Short And Long Run Dynamics Economics Essay WHAT IS THE RELATIONSHIP BETWEEN STOCK AND HOUSE PRICES IN THE NETHERLANDS? Author: R.J.W. Goosen Academic year: 2012-2013 Coach: R.H.G.M. Cox Co-reader: N/A Version: 2nd draft MTP Contact: Department of Finance & Investments, Rotterdam School of Management, Erasmus University Rotterdam, Campus Woudestein, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands PREFACE & ACKNOWLEDGEMENTS ABSTRACT TABLE OF CONTENTS INTRODUCTION House price fluctuations can strongly impact the real economic activity, since housing is one of the most important components of household wealth. Therefore changes in residential property prices affect consumption, expenditure and wealth perception of households. Movements in house prices can furthermore influence the real side of the national and global economy because of their effect on the stability of the financial system. One of the widely considered major determinants of the financial crisis of 2007- 2009 is the rapid rise and following collapse of the residential housing prices in the United States. The financial crisis led eventually to a deep recession and has led to an significant increase in unemployment rates, slowdown in economic activity and put a hold on consumer spending (Ludwig & Sløk, 2004; Ibrahim, 2009). The rollercoaster ride that both stock and house prices have been on the last few centuries brings up the interesting question what the relationship is between the two markets and how can we learn from historic events and crisis. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order The current recession in the Dutch economy is, to a large extent, caused in the period 1990-2000, when house prices increased with 160%, the AEX- index accrued with 450% and the total wealth of households augmented with 1 trillion EUR (DNB,2012). These development gave a strong impulse to household consumption during that period, even though these wealth expansion almost only existed on paper. This movement of on-going house price appreciation was a specific Dutch phenomenon, which was mainly attributed to the stimulating impact of the tax relief policy in the form of ‘Hypotheekrenteaftrek’, relaxing of the mortgage constraints by financial institutions and securitization developments such as ‘Nationale Hypotheek Garantie’ (NHG). The rising house prices were in addition also fed by hefty credit growth (260%) and had all the characteristics of a price bubble. At the time that household wealth increased the hard debt grew at the same pace. During this period household debt more than doubled from 76% to 163% of the net household income and the total balance sheet of the banking system ascended from 190% to 275% of GDP, excluding securitisations (Neuteboom, 2013; DNB,2012). The dreaded correction on the Dutch housing market has gradually taken place with relatively stable price growth in the first years of this century. Since the peak in 2008 and the subsequent credit crunch and global economic recession house prices dropped by nearly 20% and transaction volumes with almost 50% (DNB, 2012, van de Pas, 2012). Price increases in the 1990s, were stimulated by economic consumption and production through positive wealth effects and impulses to the construction and the financial sector, as does the current decline in negative wealth effects and a decreasing demand for construction activities and related financial services. The results of this cycle are enhanced by the funded pension systems, in which premiums were reduced in the 1990’s and increased again in the last few years to offset capital losses (DNB, 2012). These developments highlight the importance and value of a balanced financial development and policies that prevent excessive debt enhancement. All in all, house and stock price movements can be perceived as important macroeconomic determinants and have a strong impact on real economic activity. The outcome of this study should provide investors, regulators and other stakeholders with valuable information about the factors that explain any short and long-term relationship regarding boom- bust cycles in the Dutch economy. Problem statement Existing literature suggests that both asset prices exhibit positive correlation on the long-term, which can be partly explained by common macro-economic factors such as credit availability, consumer spending and interest. Furthermore, a direct and increasing interaction between equity and housing prices can be found. The causality between house prices and stock prices exists due to either the wealth or credit-price effect or both (Oikarinen, 2006; Ibrahim, 2010). However the general opinion about direction of influence seems to be from the stock to the housing market, where house prices with a delay of two to three years respond to stock market movements (Kakes & Van den End, 2002, 2004; Kapopoulos & Siokis, 2005; Sutton, 2002). The current global financial crisis makes financial stability an important issue for policy makers, governments and regulators. Financial stability or instability may have implications for interrelated markets and since the international connections the Netherlands is to great extend exposed to the fallout from the economic trails of other countries (Priemus, 2009). Furthermore the understanding of the relationship between these two asset markets provides an important intuition on household welfare, asset substitution and portfolio investment. Although housing is regarded as