The following article was first published in the Mortgage Finance Gazette in January 2015 and has been brought up to date for MIAC Perspectives.

Introduction

The Buy-To-Let (BTL) sector has demonstrated material growth as a sub-market of the mortgage industry in the UK.  For potential home owners, buying homes has become a serious challenge from an affordability perspective, contributed to by Mortgage Market Review rules. With house price inflation continuing to outstrip wage inflation a significant proportion of lending activity 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 for purpose. Many lenders have increased exposure to the product and, therefore, a reliance on the resilience of the BTL market generally.

Interest rate rises are a specific risk to this sector as it will put the squeeze on tenant’s affordability (debt service costs) as well as the borrower’s.  The argument that there is extra insulation against mortgage default risk when 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 – is partly substantiated by recent arrears statistics.

However, the criticality of understanding the specific risk drivers and behaviours within the sector are essential in modelling BTL portfolios for future defaults. This enables measurement of the resilience of these assets to perform within risk appetite 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, represented in Figure 1 and compiled by the CML, are a strong proxy for the market as a whole. At last count these figures make up over 90% of the mortgage market. In June 2006 BTL lending took up only 6% of first charge mortgages and now that figure has risen to 14.4% as at Q3 2014 (green line).

Figure 1: UK Residential Lending & Arrears: BTL versus Owner Occupied           Source: Council of Mortgage Lenders (CML)

The ratio of arrears numbers allocated between owner occupied and BTL from the same dataset is superimposed in Figure 1 (blue line). This clearly demonstrates an inverse relationship with the overall BTL growth since the economic crash. As BTL has become a higher proportion of the lending, the arrears proportion has reduced. Whilst this has an element of arithmetic influence (rising denominator), and lag effect from higher volumes of new lending (including 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 attributable to weaker lending criteria. During the pre-crisis lending boom many higher LTV products and self cert BTL options were available. Since the crash LTV criteria and borrower credit quality has been materially tightened and this is reflected in current arrears trends.

With many potential home owners 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. (Refer to Figure 2).

UK Rental Market Trends

Figure 2: UK Rental Market Trends      Source: Office for National Statistics (ONS)

Stress Test

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 with specific focus on a material interest rate rise.

The portfolio tested is £0.6bn of loans (c.3,900) originated between 2006 to 2014.  The average original LTV is 73% and the average current LTV is 64%.  The percentage of accounts in default, defined as 3 months in arrears to align with CML stats, is at 0.75% as at the projection point of our analysis. This is a strong proxy for the industry as it is near to the industry arrears rate detailed in CML statistics (0.78%).

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 but with a constantly rising Bank of England Base Rate from its current state of 0.5% up to 5% by 2019, rising in quarterly increments of 0.25%.

In reality, there are macroeconomic indicators that will need to adjust in order for the MPC 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 that BTL mortgages are modelled in the same way as owner occupied residential mortgages in respect of 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 ‘double’ 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 borrowers 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 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 Borough 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 under today’s market and how that might change as the economic environment changes.  This becomes more important the more seasoned the loan becomes 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.

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 the evolution of  rental incomes and understand how rental coverage ratios change at the loan level in the projections.

Macro modelling

The macro default model inputs are House prices, Unemployment, CPI, 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 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. It then follows that, as the rental coverage is eroded by interest rate rises, because there is no commensurate rise in rental income, the risk of default rises.

Results

As illustrated in Figure 3, the expected path of default rates over time (blue 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.

Figure 3: MIAC Modelled Distribution of Projected Default Rates

Figure 3:   MIAC Modelled Distribution of Projected Default Rates        Source:     MIAC | Acadametrics

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% 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%.  Whereas, on properties that are nearer the average property, and thus in most demand, the forced sale discounts tend to average nearer 30%.

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 the borrower’s resources as a backup mean that 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.