A Regional Analysis of Mortgage Possessions

8/2/2019 A Regional Analysis of Mortgage Possessions

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A REGIONAL ANALYSIS OF MORTGAGE POSSESSIONS: CAUSES, TRENDS

AND FUTURE PROSPECTS1

By John Muellbauer, Professor of Economics and Official Fellow and Gavin Cameron, Research

Officer, at Nuffield College, Oxford

Executive Summary

This paper analyses the variation in court possession actions and orders over regions and of

total UK possessions rates, in order to understand better the causes of the high rates of

mortgage possessions in the 1990s. It untangles the web of economic forces and

administrative responses, and forms a view about future prospects.

Data on court orders given actions and on suspended court orders provide evidence of a shift

in behaviour by the Courts. Data on court actions brought by lenders provide evidence of a

shift in behaviour by mortgage lenders. UK data on possessions rates reflect both shifts.

Evidence suggests that there was a temporary softening of policy in the County Courts

beginning in 1991 but that Court policy had returned to normal by 1995.

Shifts in policy by lenders are probably a mix of intended changes in behaviour and initial

delays in setting up systems to deal with possessions. The evidence suggests that this policy

softening effect is gradually decaying.

The outlook for the rate of possessions depends partly on how rapidly this effect decays, on

the hard-to-quantify effects of the tightening of DSS rules from October 1995 for claimants,

as well as on the macroeconomic fundamentals.

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The analysis suggests that the falls in the rate of possessions seen at the end of 1996 will be

followed by significant further falls in 1997-98 provided a significant rise in interest rates can

be avoided.

1. INTRODUCTION

The years 1990 to 1995 saw a record number of households, around 345,000, containing perhaps

one million individuals2 suffering the misfortune of mortgage possession. An important question

concerns the institutional response to this crisis: was there a shift in policy by the courts and by

the mortgage lenders? The purpose of this paper is to analyse the variation over regions, in order

to understand better the causes of the high rates of mortgage possessions in the 1990s, untangling

the web of economic forces and administrative responses, and to form a view about future

prospects. This paper argues that there was a temporary softening of policy in the County Courts

beginning in 1991 but that Court policy had returned to normal by 1995. There is evidence,

however, that mortgage lenders are still a little softer'' than they were before 1991.

We examine four sources of empirical evidence. First, aggregate UK data on the relationship

between rates of mortgage arrears and possessions indicate a fall in possessions beginning in

1991 while arrears continued to rise for two more years. This suggests a shift in policy or

procedures by the courts and/or mortgage lenders. Second, regional data from County Courts for

England and Wales show a rise in the ratio of suspended court orders beginning in 1991 and

suggesting a softening of policy. Thirdly, the ratio of court orders to court actions was smaller in

1991-94 than would have been expected given the economic fundamentals. These last two sets of

evidence point to a shift in behaviour by courts. Fourthly, the rate of court actions was lower in

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1992-96 than would have been expected given the economic fundamentals, pointing to a shift in

behaviour by mortgage lenders.

By pooling time series/cross section data across regions, the methodology is to identify national

policy shifts by courts and by lenders in terms of common time effects across regions,

distinguishable from economic fundamentals varying by region and by time. The importance of

these time effects are confirmed in aggregate time series econometric models for the UK rate of

mortgage possession. The paper concludes by considering prospects for the rate of court action

for mortgage possession. The paper also throws interesting light on regional variations in rates of

court actions and orders. For example, it destroys the myth that the possessions "crisis" was

largely a Southern phenomenon. Fuller details are set out in the technical appendices to Sections

4, 5 and 7 and in a data appendix.

2. COUNTY COURT DATA AND THE HISTORICAL BACKGROUND

(a) The Relevance of County Court Data

In most cases, mortgage possession involves court proceedings. Ford et al (1995) report that even

in cases where households voluntarily handed the keys of their property to their mortgage lender,

evidence of court proceedings was often a requirement to be eligible for rehousing in the social

rented sector. Thus, data on court proceedings are of more general interest for understanding the

phenomenon of mortgage possession. Data on County Court actions and orders for mortgage

possession have been published for 1986 to the present and on a quarterly basis since 1987. They

are the only source of regularly available regional data to map possessions.

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To be more specific about the nature of the data, the Lord Chancellor's Department defines terms

as follows.

Actions Entered: A plaintiff begins an action for an order for possession of residential property

by way of a summons in a county court.

Orders Made: The court, following a judicial hearing, may grant an order for possession

immediately. This entitles the plaintiff to apply for a warrant to have the defendant evicted.

However, even where a warrant for possession is issued, the parties can still negotiate a

compromise to prevent eviction.

Suspended Orders: Frequently, the court grants the mortgage lender possession but suspends

the operation of the order. Provided the defendant complies with the terms of the suspension,

which usually requires the defendant to pay the current mortgage instalments plus some of the

accrued arrears, the possession order cannot be enforced.

(b) Historical Background

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Between 1980 and 1990 average mortgage debt in the UK more than doubled relative to income.

The house price boom of the mid to late 1980s3 came to an end amid sharp rises in interest rates

in 1988-1990, the response of the policy makers to the deterioration in the UK's balance of

payments and inflation. House prices fell in nominal terms for the first time since the late 1950s,

particularly in the Southern regions of the UK. This left many households, particularly those who

had bought near the top of the boom, with negative equity. The combination of negative equity,

and the drain on cash flows from the high interest rates of the 1988-1992 period and from income

loss or business failure, was a particularly critical mix of circumstances. When households have


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positive equity, the opportunity to trade down is available as a response to cash flow problems.

When households have negative equity, this is unavailable unless mortgage lenders and their

regulators have a particularly tolerant attitude and allow households to trade down while

retaining negative equity.4 Initially, negative equity products were not offered by most mortgage

lenders, though these did become available increasingly over time (see the interview evidence

cited by Ford et al (1995), ch 4). It appears that practices by the County Courts also altered in the

1990s, with longer repayment periods for households in payment arrears being permitted, see

Ford (1994) and Ford et al (1995), ch 5.

To appreciate the context in which the policy of mortgage lenders and of courts is likely to have

shifted, recall that in 1991 mortgage possessions were frequently headline news. Heightened

public concern was reflected in the implicit contract agreed between the Government and

mortgage lenders in November-December 1991, 6 months before the 1992 General Election, to

reduce possessions. On the Governments side this included the commitment to pay DSS

mortgage support payments direct to lenders rather than to mortgagors and to stimulate the

housing market by raising the Stamp Duty ceiling for a year and by giving ear marked grants to

housing associations to buy up properties originally intended for owner occupation, see Stephens

(1996) for fuller details. By the time of the November 1994 Budget, however, public concern

about possessions had faded sufficiently that the Government felt able to announce major

reductions in the DSS safety net for new mortgagors and some reductions for existing

mortgagors to take effect from October 1995, see Dale (1995 ) and Stephens (1996).

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The Courts are unlikely to have been immune from these shifts in public and official concern. It

can also be argued that the Courts may have placed more of the responsibility for mortgage

default in the early 1990's on lending practices in the 1980s of some mortgage lenders and on


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macro policy mismanagement by the Government. This would also have led to a softening of

court policies. The passage of time and a declining proportion of cases coming to court of

mortgages originating in the 1980's would in due course cause a return to normal practices.

It is also possible that what we interpret as a purposive temporary softening of policy may have

been partly the result of the inability of existing court facilities to cope with the flood of

possessions cases. This would imply an apparent softening of policy as the number of cases

peaked, followed by an apparent hardening as the number of cases declined again. Mortgage

lenders would have been subject to the same set of influences as defaults increased, including

lags in training staff and setting up systems for dealing with cases. Interview evidence in Ford et

al (1995) suggests this was the case.

This view contrasts with another interpretation of a pattern of high possessions rates followed by

lower rates. This emphasizes the efficiency and speed of reaction of mortgage lenders in

possessing the most disastrous first, implying a later reduction in possessions rates. The latter

would result from the change in the composition of the population at risk with the removal of

many of the highest risk cases. As we will see, there are several important pieces of evidence

against this interpretation.

