Master Thesis
The effect of the 2015 Housing Act on the performance of housing corporations. D.C. van Buuren
s2222213 University of Groningen Faculty of Economics and Business
Supervisor: dr. E.G. van de Mortel Co-assessor: dr. S. Girdhar
Abstract
The aim of this study is to investigate the effects of the revised Housing Act 2015 on housing corporations in the Netherlands. The Housing Act was revised after the misconduct that happened in the sector and the parliamentary inquiry that resulted from this misconduct. This study investigates the effect of the Housing Act on the financial risks and social performance of housing corporations. This study draws on New Institutional Sociology to describe the pressures that result from the Housing act to which housing corporations are exposed to in order to make a prediction. Furthermore, differences in financial risks between regions in the Netherlands are explored. This study uses a database that extends a period from 2012 to 2017 with 307 corporations. The results show a decrease in the financial risks of housing corporations. Further, a decrease in the social performance of housing corporations was found. Additionally, the results indicate that financial risks in the Randstad are higher than in the rest of the Netherlands. On the other hand no differences in financial risks were found between regions with a population decrease and the rest of the Netherlands. The possible explanations for the results are discussed with the aid of interviews. Finally, the limitations are discussed and recommendations for further research is done.
Table of Contents 1. Introduction 4 1.1. Relevance 4 2. Sector description 6 2.1. Background 6 2.2. Housing Act 2015 8 3. Literature review 10 3.1. Institutional theory 10 3.2. Coercive forces 11 3.3. Mimetic forces 12 3.4. Normative forces 12 3.5. Hypothesis development 13
3.6. Housing market region 14
3.7. Population decrease areas 15
4. Method 15
4.1. Dataset and sample selection 16
4.2. Dependent variables 16 4.3. Independent variables 20 4.4. Control variables 20 4.5. Research Design 22 5. Results 23 5.1. Descriptive statistics 23 5.2. Results of analyses 23 6. Discussion 30 6.1. Financial risks 30 6.2. Social performance 32 6.3. Randstad 33
6.4. Population decrease areas 34
6.5. Limitations and future research 35
7. References 36
The effect of the 2015 Housing Act on the performance of housing corporations. In the Netherlands the social housing market is dominated by housing corporations, these organizations are privately run but carry out a public task. With more than 30 percent of the Dutch housing stock owned by corporations (CBS, 2016). The Netherlands is in this perspective by far number one within Europe (Scanlon, Fernández Arrigoitia, & Whitehead, 2015). These housing corporations focus on building, managing and rent out housing with an affordable rent to people with a low income. They had a vital role, after World War II, in solving the housing shortage. Today, these corporations still play an important role in providing the Netherlands with an affordable housing stock.
In recent year, due to various incidents, housing corporations gained increased public
attention. These incidents ranged from integrity scandals to financial maladministration which resulted in billions of euros lost. This led in 2013 to a parliamentary committee of inquiry1. They concluded that there was inadequate control of the performance of housing corporations. Additionally, it was unclear to whom corporations had to take responsibility. The committee recommended, among others, increased transparency in the performance and resource usage of housing corporations. This was to be achieved by reconsidering the current institutional design (Elsinga, Hoekstra, van ’t Hof, van der Leij, & van Rijn, 2014; Veenstra, Koolma, & Allers, 2017).
In 2015, this led to the revising of the Housing Act, the tasks of the housing corporations were restricted and recorded. The Act is supposed to limit the financial risks, secure the quality of the social housing and arranges allocation of housing to people with a low income. The latter two will further be described as social performance. In addition, the authority housing corporations2 was introduced. It is engaged with monitoring the housing corporations and has the ability to impose sanctions
(Rijksoverheid, 2015). With this revised Housing Act a fitness and reliability test was introduced for the directors and the board of directors. This so called ‘fit and proper test’ is intended to improve the quality and integrity of the directors and internal supervision (Weebers, 2015).
Housing corporations have been under a lot of external pressures to implement changes. Especially the recent pressures from the revised Housing Act. These changes were intended to reduce financial risks and increase social performance. However, so far no research has been done into the effect of these pressures from the Housing Act on the financial risks and social performance of housing corporations and whether these changes happened abruptly or gradual.
1.1 Relevance
In 2018 the committee ‘Van Bochove’ investigated the effects of the law. The prospective of their investigation was the social responsibility of housing corporations. Van Bochove et al. (2018, p.5) concluded that “the objectives of the Housing Act have largely been achieved and notes that housing corporations comply with the law”. Further, they concluded that both internal and external
supervision on corporations had improved. However, the orientation of their study was mainly focused on the administrative level. Therefore, giving little insights in the effect of the Housing Act on other aspects. Additionally, to be able to justify intervening changes in the sector there needs to be enough information to substantiate such decisions. Priemus (2003) noticed that this wasn’t the case. This led Veenstra, Koolma, and Allers (2013) to investigate the efficiency of housing corporations. The authors concluded that in the sector there is still a lot to be gained in terms of efficiency. This study aims to fill these gaps by investigating whether the revised Housing Act have led to reduced financial risks and a better social performance of housing corporations. The knowledge gained with this research can moreover be used to make further improvements to the Housing Act. Because of the quantitative nature of this research it gives an insight in the effect of the Housing Act for the whole sector.
As mentioned before housing corporations own more than 30 percent of the Dutch housing stock, which are 2,3 million houses (CBS, 2016). It is thus clear that corporations have an important role within the Dutch society and an important social responsibility. Therefore the performance of corporations, both social and financial, are of vital importance for the stakeholders (Veenstra et al., 2013). Additionally, because corporations’ loans are guaranteed by a governmental bail-out scheme it is important that their resources are used in a responsible and optimal way.
Corporations operate between the spectrum of the public and non-profit sector. Although they execute a public task, they can be characterized as non-profit enterprises (Veenstra et al., 2017). This position causes paradoxical demands on corporations. On the one hand housing corporations are non-profit and thus operate under a non-distribution constraint, which means they are not allowed to distribute any profit (Jensen, 2003). Which may reduce the incentive to optimize efficiency (Walker & Murie, 2004). On the other hand, corporations have to be self-sufficient and serve a public task. Additionally, corporations have to comply with a large quantity of legislation and requirements. Furthermore, this legislation has been affected by multiple changes in the past (Elsinga et al., 2014). All in all housing corporations have been and are exposed to, sometimes contradicting, pressures and changes.
