DOI QR코드

DOI QR Code

Time Series Analysis of the Relationship between Housing Consumer Sentiment and Regional Housing Prices in Seoul

서울시 주택소비심리와 권역별 주택가격의 시계열적 관계분석

  • Yang, Hye-Seon (Department of Urban Planning and Real Estate, Chung-Ang University) ;
  • Seo, Won-Seok (Department of Urban Planning and Real Estate, Chung-Ang University)
  • 양혜선 (중앙대학교 도시계획.부동산학과) ;
  • 서원석 (중앙대학교 도시계획.부동산학과)
  • Received : 2020.03.06
  • Accepted : 2020.06.12
  • Published : 2020.06.30

Abstract

This study investigated the time-series relationship between housing consumer sentiment and housing prices in the five major districts in Seoul and also analyzed the effect of the housing consumer sentiment on housing prices using Granger Causality and VEC (Vector Error Correction) models. To describe the key results, first of all, housing consumer sentiment and regional housing market prices were closely related to each other, and the consumer sentiment strongly affected the change of housing prices. Second, the housing consumer sentiment was confirmed to have a discriminatory effect on the housing prices among the districts in Seoul in the short term. Specifically, the housing price of the east southern district (ESD) was the main reason for the change in housing consumer sentiment in Seoul, and that the resulting impact was transferred to other districts. Third, it was analyzed that regions other than the ESD would increase the housing prices in the long term as the housing consumer sentiment turned positive, but that the ESD would see a steady tone. Fourth, in the case of relative influence by district, housing (apartment) price fluctuation in a district was generally found to be most affected by adjacent or competitive districts. Through these findings, this study confirmed that there is a clear causality between housing consumer sentiment and housing prices in each district of Seoul and that there is a discriminatory influence on housing consumer sentiment among the districts.

본 연구는 서울시 5대 권역을 대상으로 주택소비심리와 주택매매가격 간 시계열적 인과관계를 파악하고 소비심리 변동이 권역별 주택가격에 미치는 영향을 그랜저인과(Granger Causality)모형과 VEC (Vector Error Correction)모형을 이용해 실증분석하였다. 주요 결과를 요약하면 첫째, 주택소비심리와 권역별 주택가격은 밀접한 관계가 있으며, 소비심리는 주택가격에 강하게 영향을 미치는 것으로 나타났다. 둘째, 주택소비심리는 단기적으로 서울시 권역별 주택매매 가격에 차별적인 영향을 미치고 있었는데, 동남권 주택가격은 서울 주택소비심리 변화의 주요한 원인으로 작용하며 이에 따른 영향은 비동남권으로 전이되는 것을 확인하였다. 셋째, 동남권 이외 권역은 주택심리가 긍정적임에 따라 장기적으로 주택가격을 상승시키지만, 동남권은 보합세가 나타나는 것으로 분석되었다. 넷째, 권역별 상대적 기여도의 경우 대체로 아파트가격 변동은 인접권역 또는 경쟁권역의 영향을 가장 많이 받는 것으로 파악되었다. 이러한 결과를 통해 본 연구는 주택소비심리와 서울시 권역별 주택가격이 상호 간 명확한 인과관계가 있다는 점과 권역 간에도 차별적인 주택소비심리 영향이 나타나고 있다는 점을 확인하였다.

