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Realisation of mortgage property: Survival analysis

Author

Listed:
  • Rumyantseva, Ekaterina

    (Agency for Housing Mortgage Lending, Moscow, Russian Federation)

  • Furmanov, Kirill

    (National Research University Higher School of Economics, Moscow, Russian Federation)

Abstract

Time to realisation of mortgage property is studied using data from Russian mortgage agency. Factors indicating high risk of non-realisation are revealed. Obtained estimates indicate that time to realisation is determined mainly by a loan-to-value ratio, a type of mortgage property and its location in economically developed region.

Suggested Citation

  • Rumyantseva, Ekaterina & Furmanov, Kirill, 2017. "Realisation of mortgage property: Survival analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 22-43.
    • Handle: RePEc:ris:apltrx:0329
    as

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    References listed on IDEAS

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    1. Edward Altman & Andrea Resti & Andrea Sironi, 2004. "Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 33(2), pages 183-208, July.
    2. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    3. A Matuszyk & C Mues & L C Thomas, 2010. "Modelling LGD for unsecured personal loans: decision tree approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 393-398, March.
    4. Rumyantseva, Ekaterina & Furmanov, Kirill, 2016. "Modeling mortgage survival," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 41, pages 123-143.
    5. Schmidt, Peter & Witte, Ann Dryden, 1989. "Predicting criminal recidivism using 'split population' survival time models," Journal of Econometrics, Elsevier, vol. 40(1), pages 141-159, January.
    6. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2012(1), pages 6-27.
    7. Greg M. Gupton, 2005. "Advancing Loss Given Default Prediction Models: How the Quiet Have Quickened," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 34(2), pages 185-230, July.
    8. Lozinskaya, Agatha & Ozhegov, Evgeniy, 2014. "Estimation of mortgage lending credit risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 3-17.
    9. Карминский Александр Маркович & Лозинская Агата Максимовна & Ожегов Евгений Максимович, 2016. "Методы Оценки Потерь Кредитора При Ипотечном Жилищном Кредитовании," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(1), pages 9-51.
    10. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
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    More about this item

    Keywords

    mortgage; property realisation; survival models;
    All these keywords.

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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