To describe these pressures and changes that housing corporations are exposed to this study draws on institutional theory, and in particular new institutional sociology (NIS), in order to make a prediction about the financial risks and social performance of corporations. Because it is argued that external pressures largely shape organisational structures instead of efficiency objectives (DiMaggio & Powell, 1983). These external pressures are exercised by institutions which influence human
interaction by putting formal and informal constraints upon them. Complying with these pressures provides legitimacy for organizations (Burns & Scapens, 2000). In other words, institutions influence housing corporations because of formal and informal constraints to which they are exposed. An example of these constraints is the revised Housing Act.
outcomes3. Previous studies have investigated performance in the public sector (Afonso, Schuknecht & Tanzi, 2005) and the efficiency of housing corporations (Veenstra et al., 2013). But research on housing corporations and their efficiency remains scarce (Veenstra et al., 2017). Additionally,
empirical research into the effect of legislation on the performance in the (semi) public sector happens not very often. The relationship between law and institutional theory has had some attention, mainly with regard to new institutional economics (NIE) (Drobak, 2008). However, the relationship between law and NIS has received little attention, especially in the (semi) public sector. This is needed because NIS is involved with institutional pressures and law and regulations have, among others, a major part in these pressures. Particularly with regards to formal and coercive pressures (Drobak, 2008). The housing corporation sector is suitable for this research because it is a complex sector as a result of the legislation (Elsinga et al., 2014). To summarize, it is interesting to see if the Housing Act, or
legislation in general, can lead to a change in financial risks and social performance by housing corporations. Additionally, differences in financial risks between regions are investigated as this was requested by the WSW. This leads us to the following research questions: Did the introduction of the Housing Act lead to a reduced financial risks for and better social performance of housing
corporations? And did these changes happen abruptly or gradual? Furthermore, are there any differences in financial risks between regions?
This study is organized as follows. In the next chapter an overview of the sector will be provided. In which the background and Housing Act will be described. In chapter 3 the theoretical background will be discussed and hypotheses will be formulated. Chapter 4 will describe the sample, variables and research design. In chapter 5 the research results will be presented. In the final chapter the results will be discussed with three additional interviews and a conclusion will be formed. Further, the limitations and future research will be discussed.
2.0 Sector Description 2.1 Background
In the Netherlands housing corporations are responsible for building, managing and renting out housing with an affordable rent to people with a low income. Halfway through the nineteenth century the first housing associations were established. This was a private initiative by prosperous citizens and industrialists to offer a solution for the housing shortage and bad housing conditions of primarily the working class. The motive for this was their concern for well-being and work capacity of the working class. At the time there was no government interference, this came later with the
introduction of the Housing Act in 1901 (Gerrichhauzen, 1990).
With the introduction of the Housing Act in 1901 the government recognized these housing associations as authorized institutions. Today, this law is still the foundation on which the institute
housing corporation is build. This law states the conditions under which the corporations are
authorized as special institutions. As such they were entitled to subsidy from the government, as long as they operate solely in the interest of public housing (Gerrichhauzen, 1990). After World War II the housing corporations had an important role in reducing the housing shortage. However, this role went along with an increase in governmental interference. Municipalities did the commissioning for
construction, decided most of the financial aspects for the corporations and had say over the allocation of houses based on the living space act 19474. In addition the national government determined the maximum rental price (Wolters & Verhage, 2001). As a result of this the housing corporations lost their independence and private character (Elsinga et al., 2014). Despite the growth of housing corporations in the 1950s, there was discussion about the crisis of the housing corporations (Gerrichhauzen, 1990).
As a response to this in 1958 the committee De Roos was introduced to investigate whether corporations could be given more independence. The report of this committee was published in 1964 and a lot of recommendations from the report were (much) later converted into policies. With the adjustment of the Housing Act 1975 and the introduction of the decree admitted institutions public housing 19765 the position of the housing corporations was strengthen against municipalities. For example a priority rule was introduced which stated that municipal housing companies were only allowed to construct if local housing corporations had no interest (Wolters & Verhage, 2001).
In the 1990s the privatization accelerated with the policy paper ‘Volkshuisvesting’ by state secretary Heerma. Privatization was one of the main theme’s of this note. As a result a discussion about the necessity, effectivity and efficiency of subsidies for the corporations took place. These events marked the end of subsidization of the housing market. In practice this led to the financial privatization of corporations. Corporations became independent and no longer received subsidies (Wolters & Verhage, 2001; Elsinga et al., 2014). However, corporations’ loans were, and still are, guaranteed by a bail-out scheme giving them access to low financing costs. So that corporations still receive indirect state funding. Because of the subsidies that corporations obtained in the past they still had a favorable financial position (Veenstra et al., 2017).
Eventually housing corporations were encouraged by the authorities to obtain a lot of side activities beside their core tasks (Elsinga et al., 2014). This meant that state funding was used for other purposes than housing people with a low income. Because state funding was used for other activities than ‘services of general economic importance’ (daeb), it did not meet the requirements of the European Commission (Rijksoverheid, 2015). Additionally, misconduct was observed in parts of the housing corporation sector such as administrative and financial failure. With some commercial projects done by housing corporations, unacceptable financial risks were taken. Most notably the
4 Woonruimtewet 1947
Vestia affair, with over 700 million in remediation aid (Hoekstra, Hoogduin, & van der Schaar, 2015). In 2013 this led to a parliamentary committee of inquiry (Elsinga et al., 2014). This inquiry provided the foundation which lead to the Housing Act 2015 (Rijksoverheid, 2015).
2.2 Housing Act 2015
With the revised Housing Act 2015 the government imposed strong limitations to the work domain of housing corporations, making them return to their core task. Primarily focusing on ‘services of general economic interest’ (DAEB) activities. Which are; the construction, rent out and managing of social rental housing to people with a low income. Therefore, the revised law states more explicit that tenants needs to be housed according to their wage. In that tenants with lower income should be housed in the cheaper homes. So that people who can afford to pay more are less eligible for a cheap home, since those homes are scarce. Below some specific changes of the revised Housing Act are discussed that are relevant for this research (Aedes, 2016).
2.2.1 Separation DAEB and non-DAEB. As mentioned before, the introduction of the Housing Act 2015 emphasizes the primary goal of housing corporations. Which is the construction, rent out and managing of social rental housing to people with a low income, also known as DAEB activities. As of 2017 corporations are obligated to separate their DAEB and non-DAEB activities. Corporations can chose either an administrative or legal separation. With an exception for smaller corporations (Annual Revenue <30Mln.) and a small share of non-DAEB activities (<5% of
Revenues). These corporations can suffice with a separation of income and expenditures at the end of the year (Aedes, 2016).
2.2.2 Market and financial test. In addition, non-DAEB activities are met with strong requirements, these include a market and a financial test. The market test is meant to find out if there are commercial parties interested to carry out the desired activity, and is carried out by the
municipality. When no suitable commercial party is interested, an interested housing corporation can carry out the activity. In addition, the authority housing corporations needs to give explicit permission in order to allow the housing corporation to execute the activity. The authority housing corporations tests whether the municipality has carried out the market test correctly and if the activity fits the work domain of the corporation. In addition, the Guarantee Fund Social Housing 6 (WSW) tests whether the activity yields a positive return and doesn’t bring any unacceptable financial risks to the corporation that could harm the DAEB activities (Rijksoverheid, 2015; Aedes, 2016).
2.2.3 Governance and internal supervision. Since the introduction of the revised Housing Act, nominated directors and supervisory board members for a corporation need to be tested by a fitness & reliability test. This is done by the authority housing corporations on behalf of the Minister of the Interior and Kingdom Relations. To test suitability, one can think of criteria formulated in terms of personal competencies. The presence of criminal, financial and fiscal antecedents are used to judge
reliability. These subjects are used to judge whether the (re)nominated director or supervisory board member will be suitability and reliability for the job (Aedes, 2016).
