房價變動對經濟成長及消費支出之影響 - 政大學術集成

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(1) . 國私. 立立. 政中. 治. 大. 國. 學地. 地政. 政研. 學. 系 . 究. 碩士論文所 . . . 房價變動對經濟成長及消費支出之影響 The Influence of House prices on Economic Growth and Consumption. 研究生: 林佑倫指導教授: 林左裕博士徐士勛博士. 中. 華. 民. 國. 1. 0. . 4. 年. 6. 月.

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(3) The Influence of House prices on Economic Growth and Consumption . . 謝誌回想當初光要拿到就讀政大碩士班的入場卷，說來慚愧，前後也考了第三次才考上，大學畢業期間至空軍松山指揮部基勤隊的膳勤分隊完成服役，後續透過地方三等特考至新北市汐止地政事務所工作，看似曲折繞了些路，但也由於服役及職場上的這段經歷，讓我能夠帶著不同以往的學習態度回到母校繼續攻讀碩士，撰寫論文的過程雖艱難又孤獨，但不蠻你說，這篇謝誌寫的快比論文還久，因為想要感謝的人太多，因為有你們，論文才能順利完成。首先，要感謝恩師林左裕老師，能收我為左家研究生，平常從老師的英文授課課程中，學習到不動產投資與金融的專業知識外，與班上國際學生的交流，更了解到各個國家所面臨不動產之議題，也以英文訓練口說並勇敢表達自己的想法，皆培養了學生們的國際觀並從中提高自身的競爭能力。於尋找論文題目上，謝謝老師的信任，讓我研究如此重要的議題。同時間老師還擔任學校總務長，在如此繁忙的公務及課程教學下，仍然抽出許多時間於課前空檔甚至假日時間，努力地持續指導，每次的 meeting 老師提供的思維與邏輯，讓我對論文研究上有更多的了解並體會到研究上應有的嚴謹，並於論文撞牆時期，適時地引領我避免迷失於研究大海中，而研究方法上很有遠見地建議我找經濟系徐士勛老師共同指導，在修過士勛老師精實的時間序列課程後，除了深深被老師的特質吸引外(政大言承旭?!)，與學生的零距離感，更使我不斷厚著臉皮請教老師並不斷地修改研究模型，才能使本篇論文順利地完成。真的非常感謝左裕老師及士勛老師，這段不長不短的碩士學程加強了本身思考能力及獲得碩士學歷外，更重要的是認識了你們，從你們平時認真的教導及研究態度，皆讓我深刻體會要發揮所長，盡己所能付出，對社會有所貢獻，了解此種工作態度是這趟碩士學程中獲得最棒的附加價值。其次，謝謝口試委員林哲群老師及彭建文老師於口試時的撥冗參與，給了許多精進論文的寶貴建議，使口試後論文的修改方向明確許多。感謝江穎慧老師、袁淑湄學姊、盧建霖學長於期初及期末報告擔任評論人，使我了解論文架構以及各篇章論述應加強的地方，尤其是建霖學長（謝謝健宇兄的推薦！），分享許多做研究的心得，甚至給予後續如要投國際期刊應努力及注意的地方。謝謝林森田老師、陳奉瑤老師、林子欽老師、蔡育新老師及張鈺光老師於碩士班所教授的專業知識，亦使我於撰寫論文時有多元的思維。再來，要謝謝研究所認識的各位朋友及夥伴，謝謝左家的每位學長姊，於左家聚餐中分享碩士學程應當好好把握及努力學習的地方，謝謝黃斐學姊，於期初到口試樹立優秀的典範，並傳承許多研究經驗，謝謝虹芢及家寶，共同互相扶持 . I .

(4) The Influence of House prices on Economic Growth and Consumption . . 度過期初、期末、研討會及每次的 meeting，謝謝左家承瞱、世豪及筱真於行政事務、計畫案、期初、期末及研討會上的協助，讓我們能專心於研究及報告上。謝謝各位 102 級碩士班的同學們，各種研究室內的談天說地、謝師宴的跳舞表演、聖誕節的交換禮物、無數次的課後聚餐、水池慶生、羽球比賽及每個禮拜五的導生聚，因有你們，辛苦的研究生活中卻也點綴出些許美好的回憶。另外要特別感謝新北市汐止地政事務所的長官、先進及同事們，尤其是王秘書珮榕及詹課長旻華，謝謝你們全力支持與准予，還幫小弟的留停公文修改的面面俱到，有你們這麼挺課員的課長，實在幸運，內心除了滿懷感激外，還是感激，還有不怕任何案件只怕案件不見的神之複審-阮大哥舜輝及李大哥哲光，除了平時案件上的教導外，更叮嚀未來在公家職場上需要學習的課程，不斷給予實務與理論上互相學習的建議，謝謝你們耳提面命的像教導自己小孩般的熱情，還有各位審查好夥伴，書騰、凱超、宇倫、淑媛姊、陽榆學姊、孝瑜哥、康哥、倫哥、侑成哥、曉彤學姊及楚鵬，當初有你們工作上的支援，小弟才能順利回到學校完成學業，攻讀碩士期間沒能和你們站在審查第一線一起共同面對龐大的登記案件，實在深感抱歉，謝謝你們~汐止 Family。最後，也是最重要的，要感謝一路養育我到大的父母，林錫增先生與楊鳳卿女士，謝謝你們的無私奉獻，讓我於任何求學階段皆能衣食無缺，專心念書，儘管於攻讀研究所期間，時常把家當旅館，一早就跑學校，半夜才回家，但回家時常看到餐桌上留了碗熱湯，一張充滿關懷字句的紙條，感受到你們的愛，時常讓我淚流滿面，能夠生長在這麼溫暖、幸福的家庭，謝謝老天、謝謝爸媽。感謝我親愛的老哥-林佑軒，一路上是我最佳的榜樣，同時間赴美攻讀博士的你，每個禮拜不忘從繁忙的生物實驗及留學生活中抽出空檔，在那短暫的白晝時差交錯點，彼此深談生活上發生的大小事，每次應當更關懷獨自前往美國留學的你，反倒都被視訊那頭的你給關心，有你當我的「阿兄」，應當是八輩子修來的福氣，希望未來在美國發展的你，能注意安全，保持身體健康。感謝親愛的汝晏，總是女神般冷靜地聽我講述研究上所發生一切的大小事，我愛你！佑倫謹誌 2016 年 10 月 . . . II . .

(5) The Influence of House prices on Economic Growth and Consumption . . 摘要臺灣不斷攀升的房價已使民眾的購屋貸款負擔能力惡化，尤其在次級房貸風暴後所實施的寬鬆貨幣政策，在低持有稅及交易稅的環境下，臺北市於 2014 年的房價所得比已高達 16 倍，高於世界多數城市。高房價所帶來的高貸款負擔率，似乎也已排擠了消費能力及經濟成長。有鑑於此，本文透過向量自我迴歸模型分析探討房價對消費支出的影響，研究結果顯示，臺灣消費支出受到加權股價指數及消費支出落後期顯著的正向影響;而不斷攀升的房價對消費支出則有負向影響。透過衝擊反應分析發現，房價指數對本身的衝擊反應最顯著，效果也最長，消費支出的衝擊反應次之。研究結果指出的高房價已明顯排擠消費支出，並導致經濟成長緩慢甚至衰退，透過此結論政府應深思期以不動產為所謂的「火車頭工業」刺激房價時對消費及經濟所產生的負向連鎖效應。關鍵字:房價,經濟成長,消費支出,費雪方程式,向量自我回歸模型,衝擊反應函數 . . III .

