How US/UK Housing Sector caused, and are affected by, the 2008 Credit Crunch – Dissertation Proposal 3500 words

From Boom to Bust: How The USA and The UK Housing sectors caused, and are affected by, the Credit Crunch






















1        Background


Starting in 2007 and throughout 2008 major financial institutions in the USA and the UK started reported considerable losses in subprime mortgages which they had heavily invested in over the previous few years. Between January 2007 and May 2008, the financial institutions in the USA for example, lost US$ 379 Billion in asset writedowns and credit losses (Onaran, 2008) (Please see Appendix I for detailed breakdown). There is almost a consensus among experts that the heavy lending to subprime borrowers with heightened perceived risk of default, a history of loan delinquency or default, recorded bankruptcy and limited debt experience led to these losses. Thus, although the financial sector’s heavy but risky lending in the housing market initially resulted in an increase in house prices and a boom in the housing industry, the subsequent losses suffered by the financial sector in turn resulted in bursting the bubble in the housing sector by reducing demand due to limited liquidity. This resulted in rapid reduction in house prices in USA since 2007 as illustrated in figure 1.



These facts point towards important relationships between the housing sector, the financial industry and the credit crisis that is expected to impact not only on these but on other sectors of the economy as well and may have – and may well already be having – dire consequences for the real economy. It is, therefore, imperative to study its ‘cause and effect’ mechanism to understand its true nature and thus contribute towards building theories and strategies to help avoid these situations in the future.


2        Research Purpose


The objective of this project is to analyse how the housing sectors in the UK and the USA have contributed to causing the current credit crunch, and how in turn these have been affected by it. The study intends to put the current financial crisis in historical context by pointing to the similarities and differences between this crisis and those experienced earlier, along with unravelling the complex ‘cause and effect’ mechanism of the phenomenon.


The study aims to develop deeper understanding of the relationship between the credit crunch, the housing sector and the financial sector and is expected to have key implications for regulators, financial institutions and the housing industry. It will also lead to recommendations on avoiding such crisis in the future.


3        Key Concepts


Credit Crunch: This concept is often ill-defined. Many people use this term loosely and to describe a variety of phenomena including: the tightening of monetary policy, credit rationing by banks, and shortage in the supply of funds. Commonly the alliterative term credit crunch, also referred to as credit crisis or credit slowdown, can be described as ‘a sudden reduction in the general availability of loans or credits, or a sudden increase in the cost of obtaining loans from banks. Credit crunch implies changes in the relationship between credit availability and interest rates’ (Ding et al, 1999:7).


Subprime Borrowers: These are also sometimes referred to as “under-banked” borrowers and represent those who have higher perceived risk of default due to limited debt experience, a history of defaults, recorded bankruptcy or any other reason that leads to a low credit score.


4        Motives and Goals


The main motive is to fill the gap in literature by extending the understanding of the relationship between the housing sector and the financial sector, and the cause and effect mechanism of the credit crunch.


5        Research Questions


This project will address the following research questions:


  • How did the housing sectors in the UK and the USA contribute to the recent financial crisis.
  • How are the housing sectors in the UK and the USA being affected by the ongoing credit crunch.



6        Literature Review


The dominant discourse over the last year has resulted in the inclusion of, ‘credit crunch’, once considered an arcane economic term, to the latest edition of Oxford English dictionary. Research shows (Allen, 2001) that contrary to conventional financial theory, world financial systems that are subject to market forces are prone to periodic financial crises. Historic examples of these crises include Dutch ‘Tulipmania’ of seventeenth century, the ‘South Sea Bubble’ in England, the 18th century Mississippi bubble in France, and the US great crash of 1929. Similar events occurred in Norway, Finland and Sweden in the 1980s and in the mid-90s most East Asian countries started to see the melt down of their economies (ibid; Kaminsky and Reinhart, 1996; Kaminsky and Reinhart, 1999). Although initially many experts suggested that the market failure caused in South East Asia market was linked to the corruption and weak political systems in these states, when many American countries started suffering from this financial meltdown it became clear that this crisis is rooted into global financial system and its policies.


