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- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Relationship Between Housing Prices and Expected Housing Prices in the Real Estate Industry
Abstract
Purpose - In Korea, there has been a recent trend that shows housing prices have risen rapidly following the International Monetary Fund crisis. The rapid rise in housing prices is spreading recognition of this as a factor in housing price volatility. In addition, this raises the expectations of housing prices in the future. These expectations are based on the assumption that a relationship exists between the current housing prices and expected housing prices in the real estate industry. By performing an empirical analysis on the validity of the claim that an increase in current housing prices can be correlated with expected housing prices, this study examines whether a long-term equilibrium relationship exists between expected housing prices and existing housing prices. If such a relationship exists, the recovery of equilibrium from disequilibrium is analyzed to derive related implications. Research design, data, and methodology - The relationship between current housing prices and expected housing prices was analyzed empirically using the Vector Error Correction Model. This model was applied to the co-integration test, the long-term equilibrium equation among variables, and the causality test. The housing prices used in the analysis were based on the National Housing Price Trend Survey released by Kookmin Bank. Additionally, the Index of Industrial Product and the Consumer Price Index were also used and were obtained from the Bank of Korea ECOS. The monthly data analyzed were from January 1987 to May 2015. Results - First, a long-term equilibrium relationship was established as one co-integration between current housing price distribution and expected housing prices. Second, the sign of the long-term equilibrium relationship variable was consistent with the theoretical sign, with the elasticity of housing price distribution to expected housing price, the industrial production, and the consumer price volatility revealed as 1.600, 0.104,and 0.092, respectively. This implies that the long-term effect of expected housing price volatility on housing price distribution is more significant than that of the industrial production and consumer price volatility. Third, the sign of the coefficient of the error correction term coincided with the theoretical sign. The absolute value of the coefficient of the correction term in the industrial production equation was 0.006, significantly larger than the coefficients for the expected housing price and the consumer price equation. In case of divergence from the long-term equilibrium relationship, the state of equilibrium will be restored through changes in the interest rate. Fourth, housing-price volatility was found to be causal to expected housing price, and was shown to be bi-directionally causal to industrial production. Conclusions - Based on the finding of this study, it is required to relieve the association between current housing price distribution and expected housing price by using property taxes and the loan-to-value policy to stabilize the housing market. Further, the relationship between housing price distribution and expected housing price can be examined and tested using a sophisticated methodology and policy variables.
- keywords
- Housing Distribution Price, Expected Housing Price, VECM, Cointegration Test, Causality Test
Reference
Cha, Moon-Joong (2004). Housing Market analysis and Policy Research : part of Cha, Moonjoong(pp.97-105). Seoul, Korea: Korea Development Institute.
Campbell, J., Lo, A. W., & Mackinlay, C. (1997). The Econometrics of Financial Markets. New York, U.S.A.:Princeton University Press.
Dickey, D., & Fuller, W. A. (1979). Distribution of Estimates for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427-431. doi:10.2307/2286348.
Davis, F. P., & Zhu, H. (2004). Bank Lending and Commercial Property Cycles: Some Cross-Country Evidence. BIS Working Papers, 150, 1-45. https://www.bis.org.
Granger, C. W. J., & Engle, R. F. (1987). Cointegration and Error-Correction : Representation, Estimation and Testing. Econometrica, 55(2), 251-276. doi: 10.2307/1913236.
Gallin, J. (2008). The Long-run Relationship between House Prices and rents. Real Estate Economics, 36(4), 635-658. doi: 10.1111/j.1540-6229.2008.00225.x.
Hofmann, B. (2001). The Determinants of Private Sector Credit in Industrialized Countries: Do Property Prices Matter?. BIS Working Papers, 108, 1-39. http://www.bis.org.
Hamilton, J. D. (1994). Time series Analysis. Princeton, U.S.A.:Princeton University Press.
Jeong, Ku-Il (2006). Analysis of the relationship between asset prices and liquidity. Financial and Economics Research, 255, 1-36.
Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegrating Vectors in Gausssian Autoregressive Models. Econometrica, 59, 551-580. doi: 10.2307/2938278.
Kim, Sei-Wan, & Kim, Eun-mi (2009). Analysis of dynamic relationship between the housing market and household lending: focusing on before and after the financial crisis. Journal of the KRSA, 25(4), 123-147.
Kim, Young-cheol (1996). A Study on Relationship Between Housing Price and Economic Variables. Land Planing, 31(6), 67-82.
Lee, Keyung-Ae, Park, Sang-Hak, & Kim, Young-Soon. (2013). Variation of Determinant Factor for Seoul Metropolitan Area's Housing and Rent Price in Korea. LHI Journal, 4(1), 43-54.
Lee, Hong-Jae, Park, Jae-Seok, Song, Dong-Jin, & Lim, Kyung-Won (2005). Eviews Practice & Analysis. Seoul, Korea : Keyung Moon Sa.
Lee, Young-Man (2000). Structural change is a sign of appreciation?-The Interpretation of the rise of the housing-price ratio of chonsei. journal of the Korea Real Estate Analysts Association, 6(1), 9-22.
Lastrapes, W. D. (2002). The Real Price of Housing and Money Supply Shocks : Time Series Evidence and Theoretical Simulations. Journal of Housing Economics, 11, 40-74. doi:10.1006/jhec.2002.0309.
McCarthy, J., & Peach, R. W. (2002). Monetary Policy Transmission to Residential Investment. FRBNY Economic Policy Review (May), 139-158. https://www.newyorkfed.org/research/epr/02v08n1.
McCarthy, J., & Peach, R. W. (2004). Are Home Prices the Next “Bubble”?. FRBNY Economic Policy Review (December), 1-17. https://www.newyokfed.org/research/epr/04v10n3.
Phillips, P. C., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrica, 75(2), 335-346. doi:10.1093/biomet/75.2.335.
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