University of Amsterdam
Effect of Subprime crisis on the Housing Market in USA
Prepared by: Sabyasachi Sengupta, M.Sc Real Estate Finance
August 2011
Student number: 6261477 Supervisor- Gönül Doğan
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INDEX
Table of Content……….. 2
Acknowledgment……….….3
Abstract……….4
Introduction ………..5
Origin of the Crisis………...12
Literature Review……….14
Data and Methodology……….22
Results………..26
Granger Causality Test………30
Conclusion………...33
Appendix……….35
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ACKNOWLEDGEMENT
My thesis "Effect of Subprime crisis on Housing Market in USA" is an attempt to understand the role of subprime loans and the crisis on the Housing market of USA before, after and during the crisis of 2008. There have been previous studies to understand the cause and effect of the Subprime Foreclosures in the Housing market. In this paper I try to analyze the role further by studying previous Literatures and also by Empirical test. I have done the regression analysis through Eviews and the Granger Causality Test through Matlab.
I would like to sincerely thank my supervisor Gönül Doğan for her support and guidance during the study. I would also like to thank the Real Estate Faculty at UvA for their support all throughout. I would also like to thank my friend Ian Menezes for his help and support.
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ABSTRACT
The origin of the financial crisis in the last decade was the housing market. The easily available housing mortgage loans, mainly the subprime mortgage loans triggered substantial growth in the housing market. This led to not only raise in house prices, but over spending in the housing market which meant higher supply of houses during the boom period leading to a bubble. When the house prices started to collapse the impact was unimaginable and unprecedented. As a result there started to be large number of defaults for house mortgage which meant substantial losses for the banks and the other financial institutions. The GDP of the USA started shrinking and unemployment levels rose to higher levels. The inflation which is often considered to have positive effect on the House prices didn't help the matter. The government of USA tried to control this huge meltdown by reducing the interest rate so that the House price fall can be prevented but the House prices kept on falling. Another important reason for this bubble was the fact that until the very bust, the housing prices were hugely influenced by the previous quarter or previous month index. Therefore the house prices before the bust was biased and very expensive.
We study the cause of the subprime crisis with respect to its origin and how it spread to the housing market and the prices started to fall. Through this study we try to understand the above discussed factors and how these macro-economic and financial factors played an important role in the growth and decline of the house prices in the last ten years. Through the various literature reviews we try and find out how the factors contributed to the housing bubble and how they have impacted or have been impacted during the bust. During the bust phase of the housing crisis the crisis spread to the other sectors of the economy and this resulted into a recession. We analyze various the factors like the GDP growth, inflation, interest rate, previous month house price and the subprime loan default or foreclosure rate. We try and analyze which of this variable has positive or negative impact on the house price and which variable the highest impact on the house prices through empirical study. We also perform the Granger causality test to see the impact of foreclosure rate on the housing price and the growth of housing price on foreclosure rate. Once the results are obtained we finally conclude the outcomes. The literature briefly covers the future of the housing price in USA and how the government can take measure to control this free fall of house prices in USA.
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INTRODUCTION
The housing sector has been one of the most dramatic sectors in the last ten years. The early 2000’s saw rapid growth in housing prices. Soon the house price started to drop and it gave rise to economic turbulence. The default on house mortgages started to rise, which was alarming not only for the banks and other financial but also for the entire economic condition. What followed was an unprecedented and almost uncontrollable crisis.
The trouble with the mortgage market rose in the middle of 2005 when the serious delinquent loans (which are due over 90 days or more) started defaulting. The mortgage default was primarily on the subprime or near prime mortgages. The subprime mortgage is the loans which are targeted to borrowers who have poor credit histories and little savings available for down payments. 700,000 subprime mortgage loans had originated by 1998-2000. (Christopher Mayer, Karen Pence and Shane M. Sherlund, 2009). This led to rise in house prices and when in the mid of 2005 the mortgage rate started to rise the house appreciation started to slow down and towards the end of 2007 it went into to negative. In USA the housing price growth was negative post 2007.
Housing market has the highest impact on the wealth of the households. Also the reduction in housing construction leads to reduction in GDP and which gives rise to unemployment. The soundness of the housing sector is therefore very important for the overall economic stability of a country. The fluctuations in the house prices led to instability in the financial and the economic sector. The rapid rise in house price leads to a housing bubble and when the bust happen the housing price collapses leading to financial turbulence across all the sectors.
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House Price Growth
-8,0 -6,0 -4,0 -2,0 0,0 2,0 4,0 6,0 8,0 10,0 2003 2004 2005 2006 2007 2008 2009 2010
House Price Growth
Figure 1 Source- OECD
In the last financial crisis when all the major economies were in recession and major banks failed the housing prices across all the major countries dropped sharply leading to deeper financial crisis. Thus it is very essential to understand the reason for the volatility in the housing market and how the various factors contribute to the house prices. In this study we analyze the factors that contributed to the house prices during the subprime crisis and also how the housing market reacted to the subprime crisis.
Despite the low borrowing rates there were still a steady flow of re-possessed homes onto the market because of tighter bank lending rules and high unemployment rates. According to the housing index, prices in 20 major metropolitan areas of USA fell by 3.6 percent in the 12 months by March 2008, lowering the prices from already suppressed levels (Freddie Mac). That was bad news for the broader economy, which depended on a buoyant housing market to keep the USA economy in sound health. As a result of poor house prices there was reduction in wealth, which reduces consumer spending. The falling prices also raised the level of uncertainty, scaring consumers from the market and leading to more foreclosures.
