread varies and is not static over time. The change in credit spread, widening and tightening range, is generally caused by certain economic conditions.
When market conditions deteriorate, the demand for corporate bonds drops and the preferred investment is Treasury bonds, which increases the price of Treasury bonds and simultaneously lowers the yield. This, while the outflow of capital from corporate bonds, makes the yield increase and the price of the bond decrease (CFI, 2021). The result of this market conditions is a widening in credit spread, which is illustrated in figure 3.10.
Figure 3.10: Deteriorating market conditions effect on credit spread
Figure 3.11: Improving market conditions effect on credit spread
When market conditions improve, the opposite is expected to happen, where investors become less risk averse and the demand for corporate bonds increases and Treasuries are being sold. Capital outflow in Treasury bonds, makes the yield increase and the price of the bond decrease. On the other hand, inflow of capital from Treasuries to corporate bonds, makes the yields decrease and the price of the bond increases. As can be viewed in Figure 3.11, in a scenario of improving market conditions the credit spread between the securities would therefore narrow. (CFI, 2021)
The yield spread of bonds varies between countries, different types of bonds and it also depends on the rating of the issuer. In figure 3.12, one can view the difference in yield spreads between Swedish zero-coupon corporate bonds, Swedish zero-coupon covered bonds and Swedish zero-coupon municipal bonds. The graphs all refer to bonds with a five-year maturity and the spreads are calculated in relation to the five-year zero-coupon Swedish government bond yield. The higher the yield spread, the higher the risk and its evident that the corporate bonds are the riskiest and most volatile between the examples.
Which is specifically evident during the outbreak of Covid-19 in the first quarter of 2020.
In figure 3.13, one can instead view the difference in yield spread for corporate bonds with five-year maturity and different ratings. The figure further shows the difference in yield spreads between corporate bonds with the same rating, issued in USD versus EUR.
Figure 3.12: Yield spreads for different Swedish bonds.
Source: The Riksbank and Statistics Sweden (SVDB)
Figure 3.13: Yield spread for 5-year corporate bonds issued in the euro area and the United states respectively. Source:
The Riksbank and Statistics Sweden (SVDB)
The yield spread between different ratings are illustrated for the BBB and the AA rating classes. The graph clearly illustrates that less risky bonds, hence higher rated, demand lower yields. The graph also shows that the European market and the US market have similar yield spreads for bonds within the same rating class.
3.3 Financial models
In order to perform a simulation of the theoretical optimal capital structure for the examined real estate firms, this section presents the chosen models used and its different components are described.
3.3.1 Asset pricing model
As cost of equity is unobservable, unlike the cost of debt, practitioners have to rely on an asset pricing models in order to estimate it. The best-known models for this, according to Credit Suisse´s practical guide for “Estimating the Cost of Capital”, are the Fama-
French Three-Factor model, the arbitrage pricing theory (APT) and CAPM. The most popular method used is the capital asset pricing model and therefore also chosen in this research (Callahan and Mauboussin, 2013).
Ex-ante and Ex-post are Latin terminologies, meaning “before the event” and “after the event”, that is used when estimating the returns of a security. Ex-ante predictions are often uncertain as it accounts for multiple variables affected by market forces in the future. Ex-post accounts for results that already occurred, such as actual or historical return, and could be used to forecast future returns. This research uses ex-post empirical data to estimate the betas of targeted firms for the simulations. The CAPM equation allows the researchers to calculate the ex-ante cost of equity in order to finally estimate the weighted average cost of capital.
The Capital Asset Pricing Model is used to describe a relationship between the expected return and risk of investing in a security. CAPM is widely used within finance to
determine an appropriate required rate of return of an asset, oftentimes on stocks. CAPM shows that expected return is equal to the risk premium, on a security/asset, plus the risk-free rate, see equation 3.3.
𝑅𝑒 = 𝑅𝑓 + 𝛽𝑒 ∗ (𝑅𝑚 − 𝑅𝑓) [3.3]
Re: Cost of equity Rf: The risk-free rate βe: Beta equity
Rm: Expected market return
Figure 3.14: The Capital Asset Pricing Model
The risk premium is based on the beta of the investigated security and is illustrated below, figure 3.14.
