- Email: [email protected]
Housing costs, house price shocks and savings behaviour among older households in Britain
Regional Science and Urban Economics 32 (2002) 607–625 www.elsevier.com / locate / econbase
Housing costs, house price shocks and savings behaviour among older households in Britain Richard Disney a,b , *, Andrew Henley c , Gary Stears d a
School of Economics, University of Nottingham, University Park, Nottingham NG7 2 RD, UK b Institute for Fiscal Studies, London WC1 E 7 AE, UK c School of Management and Business, University of Wales Aberystwyth, Penglais, Ceredigion, Wales SY23 3 DD, UK d Association of British Insurers, London EC2 V 7 HQ , UK Received 25 April 1999; received in revised form 12 June 2001; accepted 25 June 2001
Abstract Most households around retirement age hold much of their non-pension wealth in the form of housing. The paper uses a two wave panel to examine housing wealth and saving behaviour by the elderly in Britain between 1988 and 1994. It examines the response of households to house price shocks, and also to ‘excess’ housing costs relative to income. The results differ between non-movers and movers, and are corrected for the non-random nature of the moving decision. It finds a marginal propensity to save to offset the house price shock of 0.96 for movers, but an insignificant impact for non-movers. ‘Excess’ housing costs do not appear to influence the moving decision and are positively associated with saving, suggesting strong heterogeneity in tastes for overall asset holding. 2002 Elsevier Science B.V. All rights reserved. Keywords: Housing equity; Home ownership; Saving; Retirement JEL classification: D12; J26
*Corresponding author. Tel.: 1 44-115-951-5151; fax: 1 44-115-951-4159. E-mail address: [email protected] (R. Disney). 0166-0462 / 02 / $ – see front matter 2002 Elsevier Science B.V. All rights reserved. PII: S0166-0462( 01 )00086-2
608
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
1. Introduction This paper considers the saving behaviour of a sample of older owner occupiers in Britain in the late 1980s and early 1990s, using a short panel of household data. In particular, it focuses on the relationship between changes in household housing wealth, housing costs, and saving in financial assets. The paper makes two broad contributions to the existing literature. The first concerns the impact of exogenous shocks to housing wealth on household saving behaviour. This period in the UK is particularly interesting insofar as real average house prices fell by 30% (11% in nominal terms), albeit with a good deal of regional variation that we exploit as a proxy for expected household housing wealth changes. We look for evidence of life cycle consumption and wealth smoothing — that is, for adjustments of household saving levels to offset housing wealth changes such as to leave total wealth and, therefore, prospective consumption unchanged. Our empirical results show that households that moved home in the period almost completely offset these shocks to housing wealth by adjusting their financial asset holdings. In contrast, households that did not move made no discernible adjustment to their saving behaviour. Of course, the majority of households did not move; moreover, the decision to move is non-random and the estimates of housing wealth-saving offsets are corrected for the selectivity of the mover-stayer decision. Our second finding concerns the relationship between moving behaviour, financial asset holding and the ratio of holdings of housing wealth to current ‘needs’ and income. It has been suggested that households with ‘excess’ costs of housing relative to current income and family size are more likely to move in order to bring housing costs into equilibrium with financial flows. Among older households, this seems a particularly pertinent issue since households in later life typically experience changes in family needs (children becoming independent) and changes in current income (such as a fall after retirement) which might induce them to ‘downsize’ their housing stock. Unlike the first issue, therefore, which considers the overall level of wealth, this finding focuses on the portfolio composition of household wealth and in particular on the possibility that wealth illiquidity may induce ‘financial stress’, especially among older households, leading them to downsize housing. We do indeed find evidence that moving behaviour is associated with family ‘events’, such as inheritance of a house or the spouse retiring. However, we find no evidence of moving behaviour being related to our measure of ‘financial stress’: the ratio of housing costs to current income. Moreover, in a number of specifications, housing expenditure relative to income is positively associated with saving in financial assets, suggesting that we are observing strong heterogeneity in tastes for asset-holding, rather than adjustment of assets to a common wealth– income ratio at this stage of the life cycle.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
609
In all this, however, there is one potential ‘puzzle’ (at least, for the standard consumption smoothing model). Households that move do generally downsize their housing wealth, and increase financial assets to maintain their overall wealth position. But moving behaviour itself is uncorrelated both with the regional change in house prices (the proxy for expected changes in household housing wealth) and with the household ratio of housing costs to income — variables that might be expected to affect the probability of housing wealth adjustment. It may be that this particular period is unusual given that it was dominated by house price falls, which undoubtedly made older households reluctant to move. Indeed, there is some tentative evidence elsewhere of a correlation between moving behaviour and ‘excess’ house size over a slightly longer period for Britain for this age group (Ermisch and Jenkins, 1999). However, we reiterate that this line of argument does not explain the general positive relation between housing costs (conditioned on income) and saving that is observed in our data. Further work on this issue is required in the UK context. Our interpretation of why households move, consistent with our results, is slightly different. We find that actual moving behaviour is related to moving intentions and to residential tenure, as well as to household retirement outcomes and bequests. We interpret this as suggesting that households plan housing moves in relation to life cycle ‘events’ and also exhibit strong heterogeneity in moving behaviour, which may reflect tastes, or housing-specific differences in moving costs. It is impossible to pin this down further given the limited small number of moves in the data [see Venti and Wise (1984), for a fuller treatment]. Thus, overall, we conclude that households in this age group that do move use the opportunity to adjust wealth holding consistent with life cycle consumption smoothing, but that other households simply absorb wealth shocks without changing saving behaviour. The data utilised in the paper comprise the two waves of the UK Retirement and Retirement Plans Survey, conducted in 1988–89 and 1994 [hereafter RS, and described in greater detail in OPCS (1992) and Disney et al. (1997)]. This Survey, carried out by the Office of National Statistics (previously the Office of Population Census and Surveys), is a random sample of over 2500 households that contained at least one person aged 55–69 in 1988–89. The survey contains information on a wide variety of socio-economic characteristics as well as evidence on housing tenure, housing costs and other wealth holdings.1 It also contains a full event history concerning labour market states, and a family history. Furthermore, the survey contains interesting questions concerning peoples’ intentions and expectations about retirement and their economic behaviour, including (dis)saving behaviour since retirement. Although sharing much in common with the US Health and Retirement Survey and AHEAD, the Retirement Survey has only two 1 On the evolution of wealth holdings, including financial and housing wealth, and imputed social security and pension wealth, see Disney et al. (1997) and Disney et al. (1998).