(6) The Influence of House prices on Economic Growth and Consumption . . Abstract The soaring housing price in Taiwan has deteriorated the home affordability of citizens in Taiwan, especially after the Quantitative Easing (QE) monetary policy after the subprime crisis as well as in the low tax environment. For example, the Price-to-Income (PTI) Ratio in Taipei City reached 16 in 2014 is higher than most cities in the world. The pressure of mortgage payment for households seems to crowd out the consumption capability and therefore the economic growth. To discover the influence of housing prices on consumption and economy, we employ the Vector Autoregression Model (VAR) for empirical analysis. Results show that consumption in Taiwan is highly and positively influenced by the stock price index and the lagging consumer expense, whereas interest rate plays a minimal role. On the other hand, rising house prices have negative effects on consumption. Through the analysis of impulse response function, we discover that housing index has a significant impact on itself with long period effect, and secondly on the consumption. We also find that high housing price has a crowding-out effect on consumption and in turn results in the sluggish economic growth in Taiwan. The findings of this study provide the government with a policy direction in deciding whether to stimulate or control housing prices for long-term housing affordability and economic sustainability. Key words: House prices, Economic growth, Consumption, Fisher Equation, VAR, IRF. . IV .

(7) The Influence of House prices on Economic Growth and Consumption . . Context CHAPTER 1 INTRODUCTION ................................................................................ 1 1.1 BACKGROUND AND MOTIVATION ......................................................................... 1 1.2 RESEARCH METHOD AND SCOPE ........................................................................... 6 1.3 RESEARCH OVERVIEW .......................................................................................... 8 CHAPTER 2 LITERATURE AND THEORY REVIEW ...................................... 11 2.1 LITERATURE REVIEW .......................................................................................... 11 2.2 THEORY REVIEW ................................................................................................. 17 2.3 REVIEW OF VARIABLE SELECTION ...................................................................... 20 CHAPTER 3 RESEARCH METHOD AND DATA INFORMATION ................ 21 3.1 METHODOLOGY .................................................................................................. 21 3.2 DATA DESCRIPTION AND PROCESSING ................................................................ 29 CHAPTER 4 EMPIRICAL RESULTS ................................................................... 35 4.1 STRUCTURAL CHANGE ........................................................................................ 35 4.2 UNIT ROOT TEST ................................................................................................. 37 4.3 VECTOR AUTOREGRESSION MODEL .................................................................... 38 4.4 IMPULSE RESPONSE FUNCTION ........................................................................... 43 CHAPTER 5 CONCLUSIONS AND DISCUSSION ............................................. 51 5.1 CONCLUSIONS ..................................................................................................... 51 5.2 SUGGESTIONS ...................................................................................................... 53 REFERENCE ............................................................................................................. 57. . V .

(8) The Influence of House prices on Economic Growth and Consumption . . List of Figures Figure 1-1 Trend of Taiwan Housing Price Index ......................................... 2 Figure 1-2 Trend of GDP and HPI (year % change) ..................................... 3 Figure 1-3 The constituted of GDP in 1980-2014 ......................................... 3 Figure 1-4 Trend of Consumption and HPI (year % change) ........................ 4 Figure 1-5 Consumer Life Cycle ................................................................... 7 Figure 1-6 The Research Process ................................................................... 9 Figure 3-1 Process of Research Method ...................................................... 22 Figure 3-2 Process of ADF Test .................................................................. 25 Figure 3-3 Time Series Charts – Source Data ............................................. 30 Figure 3-4 Comparison of Source Data and Seasonal Adjustments ............ 32 Figure 3-5 Logarithms Difference of Rate of Change ................................. 33 Figure 4-1 Result of CUSUM Test .............................................................. 36 Figure 4-2 Impulse Response Function - GDP ............................................ 44 Figure 4-3 Impulse Response Function - CS ............................................... 44 Figure 4-4 Impulse Response Function - HPI.............................................. 45. List of Tables Table 3-1 The Variable Descriptions ........................................................... 29 Table 3-2 Descriptive Statistics of Variables............................................... 31 Table 4-1 ADF Unit Root Test .................................................................... 37 Table 4-2 VAR Result on CS and HPI ........................................................ 41 Table 4-3 VAR Result on GDP and HPI ..................................................... 42. . VI .

(9) The Influence of House prices on Economic Growth and Consumption . . Chapter 1 Introduction This chapter is divided into three parts. The first part mainly introduces the general background and research motivation of this study. Then, the research method and scope are described in the second part. The final part presents the research framework and process.. 1.1 Background and Motivation 1.1.1 General Background Since the end of 2002, Taiwan has experienced global deflation and the impact of the U.S. subprime mortgage crisis in 2008. To promote the economy, the government implemented the quantitative easing monetary policy,1 intending to promote the economic growth through its transmission effects,2 in order to reduce the impact of global financial crisis on Taiwan in recent years. In case of implementing the QE policy against the downturn in the economy, among prices of assets, the increase of house prices in Taiwan was especially obvious (as shown in Figure 1-1). The HPI (House Price Index of Sinyi Realty Inc.) in Taiwan rose from 100 in Q1 of 2003 to 286 in Q4 of 2013, with the increase of 186%. After being adjusted by CPI (Consumer Price Index), rate of change of quarterly average HPI still has 10% increase, but in this decade the economic growth is still growing slow (as shown in Figure 1-2). According to historical data of GDP in Taiwan (as shown in Figure 1-3): GDP ＝ civic consumption (60%) + government spending (10%) + Investment (20%) + Export-Import (10%) based on the measurement of expenditure.. 1. . “Quantitative” in “quantitative easing” referred to the currency which should create the designated amount, while “easing” referred to the reduction of financial pressure of banks; the actual method was that the Central Bank increased the money supply through open market operation. During the period of global financial crisis in Taiwan, the rediscount rate was adjusted downward for 7 times with the accumulated reduction of 2.375%, and the excess reserve of bank systems was at a higher level in the amount of about NT$160 billion. 2 Transmission effect: 1. significantly changed the market’s expectations of inflation and reduced the real interest rate; 2. improved the increase of prices of assets and promoted consumption and investment through wealth access; 3. reduced long-term interest rates and promoted civic investment; and 4. caused the depreciation of exchange rate and encouraged export. . 1 .

(10) The Influence of House prices on Economic Growth and Consumption . . Looking back at Taiwan before 1990, the net export was the main power of economic growth, which accounted for about 20% of GDP. However, in recent decades, the net export has accounted for less than 10% of GDP mostly, while civic consumption reached about 60% of GDP. The consumer spending has become the most important factor in Taiwan’s GDP.. House Price Index of Sinyi Realty Inc. in Taiwan . Data Source: Sinyi Realty Inc. Figure -1-1 Trend of Taiwan Housing Price Index. . 2 .

(11) The Influence of House prices on Economic Growth and Consumption . . Year % Change . Data Source: National Statistics, R.O.C., Sinyi Realty Inc. Figure 1-2 Trend of GDP and HPI/CPI (year % change). Data Source: TEJ Database Figure 1-3 The constituted of GDP in 1980-2014. . 3 .