7 Figure 2 Source-Datastream
The GDP growth in USA declined sharply during the crisis and it went into negative. The GDP growth rate was the lowest during the end of 2008 and the GDP growth rate remained in negative till the 2010.
The negative and poor GDP growth has negative impact on the housing price and thus the housing price index was also affected. The graph below we see that the house price index in USA was at its peak during 2006 and early 2007. By the mid of 2007 the price index started dropping and post the late 2009 the fall decreased.
8 Figure 3 Source-Freddie Mac
Another reason for this high turbulence in the housing market is the nature of the housing market. The house price index is not necessarily the right indicator for the correct condition in the housing market. This is because when the house price starts to fall most of the home owners avoid selling their houses to save their wealth, as a result the number of transactions goes down. Another important reason is that the current house prices are influenced a lot by the lagged house prices. Therefore the current house price tends to impact the current prices and therefore when there is rise in price, the price keeps on rising and when the house prices fall they keep on falling. Some macro-economic factors play an important role in deciding the prices of the houses. As we do the literature review and the empirical study we would also see how the previous house prices have an impact on the current house prices.
Another important factor that has a strong influence on the house prices in the inflation. There have been studies that stated that inflation has positive impact on the house price. Through the empirical study we would study if inflation plays an significant role in deciding the house prices and if it does whether it is positive or negative.
9 Figure 4 Source-US Inflation Calculator
Another key reason for this housing debacle was the default in the subprime mortgage sector. During the peak seasons the banks and other financial institutions started to supply huge amount of mortgage credit to the market. As a result the demand for houses increased and therefore the house prices began to peak in the early 2000’s. As the house prices started to decline the home owners were left with no option but to default on the mortgage. As more and more house prices began to shrink, the default rate or the foreclosure rate jumped up, creating further pressure on the mortgage market.
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As the housing price goes down the foreclosuer rate goes up and as the foreclosuer rate goes up the house prices go down as well. This was like the vicious cycle where the US housing market was trapped into. The governmet tried its best to control the economic crisis. FED constantly tried to boost the confidence in the market specially in the housing market by reducing the interest rate during the crisis period. These changes did boost the market on a temporary basis only.
Figure 6 Source- FED Reserve
The US government made all efforts to save the economy from dipping into a recession but was not successful. The US government in its efforts to boost the economy has now lot of debt, which is higher than the average debt of all the OECD countries and also the European countries. This has created further turbulence in the market.
11 Government’s debt as a percentage of GDP
Figure 7 Source- OECD
As we go along this paper we discuss how the House price collapse happened and how the various factors contributed to the collapse. We explore the role of all these variables (the macro economic and financial factors) and how these have factors made an impact on the Housing price.
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ORIGIN OF THE CRISIS
The housing bubble and the stock bubble grew simultaneously in the mid 90s as both contributed to the growth of each other. This meant that the excess wealth creation because of tremendous increase in stock prices led to higher disposable income and as a result people preferred to buy better and more expensive homes than before and therefore increased the demand for houses. This led to increase in house prices in the initial period. An expectation that the house price will keep on increasing in the same momentum was built and as a result the prices began to grow further. Reports suggested that the housing prices in USA that were adjusted with inflation had not changed much till 1995, but post 1995 they grew by 30% although the rent had only increased by ten percent (Dean Baker, 2008). This was an indicator of the fact that housing bubble as outcome huge price rise of the houses which were not reflected through the rents. On the contrary as the house prices rose to higher levels by 2002, there was increase in constructions that led to oversupply in the rental market and which finally led to rise in vacancy rate which eventually touched record high. As the stock market began to collapse in the USA, the faith in the housing market increased substantially as housing investment was perceived as a safer investment. As the economy was recovering from the crisis of 2001, FED continued to cut interest rate and the rates on the mortgages were also reduced. FED also suggested home buyers to opt for adjustable rate mortgages (ARMs) instead of fixed rate mortgage. Therefore despite the fact that the fixed mortgage rate had been all time low, home buyers preferred to opt for ARMs. The real house price rose by 31.6 %, with an annual rate of 7.1% between 2002 and 2005. (Dean Baker, 2008).As a consequence of unprecedented growth constructions boomed and consumptions also boomed but savings dropped substantially during this period. With rise in constructions, vacancy rate shifted from rental market to the owners market.
By the end of 2007 and 2008, house prices started dropping and homeowners started facing foreclosure. The prices of the houses fell below the mortgage amount and the home owners preferred to foreclosure their homes instead of paying monthly mortgage (Dean Baker, 2008). As a result of this high foreclosure there was high supply of the houses and the demand dropped. The default rate started increasing the banks tightened their standards by having stringent rules for the home owners and asking for higher down payment. As a result the affordability for the 1st time home owners went down drastically and also made it very difficult for the home owners
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to make the high down payments. By the end of 2007 the real house price fell by 15% and 20% in case of some areas in USA. The more shocking results came in 2008 when the house prices in most of the cities fell down by 30% which meant a loss of seven trillion dollars (approx $100,000 per homeowner)(Dean Baker,2008), which was almost half of the GDP and this led to the financial crisis.