610
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
waves and it is not possible to use panel estimators that require several lags as instruments. The plan of the remainder of the paper is as follows. The next section surveys some contributions to the literature that focus on the relationship between housing wealth, moving behaviour and the change in financial assets. Section 3 provides some empirical description of house price changes, of the Retirement Survey data, and briefly examines household housing expenditure. Section 4 estimates a joint model of moving behaviour and financial asset accumulation. Section 5 summarises the main conclusions of the paper.
2. Previous literature and some hypotheses In the basic life cycle hypothesis of saving (LCH), households in the age category examined here are entering the stage of their life when they should generally be running down their household wealth, either by reducing financial assets, moving in order to ‘downsize’ housing wealth, or through remortgaging or ‘equity release’ schemes. However, two kinds of evidence cast doubt on the stylised LCH model as an adequate description of saving behaviour among this age group. First, evidence from the United States suggests that households continue to accrue net wealth through asset appreciation even late in their life (Merrill, 1984; Feinstein and McFadden, 1989; Venti and Wise, 1989, 1990, 1991; Sheiner and Weil, 1992). Second, households tend to cut consumption sharply at or after retirement, leading to the so-called ‘retirement saving puzzle’ (Banks et al., 1998). There are a number of potential explanations for this finding that older households do not decumulate assets to the extent predicted by a simple LCH model. These include the role of the bequest motive (Bernheim, 1991), shifts in preferences at retirement, precautionary saving against incapacity and disability, ¨ ceilings on consumption imposed by poor health (Borsch-Supan and Stahl, 1991), and so on. A key factor, however, is likely to be that a large fraction of the assets of older households is tied up in housing wealth. Real increases in the value of housing wealth over time therefore lead to what is commonly termed ‘passive’ saving. Nevertheless, many studies in both the UK and the US find evidence that changes in housing wealth do lead to changes in consumption and to reductions in non-housing wealth (dissaving), although the implied marginal propensities to consume from housing wealth are rather small.2 A particularly interesting finding 2 For evidence that consumption may be related to housing wealth, see Skinner (1989, 1993) in the US context, and, for the UK, Acemoglou and Scott (1994), Miles (1992) and Bayoumi (1993). For evidence as to whether housing windfalls affected consumption in the UK, see the debate between Muellbauer and Murphy (1990) and Attanasio and Weber (1994).
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
611
is that of Engelhardt (1996) who finds an asymmetric response of saving behaviour to house price changes, with saving in financial assets much more responsive to house price falls than to rises. As mentioned in the introduction, many of the households in our sample faced house price falls during the period in which their behaviour was observed. Even if housing wealth and financial wealth are imperfect substitutes, there are other potential means of adjusting wealth portfolios, which involve adjusting the stock of housing wealth directly. One possibility is to remortgage, or to utilise reverse annuity mortgages and other equity release schemes, either to finance consumption or to adjust the liquidity of the wealth portfolio. There it little evidence, either from the United States (Venti and Wise, 1991) or the UK (Leather, 1990), of older households using second mortgages as a means of financing consumption. Schemes to release housing equity have also proved relatively unpopular, especially in the UK. First, the very thinness of the market generates the perception of, and the likelihood of, adverse selection. Secondly, the present value of the annuity released through equity release is generally rather small, at least until households near the end of their life (Venti and Wise, 1991; Skinner, 1993; Hancock, 1998).3 The most direct means of adjusting housing wealth is for the household to move, generally ‘downsizing’ the family home, and thereby releasing housing equity. However, a feature of home ownership that distinguishes it from other asset holdings is that housing is both a consumption good as well as a potential form of wealth-holding (Henderson and Ioannides, 1983). Home ownership generates a flow of services, both real and psychic, and decisions concerning optimal consumption of housing services may not easily be separable from optimal asset holdings. Moreover, older households in particular may be reluctant to move house to adjust their lifetime wealth position because of high moving costs or psychic costs from leaving a known domestic environment (Venti and Wise, 1990; Megboulugbe et al., 1999) Consequently, their consumption behaviour may be constrained by asset illiquidity. This may induce risk averse households to reduce other forms of consumption expenditure or lead to an accelerated decline in holdings of other, more liquid, assets. Given this potential disjunction between the twin roles of home ownership as an investment and as a flow of consumption services, we should augment our estimates of moving behaviour and the household wealth trajectory by measures of ‘excess’ housing costs or other physical measures of ‘excess’ housing space relative to ‘needs’.4 But high measured housing costs (budget shares) may also reflect heterogeneity of preferences for housing consumption, and indeed heteroge3 Falling returns in bond markets in the UK in the 1990s also made annuitisation of housing wealth relatively unattractive. 4 See Feinstein and McFadden (1989) and Venti and Wise (1989), for the US; Disney et al. (1994), and Ermisch and Jenkins (1999), for the UK, and Stahl (1989), for Germany.