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In addition, two further arguments are often put forward. The first is that an apparent softening of

policy could have been the result of the difficulty lenders had, particularly in 1991-3, in selling

houses which had been taken into possession. A related argument concerns the fact that

mortgage indemnity policies typically insured the top 25% of the value of a property. It has been

argued that mortgage lenders therefore had incentives for taking possession rapidly when losses

were small. Typically, homes in possession appear to sell at a discount below similar regular


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homes. Thus, when nominal house prices in Southern regions in 1991-3 fell to levels 10% or

more below their 1988-90 levels, the argument is that mortgage lenders would have begun to

bear a significant part of the losses and so had reduced incentives to exercise the possession

option. Note that this argument would imply a significantly greater softening of policy in the

Southern regions where the biggest nominal falls occurred. We test for such an effect later.

There is a third, less controversial aspect to this line of argument. If, as Breedon and Joyce

(1992) argue, taking homes into possession weakens house prices further, such action by one

mortgage lender imposes negative externalities on the others by bringing about further defaults.

Thus, there is scope for collective action. The implicit contract between mortgage lenders and the

Government in November-December 1991 can be seen as providing just the required impetus for

such collective action to slow possessions rates.

To understand the variations that have occurred in rates of mortgage possession, such alleged

shifts in behaviour need to be taken into account along with the influence of variations in

economic conditions, such as the debt/equity ratio (i.e., the ratio of mortgage debt to the value of

the home) and debt service ratios (i.e., the ratio of mortgage interest payments to income),

unemployment shocks, small business failure rates, and house price developments. Indeed,

econometric studies of aggregate mortgage possession data such as Breedon and Joyce (1992),

Brookes et al (1994), and Allen and Milne (1994) estimated on data up to 1990 or 1991 break

down badly on later data. These studies use mortgage arrears to help explain mortgage

possessions. But from 1991, possessions started to fall while arrears continued to rise for some

time, introducing a break in their relationship, see Chart 1. Though this is not conclusive

evidence of a structural shift in behaviour, it is very much consistent with this hypothesis.

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Incidentally, this shift in behaviour appears not to be due to distortions in the month in arrear

data, see Muellbauer (1996), the source for the estimated 10% in arrear rate shown in Chart 1.

CHART 1: RATES OF POSSESSION, 12 MONTH AND 10% IN ARREAR

Note: The rates are log possessions rate, the log 12 month in arrear rate and the fitted log 10% balance in arrear rate

3. REGIONAL CONTRASTS

Regional data offer more scope than aggregate data for detecting such shifts in behaviour. It

seems likely that shifts in behaviour by County Courts and mortgage lenders would be

approximately uniform across the country, though short-term variations could be region

specific: there is pressure on the courts to operate a national system of justice and the major

mortgage lenders operate in all the regions. The housing market experience of different

regions has been more diverse: nominal and real house prices have moved very differently in

different regions and the timing of the build-up of mortgage debt differed across regions.

Debt service ratios differ across regions with varying ratios of debt relative to income and the

differential impact of the mortgage interest tax relief ceiling. Unemployment rates and small

business failure rates have also varied substantially across regions. In these various

dimensions, a broad North-South grouping of regions is helpful in summarizing information:

within each broad group there were more shared experiences than between groups.

To give some background to the regional data from the courts for 1986-1996, note that the

Southern regions led the strong UK upswing in house prices in 1986-88. After interest rates

rose from the summer of 1988 to a peak in 1990, house prices in the Southern regions slowed

sharply and then declined in 1990-93 by 17 to 23%, even though interest rates were by then

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CHART 2B: RATE OF COURT ACTIONS, DEBT/EQUITY RATIO AND DEBT

SERVICE RATIO (NORTHERN REGIONS)

Note: The Northern regions are defined as: North, North West, Yorkshire & Humberside, West Midlands and

Wales

To compare court actions across regions it is necessary to scale by the number of outstanding

mortgages in each region. Charts 2A and 2B show basic data on court action rates,

debt/equity ratios and debt service ratios, respectively averaged across Southern and Northern

regions. These charts show that in 1986-1988 court action rates were higher in Northern than

in Southern regions but that Southern rates rose more sharply to the 1991 peak before falling

back to similar rates in 1995. These charts explode the myth that the possessions "crisis" was

largely a Southern phenomenon.

The charts also show that debt/equity ratios started lower in Southern regions, since

historically loan-to-value ratios have been lower there, see Table 2 below. With the fall in

nominal house prices after 1989, however, debt/equity ratios rose much more strongly there.

Debt service ratios (defined in the note to Table 2) have throughout been higher in Southern

regions. This is consistent with the systematically higher ratio of house prices to earnings

found in these regions. The rising and falling pattern shows clearly the influence of the rises

in interest rates in 1988-90 and the subsequent declines. Not surprisingly, our empirical

results confirm the strong association between court action rates and debt service ratios

visually apparent in these charts.

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TABLE 2 AVERAGE LOAN TO VALUE RATIOS IN 1970-95 FOR FIRST-TIME BUYERS BY REGIONS

ND 1986 VALUES OF DEBT/EQUITY AND DEBT SERVICE RATIOSAPeriod Yorks & East East South South West North

North Humber Mid Anglia East West Mid West Wales

LVR% 84.3 83.9 83.0 80.4 79.1 79.9 82.6 84.0 83.1

Debt/Equity % 40.9 38.6 35.3 36.2 34.2 37.1 38.4 37.0 39.3

D ebt/Service % 28.7 27.4 28.4 36.0 44.5 39.3 31.3 27.8 28.6Source: LVR from DOE 5% sample. Debt/Equity = average mortgage/average 1985 second-hand house price indexed by mix-adjusted

indices. Debt Service = (average mortgage) (tax adjusted mortgage interest)/personal disposable income per capita indexed to regional

earnings index, see forthcoming CML Discussion Paper for further details.

It is instructive to look at court orders relative to court actions, see Charts 3A and 3B. Court

orders, including suspended orders, rest on specific decisions by courts whereas court actions

are largely the consequence of cases brought by mortgage lenders. There are some notable

differences in movements of the ratio of orders to actions. In particular, there is little sign of a

fall since 1992 in the South East, South West and East Anglia, in contrast to the other

regions. This can be explained partly in terms of a worse underlying debt/equity position in

the Southern regions, see Chart 2A. However, because of delays in court procedures, there is

also a lag of orders behind actions.

CHART 3A: RATIO OF COURT ORDERS TO ACTIONS, IMPLEMENTED ORDERS TO

COURT ORDERS AND CHANGE IN NOMINAL HOUSE PRICES (SOUTHERN

REGIONS)

Note: The Southern regions are defined as: East Anglia, East Midlands, South East including Greater

London, and South West

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CHART 3B: RATIO OF COURT ORDERS TO ACTIONS, IMPLEMENTED ORDERS TO

COURT ORDERS AND CHANGE IN NOMINAL HOUSE PRICES (NORTHERN

REGIONS)

Note: The Northern regions are defined as: North, North West, Yorkshire & Humberside, West Midlands and

Wales

Our econometric model is used to argue that, taking into account the deterioration in the

economic environment and the lags, there appears to have been a fall in the ratio of orders to

actions, particularly in 1991-4. This is interpreted as a relaxation of court policy. However,

this feature of the data is not obvious to the naked eye examining Chart 3A and 3B: it only

becomes apparent when controlling for the other forces acting on court orders. The empirical

evidence is set out in Section 5 and its appendix.

That there has been a shift in court policy is visually more obvious in examining the ratio of

implemented orders to total orders since 1990, the period over which the data have been

available, also shown in Charts 3A and 3B. This generally shows a decline since 1990,

despite the deterioration in the economic environment. The increasing use of suspended court

orders over this period can be taken to be a sign of a more lenient attitude by the courts

particularly in 1991 and 1992 when house price falls and rises in unemployment were at their

most severe. Charts 3A and 3B also show the rate of change of nominal house prices, clearly

revealing the negative shocks occurring in 1991-2 in the Southern regions.

The unemployment rate data throw further light both on the timing and the extent of the

recent recession in different regions. Chart 4 shows that in Southern regions, unemployment

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rates reached their low point in 1988, while in Northern regions this occurred in 1989.

Furthermore, while in most of the Southern regions, the rise by 1993 led to unemployment

levels similar to or higher than in 1986, in all Northern regions, the 1993 peak was still

substantially below 1986 levels. Thus, regional differentials in unemployment rates narrowed

sharply in the 1990s.