Risk free rate
The risk-free interest rate for a given period is defined as the rate at which money can be lent or borrowed without risk for that specific period. Just like other market prices, the risk-free rate depends on supply and demand where, at the risk-free interest rate, the supply of savings equals the demand for borrowing (Berk and DeMarzo, 2017). In practice, the risk-free interest rate is calculated by looking at government bonds. If a risk-free government bond pays X SEK in one year and currently trades at Y SEK on the competitive market with no arbitrage, the risk-free interest rate is determined by
equation 3.4 (Berk and DeMarzo, 2017).
1 + 𝑟𝑓 = 𝑋/𝑌 [3.4]
Determining an appropriate risk-free rate can, however, be more complex than that. The risk-free rate in CAPM refers to the rate at which investors can both borrow and save, and even if a loan essentially is risk free, a premium usually compensates lenders for the
difference in liquidity between their funds and government treasuries. This is making investors pay substantially higher rates to borrow money (Berk and DeMarzo, 2017).
Market risk premium
The market risk premium is an essential part of the CAPM model, and it is defined as the expected excess return of the market in comparison to the risk-free rate (Berk and DeMarzo, 2017). Accurately estimating the future expected market premium, however, is rather complex and one often has to consider a trade-off to reach a result. One way of estimating the expected premium is to look backwards on historical average excess returns. The trade-off that arises with this approach, lies in the fact that even with a 50- year period of historical data, the standard error of the result is substantial. At the same time, the relevance of the data declines with the years, as the market changes over time, and therefore using a large sample of data, to reduce the standard error, would not necessarily provide a reliable expected market premium for the future (Berk and DeMarzo, 2017).
An alternative way is to use a fundamental approach. With the requirement of estimating a constant dividend growth rate, one can calculate the expected market return by adding the dividend yield of the market index to the expected constant dividend growth rate, see equation 3.5 (Berk and DeMarzo, 2017).
𝑟𝑀𝑘𝑡 = 𝐷𝑖𝑣1/𝑃0 + 𝑔 [3.5]
Rm = Expected market return Div1/P0 = Dividend yield g = Expected dividend growth
The approach is highly inaccurate for individual firms, but the assumption of constant dividend growth has shown to be more reasonable when considering the overall market (Berk and DeMarzo, 2017).
The beta measures a stock or other securities sensitivity to market risk. The beta
represents the expected change (%) in return of a security when the return of the market portfolio increases by one percent. Further, estimating the beta of a stock is more reliable than estimating the expected market return. Regression analysis has shown to provide reliable betas with only a few years of historical data and the betas tend to remain stable over time (Berk and DeMarzo, 2017).
Cost of equity
The cost of equity refers to the rate of return that a company is expected/required to pay too its equity investors as a compensation for the risk the investors take. The cost of equity can be estimated using the CAPM model given the appropriate risk-free rate, the expected market return and the beta of the firm's assets (CFI,2021).
Cost of debt
The cost of debt is the rate of return that a company is contracted to pay its debt holders (CFI,2021). A company can have different loans and outstanding bonds with different interest rate requirements. For listed companies, one can observe the firm's annual reports and if the average cost of debt is not given, it's possible to calculate it by dividing the firm's yearly interest payments by their total debt. Further, it is possible to estimate the cost of debt related to credit ratings by looking at the yield spread at a certain rating level and add that to the risk-free interest rate. See equation 3.6 (Damodaran, 2009).
There is a significant correlation between the default risk and the corporate spread calculated by the rating agencies, through the credit rating model (CRM), leaving the cost of debt to be calculated by adding Rf to the spread. (Damodaran, 2009)
𝑅𝑑 = 𝑆𝑝𝑟𝑒𝑎𝑑 + 𝑟𝑓 [3.6]
Rd = Cost of debt rf = Risk free rate
The weighted average cost of capital reflects the average rate that is expected/required to pay to all its investors, both debt and equity holders. In a world with taxes, where
interest payments are tax deductible, the WACC reflects the effective after-tax cost of capital for the firm, see equation 3.7 (Berk and DeMarzo, 2017).