(12) The Influence of House prices on Economic Growth and Consumption . . Many economic factors, such as income, wealth, consumer price index, money supply policy, and interest, could directly or indirectly influence consumer behaviors. Among all factors income and wealth are most important, according to Friedman (1957), except the income from labor, the assets of income will also influence the consumption. Under the easing monetary policy of the transfer effect, the real estate should be able to bring the wealth effect, which can boost consumption and investment. However, the annual growth of consumption decreased since 2010 departure with House Prices Index. (as shown in Figure 1-4) Year % Change . Data Source: National Statistics, R.O.C., Sinyi Realty Inc. Figure 1-4 Trend of Consumption and HPI/CPI (year % change). 1.1.2 Research Motivation Based on the background above. In recent years, many countries have experienced issues of real estate bubbles and economic growing slow generated under quantitative easing policy. There were few domestic references exploring the relationship between house prices and consumption, international references regarding relevant study on house price and consumption varied significantly.3 Many research thought that wealth of real estate or house prices had a significant positive influence on consumer spending; however, few research. 3. . Discuss the detailed in chapter 2: Literature and Theory Review. 4 .

(13) The Influence of House prices on Economic Growth and Consumption . . indicated that no causality existed between prices of real estate and consumption, and consumer spending was changed by macroeconomic factors. As a result, references conflicted with each other. Most of references were relevant research conducted before the implementation of easing monetary policy. In Taiwan, the pressure of mortgage payment for households seems to crowd out the consumption capability and therefore the economic growth. The study aimed to explore the impact of changes in house prices on economic growth and consumer spending in Taiwan under the quantitative easing background. In order to avoid property bubbles occur in Japan or PIGS countries in Europe (Klotz et al. 2013). The research of this study is a pressing need for the government to discover the influence of housing prices on consumption and economy.. 1.1.3 Research Purpose Based on the motivation above, this research aims to address following questions: 1.. Through the literature review to explores the influence of house prices on economic and consumption, and to explore the factors which affect consumption based on the Fisher equation and consumption theory. The study aimed to explore the impact of changes in house prices on economic growth and consumer spending in Taiwan under the quantitative easing background. Through viewpoints of models and empirical results of time series analysis to forecast how important factors affect economic and consumption. In addition, this study attempts to provide the government with a policy direction in deciding whether to stimulate or control housing prices for long-term housing affordability and economic sustainability.. 2.. . . 5 .

(14) The Influence of House prices on Economic Growth and Consumption . . 1.2 Research Method and Scope 1.2.1 Research Method 1.. Theory and Literature Review. Through the relevant literatures and theories to explores the influence of house prices on economic and consumption, and to explore the factors which affect consumption, according to the past researches. These methods and factors will provide reference for the empirical analysis in this study.. 2.. Modeling and Empirical Analysis. The empirical modeling analysis is applied to examine the influence of house prices and other important factors on economic and consumption. Through the use of Eviews statistical software, we employ the Vector Autoregression Model to explore variables of lag period affect on the current period of economic and consumption. And apply the Impluse Responds Function to know the cross-period dynamic responses of other variables caused by changes of observed variable. From the viewpoints of models and empirical results to predict the trend in the future.. 1.2.2 Research Scope 1.. Research Objects. This study tries to explore the influence of house prices and some variables on economic and consumption,4 the US Bureau of Economic analysis of consumer life cycle(as shown in Figure 1-5),5 showing the person's life at different ages have different spending power and 4. . Because a person's life at different ages have different needs for real estate, consumer spending was originally intended to collect information of different ages, to analysis of different ages have different wealth effects on results, in order to further analyze the impact of real estate wealth effect. But this part of information in the country is available or publicly still hard to confirm, because of data limitations, we use total spending of all citizens consumption as an alternative variable. 5 The Consumer Expenditure survey has been conducted annually by the US government since 1984. This survey asks 5000 respondents to list all income and expenditures out of their personal budgets for a period of 6 weeks. From sodas, movies, and mortgage payments to car, pizza, and insurance - literally anything paid out of the household is recorded. Respondents are then asked demographic questions such age, education, number of children, and where they live. From this body of research to learn a tremendous amount about average spenders and what . 6 .

(15) The Influence of House prices on Economic Growth and Consumption . . has different demand for real estate. This study focuses on the macroeconomic variables, we select gross domestic product and civic consumption as dependent variable for research and analysis.. Data Source: The US Bureau of Economic Analysis Figure 1-5 Consumer Life Cycle 2.. Time and Spatial Scope. This study select gross domestic product and civic consumption as dependent variable, and find the most important factors including house prices index as independent variable. Due to the background information above and the limitation of data, the study period ranges from the first quarter of 1998 to third quarter of 2013. The data type is seasonal information, and the scope of research is Taiwan’s macroeconomic variables. . they’re doing. Then take this data to see how people spend money at different ages and on what products and services. . . 7 .

(16) The Influence of House prices on Economic Growth and Consumption . . 1.3 Research Overview 1.3.1 Research Framework This study consists of five chapters as follows. Chapter 1 is “Introduction”, which mainly contains the general background, research motivation and research purpose. Besides, the research method and scope as well as the research overview are introduced in this chapter. Chapter 2 is “Literature and Theory Review”, exploring the influence of house prices on economic and consumption, and find the important factors which affect consumption. Chapter 3 is “Research method and Data Information”, presenting the methodology and the data used in this study. Chapter 4 is “Empirical Results”, which illustrate the empirical results and analyses the practical meanings of these results. Chapter 5 is “Conclusion and Discussion”, which summarizes the implications of this study and provides suggestions for economic policies and housing policies.. . 8 .

(17) The Influence of House prices on Economic Growth and Consumption . . 1.3.2 Research Process General Background and Research Motivation. Literature Review. Review the Influence of House prices on GDP and Consumption. Fisher Equation / Consumer Theory . Review of Variable Selection. Research Method and Data Information Empirical Results. CUSUM / ADF Test. VAR Test. Conclusions and Discussion Figure 1-6 The Research Process. . . 9 . IRF Test.

(18) The Influence of House prices on Economic Growth and Consumption . . . . 10 .

(19) The Influence of House prices on Economic Growth and Consumption . . Chapter 2 Literature and Theory Review This chapter mainly divided into three sections. We first review the Influence of house prices on economic and consumption. Next we explore Fisher equation and Consumer theory to discusses the role of consumption. At last, the third section is variable selection in this study based on the first and second section.. 2.1 Literature Review 2.1.1 The Relation between House prices and Economics Looking back at the role of the real estate market in economic development, one could find that housing was considered the social spending during the post-war period from 1945 to 1960, when houses were consumer goods and had no direct productivity to consumers. Investment in housing did not fit the bill. The house building industry relied on hand-powered technology, used limited capital, and was characterised by low wages and productivity. Worse, housing itself was seen as a consumer good which had a limited and indirect effect on the productivity of its consumers. As Strassman(1970) observed, from this point of view ‘‘financing housing without raising productivity was throwing money into a bottomless pit.’’ Indeed, such investment could appear to be the very worst type of consumer spending since it tied up capital for such long periods of time. During 1950-1960s, those development economists who considered the economic significance of housing found it impossible to dismiss the subject entirely, residential investment were already deemed to be helpful to economic growth. In addition to many industrial activities driven by the construction industry, economic growth was further influenced by its impact on employment, savings, investment and labor productivity (Turin, 1969; Liu et al., 2004; Dlamini, 2012; Chang et al. 2013). During 1960-2000s, the economic development driven by the construction industry made the real estate begin to be considered a. . 11 .