consumption good, it is also considered as an alternative investment to equity. The dynamics between the variables will affect now more than ever the financial positions of households as a result of the increasing part of their wealth is in housing and shares due to investment mortgages. This contemporary negative trend is also seen in the current Dutch economy where households are reserved in their expenditures due to decreasing house prices, volatile stock markets and uncertainty of future developments in terms of labour and pension (DNB, 2002). Research question(s) After evaluating the insights and contributions of previous studies in the field of stock and housing markets, it can be concluded that there are gaps in the existing literature. This paper focusses in particular on the reciprocity between stock and house prices in the Netherlands that can be seen through macroeconomic channels. The aim of this paper is to add further empirical evidence on the short- and long run relationship between house and stock prices in the Netherlands. Secondly, macroeconomic variables (wealth effect) such as GDP and interest rate are taken into account, since they are considered as important drivers for demand and supply in the Dutch housing market. Finally, credit supply (credit price effect) is discussed, as residential housing transactions are almost entirely financed with borrowed money and specially Dutch household have relatively high LTV ratio’s compared with other EU countries (Neuteboom, 2013; CPB, 2013). This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order This study will make a distinction between two periods; 1997 – 2007 and 2008-2012 to investigate whether there is a significant difference in the relation between stock and house prices within different time frames. The first period until 2007 can be considered as a period of relatively stable growth, with exception of the Dotcom bubble and stock market crash after 9/11. The second period from 2008 incorporates the credit crunch and global financial crisis which affected the real economy. In addition there a disaggregation into four housing market segments is considered. Existing literature by Kakes and van den End (2002, 2004) show there is a stronger correlation in the most expensive housing segments. To study the relationships between the different variables and corresponding correlations the following research question is defined; “What is the relationship between stock and house prices in the Netherlands during the periods 1997-2007 and 2008-2012?” To answer the research question, two sub-questions have been formulated to collectively address the main question. These questions have been extracted from the studied literature: 1. Is there a significant difference in the relationship between stock and house prices between the two time series? 2. Is there a stronger correlation between stock and house prices if the more expensive housing segments are considered? Contribution to literature This paper contributes to prior literature in several ways. In general it answers the main hypothesis about the relationship between house and stock prices in the Netherlands, but more specific it gains insight in the effect of dissimilar periods of time with emphasis on the volatility in financial system and correlation with different housing segments. Prior literature has been frequently done in the period before the financial crisis and this thesis could contribute to see what the relation is in turbulent times. With comparing the two time frames this study examines differences in correlation and influences of the variables in relative stable and volatile periods. In addition this paper extends current academic literature with extra attention to mortgage lending in the form of credit availability/ supply and interest rates. Mortgage loans have been originally used for housing but in the years upon the financial crisis also, to some extent, for consumption and purchase of securities. Economic growth and low interest rates increased mortgage lending and households’ borrowing capacity significantly, thereby stimulating both stock prices and consumption. Besides economic growth also structural causes were essential such as tax legislation (deductibility of interest) and changes in banks’ acceptance policies and lending criteria (de Greef & de Haas, 2000). Finally recent data up to 2012 is used to examine the main relationship between stock and house prices in the Netherlands whereas prior literature had only data till 2002 (Kakes & van den End, 2002, 2004; Sutton, 2002). Research methodology and data Consistent with prior studies on this topic a standard VAR approach will be used, outlined in the empirical approach. The primary focus is on the relationship between house and stock prices, but control variables need to be added because the variables might be unauthentic reflecting common factors (Quan & Titman, 1999; Lean, 2010; Ibrahim, 2010). Data is obtained from DataStream and house price data are published by the Dutch realtors’ association NVM. The broad AEX stock index (both deflated by the consumer price index) will be taken for equity prices (Kakes & van den End, 2004). Quarterly observations for a sample period that runs from 1997 up to and including 2012 are used in this study. Preview findings Previews of the results obtained in this thesis indicate that………….. LITERATURE REVIEW This part reviews and evaluates existing literature that directly and sometimes