612
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
neous tastes for saving and for holding wealth. It is hard however to separate out ‘excess’ costs of housing from preferences with only two temporal observations in the panel. Although we do not provide a definitive answer to this difficulty, we believe that exploiting the variation in exogenous price shocks across regions and judicious selection of instruments in our two-step procedure cast light on this issue.
3. Data sources and description This section provides evidence concerning holdings of both housing wealth and financial assets by age group at each wave of the survey, and measures of housing consumption for homeowners and renters. Our basic unit of analysis, the household, is defined as the administrative ‘benefit unit’ in the United Kingdom, which is a single person or couple plus dependents. We focus only on households where the head was aged between 55 and 69 in the 1988–89 wave of the survey, and who reported data in both waves of the survey.5 Just under 1400 households who survived and who provided all the requisite information are utilised in the ensuing analysis. As background information, we note that between the peak of the housing market in 1989 Q3 and 1994 Q4, real average house prices for the UK fell by 30%, and by over 11% in nominal terms. As Fig. 1 suggests, this episode was not unique in the recent history of fluctuations in real UK house prices, although unusual in that nominal prices also fell.6 However, these price falls were uneven across the country, with the largest falls concentrated in London and the South East where the boom of the mid-1980s in asset values had been most pronounced, whereas in the northern regions of England and Scotland, real house prices were more stable (Table 1).7 We exploit this regional variation to show that the rate of accumulation or decumulation of financial assets among older households is sensitive to the change in the value of housing assets. In contrast to housing wealth, however, there were real increases in the value of the equity market over the period, arising from the recovery from the ‘crash’ in 1987.8
5 There is very strong evidence that attrition of the sample, both through death and non-response, is highly non-random. Survivors are more likely to be owner-occupiers, to own more valuable houses and to have larger holdings of financial assets (Disney et al., 1998). 6 These figures were computed from the mix-adjusted house price series published by the UK Office for National Statistics, deflated in the case of the real series by the consumer price index. 7 Quarterly average house price data by UK region are from Halifax plc, deflated by the consumer price index (change over the period 25.5%). 8 The ‘shakeout’ in house prices was in part a lagged response to the bursting of the bubble in asset markets that occurred in the late 1980s, but we do not consider the behaviour of asset prices further here. On house price changes, see Henley (1998).
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
613
Fig. 1. Annual change in real and nominal average UK house prices 1957–1998. Source: Office for National Statistics.
Table 1 Real house price change 1989 Q3 to 1994 Q4 by UK region Percent Greater London South East outside London East Anglia South West West Midlands East Midlands Yorkshire and Humberside North North West Wales Scotland Northern Ireland
247.7 248.2 250.3 247.2 236.1 242.2 229.5 223.5 225.0 237.9 29.3 20.1
614
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
Table 2 Average housing and financial wealth by age and tenure status of head of household in 1988 Age, tenure No.
1988 Mean
1994
Mean change
% Change
Median
Mean
Median
Housing wealth Owning stayer 100,199 Owning mover 116,063 Owner to renting 59,545
92,256 101,305 52,233
74,163 80,079 0
68,884 76,278 0
226,036 235,984 259,545
226.0 231.0 2100.0
Financial wealth Owning stayer 20,072 Owning mover 29,069 Owner to renting 2,407
7,967 10,315 1,672
27,779 45,052 6,194
11,796 23,241 4,085
7,707 15,983 3,787
38.4 55.0 2157.3
Note: Constant January 1996 prices.
