Analyzing the Spillover effect of Housing Prices

Transcription

1 3rd International Conference on Humanities, Geography and Economics (ICHGE'013) January 4-5, 013 Bali (Indonesia) Analyzing the Spillover effect of Housing Prices Kyongwook. Choi, Namwon. Hyung, Hyungchol. Jeon, and Jegook. Kim Abstract The Housing price in Korea shows different movement and depends on the characteristics of specific regional markets, so some shock in one market impacts other regions too. Therefore, it's crucial to determine how housing price fluctuation in one region affects other regions. In this study, we used housing price index provided by Kookmin Bank and analyzed the spillover index to identify the feedback effects of market variables among housing markets of Seoul and 6 metropolises in Korea. Granger causality test on each variable indicated that housing prices in South Seoul Granger-cause those in North Seoul and six Metropolises. VECM shows spillover index decrease continuously after 008. During the pre-1999 period, the housing markets in six metropolises were mainly impacted from each other, rather than from the volatility in the Seoul market. However, post-1999, the spillovers from South Seoul took a rapid upturn. Keywords Cointegration Test, Granger causality Test, Spillover effect, Vector error correction model I. INTRODUCTION HE supply in the housing market is inelastic and the housing T prices largely depend on the characteristics of specific regional markets. For this reason, applying the same market policy to multiple regions will produce different outcomes in each area. On the other hand, volatility in one market impacts other regions as well. This means that, when implementing housing market policy, the government must consider its effects on the target region and on other regions. Therefore, it is crucial to correctly determine whether and how housing price fluctuation in one region affects other regions. In this study, we used housing price index provided by Kookmin Bank and analyzed the spillover index to identify the feedback effects of market variables among housing markets of Seoul and six metropolises in Korea. We divided Seoul into two sub-markets of South Seoul and North Seoul for analysis, since the two areas feature different housing market characteristics Kyongwook. Choi, Associate professor, is with University of Seoul Department of Economics, 163 Siripdaero, Dongdaemun-gu, Seoul KOREA, lead author ( kwchoi@uos.ac.kr@uos.ac.kr). Namwon. Hyung, professor, is with University of Seoul Department of Economics, 163 Siripdaero, Dongdaemun-gu, Seoul KOREA, correspondence author ( Hyungchol. Jeon is Currently enrolled in a Ph.D. program at University of Seoul Department of Economics, 163 Siripdaero, Dongdaemun-gu, Seoul KOREA ( cheory1519@uos.ac.kr). Jegook. Kim, is Currently enrolled in graduated school at University of Seoul Department of Economics, 163 Siripdaero, Dongdaemun-gu, Seoul KOREA ( ligaya1st@uos.ac.kr). and the government policy tend to focus on South Seoul sub-market. II. HOUSING PRICE TRENDS IN KOREA The housing price data used in this study includes monthly statistics of housing trades from January 1986 to September 010. Housing trade values in Korea during these period are presented in <Figure 1> below. <Figure 1> Housing Trade Values in Korea. The housing price in Korea begin rising in This was propelled by increased liquidity influx into the real estate market, resulted from Korea s high performance in exports and subsequent surplus in international trades. In 1996 and 1997, housing prices rose with a small margin, before the market hit a downturn at the end of 1997 due to the currency crisis causing an economic decline and severe credit strains in Korea. The market sharply fell in <Figure > Housing Prices in South Seoul, North Seoul, and Six Metropolises S_Seoul N_Seoul 6_Metro Note: S_Seoul: South Seoul, N_Seoul: North Seoul, 6_Metro: six metropolises Korea s housing market has maintained regional discrimination. Housing price trends in each regional markets show 64.4% increase for properties in South Seoul, as of September 010 compared to January 1986, while the figure remains at 103.4% and 114.% for those in North Seoul and six metropolises respectively. The statistical data used in this study, housing prices and changes during January 1986 September 010, are shown in <Table 1> and <Table >. 138