In addition, for investments higher than three million the board of directors needs permission from the supervisory board. Another task of the supervisory board is to inform the Minister of the Interior and Kingdom Relations about important affairs. One can think of affairs like as a dispute between the board of directors and the supervisory board, potential problems with integrity, liquidity or solvency. Generally, affairs the Minister needs to know to be able to do his/her duties properly (Aedes, 2016).
2.2.4 External supervision. The external supervision of housing corporations is entrusted to the authority housing corporations. Additionally, the WSW has a similar function although more informal. The tasks of both organizations will be described below.
2.2.4.1 Authority housing corporations. In order to ensure that corporations concentrate on their core task, the authority housing corporations has been established. Which is part of the Human Environment and Transport Inspectorate7. It is tasked with the financial supervision and is responsible for monitoring the governance and integrity of corporations. Part of their duties is the implementation of the Housing Act. The authority housing corporations has the ability to appoint a supervisor and has the authority to impose a fine (Aedes, 2016).
2.2.4.2 WSW. The Guarantee Fund Social Housing (WSW) is established in 1983 and its core task is objective risk management. Though this organization is not new, their tasks are extended. For example, they are now responsible for the financial test for non-DAEB activities. Additionally, they conduct a risk assessment for housing corporations, in which they assess the corporations on their financial- and business risks. Through this risk management the organization contributes to optimal financing for corporations. This is achieved because of the risk assessment and in addition the affiliated corporations have their interest- and repayment obligations guaranteed by the WSW. This gives these corporations the ability to finance their activities at optimal financing costs (WSW, NA).
2.2.5 Housing market region. As of 2016 the Netherlands is divided in housing market regions. The arrangement of these regions originated from joint proposals from municipalities and housing corporations. All municipalities and housing corporations are part of a housing market
regions, except for categorical corporations. These are corporations that cover multiple housing market regions, such as corporations that focus on student housing or nursing homes. With the introduction of housing market regions every housing corporations, except for categorical corporations, now has a work focus area. The aim of the introduction of housing market regions is to aid the communication between housing corporations, municipalities and tenant organizations. This is, for example, helpful for conversations about construction of new social housing. Because it is now clear which
corporations are eligible for the implementation of the project. In addition, it strengthens the bond between the corporation and the region (Ministry of the Interior and Kingdom Relations, 2016).
There are several conditions for a housing market region which are stated in the Housing Act 2015 and decree authorized institutions public housing. A housing market region must consist of at least two municipalities, which together contain at least 100.000 households and are in coherence from the point of view of the housing market. Corporation are only allowed to be active and expand in their specific region. There are a few exceptions to this rule. Corporations that were active in a different region before the introduction of housing market regions may continue to exploit their current
property. In addition, corporations can submit a request for exemption from this rule. This request will be granted if there is insufficient investment capacity, with the current housing corporations in the region, to meet the housing demand (Ministry of the Interior and Kingdom Relations, 2016). An overview of the housing market regions is provided in appendix A.
3.0 Literature review 3.1 Institutional theory
As mentioned in chapter 1, institutional theory will be used to make a prediction about the financial risks and social performance of housing corporations. Institutions structure human interaction through humanly constructed constraints. They are the structural qualities which characterize the activities of, and relations between members of specific groups or communities (Burns & Scapens, 2000). These institutions are devised of formal and informal constraints and enforcement components. They have, throughout history, been made up by humans to regulate and lower uncertainty in
exchange (North, 1991; North, 1994). Housing corporations can be seen as a vehicle that exchange public means in affordable housing for people with a low income. They are influenced by institutions because they have the statutory duty to carry out public tasks (Veenstra et al., 2017). Thus housing corporations are exposed to formal and informal constraints by the institutions that surround them.
When dealing with public means organizations need to legitimize their decisions. As organizational decision makers are limited by bounded rationality. Which means they have limited information processing and therefore can’t consider all available alternatives (Dow, 1987). This leads to decision makers having the tendency to limit their search for available alternatives and aim for satisfying solutions, instead of optimal solutions (Cyert & March, 1963). Consequently, decision makers are susceptible for external forces outside the organization to legitimize their decisions. This is in line with the institutional approach which argues that organizational change is not only driven by rational choice or technical considerations (Westney, 1993; Ashworth, Boyne, & Delbridge, 2007). Instead the focus within institutional theory is to explain organizational change as a result of
uncertainty and/or legitimation processes (DiMaggio & Powell, 1983; Oliver, 1992; Beckert, 2010). According to isomorphism there are three types of external forces that pressure an
Similar to Cyert and March (1963), it is argued that greater legitimacy is the fundamental objective of organizational change instead of performance (Ashworth et al., 2007). DiMaggio and Powell (1983) predict that, over time, organizations will yield to these isomorphic pressures that they encounter in order to secure their external support and eventually their continuity. In the next section I will elaborate on these external forces and discuss their applicability to housing corporations. 3.2 Coercive forces
Coercive forces arise when organization are subjected to external pressures. These pressures are exercised by governmental institutions or other external organizations on which the organization depends. These institutions or organizations put formal or informal pressure on an organization to adopt the structures or systems that they support. In some cases governmental regulations are a direct cause for organizational change. These pressures can also result from cultural expectations from the society, such as social norms (Carruthers, 1995; DiMaggio & Powell, 1983).
Organizations that rely on scarce resources are likely more exposed to coercive forces. In line with the resource dependence model. When an organization is dependent for resources on other organizations, the organization is viewed as constrained by those on which they are dependent (Pfeffer & Salancik, 1978). Such constraints could pressure an organization to adopt structures and strategies desired by those on which they are dependent (DiMaggio & Powell, 1983; Mizruchi, & Fein, 1999).
With the introduction of the Housing Act 2015, housing corporations have experienced coercive pressures on multiple facets of their organization in order to meet regulatory requirements (Rijksoverheid, 2015). One of these regulations had a direct impact on the organizational structure of corporations. Which is the previously discussed requirement for large corporations to separate their DAEB and non-DAEB activities (Aedes, 2016). However, since this separation is executed in the course of 2017, it is not expected to have influence yet on the performance of housing corporations.
Another coercive force is the introduction of governance supervision and the fit and proper test. As of 2015, (re)nominated (supervisory) board members of housing corporations are reviewed by the authority housing corporations. This review process is intended to improve the governance quality of housing corporations. Considering the fit and proper test was introduced in 2015 and our sample includes data up to 2017. It can be expected that the effect of this legislation is gently discernible. Although this study doesn’t measure governance quality directly, it is expected to have an effect on the financial and social performance of corporations. As previous research has found a positive relation between governance quality and firm performance (Brown & Caylor, 2009; Dey, 2008).
The new market test for corporations’ non-DAEB activities will have a substantial influence on corporations as well. Because the test is to find out whether commercial parties are interested to do the project, one could conclude that the projects that remain are commercially less viable. A result of this can be that non-DAEB activities will be less profitable for corporations and diminish their
corporations. Taken together it is likely that these two tests will prevent corporations from obtaining a profitable project, because commercial parties will execute the project. But will prevent corporations as well from obtaining a project with unacceptable financial risks.