Table 2 depicts predicted mean and median holdings of housing and financial wealth among owner-occupier households observed in both waves of the Retirement Survey, by (changes in) tenure status in 1988–89 and 1994 (all values are in January 1996 prices). Household housing wealth values are derived from banded information on house values provided by the respondents themselves. Predictions are then estimated using a grouped dependent variable (GDV) estimator.9 There are some striking statistics in Table 2. Owner occupiers in the survey on average saw a sharp decrease in real housing wealth between 1988–89 and 1994, mirroring the national decline across all households. This is wholly due to house price deflation dominating the offsetting effects of the net decline in the mortgage liability of households in this age range. Those households who moved typically had a slightly higher value of housing wealth in 1988–89 than non-movers, and the average decline in the value of their housing wealth between 1988–89 and 1994 was greater than that of non-movers. Only a small number of owner occupiers in 1988–89 had moved to rented status by 1994, and indeed they were outweighed by a few moves in the opposite direction (not shown in Table 2). In contrast, average real financial wealth of households in the survey increased sharply between the two dates, although the absolute magnitude of the change was
9
The GDV estimator treats each value of the banded variable as a latent variable within known limits, and finds a best prediction of that value given exogenous information and an assumed overall underlying distribution. The additional regressors comprised a quadratic in age, and gender, marital and employment status of the head of household, socio-economic group, regional dummies, and whether the property was connected to mains drainage and water supplies. The assumed underlying distribution is log-normal. The estimator is described in Stewart (1983), although here the estimates were obtained directly by Maximum Likelihood. For further details of the method used, see Disney et al. (1997) and Disney et al. (1998). To move from a measure of house value to housing wealth, the value of outstanding mortgages was estimated and deducted from each household’s calculated house value.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
615
less than the average decline in real housing wealth.10 As suggested above, this partly reflected the ‘passive saving’ effect of a rising equity market. Overall, movers saw a greater real increase in their financial wealth than non-movers, suggesting that some ‘downsizing’ took place for portfolio reallocation purposes. Specifically, it can be noted that the housing wealth of movers fell, on average, by just under £10,000 more than non-movers, while the financial assets of movers grew by just over £8000 more than non-movers, between 1988–89 and 1994. Next, we examine housing costs to the household, which might also influence moving and saving behaviour. To do this, we calculate the housing budget share (HBS), for which we need a measure of household income and a flow measure of expenditure on housing. Our income measure for the household unit is defined as the sum of earnings, investment income, private pensions, social security and other income, aggregated up from the individual level. Welfare payments, including payments of housing benefit, council tax benefit (paid to offset local property taxes), rent and rate rebates and rent contributions from other resident individuals are also included in gross income.11 It is of course not possible to measure directly the flow of housing services provided by home ownership but one can measure expenditure on housing services and maintenance, and this measure of ‘user cost’ is feasible given data available in the Retirement Survey. Our measure of the ‘user cost’ of housing is defined as the sum of mortgage payments, rates, water rates, maintenance and insurance costs, and other costs.12 The maintenance costs are imputed to equal 1.0% of nominal house value. Insurance costs are assumed to be 0.25% of nominal house value in 1988–89 and 0.325% of house value in 1994, reflecting the relative increase in insurance costs to house values (because of static or falling nominal house prices) between the two periods. We define the ratio of out-of-pocket housing costs to current after-tax income as the predicted housing budget share (HBS). Table 3 examines mean and median HBS in 1988–89 and 1994 for the same household types as Table 2, separating those who stayed in the same house between waves from those who moved, and differentiated by whether they thereby
10
The financial asset estimates were obtained by a similar procedure to those for household housing wealth. After aggregating individual reported assets to form an overlapping set of household asset bands, a GDV estimator was used. The additional regressors comprised information on individual assets held, an estimate of overall income from assets, and a similar set of household characteristics. After a certain amount of experimentation, it was found that a gamma distribution best fitted the overall distribution of the variable (in the sense that the predicted values of wealth were successfully located in the reported band). Further details are obtained in Disney et al. (1998). 11 There are two ways in which welfare payments for housing costs can be treated: either the way chosen here, or as income net of payments with the sum of payments also deducted from housing costs. Either is a consistent treatment of the problem of state support provided to low income renters and home owners. 12 Other costs include service charges, ground rent, Scottish feu duty and compulsory maintenance charges.
616
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
Table 3 Housing budget shares by tenure type and tenure transitions Tenure type
No. in sample
Owner of which: Owning stayer Owning mover Owner to renter
1988
1994
Mean
Median
Mean
Median
958
0.201
0.169
0.201
0.176
889 61 8
0.202 0.181 0.184
0.170 0.167 0.187
0.200 0.186 0.445
0.167 0.159 0.485
changed tenure type. Interestingly, there is very little change by tenure transition other than a sharp rise for the small number who moved from owner occupation to rented accommodation (we can speculate as to the reasons for this but the small sample size limits any inferences). In particular, there is no evidence of significant changes in mean HBS arising from ‘downsizing’, although the median HBS of owners who moved between the sample dates is slightly lower in 1994. The average HBS for owners is very similar to the averages for the United States described by Feinstein and McFadden (1989), Table 2.4. In the analysis of the next section, we utilise an auxiliary ‘residual HBS’ variable to examine the impact on moving behaviour and financial asset accumulation of ‘excess’ (or deficient) expenditure on housing conditioned on current income and other characteristics. Table 4 therefore presents a household expenditure regression for 1988–89 for those owner occupiers who were also present in the 1994 wave of the Survey. Log housing expenditure is strongly related to log income (although the coefficient is significantly different from one), household demographics and other characteristics, and region.
4. Modelling the evolution of financial assets This section examines the impact of the house price ‘shock’ and of potential ‘excess’ consumption of housing services on the evolution of household saving. Given that the trajectory of financial wealth should be conditioned on whether the household moved or not, a simultaneous model of the moving decision and of the evolution of financial assets is constructed, based on Lee’s (1978) empirical methodology.13 Although other motives will also underpin the decision to move home, the two step model structure permits the household to select into a state where housing equity can be released which may be used to restore the wealth 13
Lee used the model to assess the impact of union status on wages, where union status and wages are modelled in a two-stage process to allow for selection into union membership / non-membership to be determined by the difference in wages in the two sectors.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
617
Table 4 Housing expenditure regression (1988–89) — Dependent variable: Log(monthly housing expenditure) Variable
Coefficient
S.E.
