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Buy-to-let sector resilient to interest rate rises?

Joe Macklin, senior manager, risk & analytics at MIAC Acadametrics, believes lenders should analyse their buy-to-let portfolios in order to keep on top of possible defaulting loans when the interest rate environment changes

The buy-to-let (BTL) sector has demonstrated material growth as a sub-market of the mortgageindustry in the UK. For potential homeowners, buying homes has become a serious challenge from an affordability perspective, compounded by the launch of the Mortgage Market Review. As house price inflation continues to outstrip wage inflation, a significant proportion of lending has been to BTL investors seeking to take advantage of the buoyant private rental sector.

As a result of this growth there is extra focus on the importance of robust stress testing methods. Many lenders now have increased exposure to the product and are thus more reliant on the resilience of the BTL market.

Interest rate rises are an important risk for this sector, as any rise would put the squeeze on both the tenant’s and BTL borrower’s affordability (debt service costs). There is, however, extra insulation against the risk of mortgage default compared to owner-occupied lending – due to the rent being the primary source of paying the mortgage and the borrower’s resources as a backup. The insulating effect of this “double covenant” is partly substantiated by recent arrears statistics.

However, it’s critical to understand the specific risk drivers and behaviours within the sector in order to model BTL portfolios for future defaults. This enables the measurement of the resilience of these assets to perform within risk appetite under stress under stressed macroeconomic conditions.

Market insight

The proportion of the first charge mortgage market taken up by BTL versus owner-occupied has been growing consistently since 2006. These statistics, compiled by the Council of Mortgage Lenders, make up over 90 per cent of the mortgage market, so they are a strong proxy for the market as a whole. In June 2006, BTL lending took up only 6 per cent of the first charge mortgages and now that figure has risen to 14.4 per cent as at Q3 2014 (navy line on Graph 1, left).

The ratio of arrears numbers allocated between owner-occupied and BTL from the same dataset is superimposed on the same chart (orange line). This clearly demonstrates an inverse relationship with the overall BTL growth since the economic crash – i.e. as BTL has become a higher proportion of the lending, the arrears proportion has reduced. Whilst this has an element of arithmetic influence and there is a lag effect from higher volumes of new lending (and the time it takes for arrears to emerge) there are undoubtedly wider underlying explanations. The relatively high arrears at the start of the time series can be attributed to far weaker lending criteria, as the lending boom peaked pre-crisis when higher loan-to-values (LTVs) and self-certified BTL options were available. Since the crash, loan-to-value criteria and borrower credit quality has been materially tightened and this is reflected in current arrears trends.

With many potential homeowners unable to afford to buy, market forces dictate that demand will be high in the rental market. Many investors seeking returns on their available wealth have been understandably choosing BTL as a route for their investment with the returns in other options low due to the interest rate environment.

In terms of underlying price trends within the wider rental market, our analysis demonstrates that London and the South East in particular have seen the highest rental inflation. Much like the house purchase and general economic trends, there is a divide between North and South here (see Graph 2, below).

 

Stress testing

Portfolio – To produce some meaningful analysis on the subject at hand it was important to process a relatively typical BTL portfolio through a stress test scenario that focused on a material interest rate rise.

The portfolio tested is £0.6 billion of lending (made up of around 3,900 individual loans) originated between 2006 to 2014 with the majority advanced before or just after the credit crash. The average original LTV is 73 per cent and the average current LTV is 64 per cent. The percentage of accounts in default, defined as six months in arrears, is at 0.51 per cent as at the projection point of our analysis. The mortgages that are more than three months in arrears as at August 2014 is 0.75 per cent which is near to the industry arrears rate detailed in CML statistics.

Scenario – In order to try to isolate how sensitive default rates are to interest rates within the BTL market we have designed a macroeconomic scenario that performs broadly to expectation on all other measures with a constantly rising Bank of England base rate, from its current state of 0.5 per cent up to 5 per cent by 2019, going up in quarterly increments of 0.25 per cent.

In reality, there are macroeconomic indicators that will need to be adjusted in order for the Monetary Policy Committee to raise interest rates – with additional focus being placed on wage inflation in relation to price inflation.

Modelling focus – In our experience it is still relatively commonplace for BTL mortgages to be modelled in the same way as owner-occupied residential mortgages with respect to default and loss.

At the same time, it is widely acknowledged that the credit risk drivers are different between these two product types.

