Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
권호 표지
DOI QR코드

DOI QR Code

A Simulation Model Development for Analyzing Ripple Effect of Housing Policy by Region

주택 정책의 지역별 시장 파급효과 분석을 위한 시뮬레이션 모델 개발

  • Yoon, Inseok (Department of Architectural and Architectural Engineering, Seoul Natinoal University) ;
  • Park, Moonseo (Department of Architectural and Architectural Engineering, Seoul Natinoal University) ;
  • Lee, Hyun-Soo (Department of Architectural and Architectural Engineering, Seoul Natinoal University)
  • Received : 2018.10.27
  • Accepted : 2018.12.28
  • Published : 2019.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, housing prices have surged, and the government has implemented various regulations, such as finance and taxes. Because of the policy, the nationwide housing price have stabilized, but polarization has occurred. Some argue that regulation can adversely affect the actual demand. Therefore, not only the correlation between market variables but also ripple effect of policy has to be analyzed in policy planning and analysis from a microscopic point of view. In this study, a simulation model was developed by integrating system dynamics for analyzing market structure and agent-based model for modeling decision process of market participants. This research applied the financial regulation and the tax regulation to the model and evaluated the policy effectiveness. This study reveals which feedback dominates according to the policies, which have same purpose. It is because market participants make different decision for each policy. Furthermore, there were other ripple effects not only in the policy target submarket but also in other submarket.

최근 주택 가격이 전국적으로 상승하였고 정부는 실수요자 보호를 위해 금융, 세금 등 다양한 규제를 지역에 따라 시행하였다. 정책 시행 이후 전국적인 주택 가격 평균은 안정되었으나 지역 별로 살펴보면 양극화 현상이 발생하였다. 또한 규제로 인해 오히려 실수요자가 피해를 볼 우려에 대한 논의도 지속되고 있다. 따라서 정책 계획 및 분석에 있어 시장 변수 간의 상관관계 뿐 아니라 미시적으로 시장 참여자들에게 어떻게 정책의 효과가 파급되는 지 분석할 필요성이 있다. 이에 본 연구에서는 변수 간 인과관계로 구성되는 시장 구조 분석을 위한 시스템 다이내믹스와 시장 참여자들의 의사 결정 과정 모델링을 위한 행위자 기반 모델을 통합하여 시뮬레이션 모델을 구축하였다. 개발한 모델에 금융 규제와 세금 규제를 적용하여 주택 가격의 변화와 이에 시장 참여자들이 어떻게 행동하는 지에 대한 분석을 통해 정책의 실효성을 평가하였다. 본 연구를 통해 가격 안정화라는 같은 목적을 가진 두 규제에 대해 시장 참여자들이 다른 의사결정을 함에 따라 시장을 지배하는 피드백의 차이를 확인했다. 이에 따라 정책 대상 지역 뿐 아니라 그 이외의 지역에 대해서도 다른 파급효과가 나타났다.

Keywords

GGRHC4_2019_v20n2_68_f0001.png 이미지

Fig. 1. Housing market model framework in this research

GGRHC4_2019_v20n2_68_f0002.png 이미지

Fig. 2. Housing market equilibrium according to change of demand (Adapted from Hwang et al., 2010)

GGRHC4_2019_v20n2_68_f0003.png 이미지

Fig. 3. Impact of market participants expectation formation on market price

GGRHC4_2019_v20n2_68_f0004.png 이미지

Fig. 4. Net asset of Korean household (Statistics Korea, 2017)

