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When is mortgage indebtedness a financial burden to British households? A dynamic probit approach

  • Orla May
  • Merxe Tudela

Since the mid-1990s the volume of secured lending to households has expanded rapidly, both in absolute terms and in relation to household incomes. This paper examines the determinants of households' ability to service this stock of secured debt. It estimates a random effects probit model for the probability of households having mortgage payment problems. It is found that past experience of payment problems increases the probability that the household has difficulties servicing its secured debt today. At the household level, becoming unemployed, interest income gearing of 20% and above, high loan to value ratios and having a heavy burden of unsecured debt are all associated with a significantly higher probability of mortgage payment problems. Saving regularly and having unsecured debt which is not a problem are both associated with a significantly lower probability of mortgage payment problems. The only non-household-specific variable to have a significant effect is mortgage interest rates - the probability of payment problems increases with the level of mortgage interest rates. An aggregate measure of debt at risk is calculated. This has decreased between 1994 and 2002, as falls in the probability of mortgage payment problems have more than offset increases in the stock of mortgage debt outstanding. It is found that the fall in the probability of mortgage payment problems has been greatest among the most highly indebted households.

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Paper provided by Bank of England in its series Bank of England working papers with number 277.

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Date of creation: Oct 2005
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Handle: RePEc:boe:boeewp:277
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  1. Victoria Redwood & Merxe Tudela, 2004. "From tiny samples do mighty populations grow? Using the British Household Panel Survey to analyse the household sector balance sheet," Bank of England working papers 239, Bank of England.
  2. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568 Elsevier.
  3. Ana Lasaosa, 2007. "Learning the Rules of the New Game? Comparing the Reactions in Financial Markets to Announcements before and after the Bank of England's Operational Independence," Ekonomia, Cyprus Economic Society and University of Cyprus, vol. 10(1), pages 18-41, Summer.
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  6. Boheim, Rene & Taylor, Mark P., 2000. "My Home Was My Castle: Evictions and Repossessions in Britain," Journal of Housing Economics, Elsevier, vol. 9(4), pages 287-319, December.
  7. Reinhart, Carmen & Rogoff, Kenneth & Savastano, Miguel, 2003. "Debt intolerance," MPRA Paper 13932, University Library of Munich, Germany.
  8. Andrew Henley, 2004. "Self-Employment Status: The Role of State Dependence and Initial Circumstances," Small Business Economics, Springer, vol. 22(1), pages 67-82, 02.
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  10. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  11. Arulampalam, Wiji & Booth, Alison L & Taylor, Mark P, 2000. "Unemployment Persistence," Oxford Economic Papers, Oxford University Press, vol. 52(1), pages 24-50, January.
  12. Brookes, Martin & Dicks, Mike & Pradhan, Mahmood, 1994. "An empirical model of mortgage arrears and repossessions," Economic Modelling, Elsevier, vol. 11(2), pages 134-144, April.
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