2 3rd International Conference on Humanities, Geography and Economics (ICHGE'013) January 4-5, 013 Bali (Indonesia) <Table 1> indicates the standard deviation is greatest for South Seoul housing prices, at As shown in <Figure 3>, South Seoul housing market features the greatest volatility. Among six metropolises, Incheon has greater volatility than others, at <Table > shows statistical data of housing price fluctuation. During the period included in the analysis, the average fluctuation in South Seoul is greater than that in other regions at Seven of the regions (excluding Ulsan) shows a positive () skew. Heavier distribution on the right from the average indicates there were more months when the housing prices went up than the months when the prices went down. <Table 1> Housing Prices Statistics Average Standard deviation Skewness Kurtosis Observed value <Table > Housing Price Fluctuation Statistics Average Standard deviation Skewness Kurtosis Observed value III. EMPIRICAL STUDY MODEL Diebold and Yilmaz (009) proposed the use of the return spillover index, also known as the volatility spillover index, and demonstrated that the index is useful in identifying a spillover or rapid change regardless of the presence of a financial crisis. Diebold and Yilmaz (009) used a VAR model as the framework for measuring return spillovers. The method used by Diebold and Yilmaz (009) is described below. The spillover index is measured using the VAR model variance decomposition The 1-step ahead forecast error is: a0,11 a0,1 u1, t 1 et 1, t = Xt 1 Xt 1, t = Au 0 t 1 = a0,1 a0, u, t 1 And the covariance matrix for the error is: E ( e e t t t t) = AA 1, 1, 0 0 Where A s = [a s,ij ] is a k k matrix. Using variance decomposition, the forecast error variance can be decomposed into the shock to itself and forecast error variance caused from the shock from other variables. In the above example, the 1-step ahead forecast error variance for X 1,t is decomposed to a 0,11, the effect caused by itself, and a 0,1, the effect caused by X,t. Similarly, variance decomposition can be applied to a 0,1 a 0,, the 1-step ahead forecast error for X,t. Therefore, from the overall forecast error volatility of a 0,11 a 0,1 a 0,1 a 0,, the proportion of a 0,1 a 0,1 indicates the spillover, corresponding to the effect caused by other variables. The spillover index is derived as: a a S = 100 a a a a 0,1 0,1 0,11 0,1 0,1 0, When using the generalized decomposition for h-step ahead forecast error variance in the VAR(p) model, the spillover index is: h k as, ij s= 1 i, j= 1, i j h k as, ij s= 1 i, j= 1 S = 100 In this study, we used the spillover index to identify the feedback effect between the variables of the housing prices in the regional markets of South Seoul, North Seoul, and six metropolises. IV. UNIT ROOT AND GRANGER CAUSALITY TEST Prior to the empirical study, we conducted two sets of unit root tests using ADF (Augmented Dickey-Fuller) and PP (Phillips Perron) methods, to verify that the samples we had for analysis were stationary time series data. The results are shown in <Table 3>. At the first order, the null hypothesis that all eight variables present unit root was not rejected. However, with the differenced variables, the null hypothesis was rejected at the significance level of 1%. All variables presented unit root at the first order. Cointegration test is necessary when using a VAR model with first-order variables presenting unit root. We used Johansen s λ_trace test for cointegration and selected the lag order as based on the SIC. The results are shown in <Table 4>. The cointegration result indicated that there were six cointegrated correlations. Based on the result, we accepted six cointegrations to use the VECM model. Before proceeding to VECM analysis, we conducted a Granger causality test using the bivariate VAR model. We converted housing price values to returns to use in the bivariate VAR model. In general, correlation between two variables does not mean prediction is possible. When the null hypothesis is rejected in a Granger causality test, it is interpreted as a variable (A) Granger-causes another variable (B), meaning you can predict the movement of (B) by observing (A). We selected the lag order in the VAR model based on SIC. The results of the Granger causality test are shown in <Table 5>. <Table 3> Unit Root Test 139

3 3rd International Conference on Humanities, Geography and Economics (ICHGE'013) January 4-5, 013 Bali (Indonesia) Level ADF Test Phillips-Perron Test Constant, Constant Constant, Trend Constant Trend S_Seoul -0.1(0.945) (0.85) 0.63(0.990) -1.13(0.9) N_Seoul (0.874) (0.77) 0.533(0.988) (0.96) Busan -.167(0.19) -.53(0.458) -.55(0.188) -.190(0.493) N_Seoul does not Granger Cause S_Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan (0.181) (0.01) (0.04) (0.017) 5.06 S_Seoul does not Granger Cause N_Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan (0.001) (0.001) Daegu -.717(0.07) -.504(0.36) -.580(0.098) -.53(0.458) Incheon (0.957) (0.88) 0.349(0.981) -1.41(0.899) Gwangju -.189(0.11) -.36(0.418) -.084(0.51) -.16(0.508) Daejeon 0.18(0.967) (0.831) 0.113(0.966) (0.81) Ulsan -.16(0.1) -.165(0.507) (0.307) (0.66) Difference S_Seoul (0.001) N_Seoul (0.001) (0.007) Busan Daegu V. EMPIRICAL STUDY We accepted six cointegrations to use the VECM analysis. We selected the lag order as using the minimum value based on SIC. And then we performed the VECM analysis to derive the VAR model coefficients, and calculated six-month-ahead forecast error and the spillover index. The calculations used the Cholesky decomposition and generalization methods. We will discuss the empirical study results in this paper mainly based on the latter. We calculated the second spillover index by applying the rolling method. The results are shown in <Figure 3>. Incheon Gwangju Daejeon Ulsan Note) Test statistics, ( ) indicates p-value Cholesky Generalized <Figure 3> The Spillover Index Trend Spillover Indices, VECM() Number of Cointegration equation <Table 4> Cointegration Test Statistic Critical Value P-Value* None At most At most At most At most At most At most At most Note) *MacKinnon-Haug-Michelis (1999) p-values The housing prices in North Seoul Granger-cause those in six metropolises, but not in South Seoul, at the significance level of 5%. The housing prices in South Seoul Granger-cause those in all other regions, including North Seoul and six metropolises, at the significance level of 1%. This means that observing the housing prices in South Seoul allows prediction of housing price trends in all other regions included in this study. <Table 5> Granger Causality Test Time As shown in the second panel of <Figure 3>, the spillover index was on a downturn until 007, when it made an upward turn, and then reached the peak in the later part of 008. After then, the spillover continued to decline. The figure also shows that the overall fluctuation range of the spillover remains stable between 55-65%. Since the spillover index indicates the spillover of volatility over multiple factors, its consistent decrease means that the housing prices in a region largely depend on the characteristics that are unique to that particular region. Our explanation for this result is that the financial crisis of late 008 caused a shock in the overall economy and magnified the feedback effect between regions. We also interpret the continuing decrease of the spillovers as an indicator for a decline in the housing market and the subsequent trend of growing impact of the regional factors on pricing, such as the actual demand in the region. On the other hand, segmentations of the volatility spillover revealed significantly different results. The results of the spillover segmentation by region are shown in <Tables 6>, <Table 7,> and <Table 8>. For instance, North Seoul (N_Seoul), shown in the second row in <Table 6>, had the highest impact from South Seoul (S_Seoul, 40.9%). The region had relatively low impact from six metropolises. For Gwangju, it is notable that the impact from Seoul submarkets 140