GGRHC4_2019_v20n2_68_f0005.png 이미지

Fig. 5. Agents behaviorʼs rule

GGRHC4_2019_v20n2_68_f0006.png 이미지

Fig. 6. Comparison of adaptive and rational expectation : simulation result

GGRHC4_2019_v20n2_68_f0007.png 이미지

Fig. 7. Housing price changes after a boom

GGRHC4_2019_v20n2_68_f0008.png 이미지

Fig. 8. Number of demands after a boom

GGRHC4_2019_v20n2_68_f0009.png 이미지

Fig. 9. Assets average of demands after a boom

GGRHC4_2019_v20n2_68_f0010.png 이미지

Fig. 10. Price change applying LTV policy by scenarios

GGRHC4_2019_v20n2_68_f0011.png 이미지

Fig. 11. Number of demands applying LTV policy by scenarios

GGRHC4_2019_v20n2_68_f0012.png 이미지

Fig. 12. Asset average of demands applying LTV policy scenarios

GGRHC4_2019_v20n2_68_f0013.png 이미지

Fig. 13. Price change applying tax policy by scenarios

GGRHC4_2019_v20n2_68_f0014.png 이미지

Fig. 14. Number of demands comparison of tax and LTV policy

GGRHC4_2019_v20n2_68_f0015.png 이미지

Fig. 15. Asset average of demands comparison of tax and LTV policy

Table 1. Attributes of demand agents

GGRHC4_2019_v20n2_68_t0001.png 이미지

Table 2. Initial value of variables in the model

GGRHC4_2019_v20n2_68_t0002.png 이미지

Table 3. Housing market behaviour during policy implementation

GGRHC4_2019_v20n2_68_t0003.png 이미지

References

  1. Borshchev, A., and Filippov, A. (2004). "From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools." In Proceedings of the 22nd international conference of the system dynamics society, 22, Oxford: System Dynamics Society.
  2. Cagan, P. (1956). "The monetary dynamics of hyperinflation." Studies in the Quantity Theory if Money.
  3. Cho, G., Lee, S., and Jeong, D. (2017). "An Empirical Analysis on the Impact of Capital Gain Tax and Economic Variables on Apartment Price." Review of Real Estate and Urban Studies, 9, pp. 89-109.
  4. Choi, Y., Lee. C., and Choi, M. (2004). "Relationship between the Present Price and Expectations on Future Capital Gains in the Housing Market - Adaptive Expectation and Rational Expectation Hypotheses." Korea planning association, 39(2), pp. 131-141.
  5. Coulombel, N. (2010). "Residential choice and household behavior: State of the Art." Ecole Normale Superieure de Cachan.
  6. DiPasquale, D., and Wheaton, W.C. (1996). "Urban economics and real estate markets. Englewood Cliffs." NJ: Prentice Hall.
  7. Dorsey, R.E., Hu, H., Mayer, W.J., and Wang, H.C. (2010). "Hedonic versus repeat-sales housing price indexes for measuring the recent boom-bust cycle." Journal of Housing Economics, 19(2), pp. 75-93. https://doi.org/10.1016/j.jhe.2010.04.001
  8. Haddad, W.D., and Demsky, T. (1995). "Education Policy-Planning Process: An Applied Framework." Fundamentals of Educational Planning 51. UNESCO, 7 Place de Fontenoy, 75700, Paris France.
  9. Hwang, S., Park, M., Lee, H.S., Lee, S., and Kim, H. (2010). "A Dynamic Approach for Evaluating the Validity of Mortgage Lending Policies in Korean Housing Market." Korean Journal of Construction Engineering and Management, KICEM, 11(5), pp. 32-40. https://doi.org/10.6106/KJCEM.2010.11.5.32
  10. Hwang, S., Park, M., Lee, H.S., Lee, S., and Kim, H. (2012). "Dynamic feasibility analysis of the housing supply strategies in a recession: Korean housing market." Journal of Construction Engineering and Management, 139(2), pp. 148-160. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000577
  11. Hyun, H., Lee, H., Park, M., and Hwang, S. (2014). "Housing Market Participantsʼ Decision Process and The Dynamics of Ripple Effect on Korean Housing Market - Focusing on The Cause of Housing Market Stagnation and Housing Policies After 2008 Global Financial Crisis." Korean Journal of Construction Engineering and Management, KICEM, 15(5), pp. 147-159. https://doi.org/10.6106/KJCEM.2014.15.5.147
  12. KB Bank of Korea (2018). "Housing price trend - monthly report" (Aug. 31, 2018).
