Detection and Prediction of House Price Bubbles: Evidence from a New City

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)


In the early stages of growth of a city, housing market fundamentals are uncertain. This could attract speculative investors as well as actual housing demand. Sejong is a recently built administrative city in South Korea. Most government departments and public agencies have moved into it, while others are in the process of moving or plan to do so. In Sejong, a drastic escalation in house prices has been noted over the last few years, but at the same time, the number of vacant housing units has increased. Using the present value model, lease-price ratio, and log-periodic power law, this study examines the bubbles in the Sejong housing market. The analysis results indicate that (i) there are significant house price bubbles, (ii) the bubbles are driven by speculative investment, and (iii) the bubbles are likely to burst earlier here than in other cities. The approach in this study can be applied to identifying pricing bubbles in other cities.


Newly developed city Real estate bubble Complex system 


  1. 1.
    Abraham, J.M., Hendershott, P.H.: Bubbles in metropolitan housing markets. National Bureau of Economic Research, w4774 (1994)Google Scholar
  2. 2.
    Ahn, K., Dai, B., Targia, D., Zhang, F.: Predicting the critical time of financial bubbles. PHBS Working Papers, 2016008 (2016).
  3. 3.
    Bourassa, S.C., Hoesli, M., Oikarinen, E.: Measuring house price bubbles. Real Estate Econ., 1–30 (2016)Google Scholar
  4. 4.
    Bree, D.S., Joseph, N.L.: Testing for financial crashes using the log-periodic power law model. Int. Rev. Finan. Anal. 30, 287–297 (2013)CrossRefGoogle Scholar
  5. 5.
    Campbell, J.Y., Shiller, R.J.: Cointegration and tests of present value models. J. Polit. Econ. 95(5), 1062–1088 (1987)CrossRefGoogle Scholar
  6. 6.
    Canning, D., Amaral, L.A.N., Lee, Y., Meyer, M., Stanley, H.E.: Scaling the volatility of GDP growth rates. Econ. Lett. 60(3), 335–341 (1998)zbMATHCrossRefGoogle Scholar
  7. 7.
    Clark, A.: Evidence of log-periodicity in corporate bond spreads. Phys. A Stat. Mech. Appl. 338(3), 585–595 (2004)CrossRefGoogle Scholar
  8. 8.
    Diebold, F.X., Yilmaz, K.: Measuring financial asset return and volatility spillovers with application to global equity markets. Econ. J. 119(534), 158–171 (2009)CrossRefGoogle Scholar
  9. 9.
    Diebold, F.X., Yilmaz, K.: Better to give than to receive: predictive directional measurement of volatility spillovers. Int. J. Forecast. 28(1), 57–66 (2012)CrossRefGoogle Scholar
  10. 10.
    Faggio, G.: Relocation of public sector workers: Evaluating a place-based policy. University of London, October 2016Google Scholar
  11. 11.
    Faggio, G., Schluter, T., Berge, P.: The impact of public employment on private sector activity: evidence from Berlin. University of London, November 2016Google Scholar
  12. 12.
    Fantazzini, D.: Modelling bubbles and anti-bubbles in bear markets: a medium-term trading analysis. In: The Handbook of Trading, McGraw-Hill Finance and Investing, pp. 365–388. New York, U.S. (2010)Google Scholar
  13. 13.
    Filimonov, V., Sornette, D.: A stable and robust calibration scheme of the log-periodic power law model. Phys. A Stat. Mech. Appl. 392(17), 3698–3707 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Johansen, A., Ledoit, O., Sornette, D.: Crashes as critical points. Int. J. Theor. Appl. Finance 3(2), 219–255 (2000)zbMATHCrossRefGoogle Scholar
  15. 15.