Lastly, another coercive force, is the influence of the external supervision bodies. They have substantial influence on the policy of housing corporations, particularly in case of poor performance. For instance, the authority housing corporations can impose a fine or appoint a supervisor. Because the WSW guarantees a corporations’ loans, the affiliated corporations are obligated to comply with their rules and regulations. The influence of these supervision bodies is intended to safeguard corporations from defaulting on their loans and ensure they concentrate on their core task.
3.3 Mimetic forces
Mimetic forces are pressures to mimic or pursue other prosperous and acknowledged
organizations’ structures, systems, practices or processes (DiMaggio and Powell, 1983). One could say that with coercive pressures an organization is pushed towards specific organizational change. But with mimetic forces an organization is pulled towards specific organizational change because the change is an attractive solution to the encountered problems. In a complicated environment in which organizational effects can only be evaluated ex post. Beside the effects of bounded rationality, decision makers can’t always identify optimal solutions and their consequences in advance and therefore can’t make a rational decision. In such a situation mimicking another organization can make up for the absence of rationality (Beckert, 2010). This mimicking behaviour is done to enhance the legitimacy of the decisions for the organizational decision makers and is useful in situations of uncertainty when decision makers cannot be certain of the results of their actions (DiMaggio and Powell, 1983). It is likely that housing corporations that struggle with their implementation of the new regulations and/or perform below average mimic more successful corporations. However, this
something for the long term because the scope of this study is six years. Additionally, coercive and normative pressures are more present in the Housing Act and therefore more related to the problem. As a consequence this mimicking behaviour and their effect is beyond the scope of this study. 3.4 Normative forces
Normative forces are the effect on organizational characteristics that stem from professional norms and communities. This is also known as professionalization. DiMaggio and Powell (1983) define professionalization “the collective struggle of members of an occupation to define the conditions and methods of their work” (p. 152). These normative forces have their influence on corporations through the fit and proper test and the monitoring bodies. The fit and proper test is meant to emphasize professionality and integrity (Rijksoverheid, 2015). These board members will bring their norms and values which they gained during their education, training and career with them. When confronted with organizational decisions their norms and values will influence their decisions and thus eventually the organizational structure. In addition, since the authority housing corporations is
values that are more in line with the supervisor. And thus, emphasize the overarching vision of housing corporations. Further, both the authority housing corporations and WSW will influence corporations, beside their coercive forces, with their norms and values through other channels as well. Together, these norms and values that are brought upon corporations will influence the characteristics of the corporations (Ashworth et al., 2007).
3.5 Hypothesis development
Take together these coercive, mimetic and normative forces have their influence on housing corporations and their decision makers. The question is whether these developments have led to reduced financial risks and improved social performance. Additionally, the question is whether these changes were abruptly or gradual. It is expected that the coercive forces have had the most substantial impact on corporations. Because corporations are obligated to obey the Housing Act. This is in line with Ashworth et al. (2007) who suggested that the extent to which organizations rely on state funding might mediate the effect coercive pressures have on an organization. As public sector nonprofit organizations that rely on scarce funds go to great lengths to appear accountable as a result of intense pressures to maintain their legitimacy as the proper receivers of the funds (Lazarevski, Irvine, & Dolnicar, 2008). Although corporations do not directly rely on state funding, they are still dependent on the government because of regulations and indirect state funding. Therefore, it is expected that the same principles apply. Additionally, research has found that firms that gain legitimacy, through adopting institutional norms, have a lower unsystematic stock market risk (Bansal & Clelland, 2004). Even though housing corporations are not listed on a stock exchange. The literature on financial risks in the (semi) public sector is limited. Therefore, this comparison is made as an approach.
It is expected that the fit and proper test and the coercive influence of the authority housing corporations and the WSW will lead to reduced financial risks and better social performance. For instance, the fit and proper test should improve a corporations governance, which should lead to improved performance (Brown & Caylor, 2009; Dey, 2008). Even though before the revised Housing Act there was a governance code. It is argued that there was insufficient control (Veenstra et al., 2013; Elsinga et al., 2014; Veenstra et al., 2017). Additionally, the coercive influence of the WSW, which obligates affiliated corporations to manage their financial risks. This should lead to a proper risk management and reduce the financial risks. The financial and governance supervision of the authority housing corporations will have a similar effect. Alternatively, the effect of the market and financial test will most likely equalize. Whereas, the market test might lower a corporations financial
performance, because the more profitable projects are given out to commercial parties. The financial test will prevent corporations from facing unacceptable financial risks.
it is expected that, over time, corporations will become more successful. The normative forces will ensure that the professionals within the corporations share the norms and values that emphasize the goal of housing corporations (providing affordable houses to people with a low income). This will likely result that board members will act more in the interest of housing corporations and pursue reduced financial risks and improved social performance.
The question remains whether these changes will be abrupt or more gradual. North (1994) argues that institutions evolve over time as a result of a learning process and that human beings shape institutions through this learning process. Thus, predicting a more gradual change. However,
organizations have to comply with the new regulations that took effect in 2015. So they are under great pressure to make the necessary changes. Therefore, it is expected that the pace of these changes is quite abrupt.
Overall, it is expected that these forces that were introduced with the Housing Act 2015 will lead to reduced financial risks and improved social performance.
This leads to the following two hypotheses:
H1: The financial risks in the period (2015 - 2017) after the introduction of the Housing Act will be lower compared to the period (2012 - 2014) before the introduction of the Housing Act.
H2: The social performance in the period (2015 - 2017) after the introduction of the Housing Act will be higher compared to the period (2012 - 2014) before the introduction of the Housing Act. 3.6 Housing market region
With the introduction of the Housing Act 2015 the Netherlands was divided in housing market regions. This division was executed in 2016 and resulted in 19 different housing market regions. The demand for houses is different between these housing market regions and so is the value of the properties. Figure 3.1 shows us that the price index for houses in the provinces Noord-Holland, Zuid-Holland, Utrecht and Flevoland are higher than in the rest of the Netherlands. Not entirely inadvertent these four provinces are regarded as the Randstad8 (Randstad Monitor, 2017). On the other hand, the rental price that corporations may charge is subjected to rules. Which results in approximately similar rental prices nationwide, independently of the demand for houses. Consequently, it is expected that outside the Randstad corporations receive a higher rental price per invested euro than in the Randstad. Additionally, in the Randstad the housing shortage is considerable. This requires corporations in the Randstad to invest more to keep up with demand. These increased investment should have a negative impact on the financial risks of corporations in the Randstad. All in all, because corporations in the Randstad receive a lower rental price per invested euro and have to invest more, it is expected that their financial position is more under pressure. Which leads to the third hypothesis:
H3: The financial risks of housing corporations in the Randstad will be higher than in the rest of the Netherlands.