Constant Log(monthly gross income) Age Age squared / 100
2.684 0.302 20.004 20.000006
(0.517)* (0.023)* (0.014) (0.0001)
Head of household: Single Widowed Divorced Female Working part time Unemployed Retired Housewife Self-employed Disabled Spouse employed Has private pension rights Owns outright
20.139 20.164 20.142 20.100 0.136 0.049 0.089 0.195 0.183 0.032 20.073 20.047 20.371
(0.047)* (0.047)* (0.058)* (0.039)* (0.045)* (0.067) (0.037)* (0.105)1 (0.066)* (0.049) (0.041)1 (0.028)1 (0.028)*
Region: Scotland Northern England York & Humber North West East Midlands East Anglia Greater London South East South West Wales
20.146 20.220 20.155 20.101 0.082 0.271 0.343 0.388 0.271 20.307
(0.070)* (0.065)* (0.052)* (0.052)1 (0.057) (0.067)* (0.054)* (0.046)* (0.054)* (0.062)*
Number in sample R2 R.M.S.E.
958 0.5662 0.3534
Notes: Default categories are: married couple, headed by a man, employed with inactive spouse, with no private pension, with a mortgage, living in the West Midlands. Coefficients (standard errors in brackets); significance at 5% level indicated by *, 10% by 1 .
portfolio (allowing for moving costs). Thus the conditional mover / stayer decision is determined by the difference in the size of financial asset holdings in the two states. The model structure is as follows (see Maddala, 1983, pp. 236ff): A Mi 5 uM 0 1 XMiuM 1 1 ´Mi
(1)
A Si 5 uS 0 1 XSiuS 1 1 ´Si
(2)
618
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
I *i 5 d0 1 d1 (A Mi 2 A Si ) 1 d2 Zi 2 yi
(3)
A Mi and A Si are (log) financial asset holdings in 1994 under each state (mover or stayer), although only observed for each household i in whichever transition occurs between 1988–89 and 1994. I *i is a latent indicator variable capturing the net benefits of moving, such that moving occurs if I *i . 0 or not otherwise. X and Z are vectors of covariates, with identification of the model obtained by elements of Z not in X, uM 0 , uS 0 and u0 are intercepts, uM 1 , uS 1 and u1 are coefficient vectors and ´Mi , ´Si and ni are independently normally distributed disturbance terms. Substitution of the asset Eqs. (1) and (2) into the selection equation gives a reduced form equation: I *i 5 g0 1 g1 Ki 2 y *i
(4)
where Ki contains all the exogenous variables in X and Z. With the disturbance normalised to have unit variance, Eq. (4) can be estimated as a conventional probit, and the results used to provide a selectivity correction. Conditional on mover / stayer status the financial asset equations are thus given by:
F G
f (ci ) A Mi 5 uM 0 1 XMiuM 1 2 s1 y * ]] 1 hMi F(ci )
F
G
f (ci ) A Si 5 uS 0 1 XSiuS 1 2 s2 y * ]]] 1 hSi 1 2 F(ci )
(5) (6)
where ci 5 g0 1 g1Wi and sj y * 5 cov(´j , y * ), j 5 M, S. The right hand side term in brackets is a selectivity term, expressed in terms of the density (f ) and cumulative distribution (F) of a standard normal distribution. Given this formulation of the model we predict a negative selectivity coefficient in Eq. (5) (movers) and a positive one in Eq. (6) (stayers). Assuming valid identifying restrictions, consistent estimates can be obtained through staged estimation of Eqs. (4), (5) and (6). We hypothesise that the decision to move and the (log) level of financial assets at the point of the second wave will depend on three categories of variables: household characteristics, state variables in 1988–89, and changes in circumstances between 1988–89 and 1994. Identification is obtained by excluding from the financial asset equation 1988–89 state variables. This identification strategy is somewhat ad hoc, but we note that two of the state variables that turn out to be individually significant are years of tenure in the residence ( in 1988 – 89) and would like to move within 5 years ( in 1988 – 89). Both of these might reflect individual preferences for (not) moving per se. A positive and significant relationship between the propensity to move and this ‘residual HBS’ variable, described in the previous section, would suggest that households are able and willing to move in response to housing budget disequilibrium. Any other sign suggests that we are merely observing preferences or ‘taste’ for high housing consumption.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
619
Among the variables capturing changes in circumstances between 1988–89 and 1994, we are particularly interested in the impact of the house price ‘shock’. Since observed house wealth is endogenous, we proxy this by the regional house price change. There are however at least two factors at work here. Where the house price decline was disproportionately large and expected to continue, the household might be inclined to shift its portfolio towards financial assets by downsizing. Alternatively, if the household expected the price change to reverse, or was unwilling to taking a nominal loss in housing wealth, it might stay put through the downturn. This would be apparent in a lower level of mobility per se. There is some evidence that this latter strategy predominated: less than 1% of households moved from owner occupation into renting over the 5-year period and 7% in all moved.14 In consequence, there are only 69 movers who were in owner-occupation in 1988–89 in the selection equation. Ermisch and Jenkins (1999), using the British Household Panel Study, found a rate of movement among all households headed by a person aged 55 and over of 3.3% per annum between 1991 and 1995. However these rates are not strictly comparable with ours for two reasons. Firstly, Ermisch and Jenkins include renters, among whom mobility is generally higher, and secondly the period in question is overall a period of slow recovery in nominal house prices after the dramatic collapse in the 1989–1991 period (Fig. 1).15 Nevertheless we would still expect movers to reallocate their portfolio towards financial assets, while non-movers might respond by increasing their saving. On the relationship between financial assets (relative to 1988–89) and the adverse house price ‘shock’, for movers a negative sign would suggest some attempt to release housing equity, and for stayers an attempt to increase saving to compensate for the decline in the overall asset value. The relationship between housing budget share (HBS) in 1988–89 and financial asset changes is harder to predict a priori. If ‘high’ HBS in 1988–89 indicates an ‘unaffordable’ level of housing, then, conditioned on not moving, we might observe either a lower level of consumption or a reduction in financial assets over that period, and thus the potential for a negative correlation between HBS and the change in financial assets. A second possibility is that a ‘high’ HBS reflects a preference for housing 14
Those that did move reported an even greater reduction in housing equity, which may have arisen arise from explicit ‘downsizing’ of housing equity. However, given house values are self-reported for movers and non-movers alike, the decline may arise simply from a comparison of an over-inflated valuation with a market value, see Disney et al. (1997). It is of interest that 182 individuals in the original 1988–89 sample reported that they intended to move house in the next five years in order to withdraw equity. Of this number, just over 40% reported the motive ‘to make other investments’ whereas almost all others reported as their motive some form of consumption decision. 15 There is however a significant rate of attrition in the RS, not all of which is related to mortality (see Disney et al., 1998). If movers are more likely to attrite, relative to, say, the BHPS, this would also explain some of the discrepancy between the two surveys other than the different time periods involved.