(20) The Influence of House prices on Economic Growth and Consumption . . type of investment products (Harris and Arku, 2006). According to international references regarding real estate and economic growth, three outlooks on the real estate market had different degree of influence in economic growth under different backgrounds and national policies. First: the real estate was simply a type of consumer products; second: the real estate was the locomotive of economy, which brought activities and drove economic growth; third: the real estate generated wealth and transferred into a type of investment products. Three viewpoints have always co-existed, while the balance among them has shifted. Dlamini (2012) indicated that Construction is a major industry throughout the world accounting for a sizeable proportion of most countries’ Gross Domestic Product (GDP), using time series statistical analysis of construction share of GDP for South Africa. Found that there is evidence of the existence of a very strong relationship between construction activity and economic growth. As an investment sector, construction has the potential to impact positively on short-run growth. Construction can thus be regarded as a major component of investment, particularly for developing economies like South Africa. But the trend could not be sustainable for continuous economic growth, the sharp decline in construction share of GDP from 15% in 2007 to 1.5% in 2010. Considering the fundamental significance of the construction sector in employment creation, capital formation and its aggregate spillover effects, it is clearly an important sector in the economy. Turin (1969) showed that the developed national construction industries during 1955-1965 accounted for 5-8% of GDP, and for developing national construction industries accounted for 3-5% of GDP. Consider construction industry for the development of the country plays a very important role, can create a lot of employment opportunities and labor productivity through construction. Furthermore, Wells (1985) pointed out that when a country’s GDP rises, the construction industry to GDP ratio will rise, and the rising speed of proportion will increase as income increase. The study of Liu et al. (2004) using Granger causality analysis, this paper examines the . 12 .

(21) The Influence of House prices on Economic Growth and Consumption . . interaction between housing investment and economic growth as well as that between non-housing investment and economic growth, showed that residential investment had stronger short-term effect on economic growth than non-residential investment, and residential investment had a long-term effect on economic growth, while economic growth has a log run effect on both housing and non-housing investment. Suggest that housing investment is an important factor for the short-term fluctuations of economic growth. Chang et al. (2013) also use Granger causality analysis the causal relationship between housing activity and growth in nine provinces of South Africa for the period 1995-2011, found that nine of the seven provinces has significant relation between house prices and economic, but changes of GDP is not significant to house prices. Furthermore, Lin et al. (1996) use the method of composite index of business cycle for real estate cycle indicators, reveals the macro-variables, such as GDP, M2, the index of stock market, CPI ect., tend to be leading indicators of real estate activities over twelve months approximately. Means the macro-variables can forecast the trend of real estate cycle in the future. According to references regarding real estate and economic growth, the real estate market had different degree of influence in economic growth under different backgrounds and national policies. Based on the background above, in recent years many countries have experienced issues of real estate bubbles and economic growing slow generated under quantitative easing policy. The study aimed to explore whether house prices had wealth effects and positive influences on economic growth in Taiwan under the quantitative easing background.. 2.1.2 The relation between House Prices and Consumption There are many references regarding relevant study on house prices and consumption, research indicates that house prices may affect consumption patterns through the following: 1. Realized or unrealized of wealth effect: When the house price increased, owners realized the capital gains from real estate . 13 .

(22) The Influence of House prices on Economic Growth and Consumption . . through acts of sale, which further influenced consumer spending of residents. The increase of house prices added owners’ wealth of real estate; although residents did not cash increased wealth through withdrawing capital gains, their expectations made them think that they were more wealthier than before and increased consumer spending in the current period; Cooper (2010), for example, using data from the Panel Study of Income Dynamics, show that during the height of the house-price boom (the 2003–2005 period) a one-dollar increase in equity extraction led to 14 cents higher household expenditures. Households also spent 21 cents of their extracted equity on home improvements and additions and saved roughly 19 cents of each dollar extracted through balance-sheet reshuffling; indicated that house prices could generate realized or unrealized wealth effects. However, Buiter (2010) represent agent model and in the Yaari-Blanchard OLG model, found that there is no pure wealth effect on consumption from a change in house prices if this represents a change in fundamental value, while the changes of house prices were measured by speculation and bubbles, they could bring wealth effect to consumption. And a decline in house prices reduces the scope for mortgage equity withdrawal. For given sequences of future after-tax labor income and interest rates, this may depress consumption in the short run while boosting it in the long run.. 2.. Real estate mortgage or liquidity constraints affect: Over the past ten years, many homeowners have taken advantage of lower mortgage. interest rates and higher home values and have refinanced their mortgage loans. After the house price increased, households would relieve their liquidity constraints by way of residential mortgage, which will further benefited the increase of consumption. Glenn et al. (2006), for example, studied US household refinancing behavior between 1983-2001. Refinancers taking cash out spent 35 percent of liquefied equity on home improvements and used 26 percent to pay off other debt. They used 16 percent of the funds for consumer expenditures, 10 percent for real estate or business investments, 11 percent for stock market . 14 .

(23) The Influence of House prices on Economic Growth and Consumption . . investments, and 2 percent for taxes. Pointed out that part of refinancing is reason for consumption. As well as Haurin and Rosenthal (2006), find that for each dollar of house price appreciation, households take on roughly 15 cents additional debt, and use to finance consumer expenditures. The debt response to house price appreciation generally increases with age and income, but is markedly lower among individuals over age 65. Homeowners save most of their housing capital gains and also helped to prop up consumer spending. Mian and Sufi (2009), employing land topology-based housing supply elasticity as an instrument for house price growth, estimate that the average homeowner extracts 25 to 30 cents for every dollar increase in consumption or home equity. 3.. The influence of budget constraints on tenant: The increase of house prices would increase the housing cost of tenants, under the. condition of unchanged income, will decreased the non-residential consumer spending of tenants. Li and Yao (2007), pointed out that house prices in the U.S. had an influence of budget constraints on tenants, which influenced consumption. House prices should bring different wealth effect to different consumer groups. Based on different ages and statuses of homeowners, each 11.5% increase of house price would bring the 2% real wealth effect to homeowners who aged more than 65. For young homeowners between late 20s to mid 30s face a long horizon of future housing consumption, and on average, are expecting to move up in the housing ladder. Thus, their investment gains from existing housing positions are not sufficient to compensate them for the increase in their lifetime housing consumption costs. So a positive housing shock incurs about a 2 percent utility loss for young homeowners. Observe renters' welfare since they have to bear the higher cost of acquiring lifetime housing services without receiving any housing wealth gains. A positive house price shock of 11.5 percent leads to a welfare loss of around 4.5 percent. Overall, the increase of house prices only improved the wealth effect of old homeowners, but had a negative influence on tenants and young homeowners. . 15 .

(24) The Influence of House prices on Economic Growth and Consumption . . 4.. The influence of the purchase new homes: While house prices rising the payment of principal and interest also increase, in order to. purchase house, the young people or tenants have to increase savings resulting consumption decreased. In addition, some studies indicate house prices change will not necessarily impact on consumption. Attanasio et al. (2009), for example, indicated that the wealth and collateral channels are important for some households at some points in time. But, on average over the past 25 years, pointed out that wealth of real estate and mortgage constraints were not the main reasons that influenced consumer spending. As a result, references conflicted with each other, and most of references were relevant research conducted before the implementation of easing monetary policy. The study aimed to explore the impact of changes in house prices on consumption in Taiwan under the quantitative easing background. To study whether house prices had wealth effects and positive influences on consumer spending, and further promoted economic growth. . . 16 .