CHART 4: UNEMPLOYMENT AND VAT DEREGISTRATION RATES

Note: The Southern regions are defined as: East Anglia, East Midlands, South East including Greater

London, and South West and the Northern regions are defined as: North, North West, Yorkshire & Humberside,

West Midlands and Wales

The peak year for business failures, as measured by de-registrations of firms from the VAT

register shown in Chart 4, came one year earlier, in 1992, in all regions, though the peak was

a little higher in Southern regions.

4. ECONOMETRIC MODELS OF MORTGAGE POSSESSIONS

In our view, the probability of possession is the result of the simultaneous occurrence of two

factors: a vulnerable debt/equity position and a trigger factor including elements such as an

unfavourable cash-flow position and unfavourable expectations for an improvement. A

rational borrower would not default on a mortgage just because of cash-flow problems if the

equity cushion relative to debt was sufficient to allow trading down or out.

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Similarly, someone able to meet mortgage payments is unlikely to seek possession: UK

borrowers who default face a high probability of being pursued for their unpaid debt in the

future and of being denied access to credit for at least some years. The theoretical

background to this is set out in more detail in the Appendix to this section.

This is a more general view of mortgage possession than the option pricing approach popular

in the US literature. (see Kau et al (1992), applied to UK data by Ncube and Satchell, (1994)

and discussed by Dale (1995).) The Box below summarizes the key points of the option

pricing approach and objections to applying this approach in the UK.

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THE OPTION PRICING VIEW OF MORTGAGE POSSESSION

Assumptions

(a) as in many US states, the borrower's liability ends when he or she gives up the

property deeds to the lender.

(b) The possession decision is in the hands of the borrower.

(c) There are no transactions costs.

(d) There are no restrictions in access to credit: thus no risk of acquiring bad credit

record.

(e) rationality and good information.

Conclusions

Given assumptions (a) to (e), the rational borrower calculates the present value of

mortgage payments and defaults if this exceeds the value of the house by some margin.

This margin is not zero because by defaulting now, the borrower would give up the

option of recovery or of defaulting in the future which has some value. Option pricing

theory, assuming that house prices and interest rates follow simple statistical processes, is

used to compute this margin.

Objections

Vandell (1995) reviews this US literature and the empirical evidence and argues that this

approach is defective in a number of ways since it omits transactions costs, credit market

imperfections, trigger events such as divorce or shocks to cash flows such as

unemployment, and also omits the behaviour of mortgage lenders.

Moreover, assumption (a) is violated in the UK: borrowers remain liable for their debt

even after possession and lenders or their insurance companies can pursue borrowers for

the negative equity and costs that remain after the possessed house has been sold off.

Nevertheless, a precarious net equity position is undoubtedly an important element in the

probability of possession and can be defined by the mortgage debt/equity ratio exceeding

some threshold, where mortgage debt includes arrears. However, the probability of

possession also depends on a trigger function which depends on current cash flows and

shocks to income and house prices. Specifically, we assume the trigger rises with the debt

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service ratio and the change in the unemployment rate and falls with positive house price

shocks as measured by the rate of change or the rate of acceleration in house prices.

One can think of default arising from the intersection of the events `debt/equity ratio exceeds

some threshold' and `trigger exceeds another threshold'. We use standard probability

argumentsto arrive at an expression for individual default probabilities and hence aggregate

default rates by region. The economic fundamentals are represented by debt/equity ratios and

the trigger effects mentioned above. See the appendix to this section for a fuller explanation

of these points.

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We model three aspects of the legal process dealing with mortgage default using County

Court data by region: the ratio of implemented to total court orders, the ratio of total court

orders to actions and the rate of actions itself. In each case, the outcome should depend on the

economic fundamentals varying by region and by time, as discussed above, and on time

effects for 1991 to 1996, the same across regions, capturing potential shifts in policy and the

delayed reaction of courts and lenders to the possessions crisis. In addition, the equation for

every region includes a factor specific to that region as a 'fixed effect' to capture long-run

differences between regions such as differences in age and occupational structure, the

ownership of financial assets and inequality within regions. See the appendix to this section,

eq(5), for an illustration of the actions equation. Worse economic fundamentals should raise

the court actions rate, decided by lenders, but also the ratio of implemented orders and the

rate of court orders, determined by the courts, given actions brought. With worse economic

fundamentals, the probability of a household avoiding eventual default falls so that, ceteris

paribus, a higher proportion of actions brought results in court orders granted and

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To the list of economic fundamentals discussed above, we also added proxies for lending

quality. For example, one of these was derived from changes in the market share of

centralized mortgage lenders in the previous five years. Ford et al (1995) provide evidence on

the higher default rates of mortgages from this source. Poor lending quality in the past should

be associated with higher default rates. This effect shows up only in the equation for the rate

of court actions. The previous year's rate of VAT deregistration as a proxy for small business

failure was also found to affect the rate of court actions, but not the ratio of implemented to

total court orders or the ratio of orders to actions.

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Appendix to Section 4: Theoretical Background

A precarious net equity position is undoubtedly an important element in the probability of

possession and, when c is small, can be defined by

ln (mortgage debt/equity) > c (1)

where mortgage debt includes arrears. However, the probability of possession also depends on a

trigger function which depends on current cash flows and shocks to income and house prices.

Suppose

trigger = f(debt service ratio, ur, lnhp) (2)

and the debt service ratio,

(mortgage debt)r

dsr = -------------------- (3)

y

where r is the tax adjusted mortgage interest rate and y is personal disposable income, ur is the

unemployment rate and hp is an index of house prices.5 The change in the unemployment rate

ur is taken as a measure of income shortfalls and the rate of change of house prices lnhp as a

proxy for shocks in the equity position. The debt service ratio and the change in the

unemployment rate are expected to have a positive effect on the trigger, while the rate of change

of house prices should have a negative effect.

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Formally speaking, one can think of default rising from the intersection of the events

ln(mortgage debt/equity) > c and trigger > c0. Then:

Prob(default) = Prob(bad debt/equity) x Prob(bad trigger given bad debt/equity)

(4)

If the two events, 'bad debt/equity' and 'bad trigger' were independent,

Prob (default) = Prob (bad debt/equity) x Prob (bad trigger)

In practice, the two events will be positively correlated. In any event, we will be taking an

approximation of (4) using the log mortgage debt/equity ratio and the variables in the trigger

function by translating these probabilities for individual households into relative frequencies of

regional populations of households.

We have regional data on average house prices and average mortgages but not on the distribution

of debt/equity ratios from which the vulnerable tail of the distribution could be analysed. Instead

we rely on the existence of a relatively stable relationship between the vulnerable tail and the

mean of the distribution.

To make this more concrete, consider the distribution of log debt/equity illustrated in Figure 1.

Define the vulnerable tail as being the part of the distribution where log debt/equity exceeds c.

Note that the point 0 marks the point where debt equals equity. The area under the vulnerable

tail of the distribution with mean M1 is shaded in grey. Now, suppose there is a fall in average

house prices which shifts the distribution, denoted by the dotted line, to the right, so that the new

mean is M2. The area under the vulnerable tail is now the bigger hatched area. Generally, as the

mean M moves to the right, the vulnerable tail area increases. We now suppose that the log of

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the vulnerable tail area can be approximated as a linear function of log mean debt/equity6, on

which we actually have data.

The theory above has been developed for mortgage possession considered broadly. In practice,

possession can be initiated by mortgage borrowers or by lenders. Given information

asymmetries between them and different objectives the possession probability given by (4) will

not have exactly the same relationship with economic fundamentals for borrower or lender

initiated possessions. However, one would still expect the same set of economic fundamentals to

be operative in both cases. Most court actions fall into the latter category, but not all. As Ford et

al (1995) observe, for households in possession to obtain access to local authority housing,

possession has typically to be the result of court proceedings. In some cases, therefore, court

actions may follow at the initial request of the borrower. However, it is safe to assume that the

number of actions is not under the direct influence of the courts, though it may depend on the

perceived probability of success, which may depend on court policies in the recent past.