𝑊𝐴𝐶𝐶 = (𝐸/𝑉 ∗ 𝑅𝑒) + (𝐷/𝑉 ∗ 𝑅𝑑 ∗ (1 − 𝑇𝑐)) [3.7]
E: Market value of the firm's equity D: Market value of the firm’s debt V: Firm value = D+E
Tc: Corporate tax rate Rd: cost of debt Re: cost of equity
Based on the theoretical framework, this section presents the chosen research strategy and the mixed methods chosen to reach the results of the thesis. The qualitative and quantitative analysis are described as well as the strategy to ensure validity and
reliability in the research. Finally, this section also describes the ethical considerations of the thesis.
4.1 Research strategy
In this section, the research strategy of the thesis is described.
To accurately answer the research questions and gain depth in the subject, the research strategy involves a mixed method approach where focus lies on a qualitative part in form of semi structured interview which in turn, is strengthened and supported by quantitative simulations. The quantitative simulations include statistical analysis of gathered
secondary data which, arguably, is a both time saving and cost-efficient way of gathering information (Saunders, Lewis and Thornhill, 2016). While not being the prioritized method to answer the research questions of this thesis, the quantitative simulations are considered perfectly suitable to effectively gain depth in the research while at the same time, strengthen the qualitative interviews.
4.2 Qualitative analysis
In this section, the qualitative method and the qualitative data is described.
Furthermore, this section presents the interviewees of the thesis and the strategy regarding interview questions is discussed.
4.2.1 Qualitative method
Qualitative methods, like interviews, are research strategies that emphasize words instead of numbers when analyzing and collecting data (Bryman, 2008). Interviews are in turn divided into structured, semi-structured and unstructured interviews (Saunders, et
al. 2016). For this thesis, the semi-structured version was chosen, and the interviews were conducted online, due to recommendations regarding Covid-19.
Structured interviews are typically more formal, where the interviewer uses
questionnaires based on standardized, identical sets of questions for each respondent.
These types of interviews are often made to gather quantifiable data and the interviewer should ask the questions in the same way, with the same tone for each respondent to avoid any bias. (Saunders, et al. 2016)
Unstructured interviews are informal. Unlike the structured counterpart, unstructured interviews are used to explore depth about a subject. The format doesn't use any
predetermined questions and the point is to gather qualitative data. Before the interview, the researcher needs to be clear about the aspect or aspects of interest for the research.
Once the interview begins, the interviewee is encouraged to talk freely and thereafter, the interview follows the perception of the interviewee and the topics they find interesting for the subject. (Saunders, et al. 2016)
As a combination of structured and unstructured interviews, semi-structured interviews are seen as a more flexible version than structured interviews, enabling the researcher to gain depth and expand on the respondents' answers (Alshenqeeti, 2014). It is not as time consuming and in depth seeking as unstructured interviews but still a suitable method to search for more information and observe the perception as well as opinions of the respondent, regarding complex and sensitive subjects (Barriball and While 1994). The order of the questions for each interview might vary depending on the flow of the interview but each interview often contains a list of key questions and themes to be covered, along with unique comments to enable discussion and promote the respondent to elaborate on their answers. The strategy makes it possible to pick up other important thoughts about the research topic that otherwise could have been left out (Ghauri and Grønhaug, 2010). In addition to the predetermined questions, other questions might also
come up during the interviews, depending on the need to further explore the research question (Saunders et al. 2016).
4.2.2 Qualitative data
To answer the research questions and gain the desired depth of the analysis, the interviews were targeted towards Swedish real estate managers, bank managers, investors and a rating institute. The thoughts and opinions of bank managers and investors were helpful to discover if the different actors have similar perceptions of the demand for credit ratings and to further explore underlying factors, leading up to the real estate managers aim for credit ratings. Furthermore, the views of bank managers and investors, as lenders, were useful to uncover if real estate managers potentially have suboptimal credit ratings with respect to their capital structure and profitability. The views of the rating institute provided helpful information about the rating process and what factors/key figures that are important for the final credit rating. Among other things, the response of the rating institute further contributes to exploring the demand for credit ratings.
The interviews serve as the source for primary data collection and the gathered data from the different actors were then critically analyzed and categorized through the lens of the theoretical framework.
The semi-structured interviews as a method were chosen as the primary source of data collection for this thesis and it is believed to provide the required knowledge to answer the research questions. It is considered a suitable method as it enables the researchers to have a theme of questions for several interviews while at the same time enabling and promoting the respondent to elaborate and add additional information.