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Forecasting mortgage defaults: evidence from UK portfolio–level data

Forecasting mortgage defaults: evidence from UK portfolio–level data This paper considers an equilibrium/error correction modelling (ECM) approach to identify determinants of mortgage portfolio default rates at firm level. Using mortgage portfolio data from a UK lender we estimated the lender's portfolio concentration weights in different UK regions to estimate portfolio–adjusted unemployment rate and portfolio–adjusted house price index. The modelling results suggest that the portfolio–adjusted unemployment rate and house price index along with Council of Mortgage Lenders' (CML) default rates, interest rates and household savings are useful determinants of firm–level default rates within an ECM framework. These findings may be of practical use for forecasting or back–populating default rates in financial institutions and especially for the smaller lenders where a long time–series with default rates may not be available. However, modelling and forecasting at portfolio level should be made carefully, as we argue that data quality and changes in the lending policy should be carefully monitored. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

Forecasting mortgage defaults: evidence from UK portfolio–level data

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References (13)

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2015.068677
Publisher site
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Abstract

This paper considers an equilibrium/error correction modelling (ECM) approach to identify determinants of mortgage portfolio default rates at firm level. Using mortgage portfolio data from a UK lender we estimated the lender's portfolio concentration weights in different UK regions to estimate portfolio–adjusted unemployment rate and portfolio–adjusted house price index. The modelling results suggest that the portfolio–adjusted unemployment rate and house price index along with Council of Mortgage Lenders' (CML) default rates, interest rates and household savings are useful determinants of firm–level default rates within an ECM framework. These findings may be of practical use for forecasting or back–populating default rates in financial institutions and especially for the smaller lenders where a long time–series with default rates may not be available. However, modelling and forecasting at portfolio level should be made carefully, as we argue that data quality and changes in the lending policy should be carefully monitored.

Journal

International Journal of Computational Economics and Econometrics – Inderscience Publishers

Published: Jan 1, 2015

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