  13. Kim, K., and Jeon, J.S. (2012). "Why do households rent while owning houses? Housing sub-tenure choice in South Korea." Habitat International, 36(1), pp. 101-107. https://doi.org/10.1016/j.habitatint.2011.06.005
  14. Lee, C.M., and Chung, E.C. (2010). "Monthly rent with variable deposit: a new form of rental contract in Korea." Journal of Housing Economics, 19(4), pp. 315-323. https://doi.org/10.1016/j.jhe.2010.09.005
  15. Lee, S., Park, M., Lee, H., and Hwang, S. (2014). "Degraded Performance and Restoration Plan of Lfieline Systems Considering Interdependency in the Post-disaster." Korean Journal of Construction Engineering and Management, KICEM, 15(4), pp. 139-149. https://doi.org/10.6106/KJCEM.2014.15.4.139
  16. Lee, W., Lee, J., and Shin, D. (2003). "The Characteristics of Consumer's Preference of house purchasing in the Capital Region of Korea." Korea planning association, 38(3), pp. 135-146.
  17. Lee, D. (2013). "A study on LTV.DTI and household debt, housing prices." Journal of Korea Planning Association, 48(3), pp. 361-381.
  18. Magliocca, N., Safirova, E., McConnell, V., and Walls, M. (2011). "An economic agent-based model of coupled housing and land markets (CHALMS)." Computers, Environment and Urban Systems, 35(3), pp. 183-191. https://doi.org/10.1016/j.compenvurbsys.2011.01.002
  19. Ministry of land, transport and maritime affairs of the Korean government (2017). "Stabilization of housing market through protection of real demand and restraint of short-term speculative demand." (Aug. 2, 2017).
  20. Mulder, C.H. (1996). "Housing choice: Assumptions and approaches." Netherlands Journal of Housing and the Built Environment, 11(3), pp. 209-232. https://doi.org/10.1007/BF02496589
  21. Muth, J.F. (1961). "Rational expectations and the theory of price movements. Econometrica." Journal of the Econometric Society, pp. 315-335.
  22. Ozbakan, T.A., Kale, S., and Dikmen, I. (2016). "Exploring house price dynamics: An agent-based simulation with behavioral heterogeneity." Computational Economics, pp. 1-25.
  23. Park, M., Lee, M.H., Lee, H.S., and Hwang, S. (2009). "Boost, control, or both of Korean housing market: 831 countermeasures." Journal of Construction Engineering and Management, 136(6), pp. 693-701. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000159
  24. Salle, I.L. (2015). "Modeling expectations in agent-based models-An application to central bankʼs communication and monetary policy." Economic Modelling, 46, pp. 130-141. https://doi.org/10.1016/j.econmod.2014.12.040
  25. Shim, J., and Oh, H. (2017). "Understanding Entrepreneurial Processes using Agent-Based Modeling and Simulation (ABMS)." Journal of the Korean Entrepreneurship Socieity, 12(5), pp. 195-213. https://doi.org/10.24878/tkes.2017.12.4.195
  26. Sinai, T., and Souleles, N.S. (2005). "Owner-occupied housing as a hedge against rent risk." The Quarterly Journal of Economics, 120(2), pp. 763-789. https://doi.org/10.1162/0033553053970197
  27. Statistics Korea (2017). "The Survey of Household Finances and Living Conditions (SFLC) in 2017" (Dec. 21, 2017).
  28. Sterman, J.D. (1988). "Modeling the formation of expectations: the history of energy demand forecasts." International Journal of Forecasting, 4(2), pp. 243-259. https://doi.org/10.1016/0169-2070(88)90080-5
  29. Swinerd, C., and McNaught, K.R. (2012). "Design classes for hybrid simulations involving agent-based and system dynamics models." Simulation Modelling Practice and Theory, 25, pp. 118-133. https://doi.org/10.1016/j.simpat.2011.09.002
  30. Zhuge, C., and Shao, C. (2018). "Agent-based modelling of purchasing, renting and investing behaviour in dynamic housing markets." Journal of Computational Science, 27, pp. 130-146. https://doi.org/10.1016/j.jocs.2018.05.007
  31. Zhang, X., Geltner, D., and de Neufville, R. (2018). "System Dynamics Modeling of Chinese Urban Housing Markets for Pedagogical and Policy Analysis Purposes." The Journal of Real Estate Finance and Economics, 57(3), pp. 476-501. https://doi.org/10.1007/s11146-017-9650-z