Thus we hypothesize that in the ith region the log percentage of court actions can be expressed

by the following equation which can be stated in words as follows:

log(court actions rate)it = constant + regional fixed effecti + time effectt

+ a1log (debt equity)it-1 + a2 debt service ratioit

+ a8 change in unemployment rateit + a4 VAT deregistration rateit-1

- a5 rate of change of house pricesit - a6 rate of change of house pricesit-1

+ a7 indicator of poor lending qualityt-1

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In symbols, this translates

logpait = a0 + biDregioni + ctDyeart

+ a1 ldeit-1 + a2dsrit + a3urit + a4 VATit-1

- a5lhpit -a6lhpit-1+ m5dsclt-1 (5)

where

Dregioni is a dummy variable which is 1 for region i and 0 for other regions

Dyeart is a dummy variable which is 1 for year t from 1991 onwards and 0 for other years

ldeit-1 is last year's value of the log (deb/equity) ratio

dsrit is the debt service ratio in region i in year t

urit is the change in the unemployment rate

VATit-1 is last year's VAT deregistration rate in region i, a proxy for

the small business failure rate

lhpit is the change in the log house price index

m5dsclt-1 is the 5 year moving average of the lagged change in the share

of centralized mortgage lenders in total mortgages outstanding, a proxy

for lending quality.7

Equation (5) was arrived at by testing down from a more general specification including current

and lagged values of the debt equity ratio, up to 2 lags in the debt service ratio, up to 2 lags in the

unemployment rate and in the rate of change of house prices. This should be a general enough

specification to be able to proxy reasonably well expectations by lenders of house prices, interest

rates and incomes which could be relevant to the decision to bring a possession action.

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As noted in Section 2(b) above, the time effects in eq.(5) are likely to represent a mix of

influences: policy shifts by lenders, adjustment lags by lenders and a 'discouraged actions' effect

as a result of a policy shift by the courts, resulting in a lower success rate for actions brought.

It is also conceivable that the time effects partly reflect a composition shift in the population of

households at risk of possession. Suppose, for example, that lenders brought actions against the

most disastrous cases first. This would suggest high rates of possession at first, followed by

some tailing off after the worst cases had been eliminated from the population at risk. One test of

this interpretation involves testing the alternative hypothesis that the time effects are

homogenous across regions. Since regions differ considerably, particularly in the timing and size

of house price changes but debt/equity ratios, these composition effects should occur at different

times in different regions but we can reject this. Another test of the change in composition

hypothesis comes from noting that it implies a positive time effect in 1990 to compensate for

later negative ones. We test this pattern in Section 5 and in Section 7 on UK aggregate data.

Something like a pure court policy shift is likely to be directly measured by the estimated time

effects in the equations for the rate of court orders, po, given the rate of actions brought. Court

orders are at the discretion of the courts and not of the mortgage lenders bringing the actions.

Since court procedures take time, we must build in a lagged reaction of orders to actions. But it is

possible to accept the restriction that, in the long run, orders move in proportion to actions,

provided one controls for the effect of changes in the economic fundamentals: worse economic

fundamentals raise the ratio of orders to actions. The restriction can be imposed via an

'equilibrium correction model' (see Hendry (1995)) linking logpo and logpa:

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logpoit = b1logpait + b2(logpait-1 - logpoit-1) (6)

+ effects from region and year dummies and economic fundamentals

Note that b2 < 1 and b1=0 would indicate partial adjustment of orders to last year's actions rate,

while b2 < 1 and b1=b2 would indicate partial adjustment to this year's actions rate. Eq.(6)

encompasses both.

The other evidence for a shift in court policy can be found in the data for the ratio of

implemented to total court orders, rio ie., 1-ratio of suspended to total court orders. This can be

derived from the estimated time effects in an equation similar to (5) above but formulated for the

ratio of implemented orders. We turn to the evidence in Section 5 and its appendix.

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5. EMPIRICAL FINDINGS FOR REGIONAL DATA

Full empirical evidence is set out in the appendix to this section. A summary now follows:

The Ratio of Implemented to Total Court Orders

We find that worse fundamentals, as seen in a higher debt/equity ratio, an increase in the

unemployment rate, a high debt/service ratio and negative house price shocks, all

significantly raise the ratio of implemented court orders. For example, a rise in the debt

service ratio from 0.4 to 0.5, such as might be caused by the tax adjusted mortgage interest

rate rising from 8% to 10%, after two years results in a 21% rise in the ratio of implemented

to total orders, even ignoring the knock-on effects on unemployment and house prices.

In terms of policy shifts, it appears that in 1991 and 1992, the ratio of implemented court

orders was around 19% lower than would have been expected purely on the basis of the

economic fundamentals. By 1993, this apparent softening of policy had fallen to 6% and had

virtually disappeared by 1994. Though the precision of these findings must be qualified by

the fact that these data only begin in 1990, they are consistent with the view that court

procedures softened. The regional fixed effects suggest that the Southern regions tend to have

slightly lower ratios than one might have expected given the economic fundamentals.

Court Orders Given Court Actions

24

The model incorporates a lagged reaction of court orders to court actions resulting from

typical delays in court procedures. The economic fundamentals found significant here are the

debt/equity ratio, the change in the unemployment rate, the change in the debt service ratio

and the rate of acceleration of house prices. A 10% rise in the debt/equity ratio results in the

long run in a rise of around 6% in the ratio of court orders to actions.


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In terms of policy shifts, there was a drop in court orders relative to actions of around 14 or

17% in 1991-92 compared with what might have been expected given economic

fundamentals. The effect then fades in 1993-94 to one fourth of this magnitude and we can

accept the hypothesis that it has disappeared by 1995. The fixed effects here suggest little

systematic difference between southern and northern regions.

The appendix to this section suggests that, while the general pattern is robust, the precise

estimates of the policy shift effects have some sensitivity to the econometric specification.

Modelling the Rate of Court Actions

The rate of court actions responds strongly to last years debt/equity position (a 10% increase

in the latter causing a 5 to 6% increase in the former) and in addition to the current change in

nominal house prices (a 10% decrease raising the current court action rate by 14% and to

high debt service ratios in the current year (a rise from 0.4 to 0.5 in the debt service ratio

results in a 12% rise in the rate of court actions). There are also smaller effects from

unemployment shocks and the previous years rate of VAT deregistration (for example, if

this rises from 12% to 13% there would be a 5 to 6% rise in the court actions rate). 8 The

proxy for lending quality based on the change in market lenders in the previous five years has

a significantly positive effect. It suggests that the aftermath of lower lending quality of the

late 1980s involved a rise of about 18% in the court actions rate.

25

The pattern of the time effects proxying various aspects of a policy shift by lenders and their

reaction to the policy shift in the courts suggest an effect beginning at around 30% in 1992

and gradually diminishing to around 8% in 1996. Thus, even in 1996, court action rates were


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about 8% below what would have been expected given the economic fundamentals. The

decline between a 17% effect in 1995 and the 8% effect in 1996 may also reflect the

tightening of DSS rules for benefit claimants discussed above. Thus, this evidence is

consistent with a shift in policy by lenders after the November-December 1991 implicit

contract between the Government and the lenders, and the view that lenders were generally

still sticking with their agreement not to put into possession DSS claimants whose mortgage

payments were being made direct to mortgage lenders. Assuming the proportion of such

cases in the total of households at risk has declined, one would expect this effect to fade over

time, as suggested by our estimates.

This pattern of time effects is broadly similar in an alternative, less well fitting equation, that

results when the lending quality is omitted. However, there are then notable readjustments of

the effects attributed to the economic fundamentals. For example, the debt service ratio and

the change in the unemployment rate become more important while the effect attributed to

house price changes falls. The details are discussed in the appendix to this section.

For court action rates, the regional fixed effects suggest strongly that, given economic

fundamentals, the southern regions experienced systematically lower court action rates and

Wales, in particular, systematically higher court action rates than the (other) English regions.

There could be a number of reasons for this, including the greater ownership of financial

assets, and the greater preponderance of non-manual workers in the southern regions. It is

well-known that such workers tend to be lifted more easily out of cash-flow problems by

higher earnings increases than manual workers.

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Earlier we discussed the hypothesis that lenders may have held off from possession actions

when house prices fell more than 10% below the 1988-90 average, since given selling costs

and discounts, the lenders would then be sharing losses with the insurers. A variable

measuring the number of quarters in each year when prices were more than 10% below the

1988-90 average was constructed. This would have had a negative effect had the hypothesis

been valid. However, its effect is positive in practice implying that economic fundamentals

overwhelmed such a strategic response by the lenders. We also tested the hypothesis that

policy shifts were significantly different across the North/South divide and were able to reject

this hypothesis. This also casts doubt on the change in composition hypothesis discussed in

Section 2(b) and Section 5 which attributes the decline in possessions to the early weeding

out of the most at risk cases. The hypothesis also implies a positive time effect in 1990 (and

perhaps 1991) followed by negative ones. This pattern can be rejected.