620
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
wealth over other kinds of wealth or a preference for wealth accumulation per se, which would induce a positive correlation between HBS (via the ratio of housing wealth to income) and financial wealth. Finally, a possibility is that measurement error both in HBS and financial assets rules out any correlation. Table 5 reports our preferred estimates of Eqs. (4)–(6), with a specification Table 5 Determination of household financial assets: selectivity-corrected estimates Variable
(1) Selection equation
(2) Asset equation: movers
(3) Asset equation: stayers
Coefficient
Standard error
Coefficient
Standard error
Coefficient
Standard error
Constant Log financial assets 1988–89 Housing wealth/10000 1988–89 Head of household female Age (in years — 54 in 1994) Age squared Single Disability severity score
21.479 20.016 0.022 20.326 0.006 0.0002 20.057 0.012
(0.798)1 (0.036) (0.023) (0.214) (0.118) (0.004) (0.225) (0.049)
9.008 0.220 20.032 20.408 0.082 20.004 20.273 20.083
(2.060)* (0.089)* (0.054) (0.513) (0.310) (0.012) (0.527) (0.114)
6.317 0.425 0.068 0.031 20.179 0.006 20.272 20.178
(0.618)* (0.027)* (0.017)* (0.157) (0.087)* (0.003)* (0.155)1 (0.036)*
1988–89 status Residential tenure (years) Moved after retirement Like to move within 5 years Residual HBS
20.014 20.241 1.169 20.075
(0.007)* (0.259) (0.153)* (0.182)
0.097
Change in status 1988–89 to 1994 D log(monthly gross income) 0.070 Spouse retired 0.521 Disability score worsened 20.171 Inherited property 0.630 Received pension lump sum 20.068 Regional house price change 0.660 1988–89 to 1994
(0.163) (0.171)* (0.256) (0.238)* (0.183) (0.572)
1.391 20.599 1.342 0.216 0.847 24.775
(0.366)* (0.380) (0.704)1 (0.488) (0.387)* (1.475)*
0.545 20.033 20.522 20.194 0.759 0.405
20.787
(0.347)*
0.496
Selectivity term Number in sample Log Likelihood x 2 (18) F(15,53) F(15,873) x 2 (3) Wald test joint significance of additional instruments: x 2 (3) Wald test of exclusion restrictions:
958 2205.40 85.15*
(0.334)
69 296.76
0.191
(0.165)
(0.118)* (0.148) (0.170)* (0.257) (0.142)* (0.430)
(0.449) 873 21613.02
4.19* 33.72*
61.26* 0.705
3.223
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
621
sensitivity analysis reported in Table 6. A Wald test reported beneath the selection equation in column 1 of Table 5 shows that the three 1988–89 status variables that appear as additional identifying instruments are highly jointly significant. Further Wald tests reported beneath each equation accept the exclusion of these variables from the two financial asset equations in columns 2 and 3. The preferred specification of the moving selection equation fails to find a significant impact on the likelihood of moving from 1988–89 housing ‘disequilibrium’ (a high residual HBS), or from regional price changes. Moving behaviour is related to length of tenure in the house — the longer the tenure, the lower the probability, and to the previous intention to move. Events that significantly affect the probability of moving include retirement of the spouse and receipt of an inherited property. These results are consistent with a model of moving where households plan moves in advance to coincide with family and work-related transitions, and where there is significant ‘duration dependence’ in housing tenure. Households do not appear to move primarily in order to adjust wealth portfolios although, as column 2 reveals, they do use moves to smooth wealth. The results in column 2 reveal that for movers assets depend significantly and Table 6 Alternative asset equation specifications (1) (a) Movers Regional house price change Residual HBS Selectivity term Log L (b) Stayers Regional house price change Residual HBS Selectivity term Log L
(2)
24.338 (1.197)* – 20.714 (0.356)* 2119.6
20.790 (0.376)* – 0.861 (0.429)* 21680.9
(3)
(4)
24.006 (1.379)* 0.199 (0.352) 20.780 (0.371)* 2115.1
23.995 (1.266)* 0.119 (0.333) 20.761 (0.376)* 2106.1
24.217 (1.136)* 0.013 (0.301) 20.744 (0.338)* 297.6
20.563 (0.366) 0.488 (0.161)* 0.792 (0.422)1 21648.9
20.541 (0.361) 0.486 (0.159)* 0.832 (0.453)1 21630.5
20.567 (0.356) 0.400 (0.158)* 0.729 (0.449) 21621.1
(5)
24.775 (1.475)* 0.097 (0.334) 20.787 (0.347)* 296.8
0.405 (0.430) 0.191 (0.165) 0.496 (0.449) 21613.0
(1): lagged assets only (2): lagged assets and 1988–89 status variables (3): lagged assets, 1988–89 status variables and change in status variables (4): lagged assets, 1988–89 status variables, change in status variables and change in income (5): lagged assets, 1988–89 status variables, change in status variables, change in income and 1988–89 housing wealth (as in Table 5) Note: Coefficients (standard errors in brackets); significance at 5% level indicated *, 10% by 1 .