(25) The Influence of House prices on Economic Growth and Consumption . . 2.2 Theory Review The section was divided into Fisher equation and consumption theory. The section elaborated the importance of traditional monetary policies and the connection of wealth effect resulting from house prices, and described the selected empirical variables based on the theory and reference above.. 2.2.1 Fisher Equation Irving Fisher (1911) established the exchange equation of money supply and demand (“Fisher equation”); that is, money supply (M) multiplying by velocity of money (V; the frequency of money spent in repeatedly purchasing of goods within a year) equaled the price level (P) multiplying by total output (Q): MV＝PQ. (2.1). Fisher reviewed the inflation of countries based on the equation: 1.. Under the assumption of constant V and Q, commodity prices will be affected by the money supply, when the money supply increases, much money chasing fewer goods, the inflation will be caused by money supply (M ↑ → P ↑).. 2.. Under the assumption of constant M and Q, commodity prices will rise by rising consumption rate (V ↑ → P ↑),or due to expectation of rising commodity prices, under this circumstance consumers shift to earlier consumption to avoid the losses from delay consumption(P ↑ → V ↑) . In recent years, due to real estate bubbles in Japan, the economic growth continued declining with the increasing unemployment rate and decreasing the income. Thus, the price remained stable, and the expected price by people continued declining, and people held a sideline attitude toward consumption (V ↓ → P ↓ ), causing the overstock of goods, reduction of production, decline of economic growth, and vicious circle of deflation. This was the reason why Prime Minister Abe intended to solve deflation through easing monetary policies and financial policies (P ↑ → V ↑).. 3.. Assuming that M and V were constant, the product price increased as the total output of products decreased (Q ↓ → P ↑), which caused the demand of commodity exceeds supply and leads inflation. For further exploration of the application of Fisher Equation, assuming that V and P were . 17 .

(26) The Influence of House prices on Economic Growth and Consumption . . constant and M and Q were positively mobile, when a country underwent economic growth, it could increase money supply without influencing inflation (that is, the price). The increase of money supply would drive the healthy and supportive increase of the price of assets (such as stock and real estate). This was one of the transmission effect which the current easing monetary policy intended to achieve. After the house price or stock price increased, consumption could be increased due to the increase of wealth, which promoted economic growth. According to historical data of GDP in Taiwan: GDP ＝ civic consumption (60%) + government spending (10%) + Investment (20%) + Export-Import (10%) In recent years, under the condition of long-term low interest rate caused by the easing monetary policy, a huge amount of money was invested in the real estate market. In the transaction of real estate, buyers caused the investment in GDP (Q) to increase, and sellers realized capital gains to obtain wealth, which increased the consumption rate (V) and promoted economy (Q). However, in recent years, although the house price increased significantly, the annual rate of change of consumer spending decreased gradually, which impacted on economic growth. It was inconsistent with the traditional theory. The relationship between house prices and consumer price index, Fama and Schwert (1977) pointed out that in all of the assets, only private residential real estate is a complete hedge against both expected and unexpected inflation. The assumption between expected of assets rate of return and inflation, is same as causality with the Fisher equation M and P.. . 18 .

(27) The Influence of House prices on Economic Growth and Consumption . . 2.2.2 Consumer Theory Many economic factors, such as income, wealth, consumer price index, money supply policy, and interest, could directly or indirectly influence consumer behaviors. Among them, income and wealth significantly influenced consumer behaviors. According to the consumption theory “Permanent income” proposed by Friedman (1957): !"#$% !"#$%&!. Permanent income = =. (!!!). !!! ! !!! (!!!)! +. +. !!! (!!!)!. !". !"#$%& !""#$" !. ! +…(!!!) + !. (!!!). !". !!. ! ! +(!!!) +…+(!!!) ! !. !"! ! !!! (!!!)!. (2.2). in formula：Labor Income(LI)、Assets of Income(AI)。 Consumers determined consumption based on permanent income rather than current income. Same as the life cycle theory, the permanent income theory explained consumption based on the intertemporal viewpoint. The sum of discounted value of labor income currently and in the future was called “human wealth” (. !!! ! !!! (!!!)! ),. and the sum of discounted value of. income property currently and in the future was called “non-human wealth” (. !"! ! !!! (!!!)! ).. In. addition to salary income obtained from work, wealth obtained from assets (such as stock and real estate) would also influence consumer spending. Thus, when the real estate became the wealth effect brought by investment in products, it should increase consumer spending in Taiwan.. . 19 .

(28) The Influence of House prices on Economic Growth and Consumption . . 2.3 Review of Variable Selection In view of this, the study adopted per capita national income as human wealth, and house price index as weighted average stock index as non-human wealth to review the consumption theory and wealth effect. The study also intend to empirical real estate market development, such as the construction industry, whether it could stimulate the economy or not, since the building of new homes will be counted in economic growth. As mention above, estimated price index should be selected to estimate pre-sale housing and new housing price index is more appropriate for Cathay Real Estate Index, however, limited by periods of Cathay Real Estate Index, in the study, we use Sinyi House Price Index instead of Cathay Real Estate Index. The study further test the correlation between the Cathay Real Estate Index and the Sinyi House Price Index, found a high correlation between the two variables,. 6. showing each. other can be used as a proxy variable. Given that interest rate represented M in Fisher equation on behalf of easing monetary policy, GDP represented Q, consumer price index represented P, and because V is the velocity of money, due to velocity of consumption rate is difficult to observe, we use the total private consumption expenditure (consumption) as a proxy variables, to represent when the higher consumption means the faster of velocity rate for money, in order to show the higher the rate of currency in circulation, the relation between the variable to reviewed Fisher equation. For fluctuation taking place in the real estate market in Taiwan recently, the study reviewed whether traditional theories were invalid based on exploration using updated information and model verification.. 6. . By OLS and Spearman rank-order to test the correlation between two variables, and the correlation coefficient, we found that R-squared and correlation coefficient are 0.97 and 0.98, the two variables are highly significantly correlated. And the following of this study is to test the rates of change between variables, after logarithm and difference in the first order, correlation coefficient also reach the 95%, showing that the two variables can be used as a proxy variable. . . 20 .

(29) The Influence of House prices on Economic Growth and Consumption . . Chapter 3 Research Method and Data Information This chapter is divided into two parts. The first part shows the research methods and theoretical discussed. And the second part describes data information and processing.. 3.1 Methodology The purpose of this study is to explore the dynamic relationship between GDP, consumption and other variables. As the assumptions of classical regression model necessitate that all sequences be stationary I(0) and that errors have a zero mean and a finite variance. In the presence of many economic series appear to have a non-stationary component, there might be statistical bias results a spurious regression.7 Therefore, before present study of Cointegration Test or Vector Autoregression, we use unit root test to determine whether the variable is stationary or not. If all the series are stationary, we employ VAR to analysis. If all the series are non-stationary, we employ the Cointegration Test to analysis the variables of a long-term stable equilibrium relationship, and if they have the cointegration relationship, Vector Error Correction Model (VECM) is applied for further study. Finally, we use Impulse Response Function, given known conditions, an unexpected change was made to compare with the original unchanged environment. Through the observation on differences between two expected values, the possible trend in the future could be projected.. 7. . Granger and Newbold (1974) call spurious regression has high 𝑅 ! and t-statistic appear to be significant, but the results are without any economic meaning. . 21 .