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Worse economic fundamentals in the form of the two year average of the debt service ratio, the

change in the unemployment rate and the log debt/equity ratio and the rate of change of house

prices all raise the ratio of implemented orders. The region dummies do not show very

systematic regional variations, though Wales and Yorkshire and Humberside (the reference

region) followed by the North West show the highest ratios of implemented orders, while the

South West followed by the South East show the lowest.

The time effects for 1991-96 proved significant only for 1991, 1992 and 1993. They suggest that

policy was softest w.r.t. suspended orders in 1991 and progressively tightened in 1992 and 1993,

returning to 'normal'levels by 1994.

Specification (7) was tested in two ways. First, against a more general specification of the

economic fundamentals and time effects for 1994 to 1996. An F-test of (7) against a more

general alternative involving 3 more time effects and 4 more parameters for the economic

fundamentals (adding ldeit, urit, dlhpit-1 and dsrit-1 effects) gives F7,40 = 0.422 [p=0.88] which is

insignificant at the 5% level. A second test involved adding interaction effects between the 3

time effects and a dummy for southern regions. This checks that the claimed homogeneity of

time effects across regions is statistically acceptable. Since economic circumstances differed

considerably between southern and northern regions, this is quite a powerful specification test.

The F-test here gives F3,44 = 0.774, which is also insignificant [p=0.51] at the 5% level.

It is conceivable that lhpit is endogenous in that a shock to rioit might feed back within the year

to depress house prices further. A simple check on this is to instrument lhpit using the fitted

values from a parsimonious system of regional house price equations. The potential OLS bias is

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logpoit = 0.16 +0.45 logpait +0.74(logpait-1 - logpoit-1)

(2.3) (6.5) (10.3)

+0.04DEA +0.02DEM +0.10DNN +0.05DNW

(0.01) (0.6) (2.7) (1.7)

+0.03DSE -0.05DSW +0.02DWW -0.01DWM

(1.4) (1.4) (0.4) (0.4)

+0.33ldeit-1 +0.045 urit -1.85av2lhpit

(3.8) (2.5) (9.7)

-0.20D91 -0.21D92 -0.10D93 -0.08D94

(3.3) (3.0) (2.4) (2.6) (9)

s.e. = 0.0624 R2 = 0.951, DW = 2.32

The most interesting difference between eq(8) and eq(9) is in the pattern of time effects reflecting

the softening of policy and the decay of this policy shift. Eq(9) suggests a peak in 1992 and then

a monotonic decay, which looks marginally more plausible than the pattern of point estimates in

eq(8). Given the limitations of the evidence on court policy shifts from the ratio of implemented

orders in subsection (a) above, the evidence in (8) and (9) is fairly consistent with that in (7).

Both specifications suggest little systematic regional variation in the ratio of orders to actions

between regions, though the North stands out slightly as having a high ratio where the South

West has a low ratio of orders to actions.

c. The court actions rate

The specification was discussed in the appendix to Section 4, see eq(5). As noted there, the time

effects here reflect a mix of policy shifts and adjustment lags by lenders, in part reacting to court

policy shifts. The results are as follows:

logpait = 2.00

32

(7.2)


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-0.19DEA -0.04DEM -0.06DNN +0.13DNW

(3.9) (1.3) (1.7) (3.2)

-0.20DSE -0.20DSW +0.30DWW +0.15DWM

(3.3) (4.6) (6.7) (5.2)

+0.55ldeit-1 +0.03 Durit +1.23dsrit

(5.9) (2.3) (4.8)

-1.54 lhpit -0.53 lhpit-1 +5.02VATit-1 +16.2m5dsclit-1

(10.2) (3.9) (2.6) (3.1)

-0.27D92 -0.27D93 -0.25D94 -0.19D95 -0.08D96

(8.4) (5.3) (5.4) (3.5) (1.1) (10)

s.e. = 0.0583, R2 = 0.969, DW = 1.88, sample 1987-1996, 90 observations

This specification of the three has the most comprehensive set of significant economic

fundamentals including VAT deregistration and the proxy for past lending quality. Testing this

specification against an even more comprehensive one containing also the current log debt/equity

ratio, the unemployment rate, the lagged debt service ratio, a lagged dependent variable and a

1991 dummy gives an F-test, F5,62 = 1.00 [p=0.43] which is not significant at the 5% level.9

Testing against an alternative that permits the time effects to be different for southern regions

gives an F-test F5,64 = 1.56 [p=0.18] which is also not significant at the 5% level.

Again, a check on the endogeneity oflhpit contradicts the hypothesis of an endogeneity bias.

The point estimate of the instrumented coefficient is marginally more negative and a Hausman

test confirms the absence of bias [p=0.77].

The regional dummies suggest a systematic North/South pattern of differences: compared with

the reference region, of Yorkshire and Humberside, East Anglia, the South East and the South

33


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West all have substantially lower court actions rates, while Wales, the West Midlands and the

North West have substantially higher court actions rates than economic fundamentals would

have predicted.

These regional fixed effects capture long-run differences between regions, for example, in age

and occupational structure, income-age profiles, income riskiness, and the ownership of financial

assets and hence in the relationships between the average and the size of the vulnerable tail for

the distributions of debt/equity and debt service ratios. Thus, for example, the lower court action

rates in the southern regions may reflect the greater ownership of financial assets there, see

Regional Trends, the higher proportion of white collar workers and the somewhat younger age

structure. It is well known that income-age profiles peak later for non-manual workers than for

manual workers. Since younger households typically have greater debt exposure, the fact that in

southern regions, many of these households can expect substantial age related income increases

means that for given average debt/equity and debt service ratios, it is likely that a higher

proportion of southern households would find themselves lifted away from the mortgage default

margin. This would help to account for the lower court action rates in southern regions, given

the measured economic fundamentals included in our equation.

This is all still consistent, however, with a higher 1991 peak for the court actions rate in southern

regions than in the rest of the economy: that, of course, is explained by the greater deterioration

in economic fundamentals in the southern regions.

34

As noted earlier, the time effects for court actions are likely to reflect in part the impact that

'softer' court policies would have had on the propensity of mortgage lenders to bring actions.

The lower the probability of success, the less likely is an action to be brought. This 'discouraged


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action' effect is likely to lag behind the shift in court policies. Indeed, we find no detectable

effect in 1991 of a softening of lenders policies as reflected in the court actions rate. Recall also

that the implicit contract between the mortgage lenders and the government to reduce

possessions rates in return for various government concessions was negotiated in

November/December of 1991 so that hardly any of the consequences would have shown up in

the 1991 figures.

An alternative interpretation of the negative time effects in 1992-5 discussed in Section 2(b)

suggested that they may have been the result of a shift in the composition of borrowers at risk in

which lenders dealt with the most disastrous cases first. This would then have led to a tailing off

of the proportion of actions brought with the worst cases eliminated from the population at risk.

This hypothesis, however, implies given the late 1989 peak in house prices, a corresponding

increase in 1990 and/or 1991 above levels warranted by observed economic fundamentals, in the

rate of court actions brought. To test this we included a 1990 and a 1991 year dummy with

respective coefficients -0.01(t=0.2), -0.07(t=0.6). The hypothesis can therefore be rejected in

favour of our policy softening alternative.

These estimates suggest a steady decline in the softening of lenders policies, though the effect

had still not quite faded out in 1996, with the point estimate suggesting an effect about one

quarter of the 1992 effect. This is consistent with the hypothesis that those who bought in 1988-

91, the cohort likely to have been most subject to mortgage default, are making up a lower and

lower proportion of new cases.

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If we omit the proxy for poor lending quality, the lagged dependent variable becomes significant

and we obtain a more gradual fading out, with the 1996 effect only just under one half of the

1992 effect, see eq.(11):

logpait = 1.06 + 0.30logpait-1

(2.8) (4.5)

-0.18DEA -0.04DEM -0.02DNN +0.07DNW

(3.3) (1.2) (0.4) (1.5)

-0.31DSE -0.24DSW +0.26DWW +0.09DWM

(5.8) (5.2) (5.3) (2.5)

+0.40ldeit-1 + 0.06 urit

(3.1) (5.3)

+1.83dsrit - 1.09 lhpit +5.20VATit-1

(8.4) (7.5) (2.5)

-0.31D92 -0.23D93 -0.29D94 -0.18D95 -0.13D96

(8.1) (4.2) (5.8) (3.1) (2.1)

(11)s.e. = 0.0640, R2 = 0.962, DW = 2.34

sample 1987-1996 90 observations

Whether one accepts the implications of eq(11) - slow fade out - or of eq(10) - fast fade out of

policy softening - depends on whether one regards the lending quality proxy in eq(10) as

plausible. Certainly eq(10) fits better.