4 3rd International Conference on Humanities, Geography and Economics (ICHGE'013) January 4-5, 013 Bali (Indonesia) was 5.0% (South Seoul) and 4.1% (North Seoul), while the impact from other metropolises was greater at 3.3% (Daegu) and 9.1% (Ulsan). <Table 6> Analysis for Entire Sample Data (January September 010) Daegu Incheon Gwangju Daejeon Ulsan Cholesky spillover index = 0.47, Generalization spillover index = 0.6 S_Seoul N_Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Cholesky spillover index = 0.47, Generalization spillover index = 0.57 <Table 7> and <Table 8>, each showing the results for preand post data, indicate significant differences in segmentations of the spillover of price fluctuation from between regional markets. During the pre-1999 period, the major cause for price fluctuations in six metropolises was impact from cities other than Seoul. For instance, Busan was greatly impacted by Daegu (6.3%) while Daegu was impacted by Busan (6.5%), showing the spillovers were the main cause for price fluctuation in these markets, excluding the factors within their own regions. However, during the post-1999 period, Busan had greater impact from South Seoul (3.9%) while the impact from Daegu (3.0%) decreased. Daegu also had significantly greater impact from South Seoul (19.1%) and rapidly diminishing impact from Busan (0.5%). The results indicate that the spillovers from South Seoul to other regions had a sharp upturn. <Table 7> Pre-1999 Period (January 1986 December 1998) S_Seoul N_Seoul Busan Daegu Incheon Gwangj u Daejeon Ulsan Cholesky spillover index = 0.67, Generalization spillover index = 0.66 <Table 8> Post-1999 Period (January 1999 September 010) S_Seoul N_Seoul Busan VI. CONCLUSIONS Korea s real estate market features strong regional discriminations. Since housing prices are largely dependent on the factors constricted to the region, it is necessary to accurately identify whether and how volatility in one regional market affects other regions. This study was conducted using the statistics of monthly housing price averages from January 1986 to September 010, provided by Kookmin Bank, for North Seoul, South Seoul, and six metropolises outside Seoul. Granger causality test on each variable indicated that housing prices in South Seoul Granger-cause those in North Seoul and six metropolises. The volatility spillovers, calculated using the rolling method on the VECM model, reached the peak in late 008 and maintained a downturn since then. This indicates that the shock of the global financial crisis in 008 magnified the feedback effect between the regional housing markets. Continuing decrease of the spillovers are an indicator for a decline in the housing market and the subsequent trend of growing impact of the regional factors on pricing, such as the actual demand in the region. Segmentation analysis of the volatility spillovers revealed results that are different than those from the rolling method. Housing prices in North Seoul had the greatest impact from those in South Seoul, with less significant impact from housing markets in six metropolises. We also segmented the spillovers into two periods of pre- and post The results indicated that during the pre-1999 period, the housing markets in six metropolises were mainly impacted from each other, rather than from the volatility in the Seoul market. However, in the post-1999, the spillovers from South Seoul took a rapid upturn. The results indicate that the Korean government s policies during this period, focusing on suppressing the housing price rise in South Seoul, ironically resulted in increased volatility spillovers from the region to the housing markets nationwide. REFERENCES [1] Korea Development Institute, 01. The Real Estate Market Trend Analysis for 1/4Q 01, vol., no.1 [] Kim, S., Park, K., 006. Study on Real Estate Market Factors Relative Effect, The Korean Economic Review, vol. 13, no.,

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