622
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
positively on both assets in the earlier wave and on the change in income between the waves. However for stayers in column 3 the coefficient on assets in the earlier wave is nearly twice as large as for movers. This stronger degree of persistence in assets is consistent with stayers being less able to adjust their financial assets in the face of the housing wealth shock through the release of housing equity. For this group higher housing wealth in 1988–89 is associated with significantly higher financial wealth in 1994. No significant effect arising from the residual HBS is found for either group of households in this specification (although see Table 6). For movers the selectivity term has the expected negative, significant coefficient. This indicates that these have a comparative advantage in the accumulation of financial assets over non-movers with the same control characteristics. For stayers no significant selectivity effect is found in this specification (but again see Table 6). For movers the regional house price change between 1988–89 and 1994 has a strong and statistically well-determined impact on 1994 financial assets, consistent with a strong ‘offset’ effect on saving from an adverse housing wealth shock. For stayers the regional house price attracts a positive coefficient but one that is not statistically significant in our preferred specification. Table 6 reports the key model coefficients, including the selectivity term, for alternative asset equation specifications. The size of the saving ‘offset’ for movers is robust to the broadening of the set of covariates, rising slightly between specifications (4) and (5) with the addition of 1988–89 housing wealth. A positive, though not well-determined, housing ‘disequilibrium’ effect declines as the specification is broadened. For stayers the exclusion of 1988–89 housing wealth yields a significant positive coefficient for the residual HBS variable. The inclusion of housing wealth halves the HBS effect. As the accumulation of housing wealth and financial wealth positively correlate, this supports the conclusion that both residual HBS and housing wealth are capturing a general ‘taste’ for housing amongst the more wealthy, perhaps driven by a strong bequest motive. The inclusion of 1988–89 housing wealth in the stayer equation also appears to be capturing a substantial element of the apparently significant selection effect in specifications (1)–(4). So for stayers falling house prices do not lead to a saving ‘offset’ to counteract the fall in the individual’s overall wealth, whereas by contrast for movers the ‘offset’ is pronounced. Assuming typical levels of housing wealth and financial wealth for this sample of £100,000 and £20,000, respectively (see Table 2), then a 10% adverse movement in house prices would require a 50% increase in financial assets in order to leave the total value of the individual’s portfolio unchanged. This elasticity of 5 is very close to the estimated elasticity of 4.775 for movers, comfortably within a 95% confidence interval. Individuals who are willing and able to move appear to offset almost fully the adverse house price shock of the early 1990s through increased accumulation of financial wealth. At these typical asset levels the implied marginal propensity to save to offset housing wealth loss for movers is, ceteris paribus, 0.955.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
623
5. Conclusion This paper has examined the trajectory of housing wealth and financial assets for households at, and subsequent to, retirement age. Using data from the two waves of the Retirement Survey, empirical results broadly confirm the US evidence, showing that substantial housing wealth is often retained into old age, whether for bequest or precautionary motives, or because transactions costs outweigh the gain from downsizing. But a significant minority of households thereby incurs large user costs of housing (Housing Budget Shares). Homeowners also have significant financial assets, unlike renters (in the latter case, see Disney et al., 1994, 1998). The period between 1988–89 and 1994 is an unusual one, in that real house prices fell sharply over that period, although differentially across regions, while financial asset markets remained buoyant. We examine both moving behaviour and financial asset acquisition in the light of this experience, conditioning the latter on the former. Subject to the caveat that our sample of movers is rather small, we show that the rate of house mobility among owner occupiers was rather low, but that nevertheless movers used moving to accumulate financial wealth that almost exactly offset the locally-specific adverse change in housing wealth. In contrast, stayers did not raise saving to offset the decrease in housing wealth. There is also firm evidence from the significant sample selection effect that moving and financial asset determination are interdependent. In contrast, there is no evidence that households with high HBS in 1988–89 were either more likely to move or to run down financial assets. Indeed particularly for stayers we find evidence that higher housing wealth (conditioned on other characteristics) is associated with greater saving. This suggests that what is being observed in the housing budget share is heterogeneity of household tastes for overall wealth accumulation rather than any ‘excess’ housing relative to demand, planned portfolio reallocations, or noise in the data. With strong evidence of heterogeneity of tastes for wealth, and low rates of moving home, we conclude that the relative thinness of the ‘equity release’ market may simply stem from low demand, rather than from any failure of the financial markets themselves.
References Acemoglou, D., Scott, A., 1994. Consumer confidence and rational expectations: Are agents’ beliefs consistent with theory? Economic Journal 104, 1–19. Attanasio, O.P., Weber, G., 1994. The UK consumption boom of the late 1980s: aggregate implications of macroeconomic evidence. Economic Journal 104, 1269–1302. Banks, J., Blundell, R., Tanner, S., 1998. Is there a retirement saving puzzle? American Economic Review 88 (4), 769–788. Bayoumi, T., 1993. Financial deregulation and housing saving. Economic Journal 103, 1432–1443.