As bubble started to bust the default rates especially in the subprime market began to rise. 2007 saw many leading bank and financial institutions in the US and Europe struggling by the collapse in the value of mortgage backed securities. As the default rate went high it triggered a substantial credit crunch. The major Wall Street giants saw a loss of $175 billion of capital between the period of July 2007 and March 2008 ((Yuliya Demyanyk, Otto Van Hemert, 2008). The crisis in Bear Stearns which led to a rescued merger with JP Morgan provoked panic in the market. The main reason for the credit crunch was the rising defaults among the holders of subprime mortgages in the last quarter of 2006 and early 2007, because of the fact that the interest rates were rising up to protect the falling dollar. This resulted into the failure of several large mortgage brokers in the early 2007, but the actually effect of the crisis was witnessed towards the end of 2007.
In October FED was announced that "FED has encouraged three of Wall Street’s largest banks— Merrill Lynch, Morgan Stanley and Bank of America—to set up a $70 billion fund to establish a clear value for threatened assets. This did not work. Analysts complained. The path they have taken of skimming off the cream from the top doesn’t resolve the fact that there is poison at the bottom". (Yuliya Demyanyk, Otto Van Hemert, 2008). Toward end of 2007 when the credit crunch got worse the central banks tried to inject large amount of liquidity in the market, but this didn't make any long term impact on the market. USA confirmed credit problems in early 2008 and planned a stimulus package to offer to the banks. The bankruptcy of Lehman Brothers worsened the situation. Lehman Brothers the fourth largest investment bank in the Wall Street filed bankruptcy on 15th September 2008, and AIG was also faced similar crunch but was bailed out by FED. With this financial turbulence across the entire financial sector in the world, the housing price continued to fall.
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LITERATURE REVIEW
In the article “Predicting Downturns in the US Housing Market: A Bayesian Approach” (Rangan Gupta & Sonali Das, 2008), the authors predicted the downfall of the housing price in the USA by using Bayesian Vector Autoregressive (BVAR) models and evaluated 20 largest states of the USA by using the quarterly data from the period 1976-1994 and then forecasted the housing price ahead of the house price growth of the periods between 1995 to 2006. The forecast once derived was verified by checking VAR models. They predicted the decline in the real house price growth, which was an indication of the adverse effect of fundamentals on real house prices. In the article "Housing markets and the financial crisis of 2007–2009: Lessons for the future",(John V. Ducaa, John Muellbauerc, Anthony Murphyd,2009) an unsustainable decline of credit standards was largely held responsible for a high US mortgage lending which eventually led to a housing bubble. The authors find several factors that were responsible for the Housing bubble. He states them as below-
“(1) new innovation initially works well, yielding above normal returns.
(2) Over-optimism about subsequent returns then fuels an over-investment in the new product; this initially spurs large increases in asset prices.
(3) These factors alone are usually insufficient, as some source of funding is needed to sustain the build-up, typically in the form of increased leverage and liquidity in the financial system. (4) If there is a perception that the innovation is fundamentally changing the structure of or agents use the recent past as a guide, the excess investment and asset price appreciation will be amplified.
(5) The combination of these factors gives rise to an asset price bubble."
It is also stated that the financial innovations in the market was an important factor for the housing bubble as by the mid-1990s, prime U.S. borrowers were able to obtain conventional mortgages from banks. The government sponsored enterprises (GSEs) started new kind of loans which were residential mortgage-backed securities (RMBS), which were funded by the GSEs by the help of issuing their own debt or by selling them to investors, and the debt holders were
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happily insured by the GSEs against mortgage default. Since GSE was backed by the US government the investors expected a low default rate. Moreover some non prime borrowers started obtaining loans from the Federal Housing Administration (FHA) which had minimal down-payments and also had insurance premium along with limited debt service ratios. The authors also highlighted that during the recent years private nonprime mortgages had more market shares during 2006 peaking at 40 percent of home purchase mortgage originations.
Other factors that were responsible for the excess leverage during the boom and which eventually led to the bust would be tax deductibility of mortgage interest that encouraged households to take on leverage offered by lenders and also the limited mortgage default liability of most US households. Other countries, such as Denmark and the UK removed the tax deductibility of mortgage interest and in other countries the tax regime benefitted the rental housing like in Germany. The other factors would be the risk appetite of global investors was very high from mid-2003 to mid-2007 until the huge drop in the summer of 2007. Investors with low risk appetite of stemmed from poor outcomes on highly levered non-housing investments. During the business cycle of 1984 to 2007 which is often termed as the Great Moderation the good results made the investors complacent and they failed to understand what was coming. (John V. Ducaa, John Muellbauerc, Anthony Murphyd, 2009) Other reasons for the subprime bubble would be low interest rates, lowering of the mortgage credit standards and an increased ability to tap housing wealth because of the household spending. The rise in Loan to Value ratios and the origination of mortgage loans in the non-subprime category depicts the combination of financial innovations that led to the securitized financing of subprime mortgages. The development of credit scoring technology helped loan givers to filter the nonprime applicants and then price the risk of nonprime mortgages. This led to the issue of how nonprime loans can be funded, because a lot of them were too risky for regulated banks to hold in their portfolio or else it would expose investors to a high and uncertain default risk, if labeled along into other securities.