indirectly relate to this master thesis. Generally, three topics are addressed that collectively form the spine of this paper; short and long-run dynamics, wealth effect and credit- price effect. Several studies have tried to analyse the relationship between stock and house prices. Intensified attention among academic researchers as well as policy makers have been offering new studies with mixed results in terms of magnitude, direction and significance of the connection between the two markets (Kapopoulos & Sioki, 2005; Piazzesi et al., 2007). A start will be made with a short overview of early studies about this subject in the early 1990s. Second short- and long term dynamics that explain the relationship between stock and house prices are discussed. Also more recent literature is reviewed with emphasis on emerging countries Third, the wealth effect as one of the two main transition mechanisms is considered, with credit price effect as the second one that will be taken into account to conclude the literature review. Causality remains one of the major issues in existing literature and will be discusses throughout the different subjects. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Diversification and asset returns Most of the early studies on the two asset prices were for the United States and United Kingdom and focussed primarily on correlations between stock and house returns (Gyourko & Keim, 1992; Hartzell, 1986; Ibbotson & Siegel, 1984; Worzala & Vandell, 1993). Subsequently also other countries than the above mentioned were investigated such as Switzerland (Hoesli & Hamelink, 1997) and Hong Kong (Fu & Ng, 2001). These first studies focussed mainly on asset returns and diversification benefits and left the direct or indirect relations between house and stock price variables out of the equation, since portfolio management was the key element. Portfolio diversification was based on work of Markowitz (1952) and he showed that there are firm- specific and market risks. Evidence of these studies generated mixed views regarding the correlation between stock and housing returns. However the outcomes have been found to be adequately low to infer significant diversification opportunities for the two asset prices (Oikarinen, 2010). These studies give no answer to the question whether the housing-market leads to the stock market or the opposite direction of causality is in place. Short and long-run dynamics Another set of studies focus merely on the short and long- run dynamics between stock and house prices. Housing market studies place great emphasis on fundamental factors such as demand and supply and construction cost, Chen (2001). Sutton (2002) find that house price gains in six advanced economies (the United States, the United Kingdom, Canada, Ireland, the Netherlands and Australia) can be attributed to favourable economic developments captures in stock prices, interest rates and national income. (Chen, 2001; Takala & Pere,1991; Green, 2002; Kakes & Van den End, 2004; Kapopoulos & Siokis, 2005; Sutton, 2002). All papers conclude that stock prices predict or lead housing prices on the short-run. Studies about the long-run interdependency between house and stock prices are relatively scarce. This is remarkable because real estate is typically an investment with a long horizon due to the large transaction costs involved (Lean, 2010; Oikarinen, 2010). According to Englund et al. (2002) the investment horizon is important to take into consideration in the composition of investment portfolios of households and investors containing housing, stock, t-bills and bonds. In terms of efficient portfolio allocation their conclusion is that short run should not include real estate but long term a low-risk portfolio should enclose 15 to 50% housing. This conclusion would be an underestimate of the true horizon effect if stock and house prices are cointegrated (Lean, 2010; Oikarinen, 2010). Barot & Takala (1998) and Takala & Pere (1991) did research about the interdependencies between the stock and housing markets for Finland and they found an long-run cointegration between 1970 and 1990. Oikarinen (2010) found a similar result even after deregulation of foreign ownership of stock in Finland, using quarterly data between 1970 and 2006. A recent study done by Ibrahim (2010) examined the relation and causality of stock and housing in Thailand (emerging country). His conclusion was that both markets are cointegrated on the short- and long run and that stock markets leads the housing market in Thailand. Tsai et al. (2011) suggest that a long-run equilibrium relationship exists but that adjustments from disequilibrium are asymmetric in nature. To estimate the asymmetric wealth effect between the prices a asymmetric error correction model is used. The empirical results show that the wealth effect is more significant when stock prices outperform house price by an estimated threshold level (Tsai et al., 2011). The short term dynamics between stock and house prices are not always visible. According to Kakes and van End (2002) there are a number of factors responsible. First, price movements are strongly influenced by the private market sentiment. Since expectations on both markets can develop differently, consistency between the asset prices can be temporarily disrupted. Secondly improving prosperity and economic activity lead to higher stock prices, but also to a higher real interest rate which in turn affect house prices negatively. A