Figure 3.19: Price index of existing owner-occupied houses10
3.7 Population decrease areas
Additionally, in the Netherlands there are also areas with a population decrease or anticipate areas. Anticipate areas are regions where a population decline is expected in the future (Rijksoverheid, 2018a,). As a consequence of financial considerations the social performance is under pressure in areas with a population decrease (De Jong & Lagas, 2012). Population decline can cause a devaluation of assets and vacancy, because of a decreasing demand for housing. This devaluation decreases the value of the collateral. This is likely to increase the financial risk of corporations, because in case of financial setbacks it is more difficult to sell property to be able to secure continuity. On the other hand, corporations might be able to anticipate on a population decrease and suspend new investments. Which would decrease their financial risks, since new investments bear risks. Overall, it is difficult to predict whether financial risks are higher or lower in areas with an (expected) population decrease. Therefore, the fourth hypothesis is explorative.
H4: There will be a difference in the financial risks of housing corporations that are active in areas with an (expected) population decrease and corporations that are not.
4.0 Method
This study will be using a quantitative approach. The aim of this study is to evaluate the effect of the Housing Act 2015 and therefore this study has a more deductive approach. In addition, a quantitative approach allows us to compare the financial risks and social performance of housing corporations over multiple years. Additionally, the benefits of a quantitative approach is that it involves a larger sample size, which increases the external validity of this research. Subsequently,
9 Source: Kadaster (2019)
there was an opportunity to have access to a dataset required for this type of study. Therefore, a quantitative approach is more suitable for this study.
4.1 Dataset and sample selection
The data is obtained from multiple databases provided by the WSW. These databases consist of cashflow data, balance sheet data and the ratio’s from all corporations from 2012 to 2017. The required variables were selected from these databases and combined into one dataset. The creation of the dataset that was used in this study took approximately 40 hours. The dataset consist of housing corporations that are affiliated with the WSW. Therefore, the sample will only consist of housing corporations that are affiliated with the WSW. There are about eight small corporations that are not affiliated with the WSW. Housing corporations that had a merger, acquisition or a termination of activities in the selected period are removed from the sample. The sample will consist of n = 307 housing corporations located in the Netherlands. Housing corporations that operate categorical, covering multiple housing market regions, such as corporations that focus student housing and nursing homes are removed from the analysis for hypothesis 3 and 4. This concerns 5 corporations. The remaining corporations in the dataset are used in the analysis. The collected data extends a period from 2012 to 2017. This translates to 6x307 corporation-year observations for H1 and H2 and 6x302
corporation-year observations for H3 and H4. 4.2 Dependent variables
The effect of the Housing Act will be measured with two variables. These are a financial risk score and a social performance score. This has been done to distinguish between the two aspects under study.
4.2.1 Financial risks. The financial risks of housing corporations will be measured using four financial risk ratio’s. Each financial ratio captures an aspect of the financial risk of a corporations and a poor financial ratio means a higher risk. Four financial risk ratio’s are used because they capture a more complete view of the financial risk of corporations than a single ratio’s would. The financial ratio’s used are: interest coverage ratio (ICR), loan to value (LtV), solvency and coverage ratio. These four financial ratio’s are based on the WSW Risk Score Model which is in line with the international standards of Standard & Poors (S&P) (WSW Risico Score Model, 2014). The formulae for the ratio’s can be found in table 4.1. The four ratio’s will be combined into a sum score that represents the financial risk of a corporations.
on this ratio below 1 will lead to a high risk categorization for the corporation, independently from the scores on the other ratio’s. The WSW uses a threshold value of 1,4 for housing corporations.
4.2.1.2 Loan to value. The LtV measures whether the long term cash flow generating capacity of the portfolio is in a healthy proportion to a corporations’ debt position. The ratio indicates whether a corporations’ debt is too heavily financed in the long term. A ratio of 100% means that in order to repay its debt, a corporation needs its full cash flow generating capacity. The WSW argues that there should be a maximum debt position for a certain level of cash flow generating capacity of the portfolio. The WSW uses a threshold value of 75 percent for housing corporations.
4.2.1.3 Solvency ratio. The solvency ratio provides information whether an organisation has sufficient buffers to withstand an unexpected situation or period of poor results and be able to meet its liabilities. It measures the magnitude of the corporations resilience in relation to its assets. The WSW uses a threshold value of 20 percent for housing corporations.
4.2.1.4 Coverage ratio. The coverage ratio measures how the value of the property is related to the debt that is secured by the WSW. In a situation where a housing corporation has financial
difficulties, property can be sold to be able to secure continuity. The ratio gives an indication whether the collateral has sufficient value to cover the outstanding debt. The WSW uses a threshold value of 50 percent for housing corporations.
Table 4.1
Formulae of the WSW Ratio’s.
Ratio Formulae Interest coverage ratio 𝐼𝐶𝑅 = 𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 𝑓𝑜𝑟 𝑛𝑒𝑡 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑒𝑥𝑝𝑒𝑛𝑠𝑒𝑠 𝑔𝑟𝑜𝑠𝑠 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑒𝑥𝑝𝑒𝑛𝑠𝑒𝑠 Loan to value 𝐿𝑡𝑉 = 𝐷𝑒𝑏𝑡 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝑣𝑎𝑙𝑢𝑒 𝑖𝑛 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 × 100% Solvency ratio 𝑆𝑜𝑙𝑣𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝑇𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 𝑜𝑛 𝑣𝑎𝑙𝑢𝑒 𝑖𝑛 𝑢𝑠𝑒 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 𝑏𝑎𝑠𝑒𝑑 𝑜𝑛 𝑣𝑎𝑙𝑢𝑒 𝑖𝑛 𝑢𝑠𝑒 × 100% Coverage ratio 𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 = 𝐷𝑒𝑏𝑡 𝑟𝑒𝑚𝑎𝑖𝑛𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑦 𝑊𝑆𝑊 𝑠𝑒𝑐𝑢𝑟𝑒𝑑 𝑙𝑜𝑎𝑛𝑠 𝑊𝑂𝑍 𝑣𝑎𝑙𝑢𝑒 𝑝𝑟𝑜𝑝𝑒𝑟𝑡𝑦
distance from threshold value represents the financial risk. Hereby, a lower score represents a higher financial risk and a higher score represents a lower financial risk. After these adjustments the individual sub scores are centred. The six year mean score was used for this in order to prevent levelling out any between year differences. Centring is done so each sub scores has roughly the same impact on the final financial risk score. Additionally, a factor analysis was conducted to determine whether the financial risk sub variables are suitable to create the financial risk variable. From the results it can be concluded that this is the case (table 4.3).
Table 4.2
Formulae for the financial risk sub scores.
