Practical Considerations in Evaluating a Long-term Care Securitisation
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1 Practical Considerations in Evaluating a Long-term Care Securitisation 1 DOUG ANDREWS, UNIVERSITY OF WATERLOO & JAIDEEP OBEROI, UNIVERSITY OF KENT
2 Items to be Discussed Some background on home equity release products Structure contemplated for this securitisation Data and methodology How our approach differs Practical problems and solutions Inconsistencies in housing data Determination of suitable LTC incidence rates Applying housing data to longer time periods Further research 2
3 Background on HER Products Would satisfy a real need Actual take-up far less than expected Academic analysis suggests that products poorly priced (from a consumer perspective) Non Negative Equity Guarantee (NNEG) over-valued Equity release for LTC is a specialised version of the product demand should increase as the population ages 3
4 Role of PPP in Arranging HER Loans 4 Homeowner seeking equity release PPPFI screens lenders on behalf of homeowner, determines maximum loan value, calculates swap terms, and provides NNEG Banks Insurers Other Lenders
5 Data and Methodology 5 For post codes CT1 & CT2 (Canterbury) and ME8 (Medway) all in Kent Land Registry data from January 1, 1995 December 31, 2011: 30,724 transactions Only repeat sales data retained: 18,747 transactions For mortality & morbidity used Gompertz model parameterised by Ji, Hardy & Li 2011 Start with couple both aged 65 with 1 healthy spouse (X) and 1 spouse requiring care (Y) no possibility of recovery
6 Possible Transitions 6 1. No change in state of X and Y 2. X healthy, Y deceased no prepayments 3. X requires care, Y requires care 4. X deceased, Y requires care 5. X requires care, Y deceased 6. X deceased, Y deceased In states 3 6 home sold
7 From State 2 - Further Transitions 7 7. X healthy 8. X requires care 9. X deceased In states 8 & 9 home sold
8 How Our Approach Differs Loan and NNEG unbundled Pricing of NNEG is securitised Loans offered on a variable rate basis Analysis based on actual data not a model Land Registry data used not Nationwide Index Calnea Analytics (2007) suggest Land Registry Index is preferable due to manner of construction 8
9 Inconsistencies in Housing Data Some transactions are repeated with separate IDs Some homes change in type Repeat transaction data more voluminous during the middle of the period Some transactions recorded very close to each other Some extreme returns we winsorised both tails Transaction data has exact dates and HPI is monthly we used linear interpolation on HPI values 9
10 Did Not Adjust For Under Representation HPI Homes with Repeat Sales Total Sales Jan-95 Jun-95 Nov-95 Apr-96 Sep-96 Feb-97 Jul-97 Dec-97 May-98 Oct-98 Mar-99 Aug-99 Jan-00 Jun-00 Nov-00 Apr-01 Sep-01 Feb-02 Jul-02 Dec-02 May-03 Oct-03 Mar-04 Aug-04 Jan-05 Jun-05 Nov-05 Apr-06 Sep-06 Feb-07 Jul-07 Dec-07 May-08 Oct-08 Mar-09 Aug-09 Jan-10 Jun-10 Nov-10 Apr-11 Sep-11 0
11 HPI: Upward Trend but High Volatility 11 3% 2% 1% 0% -1% -2% -3% Feb-95 Aug-95 Feb-96 Aug-96 Feb-97 Aug-97 Feb-98 Aug-98 Feb-99 Aug-99 Feb-00 Aug-00 Feb-01 Aug-01 Feb-02 Aug-02 Feb-03 Aug-03 Feb-04 Aug-04 Feb-05 Aug-05 Feb-06 Aug-06 Feb-07 Aug-07 Feb-08 Aug-08 Feb-09 Aug-09 Feb-10 Aug-10 Feb-11 Aug-11 Feb-12 Aug-12
12 Autocorrelation: A Challenge to Model Lag Order
13 Return Differences Winsorised but not De-Meaned 13 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% -22% -19% -16% -13% -10% -7% -4% -1% 2% 5% 8% 11% 14% 17% More Return Difference Bin
14 Determination of LTC Incidence Rates Desirable to have incidence rates for entry into care and duration in care, for England Unavailable NLTCS very detailed data regarding participants and care requirements Insufficient to construct incidence and exit rates Used report on private insurer data in US based on unlimited benefit period to construct sex-distinct incidence and exit rates in 5 year age bands Used Canadian insurer J&S mortality experience Appropriateness? 14
15 Applying Data to Longer Periods One million simulations 15 Annualised return differences show a duration effect with a positive skew Given propensity to age in place, it is realistic to incorporate the duration effect Limitation: our data only covers a 17 year period We winsorized the annualised return data at 15 years
16 Duration Effect 16 20% 15% 10% 5% 0% -5% -10% -15% -20% -25% -30% Years
17 Further Research 17 Urgent need to gather data by country that can be used to price HER products government action likely required to facilitate this process Consider feasibility of PPP structure and alternatives to provide NNEG and efficient underwriting and administration Regarding the variability between individual prices and the index and autocorrelation Effect of aging in place on property values Duration effect We have a paper showing premiums calculated
Home equity release for long term care financing: an improved market structure and pricing approach. Abstract
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