The funding problem was solved by two types of structured financial innovations which were the issues with nonprime mortgage-backed securities or have default insurance from credit default swaps (CDSs). The money supply growth had not triggered the extra borrowing to a great extent
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but it was more by securities issuance, which led to the increase in leverage and capital inflows. There are some arguments that large capital inflows to the US from China and few other countries were to some extend responsible for the weakening of the standards and the growth in securitization and structured financial products alongside. Although it is not clear that to how much extend financial innovation in the US were caused by global imbalances. It is also stated that there was a large current account swing which occurred in the 1980s without a major U.S. housing boom-bust (John V. Ducaa, John Muellbauerc, Anthony Murphyd, 2009). The surge in the consumption share of US GDP was larger in size than the widening of the current account deficit which implied the roles for other factors. The higher leverage played a critical role in the Housing bubble. Freddie Mac and Fannie purchased or guaranteed substantial amounts of private label RMBSs in order to meet their annual public policy goals of enabling a minimum percent of their business to expanding affordable housing. Moreover the prime mortgages packaged into RMBS was often funded the 80 percent first liens on many home purchases, the other 20 percent being funded was not guaranteed by the GSEs. In these two ways, the GSEs indirectly triggered the easing of U.S. mortgage credit standards in the early and mid-2000s (John V. Ducaa, John Muellbauerc, Anthony Murphyd, 2009). Lastly the low capital requirements and declining fee income from mortgage insurance premiums influenced the GSEs to issue more and more debt, at interest rates which was lowered because of the implicit government guarantees. This strategy initially increased the profits, leverage and mortgage lending of the GSE’s. Not just the GSE's but some large US bank holding companies created special investment tools to buy risky or longer duration assets like the nonprime RMBS’s and CDS’s by issuing short duration debt which initially had high credit ratings. These trends grew further after the Securities and Exchange Commission (SEC) raised the limit on the leverage ratios of the brokerage units of large investment banks.
According to the article "The housing meltdown: Why did it happen in the United States?" by Luci Ellis the US housing construction had peaked in early 2006 but towards the end of 2006 housing starts had fallen by around 40%. The decline in USA was also because of oversupply. This meant that the constructions during the boom phase were much more than the actual demand which led to oversupply. This led to a high vacancy rate and vacancy was high especially in houses built after 2000. The easing of the standard of getting a mortgage was
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another reason for his demand and supply mismatch. The US government wanted that homeownership was accessible to households who had historically been underserved by mortgage lenders. In addition to this, the administration also intended to reduce the GSEs’ domination on the mortgage market.
According to the article by Michael Mah-Hui Lim, when FED On September 18, 2007 lowered the Fed Fund rate by 0.5% FED attempted to prevent the financial crisis resulting into a recession. This rate cut initially gave hope to the investors of the equity markets worldwide, but this move did leave concerns about the U.S. fundamentals, as it alarmed that the housing crisis could lead to a crisis in the whole economy. From the very beginning of the crisis, despite the Fed attempt to revive the confidence in the market by lowering the interest rates four times (from 5.25% to 3.5%) in a span of four months, world equity markets started collapsing showing a lack of confidence in the Fed’s ability to control a U.S. recession. The long-term rates remained at their previous levels or even edged up which hinted that the market was concerned over inflationary pressures. Many refuted to credit card loans as the last mean of getting loans and as a result outstanding credit card loans soared in the third and fourth quarters of 2007 which was alarming towards another bubble for the economy. The banks by then had restrained in generating credit because of tighter credit lending standards and also because they can’t afford to lend. This was first time in last fifty years; U.S. banks’ reserves with the Fed have gone into negative. The total capital of the U.S. banking system is one trillion dollars and in January 2008, about $100 billion of losses were reported by banks and financial institutions. It was estimated that the losses could have gone up to $200 billion to $400 billion by Greenspan, to one trillion dollars by Roubini of New York University. This meant that that the total loss would be equal to the total capital and thus the biggest banking crisis since the Depression was expected. Michael Mah-Hui Lim believes that aggressive rate cuts by FED may have had short term positive effect on the economy, but it is no way the solution to the actual problem as the rate cuts just postpones the problem and make the way for the crisis and therefore concludes by saying that that the fall in the U.S. housing market would affect consumer spending and would drag the economy into recession. As the economy fell in recession the foreign creditors’ got worried about the strength of the U.S. dollar, which had reached a record low by then.
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The article “Intrinsic and Rational Speculative Bubbles in the U.S. Housing Market 1960-2009” also say that the USA housing market had experienced two kind of bubbles in the last 5 decades (Ogonna Nneji, Chris Brooks,Charles Ward, 2011). In the paper the data is analysed and is concluded that the first bubble that was already present in the market switched from one regime to the other during the year 1999. The earlier Housing bubble was characterized by high expected return and low volatility and the next bubble was characterized by low return and even negative return toward the end and high volatility. The second bubble was largely a rational speculation in which previous price change played the most important driver in deciding the future or present house price. The authors (Ogonna Nneji, Chris Brooks,Charles Ward, 2011) also felt that the consistent decline in interest rates and the aggressive subprime lending as the major reasons for the housing bubble. The unprecedented house price is actually held responsible for this Housing bubble which led to the subprime crisis. The huge appreciation is house price had a huge effect on the mortgage lending and also the housing index that were constructed before 2006, were largely influenced by the previous year results (William N. Goetzmann, Liang Peng, Jacqueline Yen, 2009). In the current scenario the housing market of USA has a substantial supply of houses whereas the actual sale of houses in still low, which is one of the key reasons that would depress the house price in USA for some more time (Karl Whelan, 2009).
