Risk and Equity Release Mortgages in the UK

Tripti Sharma,Donal Mckillop,Declan French

doi:10.1007/s11146-020-09793-2

Tripti Sharma, Donal Mckillop + Show 1 more

Open Access

https://doi.org/10.1007/s11146-020-09793-2

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Abstract

Accessing elderly housing wealth through equity release mortgages (ERMs) continue to be the focus of policy debates about how to pay for social care and how to support retirement incomes in the UK. We demonstrate in this paper that the spatial concentration of this market in just a few regions is not due to demand but to the risks faced by suppliers. We show that by ignoring regional variations in No Negative Equity Guarantee risk in national pricing models providers cannot profitably supply these products outside areas of high house price growth. We also show that EU Solvency II capital requirements provide a further disincentive to supply ERMs in these areas. Government subsidies to product provision are also modelled and shown to be infeasibly high. We therefore conclude that the government policy focus on equity release as a means of tackling the challenges of an ageing population is misplaced.

Highlights

Many older people in the UK have experienced significant house price growth and are increasingly been encouraged by government to see their home as a means to support retirement incomes and to pay for social care
The loan amount accumulates at a rate of 1.47% per quarter based on current rates charged to customers in the UK equity release market (ERC 2018)
We model the probability of negative equity, the No Negative Equity Guarantee (NNEG) value and the cost to the consumer in the form of a higher mortgage insurance premium

Summary

Introduction

Many older people in the UK have experienced significant house price growth and are increasingly been encouraged by government to see their home as a means to support retirement incomes and to pay for social care. The random variables in this model are – realised house value in a region upon contract termination, the time to maturity and the movement of the risk-free rate of return. The factor-augmented vector autoregressive model reduces a large set of macroeconomic time series to a small number of common components which are used to forecast real house price growth rates (Das et al 2010). To forecast regional house price inflation by fitting an FAVAR model, we first need to extract the common factors from the time series of macroeconomic variables in Appendix 1 through principal component analysis. The average number of years to contract termination based on stochastic mortality projections was found to be 22.5 years

Empirical Results

E Midlands

Conclusion