Some of the early work is on housing crisis and mortgage lending is done by Stone (1975). He wrote a series of papers studying the USA housing sector and how the two different economic approaches, capitalist and socialist, impact the housing market and financial market.  His research highlighted some major flaws in the capitalist approach and he recommended that it is of utmost importance that the USA should reconsider their existing housing and mortgage strategies. His research resulted in the formation of a transitional socialist housing program (Stone, 1978).  In the same era Rosen (1977) examined the magnitude of the USA housing crisis, its causes and effects on financial sector and outlined a proposal which could help alleviate the crisis.


Bernanke (1983) presented the case study of the 1930s US financial crisis. His work was based on the Friedman-Schwartz work. He studied the background of the 1930-1933 financial crises, its sources and its correspondence with aggregate output movements. He also explained how the runs on banks and the extensive defaults can reduce the efficiency of the financial sector. Bernanke (1983) presented a theoretical framework to understand the 1930s financial crisis. Among other sectors, he also studied the effects of the financial crisis on the housing market. In 1985 Diaz Alejandro tried to analyse the financial liberalisation in Latin America. His study focused on the imperfections in financial markets. He suggested that instead of following USA model of liberalisation, Latin American countries should find their own ways which better suited their domestic market. The key focus of his study was on the housing market.  Boddy (1989) also did research on financial deregulation and UK housing finance. His looked into the building societies act 1986 and its impact on housing market future in UK.


In the mid 90s another financial crisis hit the East Asian markets. This resulted in significant exchange rate adjustments and property and stock market reversals in Indonesia, Thailand, the Philippines, and many other countries, and raised concerns about the quality of the world financial system. In 1997 Claessens and Glaessner wrote a book which tried to highlight the flaws of the global financial system. Their research focused on developing countries and it introduced a methodology for adjusting corporate financial statement for inflation effects. Ding et al (1997) also studied East Asian countries’ financial crunch and tried to explore its aftermath. Their work follows a systematic framework to assess the occurrence and the magnitude of the credit crunch in five counties including Indonesia, Korea, Malaysia, Philippines, and Thailand.  Similarly Lauridsen (1998) studied the financial crisis in Thailand. He classed this crisis as ‘private sector failure’, and explained how the careless lending/borrowing, individual greed, political instability, indecisiveness and mismanagement at political and administrative level can lead economies to disastrous situations.  Herring and Wachter (1999) used Mark Carey model and tried to understand the link between the financial sector and the real estate market. The authors looked into the Asian financial crisis and noticed that the most affected countries first experienced a collapse in property prices which weakened their banking system before experiencing an exchange rate crisis.  The authors first discuss that how real estate prices are determined and why they are so vulnerable to deviation from long-run equilibrium prices, paying special attention to the role of banking system in determining prices. Increase in the price of real estate may increase the economic value of bank capital; however the opposite is also true.  Other authors who studied the relationship between the Asian financial crisis and the housing market include Hahm and Mishkin (2000), Mera and Renaud (2000), Krugman (2000) and Sheng and Kirinpanu (2000).


In 2003 Hunter at al (2003) published a book in which he studied the 80s and 90s credit crunches of both the industrialised and developing worlds. His research showed that both decades have seen prolonged build-ups and sharp collapses in asset markets such as housing, stock and exchange. His book examines asset price bubbles to further our understanding of the causes and implications of financial instability, focusing on the potential of central banks and regulatory agencies to prevent it (Hunter et al, 2003).  In the last half decade many researchers looked into the cause and effect of the global credit crisis and how it is linked with the housing market. This includes the research of Allen (2001), Blankenship (2002), Edelsteina, and Lum (2004) and Buckley and Kalarickal (2005). Bordo (2005) did a highly interesting and prescient study of the USA housing marketing; he showed major concern about the housing prices of US and classified it as bubble. He studied US economic history and consequently feared that this bubble would soon bust with dire consequences for the real economy.