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6. FORECAST - SCENARIOS FOR 1997

This section considers some forecast scenarios for 1997. An analysis of aggregate quarterly

data suggests that the UK possessions rate is forecast better from data on the court actions rate

(for England and Wales) in the previous four quarters, together with some economic

fundamentals, than from data on the court orders rate combined with economic fundamentals.

Since our earlier discussion suggests that court orders depend on court actions, forecasting court

actions is the key. We now consider three forecast scenarios applied to each of our respective

specifications (10) and (11) detailed in the Appendix to Section 5. These are included to make

the point that differences in the econometric specification do have forecast implications.

Specification (10) has somewhat weaker interest rate and unemployment effects and somewhat

stronger house price effects than specification (11). Specification (11) also suggests that it takes

a little longer for these effects to feed through.

The common features of the scenarios set out here are the assumption of a 1.2% fall in the

unemployment rates in South East, the South West and East Anglia and a 1% fall elsewhere,

nominal personal disposable income per head rising by 7.4% in these Southern regions and

6.4% elsewhere, the same change in the VAT deregistration rate as in the previous year, and

the assumption, as far as policy by the lenders is concerned, that the court actions rate is 5%

below the rate implied by economic fundamentals (as opposed to 8% in 1996). We have

assumed the tightening of the DSS rules is already reflected in the 1996 time effect so that

there is no additional effect in 1997.

37

Scenario I is a moderately optimistic one with stable interest rates and a significant house

price recovery. Scenario II is a pessimistic one. Scenario III is the most optimistic both

about interest rates and house prices. Table 3 summarizes the key points.


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Scenario I Scenario II Scenario III

Adjusted mortgage

interest rate in 1997

same as 1996 1 percentage

point higher

same as 1996

House price increase

in South East, East

Anglia, South West

12% 10% 17%

House price increase

in rest of England

and Wales

7% 6% 12%

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Table A.1 Regional Court Action Rates for Mortgage Possession: Forecasts and Historical

Data (Percentages)

Scenario Scenario Scenario

I II III

I II III

I II III

1991 1995 1996 1997 1997 1997

East Anglia 1.76 0.77 0.76 0.65 0.71 0.61

East Midlands 1.86 0.90 0.82 0.81 0.86 0.75

South East 2.45 0.93 0.85 0.71 0.78 0.66

South West 2.05 0.85 0.76 0.68 0.73 0.63

North 1.47 0.70 0.79 0.67 0.70 0.62

North West 1.98 1.10 1.05 0.92 0.97 0.85

Wales 2.21 1.14 1.04 0.82 0.86 0.76

West Midlands 2.08 0.94 0.86 0.87 0.92 0.81

Yorks &

Humberside

1.74 0.86 0.87 0.81 0.85 0.75

Southern Regions 2.24 0.90 0.82 0.71 0.78 0.66

Northern Regions 1.90 0.95 0.93 0.84 0.88 0.78

England & Wales 2.10 0.92 0.87 0.77 0.82 0.71

Source: see text

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Note: results based on equation 10. These results are marginally different from Table 4 in

the Housing Finance paper for reasons discussed in footnote 9.

Table A.2 Regional Court Action Rates for Mortgage Possession Forecasts and HistoricalData (Percentages)

Scenario Scenario Scenario

I II III

1991 1995 1996 1997 1997 1997

East Anglia 1.76 0.77 0.76 0.70 0.76 0.66

East Midlands 1.86 0.90 0.82 0.87 0.94 0.82

South East 2.45 0.93 0.85 0.71 0.79 0.68

South West 2.05 0.85 0.76 0.70 0.77 0.66

North 1.47 0.70 0.79 0.74 0.79 0.70

North West 1.98 1.10 1.05 0.99 1.06 0.94

Wales 2.21 1.14 1.04 0.92 0.98 0.87

West Midlands 2.08 0.94 0.86 0.91 0.97 0.86

Yorks &

Humberside

1.74 0.86 0.87 0.88 0.95 0.84

Southern Regions 2.24 0.90 0.82 0.73 0.81 0.69

Northern Regions 1.90 0.95 0.93 0.90 0.97 0.86

England & Wales 2.10 0.92 0.87 0.81 0.88 0.77

Source: see text

Note: results based on equation 11.

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The differences in specification of equations 10 and 11 noted above most relevant for

forecasting into 1997 are two: eq(10) displays smaller effects from the debt service ratio while

eq(11) displays more persistence, via the role of the lagged dependent variable. The latter is

the main reason why the fall in court action rates between 1996 and 1997 in Scenario I is larger

in Table A.1 (from eq(10)) than in Table A.2 (from eq(11)).

With a larger debt service ratio effect in eq(11), Table A.2 then shows a sharper rise in court

action rates going from Scenario I to Scenario II than does Table A.1. Note that Table A.2

suggest that even a 1 percentage point rise in the average mortgage rate for 1997 compared

with 1996, would be sufficient to undo the benefits operating via higher incomes, lower

unemployment and the upward momentum in house prices: for England and Wales,

particularly for northern regions, the court actions rate is predicted to rise. Although Table

A.1 has less negative implications, even it suggests that the magnitude of interest rate rises

which the July 2nd Budget has put in prospect may well negate the effects of other

improvements in economic conditions, particularly in northern Britain. However, much will

depend on the size of the ripple effect that rises in house prices in London and the more

affluent parts of the South East will have on other regions. Scenario III, which assumes the

strongest house price rises, shows the differences both at the regional level and for England

and Wales in comparison with Scenario I.

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7. UK EVIDENCE ON THE POSSESSIONS-ARREARS RELATIONSHIP

Chart 1 above demonstrated the shift that took place in the 1990s in the relationship between

CML data on the rate of possessions and the proportion of mortgages 12 months or more in

arrear. Previous work by Breedon and Joyce (1992), Brookes, Dicks and Pradhan (1994) and

Allen and Milne (1994) has modelled the CML (Council of Mortgage Lenders) data on

possessions in terms of arrears and other determinants. These authors impose the constraint that

in the long run, the flow of possessions moves in proportion to the number of households in long

term arrears, defined variously as over 6 months or over 12 months. Chart 1 which plots the rate

of possession, pp, plotted against the ratio, p12m, of households 12 months or more in arrear to

the number of mortgages, shows that in 1991 possessions peaked and then declined while the

stock of arrears continued to climb before turning down also. As discussed below, this cannot be

explained by the measurement error in the arrears data which arises when interest rates change.

Chart 1 also plots log p10f, the fitted value of the proportion of mortgages over 10% in arrear

against the log of the possession rate.

Econometric analysis confirms this visual impression. Using a quarterly interpolation of the

biannual CML data, we were able to find a simple relationship between the rate of possessions,

the arrears rate, the debt service ratio, the unemployment rate and the change in house prices

once time dummies for 1991-1997, reflecting policy shifts, are included. In the long run, the

ratio of possessions to long-term arrears cases rises by around 50% for an increase in the debt

service ratio of 25%, equivalent to a rise in the after tax interest rate from 8% to 10%. An

increase in the unemployment rate from 8% to 10% in the long run also implies an increase in

the ratio of possessions to arrears of around 50%.

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The time effects show a clear hump shaped pattern beginning in 1991, peaking in 1993 and

declining steadily to 1997. Their magnitude, particularly if they were given a steady state

interpretation (see appendix), is clearly larger than of the corresponding time effects in regional

court orders and court actions equations, respectively reflecting court policy shifts and policy

shifts by mortgage lenders. To some degree this is likely to reflect the fact that policy shifts by

both sets of agents are reflected in possessions outcomes. But a bigger factor is that the softening

of lenders policy that occurred after the end-1991 implicit contract with the government, not

only reduced possessions but increased the count of numbers in arrears. Indeed, the latter would

be the logical consequence of the former. Econometric models of the arrears rate confirm a

pattern of positive time effects between 1992 and 1996, also with a hump-shaped time profile.

This must mean a double element in the gap between possessions and arrears.