    Johansen, A., Sornette, D.: Predicting financial crashes using discrete scale invariance. J. Risk 1(4), 5–32 (1999)CrossRefGoogle Scholar
  16. 16.
    Johansen, A., Sornette, D.: Bubbles and anti-bubbles in Latin-American, Asian and Western stock markets: an empirical study. Int. J. Theor. Appl. Finance 4(6), 853–920 (2001)zbMATHCrossRefGoogle Scholar
  17. 17.
    Johansen, A.: Characterization of large price variations in financial markets. Phys. A Stat. Mech. Appl. 324(1), 157–166 (2003)zbMATHCrossRefGoogle Scholar
  18. 18.
    Koop, G., Pesaran, M.H., Potter, S.M.: Impulse response analysis in non-linear multivariate models. J. Econometrics 74(1), 119–147 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Korea National Statistics: National Census.
  20. 20.
    Kwon, Y.: Sejong Si (City): are TOD and TND models effective in planning Korea’s new capital? Cities 42, 242–257 (2015)CrossRefGoogle Scholar
  21. 21.
    Lin, L., Ren, R.E., Sornette, D.: The volatility-confined LPPL model: a consistent model of ‘explosive’ financial bubbles with mean-reverting residuals. Int. Rev. Financial Anal. 33, 210–225 (2014)CrossRefGoogle Scholar
  22. 22.
    Lind, H.: Price bubbles in housing markets: Concept, theory and indicators. Int. J. Hous. Markets Anal. 2(1), 78–90 (2009)CrossRefGoogle Scholar
  23. 23.
    Lyons, M.: Well Placed to Deliver? Independent Review of Public Sector Relocation. Shaping the Pattern of Government Service (2004)Google Scholar
  24. 24.
    Meese, R., Wallace, N.: Testing the present value relation for housing prices: should I leave my house in San Francisco? J. Urban Econ. 35(3), 245–266 (1994)zbMATHCrossRefGoogle Scholar
  25. 25.
    Mikhed, V., Zemčík, P.: Do house prices reflect fundamentals? Aggregate and panel data evidence. J. Hous. Econ. 18(2), 140–149 (2009)CrossRefGoogle Scholar
  26. 26.
    Pesaran, M.H., Shin, Y.: Generalized impulse response analysis in linear multivariate models. Econ. Lett. 58, 17–29 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Roche, M.J.: The rise in house prices in Dublin: bubble, fad or just fundamentals. Econ. Modell. 18(2), 281–295 (2001)CrossRefGoogle Scholar
  28. 28.
    Roehner, B.M.: Spatial analysis of real estate price bubbles: Paris, 1984–1993. Reg. Sci. Urban Econ. 29, 73–88 (1999)CrossRefGoogle Scholar
  29. 29.
    Sornette, D.: Discrete-scale invariance and complex dimensions. Phys. Rep. 297(5), 239–270 (1998)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Sornette, D.: Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press, Princeton (2009)zbMATHCrossRefGoogle Scholar
  31. 31.
    Takamura, Y., Tone, K.: A comparative site evaluation study for relocating Japanese government agencies out of Tokyo. Soc. Econ. Plann. Sci. 37(2), 85–102 (2003)CrossRefGoogle Scholar
  32. 32.
    Wang, P.: Market efficiency and rationality in property investment. J. Real Estate Finance Econ. 21(2), 185–201 (2000)CrossRefGoogle Scholar
  33. 33.
    Yoon, J.: Structural changes in South Korea’s rental housing market: the rise and fall of the Jeonse system. J. Comp. Asian Dev. 2(1), 151–168 (2003)CrossRefGoogle Scholar
  34. 34.
    Zhou, W.X., Sornette, D.: Is there a real estate bubble in the US? Phys. A Stat. Mech. Appl. 361(1), 297–308 (2006)CrossRefGoogle Scholar
  35. 35.
    Zhou, W.X., Sornette, D.: 2000–2003 real estate bubble in the UK but not in the USA. Phys. A Stat. Mech. Appl. 329(1), 249–263 (2003)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.KAISTDaejeonRepublic of Korea
  2. 2.Pepperdine UniversityMalibuUSA
  3. 3.Chonnam National UniversityGwangjuRepublic of Korea

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