The OFHEO house price index which was never in negative even in the early 1990s housing crisis was in negative during this crisis and it compares this financial crisis with the financial crisis in 1990s and declares that with respect to housing investment setback and excess inventory this housing crisis is way worse than its predecessors (William N. Goetzmann, Liang Peng, Jacqueline Yen, 2009). The foreclosure rate which is shown in the article “THE FORECLOSURE-HOUSE PRICE NEXUS: LESSONS FROM THE 2007-2008 HOUSING TURMOIL” as the ratio to the delinquency rose up as the economy was hit by recession and as a result there was high default rate. The paper concludes that this current crisis had the worst and higgest foreclosure rate.
In other study (Kristopher Gerardi, Adam Hale Shapiro, and Paul S. Willen, 2007) house price indexes and the unpaid mortgage balance is used to estimate the probability of the negative equity for the home owners. In order to obtain the correct results the Granger Causality test is done. The macro economic factors like the unemployment are explored that gives rise to
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foreclosure. They find that the foreclosure rate is not co-related to the business cycles and also that the delinquency rate which means as a missed mortgage payments have been high during the crisis of 1990 but the same rule didn’t apply to the foreclosure which on the contrary decreased. But during the crisis in the last decade both the delinquency and foreclosure rates increased significantly. Negative equity is highlighted as the main reason for the decline in house prices. The authors (Kristopher Gerardi, Adam Hale Shapiro, and Paul S. Willen, 2007) also state that the homeownerships that were financed by subprime loans were five to six times more likely to default compared to the home ownerships that were financed by prime mortgage. This is because the probability of success of the subprime loans is very sensitive to the macroeconomic environment and also to house price appreciation to a large extent. The probability of default for subprime borrowers and also for prime borrower’s increases significantly in periods with low or negative house price appreciation and therefore expected increase in the supply of housing depresses house prices and these results into default on mortgages. Foreclosures depress housing prices even further during the crisis and thus as the foreclosure goes up the housing price starts falling down. The fall in house prices creates an opportunity to increase the consumption of the current housing space but leveraging it makes it costly for homeowners to sell their current houses and buy better houses which can result into capital losses..
Owing to high foreclosure rate during the subprime crisis the government of USA implemented a new loan modification program since March 2009 (Satyajit Chatterjee , Burcu Eyiungor, 2009). The main highlights of the policy were-
"To be eligible for a loan modification, the mortgagor must have current income than his income at the time of the origination of the loan (the hardship requirement).
The ratio of mortgage payments to income must be higher than 31 percent (there are some asset restrictions as well that is ignored).
If a mortgage is eligible, the government promises to reimburse investors half the cost of reducing the mortgage payment-to-income ratio from 38 percent to 31 percent.
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The modification alters only the monthly payments; if the mortgagor were to sell the property, the outstanding loan amount has to be paid.
Also, only one modification per mortgage is permitted (i.e., a modified mortgage cannot be modified again), and the modification ends in 6 years after which the mortgage payment is gradually raised back to the original amount. The option to modify an eligible mortgage is available for only 4 years."
The authors (Satyajit Chatterjee , Burcu Eyiungor, 2009) believe that this foreclosure prevention policy has the potential to stabilize the currently highly volatile house prices and therefore they use the foreclosure policy in their model and found it to be effective in achieving its objectives. They also warn that the effects of this policy might be temporary and once the program expires in six months there is a possibility that the foreclosures might rise again and the houses prices might crash again in the future.
Foreclosure is also considered as a two-step process (Kelly D. Edmiston and Roger Zalneraitis, 2007).The very first step is when the borrowers misses a scheduled payment and become delinquent, which the lenders generally assume as the initial delinquency which would be temporary and that the borrowers would resume payments in the future again. When more than three such payments are missed the borrowers are considered to have defaulted and will not resume payments in the future again. The lenders then have the option of taking the second step by foreclosing. The mortgagor sometimes with a wealth maximization motive can at any time sell the underlying property to the lender for the outstanding balance of the mortgage. He therefore exercises this option to sell by defaulting on payments and through foreclosure he receives value because of his payment obligation on the mortgage. Such transaction increases wealth only if the aforesaid house is worth less than the outstanding balance on the mortgage and these kinds of defaults are termed “ruthless defaults” because the mortgagor has the ability to pay but opts not to make any payment. These defaulters generally have a very high Loan-to-Value ratio. The authors (Kelly D. Edmiston and Roger Zalneraitis, 2007) also prove through empirical results that there is a strong relationship between negative equity and default ratesand they also state that when the negative equity crosses more than 10% there are high chances of
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default on that mortgage. Once a borrower defaults the lender are left with no option but to action to foreclose. The time required on the Foreclosure varies largely on the lender's eagerness to re-negotiate with the borrower on the loan term, which depends mainly on the cost of foreclosure. During the crisis of 2006, the foreclosure rate increased unusually and drastically. The huge rise in subprime loans during the early 2000's is one of the main reasons for these. The new and modern subprime mortgages which were customized to meet the demand for the aspiring home owners were often characterized by high payments. Moreover when the house prices started to drop the ambitious home owners were neither able to sell their home nor refinance them and they had the burden of paying high mortgage payments. As a result the foreclosure rates spiked up in the last decade.