The combination of the aggregate time series evidence on possessions and arrears also fails to

support the hypothesis that the decline in the possessions rate from 1991 was caused by the early

weeding out by lenders of the most at risk cases. This would have resulted in respectively

positive and negative time effects in 1990 or even 1991 in the possessions and arrears equations

followed by the reversal of these effects in subsequent years. But these 1990-1991 effects are

absent.

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between possessions and court orders. In the regional orders and actions equations reported in

the appendix to Section 5 we tested and rejected the possibility of within the year feedback from

court orders and actions to house prices. In a quarterly context, feedback seems even less likely,

and so we do not test for it. The time dummies capturing shifts in policy by courts and by

lenders and possibly implementation lags by the latter enter in a moving average form - for

example, md91 is equal to 0.25 in 1990Q4, 0.75 in 1991Q1, 1 in both 1992Q2 and 1991Q3, 0.75

in 1991Q4 and 0.25 in 1992Q1. This form has the effect of centering the total annual effect

correctly at mid-year. The policy shift effect as estimated takes a hump-shaped form, building

up to its strongest (negative) effect in 1993 and declining to 1997. These effects are large and

highly significant. A proxy for lending quality, defined as in the appendix to Section 5, based on

long moving averages of the change in the market share of centralized mortgage lenders proved

insignificant. This suggests that the proportion of 12 month arrears cases sufficiently reflects the

decline in lending quality that probably took place in the late 1980s.

In order to examine parameter stability, the model was also estimated up to 1990Q3 (omitting the

policy shifts obviously). The estimated parameters are remarkably stable, with a slightly higher

feedback term and a slightly lower effect of the lagged house price change.

logppt = 1.00 + 0.33(logp12mt-1 - logppt-2) + 1.39dsrt(3.2) (4.6) (5.8)

+ 0.058urt-2 - 2.33lhpt - 1.09lhpt-1

(4.7) (6.5) (3.1) (13)

s.e = 0.0432, R2 = 0.897, DW = 1.68

Sample period 1983Q1 - 1990Q3.

45

The chi-squared statistics for respectively 1st, 2nd, 3rd and 4th order residual autocorrelation are

0.08, 0.66, 0.24, 1.51, which again confirms the lack of residual autocorrelation.


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This remarkably simple model implies that in the long run, the rate of possessions moves in

proportion to the rate of 12 month arrears. However, if the debt service ratio or unemployment

are high or if house prices are falling, then possessions will be higher than implied by arrears

alone.

The feature of eq(12) that deserves further discussion is the remarkably large coefficients on the

time dummies, for example -0.50 for the 1993 dummy. Had this been a permanent effect, it

would have implied a long-run reduction of the log possessions rate, given the 12 month arrears

rate, of 0.50/0.27 = 1.85, since the coefficient on the equilibrium correction term is 0.27. This is

far bigger than the time dummy effects for the log court actions rate estimated in eq(10) or

eq(11).

46

As noted in the main text of Section 7, a key component of the explanation of this apparently

surprising finding lies in the nature of what a policy softening by mortgage lenders implies for

the number of mortgages classified to be over 12 month in arrear. The crucial part of the implicit

contract between the government and the mortgage lenders in November/December 1991 was

that the lenders would refrain from exercising possession in cases where DSS mortgage

payments were being made directly to mortgage lenders. This would have led to many cases

creeping into the 12 months in arrear category without experiencing the possessions proceedings

that would previously have ensued.10 Thus, the policy softening should have led to a significant

increase in the proportion of mortgages 12 months in arrear. Indeed, an econometric model for

the 12 months in arrear rate shows significantly positive time dummy effects in 1992-95. Thus

the gap between the possessions rate and the arrears rate widened more than between the

possessions rate and measures of economic fundamentals such as the debt/equity ratio, debt


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service ratio etc., but excluding the arrears rate. The econometric evidence on the arrears rate

suggests that about two thirds of the widening gap between the possessions rate and the arrears

rate is due to the post-1991 shift in the arrears rate.

It is conceivable that another component of the explanation of why the 1992-5 time effects are so

much bigger in eq(12) than eq(10) and eq(11) lies in the well-known measurement biases of the

months in arrear data. When interest rates rise, monthly payments rise so that the number of

months a mortgage is classified to be in arrear temporarily falls. Thus arrears levels are

underestimated when interest rates are high and over-estimated when interest rates are low.

Since interest rates fell for much of 1991 to 1995, arrears levels would have been overestimated

relative to 1989-90, thus exaggerating the gap between possessions and arrears rates and helping

to account for the size of the 1992-95 time dummies.

Since 1993, the Council of Mortgage Lenders have published a count of the number of arrears

cases which should be free of this measurement bias based on a percent in arrears concept, eg.,

5% in arrear, 10% in arrear. Muellbauer (1996) provides estimates back to 1983 of what the

10% in arrear rates would have been, had they been collected earlier. Imperfect though these

estimates probably are, they do permit a check on the measurement bias hypothesis. Running

eq(12) with the estimated log 10% in arrear rate replacing the log 12 months in arrear rate results

in similar (indeed marginally higher) estimates of the 1992-95 time effects, suggesting that the

measurement bias hypothesis is not the explanation of the large time effects in eq(12).

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CONCLUSIONS AND OUTLOOK

We have analysed regional data on court actions, court orders and implemented court orders

to investigate empirically the influence of variations in the macroeconomic and the regional

economic environment on these indicators of mortgage default. We have found evidence of

substantial shifts in these relationships beginning in 1991 and similar across regions,

reflecting what we term policy shifts'' by Courts and mortgage lenders. As our discussion

indicates, these shifts are probably a mix of intended changes in behaviour and initial delays

in setting up systems to deal with the possessions crisis. Our evidence on the UK relationship

between possessions and long-term arrears also shows these shifts in behaviour. The court

orders data suggest that by 1995 County Court practices had returned to normal, given the

court actions submitted to them. In contrast, the data both on regional court actions and on

UK possessions cases suggest that by 1995 the mortgage lenders were still possessing at rates

significantly below what one would have expected on the basis of the economic

fundamentals. This would be consistent with the implicit promise made in 1991 not to

possess in cases where DSS mortgage payments were being made direct to mortgage lenders.

The evidence is that this policy softening effect is gradually decaying. The outlook for the

rate of possessions depends partly on how rapidly this effect decays, on the hard-to-quantify

effects of the tightening of DSS rules from October 1995 for claimants, as well as on the

macroeconomic fundamentals.

48

At least as far as the latter are concerned, there have been major improvements since 1995

with the further reduction in interest rates, rising house prices, especially in Southern regions

and falling unemployment rates. The lags in the response of court actions, court orders and

possessions rates to these macrofundamentals are considerable. This will mean that the falls


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seen at the end of 1996 will be followed by significant further falls in 1997-98 provided a

significant rise in interest rates can be avoided.

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Data Appendix

Data for 1986 to the present on court orders and actions for mortgage possessions in English

regions and Wales are available from the Lord Chancellor's Department.

We want to scale these data relative to the number of mortgages outstanding per region. These

are estimated, following methods pioneered by Anthony Murphy, as follows: from Labour Force

Survey (LFS) Housing Trailers for 1971, 1981, 1984, 1988 and 1991-93, we obtain estimates of

the fraction of owner-occupiers with mortgages, omi for region i, at the end of each year.11 We

can obtain fairly accurate estimates of this fraction for the UK as a whole by dividing, nm UK, the

number of mortgages outstanding (Housing Finance, Table 29) by ohsUK, the number of owner-

occupied houses (Housing and Construction Statistics, Table 9.3), where all figures are at year

end. Let romi be the ratio omi/omUK. By fitting a cubic in time to romi we generate interpolated

estimates for 1985 to 1995, fromi. We then define the estimated nmI, the number of mortgages in

region i by

ohsi

nmi (nmUK) (fromi) ______

ohsUK

Thus, the share of the number of UK mortgages in the ith region equals the share of owner-

occupied houses scaled by fromi.

We define, pa, the percentage of mortgages in the ith region in year t subject to Court actions as

(actit/nmit-1) x 100, where actit is the number of actions in region i, year t, and analogously for

Court orders, po. In logs, these variables are denoted logpa and logpo, respectively.