(30) The Influence of House prices on Economic Growth and Consumption . . Structural Change. Unite Root Test. Stationary Series. Non-Stationary Series. No. Cointegration Test Yes VECM. VAR. Impulse Response Function Figure 3-1 Process of Research Method. 3.1.1 Structural Change Since the stability of the regression coefficients is such an important part of the assumptions underlying the flowing regression model, it may be advisable to regard it as a hypothesis to be tested, especially our study period were 15 years. There might occurrence of various factors affecting the process of data generated, for example, the financial crisis, the stock market crash, new technologies to produce, preferences change, or some policy shocks disrupt economic development, it is necessary to confirm whether there is structural changes during the research period. In addition, structural breaks will bias the Unit Root Test statistics toward the non-rejection of a unit root ( Perron’s (2005)). Research and discussion of structural change in the past (Greene (1993); Kmenta (1986)), the relevant literature on structural econometric model of change considerations, called the stability of regression coefficients (stability or constancy) problem. In this study, we apply the Cumulative Sum of the recursive residuals (CUSUM test), the model estimated the period of sample, if there are structural changes in the value of the forward prediction will growing, . 22 .

(31) The Influence of House prices on Economic Growth and Consumption . . namely the use of recursive residuals of logic to test whether there is a structural change. The mathematical expression of CUSUM can be expressed as:. -3𝜃 𝑇 − 𝑛<𝑊! <3𝜃 𝑇 − 𝑛. (3.1). In this model, say a variable DGP is AR (1) (3.2a), with the estimated value of the DGP model to predicts the next period, and calculate the error between the predicted value and the actual value (𝑒! )(3.2b), which is the recursive residuals. And 𝜎!,! (3.2c) to represents residual variance of the predicted value. Show define w! (3.2d) as divisor of (3.2b) between (3.2c): y! =a! +a! y!!! +e!. (3.2a). 𝑒! =𝑦! − 𝑦!，t=n+1,n+2,….T. (3.2b). 𝜎!,! =Var(𝑒! ↿ 𝑦!!! ,𝑦!!! ,..,𝑦! ). (3.2c). w! =. !!. (3.2d). !!,!. Kmenta (1986) mentioned in the event of no structural changes, under H! , E(𝑒! )=0 and 𝑒! ~(0,𝜎 ! ), w! will be a normal distribution. And define 𝑊! is total w! /𝜎! of n period to t period, when t=n, 𝑊! is between: -𝜃 𝑇 − 𝑛<𝑊! <𝜃 𝑇 − 𝑛. (3.3a). when t=T, 𝑊! is between: -3𝜃 𝑇 − 𝑛<𝑊! <3𝜃 𝑇 − 𝑛. (3.3b). On the discussion above, where 𝜃=0.948 for a the significance level of 5%, and 𝜃=1.143 for a the significance level of 1%. The null hypothesis is rejected if 𝑊! crosses the boundary associated with the level of significance of the test for some t. If the coefficients are not constant, there may be a tendency of the test for a disproportionate number of recursive residuals to have the same sign and to push 𝑊! across the boundary.. . 23 .

(32) The Influence of House prices on Economic Growth and Consumption . . 3.1.2 Unit Root Test In general, many economic or financial time-series variables, have a non-stationary characteristic. The difference is that the mean and variation whether it will change over time. Stationary series for random external shocks, the impact of the time series data caused only transient impact, and will gradually disappear over time after the time-series data back to the long-term average level of convergence; non-stationary series, the result of any one of random shocks, cause time-series data can not converge to the original equilibrium, the effect for the impact on the data last forever. Dickey and Fuller (1979) consider three different regression equations that can be used to test for the presence of a unit root, however, in addition to the general nature of the variables themselves may have self-related, but the regression residuals after the estimated correspond with white noise will affect the estimated regression coefficients. If residual not correspond with white noise, which will cause DF value is incorrect. Hence, Dickey and Fuller (1981) proposed ADF regression equation to the right to add a lag period AR (p) conducted a unit root test, known as the ADF test. According to the different characteristics of the variable itself, can distinguish the following three model: A. Without an intercept term and a trend term： △ 𝑦! =γ𝑦!!! +. 𝛽! Δ𝑦!!! +𝜀!. (3.4a). B. Including an intercept term but not a trend term： △ 𝑦! =𝑎! + γ𝑦!!! +. 𝛽! Δ𝑦!!! +𝜀!. (3.4b). C. Including an intercept term and a trend term： △ 𝑦! =𝑎! + γ𝑦!!! + 𝑎! 𝑡 +. 𝛽! Δ𝑦!!! +𝜀!. (3.4c). In the model above, △ is first differential operator; 𝑎! is intercept; t is time trend; and γ represent optimal residuals term to correspond with white noise. Hence, we follow the proposal of Enders (2010) and Yang (2011), we estimate a regression equation of the model C first: . 24 .

(33) 2 1. The Influence of House prices on Economic Growth and Consumption . . △ 𝑦! =𝑎! + γ𝑦!!! + 𝑎! 𝑡 +. 𝛽! Δ𝑦!!! +𝜀!. N . Test γ＝0? . N . Y Test γ＝𝑎! =0? . N . Y . Normal distribution test γ=0. Y . N . 𝑦! does not has a unit root . 𝑦! has a unit root 𝑦! does not has a unit root . B Model Test γ=0? Y Test γ＝𝑎! =0? Y . N . N Normal distribution. Y . 𝑦! has a unit root . N . test γ=0. . 𝑦! does not has a unit root . A Model Test γ=0? Y . 𝑦! has a unit root . Figure 3-2 Process of ADF Test . 3.1.3 Cointegration Test Previous studies have found that many economic or financial time-series variables, have a non-stationary characteristic. Granger and Newbold (1974) found that with the non-stationary variables, the estimates of the classic regression model might be spurious regression result. According to the co-integration theory proposed by Engle and Granger (1987), when the cointegration existed between unsteady variables with the same order of difference, their linear combination was a stationary sequence. The regression relationship was still full of economic significance, which could be interpreted that a long-term equilibrium relationship existed between economic variables. We employ Johansen cointegration test proposed by Johansen (1998), which is based on . 25 .

(34) The Influence of House prices on Economic Growth and Consumption . . VAR approach, uses the maximum likelihood estimation to examine cointegration relationships between the non-stationary time series. The mathematical expression of Johansen test can be expressed as: △ 𝑦! =𝜋𝑦!!! +. !!! !!! 𝜋!. △ 𝑦!!! + 𝛽𝑥! +𝜀!. (3.5). where 𝑦! is a vector of non-stationary I(1) series; 𝑥! is a d-vector of exogenous variables; 𝜀! is disturbance vector;. !!! !!! 𝜋!. is the rank of the long-run impact matrix 𝜋 which equals. to the number of cointegrating vectors. Therefore, Johansen proposed the Trace and Maximum Eigenvalue test to determine the number of cointegration. ⋋!"#$% (r)=−𝑇. ! !!!!! 𝑙𝑛 (1-⋋! ) (3.6) . ⋋!"# (r,r+1)=−𝑇𝑙𝑛(1-⋋!!! ) (3.7) where T is the number of observations and ⋋! is the value of characteristic roots. The null hypothesis of the Trace test is H! ：rank≤r. For the null hypothesis of the Maximum Eigenvalue test is H! ：rank＝r.. 3.1.4 Vector Error Correction Model If there are cointegration between variables in the long-term equilibrium relationship, we can combination of cointegration in the error correction model proposed by Engle and Granger for Vector Error Correction Model. VECM is an appropriate model for a system of cointegrated variables. The mathematical expression of VECM test can be expressed as:. Δy＝a! +a! e!!! +. ! !!! a!. Δ𝑥!!! +. ! !!! b!. Δy!!! +ε!". (3.8). In this equation, e!!! is measurement deviation for t-1 period long-run equilibrium, is also called the vector of error correction terms; a! is intercept; a! is coefficient of error correction; p is the optimal lag period; 𝜀!" is white noise; ai, bi represent coefficient of short-term dynamic adjustment, can estimate the relationship between the existence of variables and how they affect each other.. . 26 .