third temporary factor can be related to real income developments. Higher nominal wages increase the borrowing capacity of households and may boost residential prices. However, at a constant labour productivity, this is at the expense of the profit margin of companies, with negative effects on stock prices. A fourth factor is that investors can take advantage of the fact that the stock and housing markets are segmented in the short term. The ‘capital switching’-model describes these negative cohesion between the markets for stocks prices and residential real estate (Lizieri et al.,1997; Kakes & van End, 2002). The question whether stock and house prices are determined by common factors on the long run, suggest that both markets are integrated when both are coherent with positive asset prices. Kakes and Van End (2002) examined the long term movement of house and stock prices of US, UK, France, Finland, Sweden and the Netherlands show that both markets known comparable movements over the period 1976-2001. Turning points in stock values were often several years later visible in house prices with a time lag of two to three years between the changes in stock prices and overflow to residential prices (DNB, 2002). For the UK cointegration exists between 1984-2001 but not for the entire period. Only Sweden shows no cointegration between the two markets (Kakes & van End, 2002). More recent studies focus on the interaction in emerging countries. Lean (2010) finds no long-run relationship between stock and house price in Malaysia. It is to be expected that there is evidence for a wealth effect in specific locations with expensive real estate and high income pockets (Green, 2002). Consistent with this perspective a wealth effect in the developed regions Penang, Selangor and Kuala Lumpur there is more evidence of stock prices leading to house prices (lean, 2010). Ibrahim et al. (2009) find results that the behaviour of housing prices in Thailand is governed by its positive relationships to stock prices, real output and consumer prices on the long run. Second, the stock market stability is critical for the stability of the housing market as well as the goods market. Finally, they conclude that substantial short-run interactions between the variables under study are found (Ibrahim et al., 2009). The relationship is also considered for China, where empirical tests provided evidence for a strong positive correlation between the markets and that stock markets lead the property market by around three months (Huang et al.,2009). This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order Wealth effect Another set of papers examines the dynamics through mainly two transition mechanisms linking house and stock prices. First is the so called wealth effect and is based upon the life cycle hypothesis proposed by Ando & Modigliani (1963). They suggests that the long-term average consumption is stable, even when the ratio of wealth to disposable income in the short term is volatile (Tsai et al., 2011). Positive economic shocks boost the value of households’ portfolio, they will increase investments and consumption due to increase in the permanent wealth (Ando & Modigliani, 1963). On the other hand the proportions of equity holding in a households portfolio will also be higher in the bull market. According to Markowitz’ portfolio theory (1952) households will relocate proportions of their portfolio allocated in the stock market to other investment targets such as housing. This theory suggest that households with unforeseen gains in wealth could motivate consumption and investments in housing. Financial wealth is largely dominated by movements in stock market in households’ portfolios. When stock prices tend to increase, also the total allocation of a households’ portfolio will increase and households will look for other investment opportunities to rebalance their portfolio. Rebalancing of households’ portfolio can be through selling shares and purchasing other assets such as housing (Lean, 2010; Markowitz, 1952). Relatively limited studies specifically distinguish between the wealth effects of housing market and stock wealth. Case et al. (2001) and Dvornak and Kohler (2003) investigate the impact of stock market as well as housing wealth using state level panel data for the Australia and the United States respectively and investigate housing wealth, using data on ownership and price indices. While Dvornak & Kohler (2003) report a lower coefficient estimate for stock market wealth than for housing wealth for most of their specifications, Case et al. (2001) find the opposite result and further extend their analysis to a panel of OECD countries. Ludwig and Sløk (2004) contribute to the existing literature by taking the same broad perspective of investigating the relative importance of both wealth components housing and stock market wealth – using quarterly data for a panel of 16 OECD countries. The first transition mechanism, wealth effect, implies in general causality running from the stock market to housing market. According to Green (2002) stock prices could have impact on house prices, besides wealth exposure, because it is expected that it reflects the profitability of a firm. Increase in share prices will lead to increase in consumption and investments from households which will lead to higher housing prices (Lean, 2010). Credit price effect Second is credit price effect which suggest that residential property prices are affected