The future of the US economy still remains a big question especially when it trying to overcome from such huge financial crisis. One of the studies stated that this crisis has shown deep and long lasting effect on the asset prices in USA. This impact would surely last for a long time (Carmen M. Reinhart, Kenneth S. Rogoff, 2009). Not just the asset prices but the country’s total output and employment has been affected to a great extend. The unemployment rise is predicted for the next 5 years. The future of the Housing prices in USA also looks very weak. The house prices are expected to fall further for the next six years, which is alarming for the investors. The authors (Carmen M. Reinhart, Kenneth S. Rogoff, 2009) finally concluded their article by saying that every financial crisis eventually ends but it leaves the economy with a substantial increase in public debt.
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DATA AND METHODOLOGY
There have been various studies conducted to understand or model the changes in house prices. The key indicators that influence the house prices would be the GDP, interest rates and inflation. Englund and Ioannides (1997) used real GDP growth, interest rates and lagged house prices when they tried to model the housing price across the OECD countries. In this study we include the economic factors like the GDP and inflation, and also use the lagged house prices as a standard to estimate the current house prices. Since this study is about understanding the effect of the subprime crisis on the housing price we have included another variable which is the foreclosure rate. Though these variables we try and understand the effect of the subprime crisis on the Housing market in USA.
The study will be examined through the below linear regression model.
Hpt = α + β1 Δ GDP + β 2 Δ INF + β3 Δ FOR + β4 Δ Int + β5 Δ Hpt-1
Where
Hpt = Housing Price growth
Δ GDP = Monthly GDP growth Δ INF = Monthly change in Inflation ΔFOR = Monthly change in Foreclosure Δ Int = Monthly change in actual FED rate
ΔHpt-1 = Growth in House prices in previous month
To build this model we have used the monthly data of the change in House price Index which gives us an idea about the current house prices in USA.
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The reasons for using the variable that we have used in our model are discussed below.
House prices are not very efficient and the prices of the houses are largely affected by the house price of the previous quarter or month. There have been many research conducted on this issue and all have by and large proved that the previous house prices have the highest impact on the current house price. Therefore we have used lagged growth rate in the housing price as a variable to understand the current house price.
Many studies have also shown that inflation rate has a positive correlation to house prices. The higher the rate of inflation, higher is the price of the houses. Therefore we have also included inflation as a variable to analyze the effect of it on the house prices especially during the crisis.
The growth in the real gross domestic product (GDP) is actually the representation of the change in wealth of a nation. It is believed that the positive change in the GDP growth can increase the housing demand and therefore accelerating the housing price. This is so because a positive growth in GDP signifies sound economic growth and this implies that there would be more economic activities and an increase in wealth for the public. This may result in rise in house price as the population would be now willing to pay more for the houses. Thus we included the change in growth in GDP as a variable that affects the current housing price.
The other variable included is the interest rate. We have used Federal funds effective rate in USA. The interest rate is a key indicator to find out the attractiveness of mortgage. The higher rate of interest has positive effect on the capitalization or the yield for the real estate market. This affects the house prices negatively and the house prices go down. When the interest rate is lowered the capitalization rate goes down and the house prices move up. In the literature review section we have observed that the interest rate has been used by the FED not only to control the falling house prices in USA but also to control the crisis. It is also believed that the cut in interest rate has a temporary effect on the market, as it only postpones the problem and not solves it. We have therefore used the change in Interest rate as a variable to see the effect on the House prices.
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In the article by Peter Englund and Yannis M. Ioannide (1997) stated that along the lagged or previous house prices, GDP and the change in interest rate have a great impact on the house prices of the current year.
"Foreclosure is the legal process by which a mortgagee, or other lien holder, usually a lender, obtains a termination of a mortgagor's equitable right of redemption, either by court order or by operation of law (after following a specific statutory procedure)." During the subprime crisis when the House prices started to fall the home owners could not pay the mortgage amount as the mortgage amount was more than the actual price of the house. This meant the home owners had negative equity and they preferred not pay the mortgage amount per month instead surrenders the house to the banks. As the house price collapsed the rate for foreclosure increased, which lead to further fall in the price. In this study we use the rate of change of foreclosure of the subprime loans as a variable component that affects the housing price.
There are various other factors that influence the house price like the population, demography, neighborhood and locations. But we have not included these data in our empirical study. Government influence also plays an important role in the house price of a country.
According to the article by Mikael Atterhög (2005), Government influence has a very important role to play in the home ownership rate which affects the housing price. But it really tough to quantify the government influence on the housing market. We did find that the government of USA does spending on the Housing market annually, but there are other involvements of the Government in the housing market which are very tough to quantify.
The relationship between the independent variables and the house price is tested using regression equation and dividing the data on a monthly basis. This is done because the previous lag data has a strong influence on the current data. We then run the regression to find out which variable has the strongest impact on the dependent variable.
We then perform a Granger causality test to find out the relationship between the growths in house prices to the change in foreclosure rate in the subprime loans and then check the result of the causality by performing regression analysis.
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House price Index of USA is collected from Freddie Mac (http://www.freddiemac.com). The GDP growth has been extracted from the data stream. The monthly interest rate has been taken from Fed website (http://www.federalreserve.gov). The rate of foreclosure of the subprime loans has also been taken from the data stream. The inflation rate has been extracted from the US Inflation Calculator (http://www.usinflationcalculator.com/inflation/historical-inflation-rates/). All the data extracted are between the time periods of 2001 to 2010.