(35) The Influence of House prices on Economic Growth and Consumption . . 3.1.5 Vector Autoregression Model Vector Autoregression model (VAR) proposed by Sims(1980), the model is appropriate to explore the dynamic interrelationships between the series. And all variables were considered endogenous variables. Each variable was shown by individual lag periods and lag periods of other variables. The model covered all information and could be used to conduct the analysis of direct effects between variables under intertemporal relation. And avoid model identification of the problem. The mathematical expression of VAR can be expressed as: 𝑦! ＝α+. ! !!! β!. 𝑦!!! +𝜀! (3.9) . In this equation, y! represents the (n×1) vector of endogenous variables like GDP and consumption in this study; y!!! is (n×1) vector composed of ｙｔ deferred i period vector like house prices, stock prices, interest rate, income, and CPI in this study; β! is (n×n) of the coefficient matrix; εｔ are the vector of disturbances. Especially, different εｔ at the same period can be interrelated but would not related to own lag value and the variables on the right side of the equation.. . 27 .

(36) The Influence of House prices on Economic Growth and Consumption . . 3.1.6 The Impulse Response Function Just as an autoregression has a moving-average representation, a VAR can be written as a vector moving average (VMA). The VMA representation is an essential feature of Sims’s (1980) methodology in that it allows us to trace out the time path of the various shocks on the variables contained in the VAR system. The VAR model use lag operator can represent as 𝑌! ＝ Φ! + Φ! 𝑌!!! +𝜀! , and use AR(1) perform MA(∞) as the following： 𝑌! ＝𝜇＋𝜀! ＋ Ψ! 𝜀!!! + Ψ! 𝜀!!! + Ψ! 𝜀!!! +…. (3.10). 𝜀!!! can interpretation for t-i period of the accident change or unexpected shock, so after differential 𝜀!!! , we can trace out the lag period effects of one-unit shocks to current period on the time paths of the variable sequences: ! !!. Ψ! ＝!!. (3.11). !!!. Through the impulse responds function, the analysis observed the impulse of specific variables to other variables. The impulse responds functions could be divided into impulse responds function decomposed by Cholesky and general impulse function. The former needed to set up the order of influences based on the degree of influence of variables, while the latter, proposed by Pesaran and Shin(1988), could be used to analyze results of impulse responses without order, which could prevent the possible distortion of causality caused by preconceptions. The study conducted the analysis based on general impulse responds function. The definition of the function was as below:. GIRF(𝑥! ; 𝑢!"# , 𝑛)=E 𝑥!!! 𝑢!"# =. 𝜎!,! , Ω!!! - E 𝑥!!! Ω!!!. (3.12). In this function, Ω!!! was the information set in t-1 period; 𝜎!,! represented the variance in the j equation in the ith variable-diagonal elements of covariance matrix; n was the length of forecast period. Which measures the effect of one standard error shock to the jth equation at time t on expected values of x at time t+n.. . 28 .

(37) The Influence of House prices on Economic Growth and Consumption . . 3.2 Data Description and Processing 3.2.1 Data Source The data employed in this study(as shown is Table 3-1), quarterly data of Sinyi house price index across Taiwan are compiled by Sinyi Realty Inc., and the weighted average stock index are provided by TEJ Database, interest rate of mortgage are provided by Central Bank of the Republic of Taiwan ; other variables (GDP, consumption, consumer price index, and per capita national income) were quarterly data published by National Statistics, R.O.C. (the original of time series data was shown in Figure 3-3 ). The research was conducted from Q1 of 1998 to Q3 of 2013 with 64 quarterly data in total. Table 3-1 The Variable Descriptions Variable. Code. Source. Time Period. Gross Domestic Product. GDP. 1998Q1-2013Q3. Consumption. CS. National Statistics, R.O.C. National Statistics, R.O.C.. Sinyi House Price Index Weighted Average Stock Index Interest Rate of Mortgage. HPI SPI. Sinyi Realty Inc. TEJ Database. 1998Q1-2013Q3 1998Q1-2013Q3. IR. 1998Q1-2013Q3. Consumer Price Index. CPI. Per-capita National Income. INCOME. Central Bank of the Republic of Taiwan National Statistics, R.O.C. National Statistics, R.O.C.. . 29 . 1998Q1-2013Q3. 1998Q1-2013Q3 1998Q1-2013Q3.

(38) The Influence of House prices on Economic Growth and Consumption . . Figure 3-3-a GDP. Figure 3-3-b CS. Figure 3-3-c HPI (Taiwan). Figure 3-3-d SPI. Figure 3-3-e INCOME. Figure 3-3-f CPI. Figure 3-3-g IR Figure 3-3Time Series Charts – Source Data . 30 .

(39) The Influence of House prices on Economic Growth and Consumption . . 3.2.2 Data Analysis and Processing According to the Table 3-2, the description of statistical variables included mean, median, maximum, minimum, and standard deviation. In terms of data processing procedures, due to differences in sizes of variables and seasonal factors, seasonality was excluded by CensusX12 in Eviews (the adjusted result was shown in Figure 3-4).8 Then, logarithm was adopted to process differences in sizes of units of variables. Furthermore, after logarithm we applied first difference in order to analysis the relationship of rate of change between the variables. (the adjusted result was shown in Figure 3-5) Table 3-2 Descriptive Statistics of Variables Variable. GDP. CS. HPI. Mean 3041548 1755367 149.88 Median 3038119 1788199 132.04 Maximum 3858380 2197859 280.87 Minimum 2282027 1298858 96.39 Std.Dev. 455358.2 248033.1 51.62 Unit Million Million 2001Q1 =100. 8. SPI. Income. IR. CPI. 6911.075 7197.557 9485.900 4299.027 1361.664 Million. 115435.6 115225.0 138538.0 92978.0 13215.65 Million. 3.62 2.43 8.54 1.63 2.25 %. 94.09 93.32 103.12 87.73 4.852 2011Q1 =100. . Census X-12 Method is a seasonal adjusted procedure established by Bureau of Census. This procedure is revised for many times and becomes one of the most usual seasonal adjusted methods. Much statistical software can perform the Census X-12 procedure. In order the seasonal variation of time series may cover up the actual trends of economic processes. The variables used in this study, except Sinyi House Price Index and Interest Rates, have obvious seasonal patterns. . 31 .

(40) The Influence of House prices on Economic Growth and Consumption . . Figure 3-4-a GDP. Figure 3-4-b CS. Figure 3-4-c INCOME. Figure 3-4-d SPI. Figure 3-4-e CPI Figure 3-4 Comparison of Source Data and Seasonal Adjustments. . 32 .

(41) The Influence of House prices on Economic Growth and Consumption . . Figure 3-5-a Rate of Change of GDP. Figure 3-5-b Rate of Change of CS. Figure 3-5-c Rate of Change of HPI. Figure 3-5-d Rate of Change of SPI. Figure 3-5-e Rate of Change of INCOME Figure 3-5-f Rate of Change of CPI. Figure 3-5-g Rate of Change of IR Figure 3-5 Rate of Change of all Variables ( After Logarithms) . 33 .