by the availability of credit (mortgage loans), because better availability of credit will likely increase demand for housing when household are credit constraint (Oikarinen, 2010). The growth in demand will be reflected in increasing house prices that can serve as collateral to credit constrained firms and households and lower cost of borrowing because the bank decreasing risk. This could fuel investments from firms and consumption of households and could ultimately lead to higher share prices, because of increased expected earnings through greater demand and firm efficiency (Sim & Chang, 2006). The credit price effect proposes that the housing market will lead the stock market and influences increased wealth effects arising from higher share prices and an upward price spiral for housing and stocks could occur (Lean, 2010; Ibrahim, 2009). McMillan (2011) suggest that the direction of causality between data from the UK and US series is one-way running from house prices to stock prices both in the long-run from both markets but also in the short-run for the US. This result supports the credit effect theory whereby increasing house prices can enable previously credit-constrained households and firms to increase expenditure and investments, leading to higher stock prices. With respect to government and regulators these results suggest that when house prices experience a bubble (either positive or negative) or credit constraints are relaxed, such that the relationship with stock prices will move in to a position of large disequilibrium, then this will ultimately have a significant impact on stock prices (McMillan, 2011). In prior empirical studies the relationship between credit constraints (lending conditions) and asset prices are commonly investigated. Kakes and van End (2002) state that especially for the Netherlands the purchase of asset titles, particularly housing, are often funded with mortgage loans issued by banks. In contrast, credit titles can also be used as collateral which was clearly visible in the Netherlands until 2007 in the form of second mortgage and private closures. Also the decline in capital market interest rates in 2000 and 2001 are considered as one of the key factors behind the continued rise in house prices. The decreasing interest rates was also correlated with the global economic slowdown that negatively affected stock prices around the globe. All in all transmission variables such as economic growth, interest and real income play an important role (Kakes & van End, 2002). These developments strongly encouraged household consumption, higher mortgage take-ups in the form of higher LTV ratio’s and investments by firms. Active portfolio management through for instance investment mortgages and household participation on the stock exchange increase the coherence between the two asset prices. This consistency is modelled by Hochguertel and van Soest (2001), which show that a rising house price via ‘spill-over’ effect leads to an increase in financial investments. This essay is an example of a student’s work Disclaimer This essay has been submitted to us by a student in order to help you with your studies. This is not an example of the work written by our professional essay writers. Essay Writing Service Dissertation Writing Service Who wrote this essay Place an Order From the literature review it appears that theoretical connection between stock and house prices are complex. It is not easy to give a decisive answer about this empirically. Also, if a connection can be shown it remains difficult, on the basis of statistical techniques namely rulings on interaction and causality. Nonetheless, in advance to expect the transmission of the shares expires earlier-to the housing market than the other way around. By strong interdependence of stock markets after all stock prices are strongly influenced by global developments, whereby the influence of the more nationally oriented housing market will be limited. In addition methodological imperfections may explain why mixed results between the house and stock markets occur. Country specific analysis is required when results and data availability for each country is different. After reviewing relevant literature, a gap is found in existing literature about the Dutch markets after the credit crunch up to 2012. At the end of the literature review a separate section should be included containing a summary of the most important/relevant findings, the identified gap(s) in the literature, your contribution and your hypotheses. CONCEPTUAL FRAMEWORK & HYPOTHESES Conceptual framework Prior literature gave many insights in the relationship between stock and house prices. Despite several academic work regarding this topic, a gap can be found in the exact relationship in the Netherlands with respect to different time frames and volatility in the Dutch economy. Besides that also the relationship of other determinants of house prices are important to address with current data including that of the credit crunch 2007-2009. Furthermore it is considered valuable to know whether different and more specific the expensive house segment have a stronger correlation with the main relationship. This study will add to existing literature by statistical testing the relationship between stock and house prices in the Netherlands. A conceptual framework in which the relations between the variables (dependent, independent, moderating and control variables) are displayed can be seen