IDEAS home Printed from https://ideas.repec.org/a/eee/joecas/v8y2011i2p91-109.html
   My bibliography  Save this article

The Role of House Flippers in a Boom and Bust Real Estate Market

Author

Listed:
  • Lee, Jin Man
  • Choi, Jin Wook

Abstract

The single-family house transactions data of the Chicago Metropolitan Statistical Area during the 1995–2010 period revealed that the peak flipper participation in the housing market occurred between 2004 and 2006 and they realized a higher return than long-term house holders, especially between 2000 and 2006 when the housing market boomed. However, flippers had higher risk than long-term holders. The estimation results of the multilevel mixed regression model showed that when more flippers entered the housing market, they created a positive upward movement in home price. The multivariate adaptive regression splines (MARS) model revealed a nonlinear relationship between housing prices and the fixed and variable effects of flipper participation in the housing market. Multiple knots indicated that flippers impacted the market differently as the frequency and magnitude of flipper participation in the housing market changed.

Suggested Citation

  • Lee, Jin Man & Choi, Jin Wook, 2011. "The Role of House Flippers in a Boom and Bust Real Estate Market," The Journal of Economic Asymmetries, Elsevier, vol. 8(2), pages 91-109.
  • Handle: RePEc:eee:joecas:v:8:y:2011:i:2:p:91-109
    DOI: 10.1016/j.jeca.2011.02.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1703494915302231
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jeca.2011.02.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Clark, Steven P. & Coggin, T. Daniel, 2011. "Was there a U.S. house price bubble? An econometric analysis using national and regional panel data," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 189-200, May.
    2. Gordon W. Crawford & Michael C. Fratantoni, 2003. "Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(2), pages 223-243, June.
    3. Coleman IV, Major & LaCour-Little, Michael & Vandell, Kerry D., 2008. "Subprime lending and the housing bubble: Tail wags dog?," Journal of Housing Economics, Elsevier, vol. 17(4), pages 272-290, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Al Refai, Hisham & Eissa, Mohamad Abdelaziz & Zeitun, Rami, 2021. "The dynamics of the relationship between real estate and stock markets in an energy-based economy: The case of Qatar," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    2. Duran, Hasan Engin & Özdoğan, Hilal, 2020. "Asymmetries across regional housing markets in Turkey," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    3. Choi, Jin Wook, 2013. "The 2007–2010 U.S. financial crisis: Its origins, progressions, and solutions," The Journal of Economic Asymmetries, Elsevier, vol. 10(2), pages 65-77.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.
    2. MeiChi Huang, 2019. "Risk diversification gains from metropolitan housing assets," Review of Financial Economics, John Wiley & Sons, vol. 37(4), pages 453-481, October.
    3. Huang, MeiChi, 2018. "Time-varying diversification strategies: The roles of state-level housing assets in optimal portfolios," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 145-172.
    4. Nichols, Joseph B. & Oliner, Stephen D. & Mulhall, Michael R., 2013. "Swings in commercial and residential land prices in the United States," Journal of Urban Economics, Elsevier, vol. 73(1), pages 57-76.
    5. William Miles, 2011. "Long-Range Dependence in U.S. Home Price Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 329-347, April.
    6. Gary Wai Chung Wong & Lok Sang Ho, 2017. "Policy-Driven Housing Cycle: The Hong Kong Case of Supply Intervention," International Real Estate Review, Global Social Science Institute, vol. 20(3), pages 375-396.
    7. Wen-Chi LIU, 2016. "Do Multiple Housing Bubbles Exist in China? Further Evidence from Generalized Sup ADF Tests," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 135-145, December.
    8. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    9. Susan M. Wachter, 2016. "Credit Supply and Housing Prices in National and Local Markets," Public Finance Review, , vol. 44(1), pages 6-21, January.
    10. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    11. Chen, Mei-Ping & Lin, Yu-Hui & Tseng, Chun-Yao & Chen, Wen-Yi, 2015. "Bubbles in health care: Evidence from the U.S., U.K., and German stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 193-205.
    12. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    13. Rose Neng Lai & Robert Van Order, 2019. "Shadow Banking and the Property Market in China," International Real Estate Review, Asian Real Estate Society, vol. 22(3), pages 361-399.
    14. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
    15. Konstantin Kholodilin, 2015. "Speculative Bubbles in Urban Housing Markets in Germany," ERSA conference papers ersa15p67, European Regional Science Association.
    16. Kuang-Liang Chang & Charles Ka Yui Leung, 2022. "How did the asset markets change after the Global Financial Crisis?," Chapters, in: Charles K.Y. Leung (ed.), Handbook of Real Estate and Macroeconomics, chapter 12, pages 312-336, Edward Elgar Publishing.
    17. Brzezicka Justyna & Wisniewski Radosław, 2014. "Price Bubble In The Real Estate Market - Behavioral Aspects," Real Estate Management and Valuation, Sciendo, vol. 22(1), pages 77-90, March.
    18. Schindler, Felix, 2009. "Volatilitätseffekte am US-amerikanischen Häusermarkt," ZEW Discussion Papers 09-048, ZEW - Leibniz Centre for European Economic Research.
    19. Daniele Bianchi & Massimo Guidolin, 2014. "Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios," The Journal of Real Estate Finance and Economics, Springer, vol. 49(1), pages 116-164, July.
    20. Khandani, Amir E. & Lo, Andrew W. & Merton, Robert C., 2013. "Systemic risk and the refinancing ratchet effect," Journal of Financial Economics, Elsevier, vol. 108(1), pages 29-45.

    More about this item

    Keywords

    House flippers; Real estates; Asymmetric information; Boom and bust; House price; MARS;
    All these keywords.

    JEL classification:

    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:joecas:v:8:y:2011:i:2:p:91-109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/the-journal-of-economic-asymmetries/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.