Variable Formulae FR_1 𝐼𝐶𝑅 − 1,4 FR_2 0,75 − 𝐿𝑡𝑉 FR_3 𝑆𝑜𝑙𝑣𝑎𝑏𝑖𝑙𝑖𝑡𝑦 − 0,20 FR_4 0,50 − 𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 Table 4.3 Factor analysis
Financial risk score Social performance score
FR2_c 0,937 FR3_c 0,930 FR1_c 0,780 FR4_c 0,765 TRR_c_r 0,790 SHR_c 0,620 SIR_c 0,483
4.2.2 Social performance. The social performance of housing corporations will be measured using three social ratio’s. Three ratio’s are used because the different social tasks of housing
corporations can’t be captured in one ratio. These social ratio’s indicate the focus a corporation has on the primary goal that was stated in the Housing Act 2015. Which is the construction, rent out and managing of social rental housing to people with a low income. The ratio’s that will be used are: target rent ratio (TRR), social houses ratio (SHR) and social investing ratio (SIR). The formulae of these ratio’s can be found in table 4.3. The three ratio’s will be combined into a sum score that represents the social performance of a corporation.
ratio provides information about how affordable a housing corporation is. As of 2016 the way in which the maximum rent is calculated has been adjusted slightly. The WOZ-value11 of the property has been incorporated in the calculation of the maximum rent. The data will be controlled to determine whether this has had a significant effect.
4.2.2.2. Social houses ratio. The social houses ratio measures how many rental properties, as a percentage of the total rental properties of a corporations, are available to house households with a lower income. The total rental properties include both Daeb and non-Daeb properties. This ratio provides information about how many resources a corporation locates to house households with a lower income.
4.2.2.3. Social investing ratio. The social investing ratio measures the investments a corporation does in its Daeb activities. To convert it in a ratio and correct for corporation size it is divided by the total rental units a housing corporation possess (VHE). This ratio provides information about the investment activity of a corporation in their Daeb activities.
Table 4.3
Formulae of the social performance ratio’s.
Ratio Formulae
Target rent ratio 𝑇𝑎𝑟𝑔𝑒𝑡 𝑟𝑒𝑛𝑡 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑟𝑒𝑛𝑡 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑟𝑒𝑛𝑡 Social houses ratio 𝑆𝑜𝑐𝑖𝑎𝑙 ℎ𝑜𝑢𝑠𝑒𝑠
= 𝑅𝑒𝑛𝑡𝑎𝑙 𝑝𝑟𝑜𝑝𝑒𝑟𝑝𝑡𝑖𝑒𝑠 𝑖𝑛 𝑐ℎ𝑒𝑎𝑝 𝑎𝑛𝑑 𝑎𝑓𝑓𝑜𝑟𝑑𝑎𝑏𝑙𝑒 𝑟𝑒𝑛𝑡 𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 𝑇𝑜𝑡𝑎𝑙 𝑟𝑒𝑛𝑡𝑎𝑙 𝑝𝑟𝑜𝑝𝑒𝑟𝑡𝑖𝑒𝑠
Social investing ratio 𝑆𝑜𝑐𝑖𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑖𝑛𝑔 = 𝐷𝑎𝑒𝑏 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝑉𝐻𝐸
4.2.2.4. Social performance. To be able to measure the social performance of a housing corporation a variable will be created that represents the social performance (SP) of a corporation. This variable will consist of the social performance ratio’s that are mentioned above. The social performance ratio’s are centred in order to create the social performance score. The six year mean score was used for this in order to prevent levelling out any between year differences. Centring is done so each sub scores has roughly the same impact on the final social performance score. Subsequently, the TRR was multiplied by minus one to reverse it. This adjustment was done to align the direction of the TRR with the other ratio’s. Since a lower score on the TRR means a better social performance. The aim is to create a score where a higher score represents a better social performance. Hereby represents a lower score a lower social performance and a higher score represents a higher social performance. Additionally, a factor analysis was conducted to determine whether the social performance sub variables are suitable to create the social performance variable. From the results it can be concluded that this is the case (table 4.3).
4.3 Independent variables
4.3.1 Dummy. To be able to measure the effect of the Housing Act 2015 a dummy variable will be created. Because this research expects the financial risks [social performance] of housing corporations to decrease [increase] after the introduction of the Housing Act 2015. The years 2015 to 2017 will be offset against the years 2012 to 2014. The dummy variable ‘Housing Act’ will have a value of 0 for the years 2012 to 2014, and a value of 1 for the years 2015 to 2017.
4.3.2 Housing market region. As mentioned in chapter 2, there are 19 housing market regions. Housing corporations will be classified to one housing market region according to the
governmental mapping (Ministry of the Interior and Kingdom Relations, 2016; Regioatlas, NA). In the situation where a housing corporation is active in multiple housing market regions, the corporation will be classified to the region where their core activities lie. This is decided by the number of municipalities a corporation is active in in each region, a corporation will be classified to the region where it is active in the most municipalities. In the situation where a corporation is active in an equal amount of municipalities in each region, the decision is made based on the highest WOZ value in 2017. Which is manually looked up. In case a corporation is active in more than half of the regions (10), it is classified as a categorical corporation. Each housing market regions will be coded with a number from 1 to 19. See appendix A for more details.
Because 19 housing market region’s are too many for multiple regression. The housing market region’s will be classified to ‘Randstad’ and ‘non-Randstad’. There is no clear definition of which geographical areas are considered to be in the Randstad. The four provinces of Noord-Holland, Zuid-Holland, Utrecht and Flevoland are considered to be (partly) in the Randstad. But generally, the cities of Amsterdam, Rotterdam, Utrecht and The Hague are considered to constitute the Randstad
(Randstad Monitor, 2017). In this study a housing market region will be classified as Randstad, if one of those four cities is located in that region. Following this logic the following housing market region’s will be considered Randstad: ‘Metropoolregio Amsterdam’, ‘Haaglanden Midden-Holland Rotterdam’ and ‘U16’. Additionally, a Randstad dummy variable will be created. The dummy variable will have a value of 0 for the Randstad, and a value of 1 for the rest of the Netherlands.
4.3.3 Population decrease regions and anticipate region. Housing corporations will be classified whether they are active in a population decrease region, anticipate region or not. This classification will be done based on a governmental mapping (Rijksoverheid, 2018b). For this classification the dummy variable ‘Population decrease’ is created. Housing corporations that are active in a region that is either classified as a population decrease region or anticipate region will be given a 1, regardless of their amount of activity in that region. Housing corporations that are not active in such a region will be given a 0. Housing corporations that are active in both a population decrease region and anticipate region are not separately classified.
4.4.1 Size. It is generally assumed that larger firms carry less risk than their smaller
counterparts. Larger firms are more resistant in times of economic hardship (Oikonomou, Brooks, & Pavelin, 2012). Contrary, when it comes to efficiency it is found that housing corporations become less cost efficient when their size increases (Koolma, 2008). Additionally, Veenstra et al. (2017) concluded that housing corporations, when it comes to efficiency, perform under diseconomies of scale. There may also be other factors at play which are a results of corporation size. Therefore, it is useful to control for these effects. The size of a housing corporation will be measured by the amount of rental units a housing corporation possess (VHE). To correct for skewness of the data the logarithm of VHE is used.