(42) The Influence of House prices on Economic Growth and Consumption . . . . 34 .

(43) The Influence of House prices on Economic Growth and Consumption . . Chapter 4 Empirical Results This chapter mainly presents the relationship between GDP, Consumption and the selected variables. First, the existence of structural change and unit root are checked. Then the results of Vector Autoregression model are interpreted. Finally, the analysis of impulse response function.. 4.1 Structural Change Before performing unit root test, it is necessary to confirm that there are no structural changes during the research period. When there are structural breaks, the various ADF test statistics are biased toward the non-rejection of a unit root (Perron’s, 2005). In order to avoid biased for following econometric analysis, we selected the Cumulative Sum of the recursive residuals (CUSUM test) to verify whether structural changes existed in variables. The results of CUSUM test are shown as Figure 4-1. All of variables, GDP, consumption, Sinyi house price index, weighted average stock index, interest rate, per capita national income and consumer price index do not exceed 5% of the critical value of significance level (dotted line), showing that each time series variable had no structural changes during the research period. In other words, there are no spurious unit root would occur in the model which was going to be discussed next.. . 35 .

(44) The Influence of House prices on Economic Growth and Consumption . . Figure 4-1-a Result of CUSUM Test for GDP. Figure 4-1-b Result of CUSUM Test for CS. Figure 4-1-c Result of CUSUM Test for HPI. Figure 4-1-d Result of CUSUM Test for SPI. Figure 4-1-e Result of CUSUM Test for CPI. Figure 4-1-f Result of CUSUM Test for INCOME. Figure 4-1-g Result of CUSUM Test for IR Figure 4-1 Result of CUSUM Test . 36 .

(45) The Influence of House prices on Economic Growth and Consumption . . 4.2 Unit Root Test To select appropriate model for relevant verification, the study adopted Augmented Dickey-Fuller (ADF) test to verify whether the unit root existed in variables rate of change. The result of ADF verification8 was shown in Table 4-1. According to the result within 10% of significance level, all rate of change rejected the null hypothesis significantly, showing that variables were stationary. Thus, we conclude that all of rate of change used in this study were I(0) series. Table 4-1 ADF Unit Root Test Variable rate of change. t-value. p-value. Lags. Result. -5.68. 0.00. 3. I(0). CS !. -8.58. 0.00. 0. I(0). HPI !". -9.07. 0.00. 0. I(0). SPI !. -5.79. 0.00. 3. I(0). INCOME !. -5.67. 0.00. 0. I(0). -7.29. 0.00. 0. I(0). -5.07. 0.00. 0. I(0). GDP. CPI. !. !. IR!. Note: The number of lags included in the ADF test is decided by the automatic lag length selection criteria based on SIC with maximum lag length of 10. c indicates that a constant term and n. ti. indicates that a constant term as. well as a linear time trend have been included in the model . indicates that a constant term and linear time trend are not included in the model。. . . 37 .

(46) The Influence of House prices on Economic Growth and Consumption . . 4.3 Vector Autoregression Model According to the results of unit root verification, all variables rate of change is I(0) series. For analysis of relationship between GDP, consumption and other variables, the VAR model proposed by Sims(1980) was adopted to estimated direct effect relationship between the variables of intertemporal, provided that each variable should be I(0) series. According to estimated coefficients of VAR model (as shown in Table 4-2). In the consumption equation, HPI, SPI and consumption are significantly, implying that under the direct effect, current CS is influence by the first lag of HPI, SPI and consumption. First, consumption (rate of change) of first lag had a positive significant influence on current consumer spending (rate of change), although under the intertemporal choice of consumption, the increase of previous consumption would substitute for the current consumer spending; in other words, the current consumer spending would decrease as the previous consumption increased, and the reduction of previous consumer spending would probably increase the current consumption. However, our consumption to GDP is 60%, when the lag period of consumption increase its means our domestic consumption demand growth, the country could avoid the environment of deflation, to cause future consumption (rate of change) positive growing. The HPI (rate of change) of first lag has a negatively and significantly effect on current consumption (rate of change). Most of previous literatures thought that the real estate could bring wealth effect and further affect economic development through the influence of employment, saving, investment, and labor productivity. The real estate investment was considered contributor to economic growth. However, the study showed that house prices negatively affected consumer spending, which caused economic depression. The reason was that the real estate played different roles in different conditions, such as research time, research methods, and research regions. Since early 1980s, the economy as well as income in Taiwan grew rapidly, and most people bought houses for their actual residence. House prices, . 38 .

(47) The Influence of House prices on Economic Growth and Consumption . . economy, and income grew at the same time, and the wealth effect came accordingly, which was consistence with past literatures. However, currently, the real estate market in Taiwan faced the low-rate and high loan-to-value, low carrying cost of real estate (land tax, house tax), and low capital gains tax (land value increment tax) caused by the easing monetary policy, which indirectly encouraged investors to enter the real estate market with low financing cost and stimulated the demand for investment, making the house price increase significantly. At that time, the real estate did not bring direct productivity to consumers; they were only investment goods. When the real estate market could not increase the actual national productivity, it was like pouring money into the bottomless hole. For the nation and consumers, such investing behaviors were inadequate types of consumer spending. The SPI (rate of change) of first lag had a positively significant influence on consumption (rate of change), showing that the stock index during the study brought wealth effects for consumer spending. The stock market and real estate market varied significantly. Compared to real estates, stocks had small prices, central market, transparent information, a number of products, high homogeneity, and flexible short-term supply, which were reasons for higher liquidity and trading frequency of stock than real estates. They also caused stocks to have more significant wealth effect on consumer spending. Moreover, development of stock market could enable companies and vendors to increase production, employment and income, promote economic growth, and issue dividends to stockholders, which benefited the public, companies, and countries. Thus, compared to the real estate market, the stock market had a steady contribution to consumer spending. In the study, the insignificant variables in consumption were IR and CPI. The interest rate continues decline under the easy monetary policy in recent years. This study showing that it was hard to encourage civic consumer spending through the easy monetary policy in Taiwan. Besides, according to Fisher equation (MV=PQ), money supply and consumption rate varied reversely. If the government intended to increase civic consumer spending through . 39 .

(48) The Influence of House prices on Economic Growth and Consumption . . the monetary policy, it should first increase economic growth or CPI in order to increase the consumption rate. However, after the impact of U.S. subprime mortgage crisis in 2008 on global economy and financial market alleviated, Taiwan promoted the easing monetary policy to encourage economic growth. So far, economic growth in Taiwan still growing slow, while the prices of assets increased, especially the price of real estates, which significantly has crowding-out effect on consumption and brought economic growing slow. The first lag of INCOME (rate of change) had a positive insignificant influence on current consumer spending (rate of change), which was inconsistent with traditional consumption theory proposed by Friedman and past literatures. The reason was that during the study “the real national income” increased by 1.6% on average annually, showing that the growth of “real income” was nearly zero; it would possibly lead to the reduction of consumer spending. However, the effect was still insignificant. The growth of income would have a positive influence on consumption, but the zero growth or decline of income could possibly inhibit consumer spending. In the consumption equation, we can learn that the first lag of HPI and SPI (rate of change) has positive significant influence on current HPI (rate of change), and when the first lag of consumption increasing, has negative influence on current HPI under the direct effect. Therefore, there has crowding-out effect between consumption and HPI. And the coefficients of current HPI is strongly affected by the first lag of IR, showing that when increase interest rate of mortgage, which can lead to house prices dropping down.. . 40 .