The financial crisis of 2007-08 began in July 2007 when investors’ loss their confidence in the value of securitised mortgages in United States resulted in a liquidity crisis.  The rise in oil prices triggered this crisis further and it became a global phenomenon, a crisis which is considered worst than Great Depression. As this is an ongoing phenomenon not a great deal of academic literature exists about it. Some key contributions have been made by Feldstein (2007), Reinhart and Rogoff (2008), Baker (2008), Muolo and Padilla (2008) and Muellbauer and Murphy (2008). The central element of their work is the analysis of the current financial crisis and how the housing bubble both contributed to and enhanced it.  They all look into the circumstances under which the bubble began to grow and discussed how financial institutions’ greed, and the lack of a proper regulator structure, allowed the bubble to grow to even more dangerous levels and eventually to burst in a way that has placed unprecedented strain on the global financial system.


7        Research Methodology


Methodology provides an overall framework and implementation strategy to conceptualise and conduct an inquiry and construct scientific knowledge (Cacez-Kecmanovic, 2001). Some of the key methodological considerations for this study are discussed below.


7.1       Research Paradigm


Cassel and Symon (1994) identify two main paradigms for conducting research: Positivistic and Phenomenological. The first is based on the belief that there is an objective real world which is orderly and predictable (Kane and O’Reilly-De Brun, 2001; Oslan, n.d.) while the later believes that reality is socially constructed. This study, however, is going to follow the ‘critical realist’ approach (Bhaskar, 1978). Bhasker (1978) differentiated between the ‘real’, the ‘actual’ and the ‘empirical’. Critical Reality argues that there is an existence of a reality that is independent of our representation of it and thus in the social world human beings both shape and are shaped by the social reality. In the context of this study, the author believes that the individuals and organisations (agents) both contributed in shaping (or causing) the credit crunch (social reality) and are also being shaped (or affected) by it, thus it is a critical realist position.


7.2       Logic of Reasoning:


Forstater (1999) describes three kinds of reasoning: deduction, induction and retroduction. Deduction is based on Positivism and seeks to verify and explicate a theory. It assumes that data may not necessarily be theory laden. Induction on the other hand is based on the Phenomenological paradigm and focuses on generating theory. It implies that theories without facts are possible (Saether, 1998).


Retroduction attempts to overcome the pitfalls of both purely inductive and deductive research processes. It uses a predictive theory but sees it as a ‘conceptualisation’ rather than an ‘ordering framework’ as considered in deduction (ibid.). This study will use retroduction and will utilise the Boom-Bust theory as predicate. The aim is not to generate a theory or to test an established theory but to apply it in order to systematically study the phenomenon.


7.3       Nature of Study


The study will be exploratory in nature as there is very little academic literature available on the current credit-crunch as yet.


7.4       Data Collection and Timing


There are various data collection methods e.g. Cross-Sectional studies, Experimental studies, Longitudinal studies, Surveys, Ethnography and Case study. This study will however be based upon two cases that provide greater opportunity for in-depth study (Yin, 1994; Voss et al, 2002).


The study will mostly rely on secondary data. Data will be collected by exploring existing literature relevant to the research topic. It is used to increase knowledge of the subject area and develop the research question. Qualitative primary data will be used for validation and triangulation. Many techniques can be used to collect qualitative primary data. These include direct observations, participant observation and interviews (Yin, 1994). This study will use interviews in the semi-natural environment for primary data collection. Interviews can be individual or group, face to face verbal interchange or telephonic conversation. They can be structured, semi or un-structured (Miles and Hubberman, 1994). This study will use in-depth, semi-structured interviews and a combination of face to face, telephone and email interviews. The effort will be to allow discussion between the interviewer and interviewee and thoughts to flow more freely in a conversation rather than a structured interview. They also help reduce the number of interviewees because of detailed discussions (Denscombe, 1998). The interviewees will be selected by random sampling and include representatives from both the housing and financial sectors. Each interview will take about thirty to forty minutes, and will be recorded and conducted by the researcher. Email and telephone calls will be used to interview participants from USA.