Keywords

References

  1. Kim G, Kim K, Lee J. 2016. An Empirical Study on the Influence Relationships between House Sub-Market, Auction Market and Consumer Sentiment Survey of Housing Market: Focused on the Apartment Market in Seoul and Seoul Metropolitan Area. GRI Review. 18(1):147-167.
  2. Kim DW, Yu JS. 2013. An Analysis on How Psychological Attitudes on the House Price Affect the Trading Volume. Housing Studies Review. 21(2):73-92.
  3. Kim MJ, Jang KH. 2002. Financial Econometrics. Kyungmoonsa. pp. 373-374
  4. Kim W, Seo W. 2018. The Impact of Space and Location Features on Regional Housing Market by New Development: Focused on Sejong City. Journal of the Korea Real Estate Society. 35(2):89-106.
  5. Kim JH, Choi YY. 2016. The Impact of Consumer Sentiment on Housing Price by Regional Hierarchy. The Geographical Journal of Korea. 50(2):185-195.
  6. Kim HK, Lee MS. 2005. Time-Series Analysis. Kyungmoonsa. pp. 459-460.
  7. Noh MJ, Yoo SJ. 2016. A Relationship between Sales Prices of APT and Consumer Sentiment. The Korea Spatial Planning Review. 89:3-13. https://doi.org/10.15793/kspr.2016.89..001
  8. Moon KS. 1997. Vector Autoregressive model: VAR. Journal of Korean Official Statistics. 2(1):23-56.
  9. Park CG, KIM TH. 2015. Analysis on the Predictive Power of the KRIHS Housing Market Survey Indices. Proceedings of the Korea Real Estate Analysts Association. 2015:53-66.
  10. Park CG, Lee Y. 2010. Analysis on the Predictive Power of the Housing Market Survey Indices. Journal of the Korea Real Estate Analysts Association. 16(1):131-146.
  11. Seo WS. 2019. Dynamic Relationship between Housing Consumer Sentiment in Seoul and Metropolitan Housing Market. SH Urban Research & Insight. 9(2):31-47. https://doi.org/10.26700/shuri.2019.8.9.2.31
  12. Yang HS, Kang CD. 2017. The Determinants of New Supply in the Seoul Office Market and their Dynamic Relationship. Journal of Cadastre & Land InformatiX. 47(2):159-174. https://doi.org/10.22640/LXSIRI.2017.47.2.159
  13. Lim JM, Lim MH. 2016. A Study of the Relationship Between Agents Sentiment and Housing Market. Journal of The Korean Regional Development Association. 28(3):147-164. https://doi.org/10.22885/KRDA.2016.28.3.147
  14. Chun HJ. 2014. A Empirical Analysis on the Impact of the Consumer Sentiment on the Housing Market. Journal of the Architectural Institute of Korea Planning & Design. 30(8):83-90. https://doi.org/10.5659/JAIK_PD.2014.30.8.83
  15. Chung EC. 2010. Consumer Sentiment and Housing Market Activities: Impact on Sales Price of Housing. Journal of the Korea Real Estate Analysts Association. 16(3):5-20.
  16. Chung JY, Yoon TK. 2008. A Study on the Preference Analysis of Apartment Purchaser using AHP Method. Journal of the Korea Institute of Building Construction. 8(3):51-58. https://doi.org/10.5345/JKIC.2008.8.3.051
  17. Cho TJ. 2014. A Study on the effect of the sentiment index to the housing market. Housing Studies Review. 22(3):25-48.
  18. Choi YG, Lee CM, Choi, MJ. 2004. Relationship between the Present Price and Expectations on Future Capital Gains in the Housing Market: Adaptive Expectation and Rational Expectation Hypotheses. Journal of the Korea Planners Association. 39(2):131-141.
  19. Choi YY, Kim J, Cho, GC. 2017. A Study on the interrelationship among interest rate, housing consumer sentiment and housing market using SVAR model. The Korea Spatial Planning Review. 95:3-20. https://doi.org/10.15793/kspr.2017.95..001
  20. Asghar Z, Abid I. 2007. Performance of lag length selection criteria in three different situations. MPRA Paper 40042, University Library of Munich, Germany.
  21. Engle R, Granger C. 1987. Co-integration and error correction: Representation estimation and testing. Econometrica. 55:251-276. https://doi.org/10.2307/1913236
  22. Seo W, Kim LY. 2020. Investigating the ripple effect through the relationship between housing market and residential migration in Seoul, South Korea. Sustainability. 12(1225):1-22.

Cited by

  1. 공시지가의 형평성에 관한 연구 - 서울특별시를 중심으로 - vol.50, pp.2, 2020, https://doi.org/10.22640/lxsiri.2020.50.2.133
  2. 주택수와 인구증가 변화를 반영한 지역별 부동산 시장 예측 vol.12, pp.4, 2020, https://doi.org/10.15207/jkcs.2021.12.4.229