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RESULTS
In order to understand the effect of the subprime crisis on the Housing price of USA we have developed the below model and we have ran regression analysis on the same.
Hpt = α + β1 Δ GDP + β 2 Δ INF + β3 Δ FOR + β4 Δ Int + β5 Δ Hpt-1
Where
Hpt = Housing Price growth
Δ GDP = Monthly change in GDP growth Δ INF = Monthly change in Inflation ΔFOR = Monthly change in Foreclosure Δ Int = Monthly change in actual FED rate
ΔHpt-1 = Growth in House prices in previous quarter
We run the regressions in Eviews. The regression was run so that it corrects for multi
collineartity and we do not have biased output. We adjusted the lagged variable as it had a very high co-relation with the dependent variable. The results that were predicted were as follows-
R-squared 0.420801
Adjusted R-squared 0.395397
Variable Coefficient Std. Error t-Statistic Prob.
C 0.013277 0.003532 3.759423 0.0003
GDP 0.053469 0.027222 1.964203 0.0519
FED Effective Rate -0.113646 0.048939 -2.3222 0.022
FORECLOSURE Rate -0.100661 0.025531 -3.9427 0.0001
INFLATION -0.078611 0.053213 -1.47729 0.1424
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The above model explains 39 percent of the house price variation during the period of 2001 to 2010. We see through the above results that the lagged variable or the previous month housing price growth had the highest significance on the current house price. The previous month house price has a positive impact on the current house price. We have already seen in the literature that the nature of the house price is such that it gets influenced by the previous quarter house price. Foreclosure rate of the subprime loans have a high significance on the current house price. Therefore we can see the literature correctly captures the effect of subprime loans default on the housing market. The foreclosure rate has a negative impact on the housing price. A one percent increase in foreclosure rate will reduce the house prices by ten percent. As the US crisis got worse the foreclosure rate increased and the housing prices dipped. The Fed effective rate does have significant effect on the house price. Although it has a negative impact on the house price but we have read in the literature that interest rates have a temporary effect on the housing price. The government during the crisis had cut the interest rate to boost the confidence among the investors but this measure by the government had only temporary effect on the investor’s market, the housing market and on the overall crisis. Inflation and GDP do not appear to have any significant impact on the housing prices.
In order to establish the relationship further between the significant dependable variables like foreclosure, FED rate and lagged house price growth on the current house price growth we perform another regression. We exclude the insignificant variable like inflation and GDP.
The results obtained were as follows
R-squared 0.386744
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Variable Coefficient Std. Error t-Statistic Prob.
C 0.013304 0.003068 4.336028 0
FORECLOSURE -0.151048 0.024563 -4.22733 0
FEDRATE -0.103838 0.047197 -3.2004 0.0018
HOUSEPRICE (T-1) 0.324797 0.092807 3.499691 0.0007
The above results show only 37 percent of the house price variation between 2001 and 2010. The results obtained after excluding inflation and GDP indicate that the lagged variable or the previous month house price growth has a very high significance on the current house price. The foreclosure rate and the Fed rate have negative significance on the current house price.
-.02 -.01 .00 .01 .02 -.03 -.02 -.01 .00 .01 .02 01 02 03 04 05 06 07 08 09 10
Residual Actual Fitted
Figure 8 Source- Empirical Results
To conclude we can say that the lagged variable or the previous month house price growth have a substantial impact on the current house price. The foreclosure rate and the Fed effective rate have negative impact on the current house price. GDP and inflation do not appear to have any significant impact on the current house price.
29 Figure 9-Source- Freddie Mac
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GRANGER CAUSALITY TEST
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another.
As we have seen earlier that the foreclosure of subprime loans have the highest impact on the prices of the houses. They have negative co-relation with each other. This means that more and more defaults to pay the subprime loans and the house prices tend to collapse more and more. Therefore we conduct this Granger causality test to find out which variable has the impact on the other. Granger causality test is a way to recognize the direction of the causality by using both the variables and checking them by time lags. This means we would check if the foreclosure rate is affecting the house price and we would also check if the house prices are affecting the foreclosure rate to the extent of 5 lags. This consideration is necessary as the causality can happen either way after a number of quarters and might not necessarily during the same lag.
The test are conducted in Matlab where-
“F” is the value of the F-statistic
“CV” is the critical value from the F-distribution
All the data are checked at statistically significant at 5% level. If F > CV we reject the null hypothesis.
The cells which are colored in green are the ones where we accept the causality between the variables and the cells which are colored orange are the cells where we could not establish the Granger causality between the two variables.
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Number of lags Direction of causality CV F-Value Decision
1 Foreclosure →Δ HP 3.922 1.143 Causality is accepted 1 Δ HP → Forclosure 3.922 3.144 Causality is accepted 2 Foreclosure →Δ HP 3.929 6.6728 Causality is rejected 2 Δ HP → Foreclosure 3.922 3.144 Causality is accepted 3 Foreclosure →Δ HP 3.926 2.32 Causality is accepted 3 Δ HP → Foreclosure 3.922 3.144 Causality is accepted 4 Foreclosure →Δ HP 3.92 4.23 Causality is rejected 4 Δ HP → Foreclosure 3.922 3.144 Causality is accepted 5 Foreclosure →Δ HP 3.9251 0.5897 Causality is accepted 5 Δ HP → Foreclosure 3.922 3.144 Causality is accepted
According to the results above we see that the Foreclosure rate of the subprime loans have Granger causality on the Housing price in the first, third and the fifth lag. The hypothesis is rejected in the second and the fourth lag. This means that growth in the foreclosure rate of the subprime loans may not have causality on the house prices but the growth in house prices have causality on the foreclosure rate. This means as in when the house prices starts falling down the foreclosure rate increases, leading to more default of the subprime loans.