4.4.2 Debt per property. This control variable was suggested by the experts of the WSW. Because when a corporation has a high debt it is expected that the corporation carries higher financial risks. Since “high debt by definition implies high financial risk” (Hurdle, 1974, p. 478). Because a higher debt means more interest obligations, which makes a corporations more riskier in case of declining results. When it comes to corporate social responsibility research has found a significant negative association with debt to assets (McGuire, Sundgren, & Schneeweis, 1988). Implying that firms with a high debt burden score lower on social responsibility. Since corporations with a higher debt have more interest obligations, less cash flow is available for their social duties. Because using total debt would resembles a corporations size as well. The debt per property will be used and calculated as follows:
𝐷𝑒𝑏𝑡 𝑝𝑒𝑟 𝑝𝑟𝑜𝑝𝑒𝑟𝑡𝑦 = 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡 𝑇𝑜𝑡𝑎𝑙 𝑉𝐻𝐸
4.4.3 Interest rate. Normally, one of the components from which interest is accrued, is the likelihood of default (Merton, 1974). This suggest that interest rate and financial risks have a positive relationship. However, housing corporations take advantage of a favorable interest rate due to the WSW bail-out system. Therefore, the component of default is minimized in the interest rate. Though differences in average interest rates among housing corporations exist due to for example the point in time and the maturity. The experts of the WSW suggested this control variable could still have an effect. Because a corporation that has to pay a relatively high interest rate on its debt, has a higher interest obligation. Thus a higher proportion of the cashflow is used for interest payments.
Additionally, there is evidence that firms that score higher on corporate social responsibility encounter less capital constraints (Cheng, Ioannou, & Serafeim, 2014). Which could mean lower interest rates. The interest rate of a corporation will be calculated with the following formulae:
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑟𝑎𝑡𝑒 = 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑠 𝐷𝑒𝑏𝑡
control variable. Because a low EBITDA gives potential to improve the position. On the other hand, a low profitability could lead in the long-term to an erosion of equity. Additionally, previous research on firms found a significant negative association between systematic risk and return on assets (ROA). Implying that less profitable firms have a higher financial risks. Regarding to corporate social
responsibility, a positive association was found with ROA. Implying that social responsible firms have a higher profitability (McGuire et al., 1988). This control variable will be calculated with the
following formulae:
𝐸𝐵𝐼𝑇𝐷𝐴 𝑎𝑠 % 𝑜𝑓 𝑟𝑒𝑣𝑒𝑛𝑢𝑒 = 𝐸𝐵𝐼𝑇𝐷𝐴
𝑅𝑒𝑣𝑒𝑛𝑢𝑒 × 100% 4.5 Research Design
The data will be, depending on the specific hypothesis, analyzed using regression analysis, paired sample t-test and/or one-way ANOVA. The analyses will be conducted in SPSS. Because extreme outliers can strongly influence the results from the analyses, all variables will be winsorized at the 1% level consistently with (Oikonomou et al, 2012). Categorical variables are an exception to this. Winsorizing means that values that lie outside of the 1st and 99th percentile will be replaced by the 1st and 99th percentile value. This method is applied to prevent extreme outliers to alter the goodness of fit of the model in their direction. The variables are winsorized within each year to prevent leveling out any difference between the years. After correcting for these extreme outliers, the analyses can be carried out.
To examine whether the financial risks of housing corporations have decreased,
a paired
sample t-test and regression analysis will be used. T
he Housing Act dummy variable will be usedin the regression analysis. The following regression equation can be drawn for hypothesis 1: FRit = β0 + β1 Sizeit + β2 Debt per propertyit + β3 Interest rateit + β4 EBITDA/Revenueit + β5
Housing Act (1)
To examine whether the social performance of housing corporations have increased,
a paired
sample t-test and regression analysis will be used. T
he Housing Act dummy variable will be usedin the regression analysis. The following regression equation can be drawn for hypothesis 2: SPit = β0 + β1 Sizeit + β2 Debt per propertyit + β3 Interest rateit + β4 EBITDA/Revenueit + β5
Housing Act (2)
To examine whether there is a difference in financial risk between corporations active in the Randstad and in the rest of the Netherlands, a one-way ANOVA and regression analysis will be used.
T
he Randstad dummy variable will be added to the regression analysis from hypothesis 1. The following regression equation can be drawn for hypothesis 3:FRit = β0 + β1 Sizeit + β2 Debt per propertyit + β3 Interest rateit + β4 EBITDA/Revenueit + β5
Housing Act + β6 Randstad (3)
regression analysis will be used.
T
he Population decrease dummy variable will be added to the regression analysis from hypothesis 1. The following regression equation can be drawn for hypothesis 4:FRit = β0 + β1 Sizeit + β2 Debt per propertyit + β3 Interest rateit + β4 EBITDA/Revenueit + β5
Housing Act + β6 Population decrease (4)
5.0 Results 5.1 Descriptive statistics
In appendix B an overview is provided of the descriptive statistics. These include the
dependent, independent and control variables. The means of financial risk and social performance are close to zero. This is due the centering of the ratio’s of which these variables consist. In addition, the descriptive statistics are split between the two periods, to display potential differences. The statistics show that the mean financial risk in the period 2015-2017 is higher (M = 0,028) than in the period 2012-2014 (M = -0,027). Further, it shows that the mean social performance in the period 2015-2017 (M = -0,024) is lower than in the period 2012-2014 (M = 0,023).
Additionally in appendix C a correlation matrix is provided. A high correlation may indicate problems of multicollinearity. According to Bohrnstedt and Carter (1971) a correlation of 0,85 or above may indicate multicollinearity. Whereas Wijnen, Janssens, Pelsmacker and Kenhove (2002) argue that a correlation should not exceed a value of 0,6. Examining the correlation matrix in appendix C shows that no correlations among the variables exceed these values. Additionally, the tolerance of a variable is also an indication of multicollinearity. Variables with a tolerance below 0,1 must be discarded (Afifi & Clark, 1984). Alternatively, a variance-inflation factor (VIF) higher than 10 may also indicate problems of multicollinearity. Whereas, in case of no multicollinearity the minimum VIF value is 1,0. (Neter, Kutner, Nachtsheim, & Wasserman, 1996). There were no tolerance values below 0,1 exist and the highest VIF factor was 1,184. Indicating that there is no evidence of multicollinearity. 5.2 Results of analyses
In the following subsection the results of the analyses will be described. Each subsection will describe one of the hypotheses.
shows that the financial risks in 2015-2017 are significantly lower (higher FR score), than the financial risks in 2012-2014 (lower FR score). From this it can be inferred that there is a decrease in the
financial risks of housing corporations after the introduction of the Housing Act.
Figure 5.1: Mean financial risk scores.
The Paired-Samples T-Test only shows whether there is a significant difference between the means of the two periods. The test cannot assess if there is any influence of control variables. Additionally, it doesn’t provide information about the variance explained by the Housing Act. To combat these restrictions a hierarchical multiple regression analysis was conducted. The assumptions linearity, homoscedasticity, normality and multicollinearity for regression are met (Appendix D). The assumptions of linearity and homoscedasticity are assessed by a plot of studentized residuals against the unstandardized predicted values, the assumption of normality is assessed by a P-P plot of the standardized residuals. As discussed above there was no evidence of multicollinearity as assessed by tolerance values greater than 0,1 and VIF values <10. This means that a regression analysis is appropriate.
financial risks. Interest rate has no significant relationship with financial risks. At last,
EBITDA/Revenue has a significant negative relationship with financial risks. Which means that corporations with a higher EBITDA/Revenue have an increased financial risk. Additionally, a regression analysis was run without interest rate as a control variable, but this did not make any difference in statistical significance among the variables (not tabulated).