This article seeks to explain how to overcome some of the main challenges in BTL mortgage modelling and discuss how the sector may perform from a default perspective under a rising interest rate environment. The “doubl” default risk insulation referred to in the introduction is the fact that many BTL loans are underwritten on the basis of rental income covering the monthly payment, with the borrower’s own financial circumstances acting as a backstop to any tenancy voids or arrears.

As with all product types, there is a spectrum of different lending criteria in the marketplace around things like ‘self-funding’ BTL, and lenders will always try to differentiate in order to gain what they see as a competitive advantage. This could be in terms of getting the highest quality portfolio or, alternatively, in tinkering with the traditional criteria in order to obtain a higher margin.

The key unknowns when modelling future portfolio performance are the value of the underlying collateral, the anticipated rental income, the default drivers at the loan level and the influence the macro economy has on default.

Collateral – When forecasting it is vital that the starting point for the collateral value is accurate, as differing house price paths will be applied against that valuation.

MIAC Acadametrics’ Collateral Revaluation tool updates property values based on their property type and geographical location. The geographical layer drills down to a granular level – county or local authority across England, Wales and Scotland, and London boroughs within the capital.

Rental – An important element of modelling a back book of BTL loans is understanding the likely rental income the borrower is receiving in today’s market and how that might change as the economic environment changes. This becomes more important as the loan becomes more seasoned, as any rental information obtained from origination loses its value.

In the case study presented here rental income has been estimated using the MIAC Acadametrics Rental AVM product. This utilises a database of comparables to optimise a rental valuation based on the postcode, number of beds and property type of the collateral.

Macro modelling – In order to understand how rental values might change under differing scenarios a model was built to understand the correlation between rental income over various regions and wider economic indicators. A statistically significant relationship was evident using change in price inflation and house price inflation to predict the changes in rents. This enables us to forecast changes in future rental incomes and understand how rental coverage ratios change at the loan level in the projections.

The macro default model inputs are house prices, unemployment, consumer price inflation, GDP and Bank base rate. When changes in these variables are compared to BTL arrears trends, this creates a default risk credit cycle which is used to predict the systematic component of future default rates, i.e. the amount of default that is attributable to the state of the economy.

Default modelling – Building PD (probability of default) models for the BTL sector usually results in some typical loan characteristics that are correlated to default. In addition, ensuring that some of those characteristics are dynamic as you forecast forward is vital in the PD being reflective of the changing market dynamics.

In many respects the macro model component of the framework covers the changing environment but with BTL, where the rental income is a vital influence on the borrower maintaining their obligations, it is sensible to reflect the idiosyncratic risks that are unique to each loan and borrower. This can be done by including the changing rental coverage ratio as a characteristic within the PD model. Thus, in simple terms, as the rental coverage is eroded by interest rate rises (because there is no commensurate rise in rental income) the risk of default rises.

This component of the model framework seeks to forecast the elements of future default rates that can be explained by idiosyncratic factors, i.e. the amount of default that is attributable to the unique borrower and loan characteristics.

Results

Graph 3 (above) charts the expected path of default rates over time (orange line) using the sample portfolio discussed. The distribution around that expectation is signified by the shades of blue. This distribution charts the different probabilities of a diversion from expectation. This can be interpreted as model error – or another interpretation is that forecasting is not an exact science and thus it is informative to understand the likelihood of outcomes other than our modelled expectation. As the distribution demonstrates, there is more tail risk above expectation than below.

This analysis has focused on the influence interest rate rises will have on default rates within the BTL mortgage sector. The level of actual crystallised credit loss those defaults will generate will be highly dependent on the portfolio, and the management of that portfolio, but the almost universal LTV ceiling of 75 per cent certainly adds a layer of loss insulation and should keep loss provisions down. However, with a material portion of the BTL collateral being on the lower end of the market, where collateral values are below average prices, the forced sale discounts are often high, anecdotally between 35-45 per cent. Whereas, on properties that are nearer the average property, and thus in most demand, the forced sale discounts tend to average nearer 30 per cent.

Conclusion

As with all future defaults they are heavily dependent on the economic environment and the credit quality of the portfolio. However, dig a little deeper into these areas in the BTL context and it is difficult to conclude that the sector is more exposed to default risk than other mortgage sub sectors. The buoyant rental market coupled with borrower’s resources as a backup means there is built-in default resilience.

Whilst lending criteria stays as prudent as it is today, and challenges remain with getting on the bottom rung of the housing ladder, the BTL market will continue to prosper. Conversely, if the market is opened up around the edges in terms of credit quality and LTV once more then the sector will become more exposed to default and losses as historical trends suggest.

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