In order to prove the causality we run another regression to estimate the relationship between the two variables.
In the first equation we see if the lagged foreclosure rate has any significance on the housing price. We use the previous month house price and previous month foreclosure rate to estimate the relationship. The result obtained is –
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Variable Coefficient Std. Error t-Statistic Prob.
C 0.004564 0.001801 2.5339 0.0126
foreclosure(-1) -0.044891 0.019067 -2.35441 0.0203
house_price(-2) 0.436272 0.090095 4.842357 0
This shows that the foreclosure has a significant impact on the house price.
We also tried to find through regression if house prices have impact on the foreclosure rate. We had the foreclosure rate as the dependent variable and the previous month house price and lagged foreclosure rate as the independent variable. The results obtained were
Variable Coefficient Std. Error t-Statistic Prob.
c 0.002367 0.00095 2.4912 0.0142
foreclosure(-1) 0.982644 0.010089 97.39334 0
house_index(-2) -0.20776 0.047356 -4.38725 0
This estimate also shows that house prices also have a significant impact on the foreclosure rate.
Thus to conclude we can say that the house price change has Granger causality on the foreclosure rate and the foreclosure growth has Granger Causality on the house price index growth.
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CONCLUSION
This paper is an attempt to understand the behaviour of the housing market of USA in the last ten years. The first five years of this decade was marked by rapid growth in house price and towards the end of the decade we saw a rapid decline in the house price. In the first half of the year the easily available subprime mortgage loans was one of the main reasons for the rise in house prices. When FED raised the interest rate the house prices began to collapse and this gave rise to high defaults, which had negative impact on the GDP and economy as a whole.
Through the empirical study we tried to find out that which factors have the higgest impact on the current house price. We found that the previous month house price has the most significant impact on the current house price which reflects a true characteristic feature of the housing market. Foreclosure rate of the subprime loans also has a high significance on the current housing price. The foreclosure rate has a negative impact on the House prices. We also seen that the other variables like the GDP and Inflation do not appear to have any significance on the current house price. The Fed rate has significant but negative impact on the current housing price.
Through the literature reviews and the empirical study we say how the house prices reacted to the various factors. One of the key component that we found was the foreclosure rate.The fall in house price resulted into negative equity for the home owners and as a result they defaulted their mortgages which led to foreclosures. As the foreclosure rate goes high the house price falls.
To understand the relationship between the foreclosure rate and the house price we conducted a Gragner casuality test between the current house prices and the foreclosure rate.The results revealed that the housing price growth has a Gragner casuality on the foreclosure rate and the foreclosure rate also has a Grangner casuality on the house price growth.
With the Housing prices still dropping, the future of the housing market in USA looks bleak. The house prices are expected to drop further, but the rate of fall of house prices is expected to reduce. The new loan modification program introduced by the government in 2009 is expected to
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control the foreclosure rate at least for the temporary period. To overcome this crisis USA would need time and the government will face substantial outstanding debts because of this crisis.
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APPENDIX
• Details of the Variables Used in the regression
GDP Inflation Foreclosure rate House Price Index Fed rate
Mean 0,017075 0,023967 0,082948333 0,001981939 0,02349
Standard Error 0,002397 0,001315 0,003769117 0,000798594 0,00167
Median 0,022 0,025 0,08 0,004204475 0,01755
Mode 0,026 0,011 0,0935 0,010625402 0,0525
Standard Deviation 0,02626 0,014409 0,041288609 0,008748157 0,018296 Sample Variance 0,00069 0,000208 0,001704749 7,65303E-05 0,000335
Range 0,137 0,077 0,1229 0,043793607 0,0587 Minimum -0,068 -0,021 0,0329 -0,025206825 0,0011 Maximum 0,069 0,056 0,1558 0,018586782 0,0598 Sum 2,049 2,876 9,9538 0,237832674 2,8188 Count 120 120 120 120 120 Confidence Level(95.0%) 0,004747 0,002604 0,007463228 0,001581296 0,003307
• Details of the Regression Analysis with Inflation and GDP
R-squared 0.420801 Mean dependent var 0.00182
Adjusted R-squared 0.395397 S.D. dependent var 0.008739 S.E. of regression 0.006795 Akaike info criterion -709.645 Sum squared resid 0.005264 Schwarz criterion -695.707 Log likelihood 4.317.868 Hannan-Quinn criter. -703.985
F-statistic 1.656.469 Durbin-Watson stat 0.314183
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• Details of the Regression Analysis without Inflation and GDP
R-squared 0.386744 Mean dependent var 0.001865
Adjusted R-squared 0.370746 S.D. dependent var 0.008763 S.E. of regression 0.006951 Akaike info criterion -7.06683 Sum squared resid 0.005556 Schwarz criterion -6.97341 Log likelihood 424.4762 Hannan-Quinn criter. -7.02889
F-statistic 24.17457 Durbin-Watson stat 0.296771
37
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