in figure (..). Fig (..): Conceptual framework of relations The independent variables as stated above have effect direct effect on the house prices and statistical test will show to what extend the correlations will be affected during different periods of time. The main relationship between stock an house prices can be influenced by GDP or real income variations, illustrated by the dotted lines in figure (..). Testing the impact of the other independent variables will enhance explanatory power of the correlations and of this scenario analysis. Also a control variable is used since it will presumably have similar effects on both stock price and house prices in the Netherlands. Hypotheses EMPERICAL APPROACH Conceptual framework (description of variables and relations) Formulate propositions/hypothesis Alternative methodologies Appropriate statistical test for hypothesis/propositions Discuss measurement of variables (referring to literature) Discuss data and sources Provide descriptive data (plots etc.) In the methodology and data section you have to discuss which methods and databases are used by relevant studies. Also motivate the method(s) and database(s) you will use. Do you master the skills to implement the method(s) and is data available? Which robustness checks are required to convince the reader of your thesis that your findings are robust (use economic arguments to motivate such checks) This analysis aims to explore the relationship between housing prices and stock prices in the Netherlands. Along with housing prices, this empirical exercise attempts to approximate the influence of credit availability and real income on the long-run relationship between the two markets. House prices (hp), subdivided into four market segments (dependent variable), are determined by the following independent macroeconomic variables interest rate (r) due to mortgage lending and real income (i) and the financial variable stock price (s). Real income or GDP (i) is evidently included in the housing price equation along with credit availability in the form of (mortgage) interest rates (r) because they influence housing demand and supply. Other determinants of house prices are not incorporated in this paper. In existing studies by Kakes & Van den End (2004), Kapopoulos & Siokis (2005), interest rates are widely used and considered as one of the strongest determinants of house prices changes besides real income (i) (Sutton, 2000). External shocks such as change in monetary policy and rapid deregulation will influence lending conditions for banks and credit constraints of households. This could lead to lower interest rates and therefore higher collateral values because of expansionary monetary policy (de Greef & de Haas, 2000). In addition, including real income Stock prices are included to address the main inquiry whether stock price movements affect house prices through the wealth effect and provide a potential interpretation and explanation for boom/bust cycles (Ibrahim, 2010). All variables are expressed in natural logarithm and the relationship in a linear form can be written as; (1) Equation (1) is a long-run equation that combines all variables together. To determine any short-run dynamic interactions, statistical verification for the validity of is needed for the first equation. The standard procedures of time-series analysis will be followed and include cointegration test, unit root tests and VAR models. VAR models investigate causality or interdependences among variables. The most commonly used test for this is the Augmented Dickey-Fuller (ADF). After the cointegration and unit root tests the Granger causality test will be used to investigate the interdependence among the variables (Ibrahim, 2010; Oikarinen, 2010). This paper uses a small Vector Autoregressive (VAR) model of the type pioneered by Sims (1980) to study the relationships and correlations. This framework permits one to study the dynamic influences of a small number of key determinants on house prices per segment (Sutton, 2000). Key advantages of the VAR approach are that all variables are assumed to be endogenously determined and only weak restrictions are placed on the dynamic behaviour of the variables of interest. The variables that are included in the VAR are the quarterly growth rate of real national income, a real interest rate, the quarterly growth rate of real stock prices and the quarterly growth rate of real house prices. In an unrestricted VAR, each variable in the system is regressed on a given number of lags of itself and the same number of lags of all other variables in the system (Sutton, 2000). Disaggregation Dutch housing market Besides the causal relation between stock and house prices in the Netherlands also disaggregation into housing market segments is considered. Existing literature by Kakes and van den End (2002, 2004) show there is a causal relation from stock to housing prices and find a stronger correlation in the most expensive markets segments. They argue that homeowners in the upper housing segments are presumably more sensitive to stock price fluctuations. This could be explained because they typically hold more equity or their income (part stock options of their income) could be more related to performance of stock markets (Kakes & van den End, 2004).). Conceptual framework: Credit Supply (c) Dependent variable Independent variable Control Moderator GDP (g) Interest Rate (i) House segment (hs) House Price (hp) Stock Price (s)