Table 5.1
Regression analysis Size, Debt per VHE, Interest rate, EBITDA/Revenue, Housing Act, Randstad with Financial Risk
Variable Model 1 (β) Model 2 (β) Model 3 (β)
Size -0,312*** -0,312*** -0,314***
Debt per VHE -0,175*** -0,167*** -0,157***
Interest rate 0,031 0,035 0,051* EBITDA/Revenue -0,189*** -0,189*** -0,198*** Housing Act 0,032 0,036 Randstad -0,135*** R² 0,185 0,186 0,203 F 103,533*** 83,312*** 77,755*** R² Change 0,185 0,001 0,018 F Change 103,533*** 2,162 40,887*** N = 1835 * p < 0.05. ** p < 0.01. *** p < 0.001
On the other hand, figure 5.1 shows a steep decline of the financial risk score between 2012 and 2013. So, it could be that the year 2012 is distorting the results. In order to investigate this possibility a regression was run without the year 2012. The results of this regression analysis are tabulated in appendix E. The results from this analysis show that the dummy for Housing Act has a positive and significant coefficient which means a decrease in financial risks. This is different from the analysis with 2012 included where the relationship between the dummy for Housing Act and financial risks did reach statistical significance. However, with a R² Change of 0,004 the
additional
variance explained by the Housing Act dummy is quite low. For the control variables, on the other hand, there are no changes in significance compared to the analysis with 2012 included.that the financial risks in 2016-2017 are significantly lower (higher FR score), than the financial risks in 2012-2015 (lower FR score). Either way, with a R² Change of 0,002 the
additional
variance explained by the Housing Act dummy is quite low.To conclude, it seems that whether there is a significant positive or negative relationship, or non-significant relationship between the Housing Act and the financial risks of corporations is
dependent on different factors. However, in all cases the additional variance explained by the Housing Act dummy is below 1%. So, if there is an effect, whether positive or negative, of the Housing Act it is quite small. Regarding hypothesis 1, the financial risks in the period (2015 - 2017) are lower compared to the period (2012 - 2014). But, we cannot conclude if this is due to the Housing Act. Therefore, hypothesis 1 is rejected.
5.2.2 Social Performance. To be able to assess hypothesis 2, whether the social performance in the period (2015-2017) after the introduction of the Housing Act are higher compared to the period (2012-2014) before the introduction of the Housing Act a paired sample t-test and a multiple
regression analysis was conducted with social performance. In figure 5.2 the changes over time of the mean social performance scores are plotted. At first sight it seems that there is a decrease in social performance. However, from a graph we can’t assess any statistical significance. Therefore, a Paired-Samples T-Test was carried out to assess whether there is a significant difference in the means between the period 2012-2014 and 2015-2017. The results indicate there was a significant difference in the scores for social performance in the period 2012-2014 (M = 0,024, SD = 0,114) and social performance in the period 2015-2017 (M = -0,023 , SD = 0,117); t(305) = -14,817, p = 0,000.
Figure 5.2: Mean social performance scores.
variables and determine the variance explained by the Housing Act a hierarchical multiple regression analysis was conducted. The assumptions linearity, homoscedasticity, normality and multicollinearity for regression are met (appendix G). The assumptions of linearity and homoscedasticity are assessed by a plot of studentized residuals against the unstandardized predicted values, the assumption of normality is assessed by a P-P plot of the standardized residuals. As discussed above there was no evidence of multicollinearity as assessed by tolerance values greater than 0,1 and VIF values <10. This means that a regression analysis is appropriate.
The results of the regression analysis for social performance (hypothesis 2) are tabulated in table 5.2. Model 1 shows the relationship between the control variables and social performance, in model 2 the Housing Act dummy is added to the equation. The results show that the dummy for Housing Act has a significant negative relationship with social performance. A negative relationship means a decrease in social performance for housing corporations. Regarding the control variables, Model 1 and 2 show that housing corporation size has a significant negative relationship with social performance. Which means that, in general, smaller corporations are able to provide a better social performance. The Debt per property has a positive relationship with social performance. Which means that housing corporations with a larger debt per property provide a better social performance. Interest rate has a significant negative relationship with social performance. Which means that corporations that pay a lower interest rate provide a better social performance. Finally, EBITDA/Revenue has a significant negative relationship with social performance. Which means that corporations with a lower EBITDA/Revenue have a higher social performance. Similar to the procedure done by hypothesis 1 additional regression analyses were conducted. Extra analyses without Debt per VHE, without the year 2012 and with the period 2016-2017 offset against 2012-2015 were ran. But no difference in statistical significance among the variables were observed (not tabulated).
To conclude, the results show a significant negative relationship between Housing Act and the social performance of housing corporations. This means that the social performance of housing corporations in the period 2015-2017 is lower compared to the period 2012-2014. This contradicts hypothesis 2, which predicted the opposite. Hypothesis 2 is therefore rejected.
Table 5.2
Regression analysis Size, Debt per VHE, Interest rate,
EBITDA/Revenue, Housing Act with
Social performance
Variable Model 1 (β) Model 2 (β)
Size -0,265*** -0,256***
Debt per VHE 0,053* 0,003
5.2.3 Randstad.
To determine whether the financial risks of housing corporations in the Randstad are higher than in the rest of the Netherlands. (hypothesis 3) a one-way ANOVA was conducted. Housing corporations were classified to either the Randstad (n = 516) or non-Randstad (n = 1319) based on their geographical activity. For each group the data was normally distributed data (Appendix H) and there was homogeneity of variances (Levene's test for equality of variances p = 0.478). The financial risks of housing corporations were significantly different between the Randstad and the rest of the Netherlands, F(1, 1833) = 31,653, p < 0,000. In figure 5.3 the financial risk scores for the Randstad and the rest of the Netherlands are plotted.
Figure 5.3: Mean financial risk scores for Randstad and the rest of the Netherlands.
Additionally, a Randstad dummy was added to the regression analysis conducted under hypothesis 1, which is tabulated in table 5.1. For hypothesis 3 we need to look at model 3. The results show that the dummy for Randstad has a significant negative relationship with financial risk. A negative relationship means that being located in the Randstad has a negative effect on the financial risk of a housing corporations. Regarding the control variables no change in significance was observed in size, debt per VHE and EBITDA/Revenue compared to model 1 and 2 under hypothesis 1.
However, in model 3 interest rate reached significance. All in all, it is concluded that housing corporations in the Randstad have a higher financial risk. Therefore, hypothesis 3 is accepted.
risks of housing corporations that are active in a population decrease area and those that are not, F(1, 1833) = 0,006, p = 0,938. In figure 5.4 the financial risk scores for housing corporations that are active in a population decrease area and the rest of the Netherlands are plotted.