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Among the explanatory variables, several rely on estimates of ami, the average mortgage

outstanding in region i. These estimates use information on rmpi, the ratio of average mortgage

interest payments per household in each region to that in the UK from the Family Expenditure

Survey for 1974-95. Data on the average UK mortgage stock could then be scaled by these ratios

to obtain estimates of average mortgage stocks in each region. These ratios are not ideal for this

purpose, being subject to large sampling variations, particularly in the less populous regions. We

use estimated ratios interpolated from cubic equations in time fitted for each region to overcome

sampling variation. Also, at times when many households are increasing their mortgage arrears,

actual recorded mortgage payments will understate the size of mortgage debts. Later, when

households are paying extra to reduce their arrears, mortgage payments will overstate the size of

mortgage debts. To the extent that this occurs uniformly across regions, these biases will cancel

out. However, we have reason to believe that mortgage arrears are higher in regions with high

mortgage possession rates. Our estimates are necessarily crude, therefore.

We estimate average debt/equity ratios for each region by scaling the average mortgage am it-1 by

hpit, an estimate of the average second-hand house price. This multiplies the Department of the

Environment's (D.O.E.) mix adjusted index for the region by the average dwelling price for

`other dwellings' in 1985 from Housing and Construction Statistics, Table 10.9. We denote the

log of the debt/equity ratio as ldeit.

51

The debt-service ratio is computed as the product of the tax-adjusted mortgage interest rate

(abmrit) and the average mortgage (amit-1) divided by an estimate of after-tax male earnings (yit)

scaled by the ratio of average UK male tax adjusted earnings to UK personal disposable income

per head. Note that this definition of the debt-service ratio omits the repayment element in

regular mortgage payments. Thus,


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dsrir = (abmrit) (amit-1)/yit

In turn, abmr is defined as (1-sittrt) (gross Building Society mortgage rate), where trt is the

standard rate at which tax relief applies and sit is an estimate of the fraction of mortgages under

the tax relief ceiling. sit varies from region to region. A simple estimate would define sit = 1 if the

average mortgage is under the tax relief ceiling of 30,000 and s it = 30,000/amit-1 otherwise. But

this would neglect the inequality of the distribution of mortgages which would, for example,

ensure that some mortgages were over the ceiling even with an average mortgage of 25,000 say.

sit incorporates an approximate adjustment12 to reflect this.

The Building Society mortgage rate comes from Housing Finance, Feb. 1996, Table 26. Up to

1987, the figures measure the annual average interest rate by dividing recorded payments by the

average mortgage stock outstanding. These figures reflect premiums or discounts as well as

delays for some societies between announcements of interest rate changes and their

implementation. However, the financial years of some societies did not coincide with the

calendar year in all cases. From 1988, the figures are an annual average of end of month rates,

which also should reflect discounts and implementation delays. Abbey National and Cheltenham

and Gloucester are excluded from July 1989 and August 1995 respectively.

After-tax male earnings by region comes from the New Earnings Survey and are April figures.

The tax adjustment also varies across regions, estimating tax rates from Regional Accounts data

on personal disposable income and personal income.

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Regional unemployment rates derive from registration records at Job Centres, refer to all workers

and come from Economic Trends.

Regional estimates of the fraction of businesses de-registered for VAT come from Department of

Employment Gazette, November 1991 and updates were kindly provided by the Department of

Trade and Industry.

A measure of credit quality was constructed from data on the share of centralized mortgage

lenders in the value of total mortgages outstanding in the UK published in the Historical

Compendium of Housing Finance Statistics. If the change in the share is denoted dscml, the 5

year moving average is denoted m5dscml and we work with the lagged value of this.

Quarterly data for Section 7 were obtained as follows. Data on the biannual flow of mortgage

possessions cases and on the end of December and end of June count of cases where arrears were

over 12 months are published by the Council of Mortgage Lenders. Interpolating (log-linearly)

the arrears data is straightforward as one would expect, for example, the September figure on

average to be half way between the June and December figures. Interpolating the biannual flow

of possessions data into a quarterly flow is less obvious. Assuming that the two quarters in each

half year had the same possessions levels or rates would be unrealistic: for example, if

possessions are trending upwards, the earlier quarter should have a lower level than the later

quarter. A moving average procedure in which, for example, the quarter 1 figure is given by one

third of the first half figure plus one sixth of the last half of the previous years figure yields a

first set of estimates. These are then scaled to ensure that in each half year the sum of the two

estimated quarterly figures adds up to the recorded figures for the half year.

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The definition of the debt service ratio for quarterly data is similar to that for annual data except

that the income measure is personal per capita disposable income. Both it and the tax adjusted

mortgage interest rate are defined at annual rates. The quarterly unemployment rate and

quarterly rate of change of house prices come from the sources quoted above.

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ENDNOTES

1. This paper is a revised and extended account of the research behind our paper, with

the same title in Housing Finance, May 1997. This research was supported by the

Council of Mortgage Lenders and the ESRC under grant R000 23 4954. We are

grateful for helpful comments to Fionnuala Earley, Janet Ford, David Hendry, Duncan

MacLennan, Anthony Murphy, George Speight, Mark Stephens, Peter Williams and

seminar participants at the Universities of Glasgow and Oxford, at University

College, Dublin, University College, London and at the CML Conference `Modelling

Possessions and Arrears', 25 February 1997.

2. The survey evidence of Ford et al (1995) suggests that the average family size of

these households was not much smaller than of all households with mortgages.

3. See Muellbauer and Murphy (1997) for an analysis of its causes.

4. That is, formally to permit loan-to-value ratios for new lending to exceed 100% by

significant margins.

5. Note that this definition excludes the repayment component of debt service cost. For a

given mortgage life, this moves in the opposite direction from the interest rate, stabilizing

slightly the true debt service ratio. However, as suggested in Muellbauer (1996), the

average mortgage life for borrowers facing possession or in arrears has fallen in the

1990s. This resulted in a substantially smaller reduction in the debt service ratio than

would have been expected from the fall in interest rates.

6. Log mean debt/mean equity is the log of a ratio of arithmetic means while mean log

debt/equity, as illustrated in Figure 1 is the log of a ratio of geometric means. These are

not identical but will move very closely together for the kinds of distributional shifts

illustrated in Figure 1.

7. It is widely believed that in aggressively bidding for market share in the latter half of the

1980s, the centralized mortgage lenders, who lacked a High Street base, were forced to

accept higher risk customers or to lend at riskier loan to income or loan to value ratios.At any rate, the evidence from Ford et al (1995) is for a higher rate of possessions

associated with mortgages from this source.

8. Note this would imply almost a one for one increase in percentage terms. Lest this

seem large, note that the trough to peak increase in the VAT deregistration rate was

only 2percentage points in Southern regions. This suggests that a little under one

fifth of the rise in the court actions rate in Southern regions can be attributed to the

rise in small business failure.

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9. An alternative specification with very similar implications returns the lagged dependent

variable but drops the lagged house price change. This fits a little worse. It was used to

generate the Table 4 forecasts reported in the Housing Finance paper which explains why

the scenarios reported below differ marginally.

10. Note that DSS mortgage payments do not include repayment of arrears or of interest on

previous arrears or on additional loans collateralized on housing. This explains why even

when DSS mortgage payments are being made, mortgage arrears levels can continue to

drift upwards.

11. Though these data are not available annually, they are based on a larger and probably

more consistently representative sample than the annual Family Expenditure Survey,

from which they show systematic divergencies.

12. Ideally, the adjustment would use the distribution of mortgages in each year in eachregion to count the fraction of mortgages under the tax relief ceiling. We do not have

such data even at the national level. Instead, we take the distribution of national mortgage

advances as a proxy for the distribution of mortgages, at least as far as the upper tail is

concerned. It is used to fit a relationship between the average mortgage advance and the

fraction under the tax relief ceiling. This relationship is then applied at the regional level,

given data on the average mortgage in each region, but subject to the constraint that sit

cannot exceed unity.

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Breedon, F J and M A Joyce, (1992), House Prices, Arrears and Repossessions: A Three

Equation Model for the UK,Bank of England Quarterly Bulletin, May, 173-179.

Brookes, M, M Dicks and M Pradhan, (1994), An Empirical Model of Mortgage Arrears

and Repossessions,Economic Modelling, 11, 2, 134-144.

Dale, Peter, (1995), Future Mortgage Default,Housing Finance, 25, February, 14-20.

Ford, J, Problematic Home Ownership, Joseph Rowntree Foundation, September, 1994.

Ford, J, E Kempson and M Wilson, (1995), QTRMortgage Arrears and Possessions:

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Documents

A Regional Analysis of Mortgage Possessions