Most of the data however, will be collected from secondary sources given the limited time and budget. However, a combination of qualitative and quantitative sources will be used to find basic statistical data and other information on the credit crisis. A detailed literature review will be conducted to study the previous crises and identify their similarities and differences with the current problem. Some of the data sources for these are books, journal articles and other documents, such as published reports, newspaper clippings and other material on mass media and online journals and databases. Archival records such as maps/charts and other geographical and demographical data will also be used (Yin, 1994).


7.5       Data Reduction and Analysis


Data reduction has already been applied to the secondary data used. A project database will be maintained and events documented. Detailed write-ups of each interview including transcription of tape-recordings and notes taken at interviews/meetings will be made to maximise recall and to facilitate follow up and filling gaps in the data (Voss et al, 2002). Documentation and the use of multiple data sources of evidence enable the researcher to highlight incidents of phenomena in the data and code it into categories (Denscombe, 1998). The data will be searched for cross case patterns and similarities and differences between cases (Bonoma, 1985).


8        Limitations and Problems


The study may have the following limitations:


  • Firstly, it can at best be considered a pilot project due to limitations of time and finances and also because there is very little secondary material on the current credit crunch.
  • Although the study may infer to the wider body of knowledge, the inherent limitations of case study research will make it impossible to generalize from it (Yin, 1994).
  • Same applies to the interviews despite the diversity of respondents due to random sampling.
  • Retroduction is considered by some authors as the weakest form of reasoning and most prone to errors (Saether, 1998).



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Appendix I


Losses of US financial institutions due to subprime mortgage failure.


Firm                    Writedown     Credit Loss      Total Citigroup                 37.3            5.6           42.9 UBS                       38.2                          38.2 Merrill Lynch             37                            37 HSBC                       6.9           12.6           19.5 IKB Deutsche              16                            16 Royal Bank of Scotland    15.2                          15.2 Bank of America            9.2            5.7           14.9 Morgan Stanley            12.6                          12.6 JPMorgan Chase             5.5            4.2            9.7 Credit Suisse              9.5                           9.5 Washington Mutual          1.1            8              9.1 Credit Agricole            8.3                           8.3 Deutsche Bank              7.7                           7.7 Wachovia                   4.6            2.4            7 HBOS                       6.9                           6.9 Bayerische Landesbank      6.7                           6.7 Fortis                     6.6                           6.6 Societe Generale           6.3                           6.3 Mizuho Financial Group     6.2                           6.2 ING Groep                  6                             6 Barclays                   5.2                           5.2 WestLB                     4.8                           4.8 Canadian Imperial (CIBC)   4.2                           4.2 LB Baden-Wuerttemberg      4                             4 E*Trade                    2.5            0.9            3.4 Dresdner                   3.4                           3.4 Natixis                    3.4                           3.4 Wells Fargo                0.6            2.7            3.3 Lehman Brothers            3.3                           3.3 Bear Stearns               3.2                           3.2 National City              0.5            2.6            3.1 Goldman Sachs              3                             3 BNP Paribas                2.1            0.6            2.7 Lloyds TSB                 2.7                           2.7 Nomura Holdings            2.5                           2.5 HSH Nordbank               2.5                           2.5 ABN Amro                   2.4                           2.4 Bank of China              2                             2 Commerzbank                1.9                           1.9 Royal Bank of Canada       1.7                           1.7 UniCredit                  1.6                           1.6 DZ Bank                    1.5                           1.5 Alliance & Leicester       1.4                           1.4 Dexia                      1.1            0.2            1.3 Caisse d’Epargne           1.2                           1.2 Hypo Real Estate           1                             1 Gulf International         1                             1 European banks not         9.2                           9.2  listed above (a) Asian banks not            7.5            0.3            7.8  listed above (b) North American banks       3              1.1            4.1  not listed above (c)                          ____          _____          _____ TOTALS*                  332.3           46.9 (d)      379.2 * Totals reflect figures before rounding. Some company names havebeen abbreviated for space.