624
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
Bernheim, B.D., 1991. How strong are bequest motives? Evidence based on estimates of the demand for life insurance and annuities. Journal of Political Economy 99 (5), 899–927. ¨ Borsch-Supan, A., Stahl, K., 1991. Life cycle saving and consumption constraints. Journal of Population Economics 4, 233–255. Disney, R., Gallagher, T., Henley, A., 1994. Excess housing costs and the behaviour of elderly households. University of Kent at Canterbury Studies in Economics: 94 / 8. Disney, R., Grundy, E., Johnson, P. (Eds.), 1997. The Dynamics of Retirement. Department of Social Security Research Report No. 72. The Stationery Office, London. Disney, R., Johnson, P., Stears, G., 1998. Asset wealth and asset decumulation among households in the Retirement Survey. Fiscal Studies 19, 153–174. Engelhardt, G.V., 1996. House prices and home owner saving behaviour. Regional Science and Urban Economics 26, 313–336. Ermisch, J., Jenkins, S., 1999. Retirement and housing adjustment in later life: evidence from the British Household Panel Survey. Labour Economics 6, 311–333. Feinstein, J., McFadden, D., 1989. The dynamics of housing demand by the elderly: wealth, cash flow and demographic effects. In: Wise, D.A. (Ed.), The Economics of Aging. Chicago University Press for NBER, Chicago, pp. 55–86. Hancock, R., 1998. Can housing wealth alleviate poverty among Britain’s older population? Fiscal Studies 19 (3), 249–272. Henderson, J.V., Ioannides, Y.M., 1983. A model of housing tenure choice. American Economic Review 73, 98–113. Henley, A., 1998. Changes in the distribution of housing wealth in Great Britain, 1985–91. Economica 65, 363–380. Leather, P., 1990. The potential and implication of home equity release in old age. Housing Studies 5, 3–13. Lee, L.-F., 1978. Unionism and wage rates: a simultaneous equation model with qualitative and limited dependent variables. International Economic Review 19, 415–433. Maddala, G.S., 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge. Megboulugbe, I., Sa-Aadu, J., Shilling, J.D., 1999. Elderly female-headed households and the decision to trade down. Journal of Housing Economics 8, 285–300. Merrill, S., 1984. Home equity and the elderly. In: Aaron, H.J., Burtless, G. (Eds.), Retirement and Economic Behaviour. The Brookings Institution, Washington, DC. Miles, D., 1992. Housing markets, consumption and financial liberalisation in the major economies. European Economic Review 36, 1093–1136. Muellbauer, J., Murphy, A., 1990. Is the UK balance of payments sustainable? Economic Policy 11, 345–383. OPCS, 1992. Retirement and Retirement Plans Survey 1988–1989. HMSO for Office of Population Censuses and Surveys, London. Sheiner, L.M., Weil, D., 1992. The Housing Wealth of the Aged. National Bureau of Economic Research Working Paper No. 4115, July. Skinner, J., 1989. Housing wealth and aggregate saving. Regional Science and Urban Economics 19, 305–324. Skinner, J., 1993. Is Housing Wealth A Sideshow. National Bureau of Economic Research Working Paper No. 4552, November. Stahl, K., 1989. Housing patterns and the mobility of the aged: the United States and West Germany. In: Wise, D.A. (Ed.), The Economics of Aging. Chicago University Press for NBER, Chicago, pp. 93–115. Stewart, M., 1983. On least squares estimation when the dependent variable is grouped. Review of Economic Studies 50, 53–73. Venti, S.F., Wise, D.A., 1984. Moving and housing expenditure: Transactions costs and disequilibrium. Journal of Public Economics 23, 207–243.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
625
Venti, S.F., Wise, D.A., 1989. Aging, moving and housing wealth. In: Wise, D.A. (Ed.), The Economics of Aging. Chicago University Press for NBER, Chicago, pp. 9–48. Venti, S.F., Wise, D.A., 1990. But they don’t want to reduce housing equity. In: Wise, D.A. (Ed.), Issues in the Economics of Aging. Chicago University Press for NBER, Chicago, pp. 13–29. Venti, S.F., Wise, D.A., 1991. Aging and the income value of housing wealth. Journal of Public Economics 44, 371–397.
Housing costs, house price shocks and savings behaviour among older households in Britain Richard Disney a,b , *, Andrew Henley c , Gary Stears d a
School of Economics, University of Nottingham, University Park, Nottingham NG7 2 RD, UK b Institute for Fiscal Studies, London WC1 E 7 AE, UK c School of Management and Business, University of Wales Aberystwyth, Penglais, Ceredigion, Wales SY23 3 DD, UK d Association of British Insurers, London EC2 V 7 HQ , UK Received 25 April 1999; received in revised form 12 June 2001; accepted 25 June 2001
Abstract Most households around retirement age hold much of their non-pension wealth in the form of housing. The paper uses a two wave panel to examine housing wealth and saving behaviour by the elderly in Britain between 1988 and 1994. It examines the response of households to house price shocks, and also to ‘excess’ housing costs relative to income. The results differ between non-movers and movers, and are corrected for the non-random nature of the moving decision. It finds a marginal propensity to save to offset the house price shock of 0.96 for movers, but an insignificant impact for non-movers. ‘Excess’ housing costs do not appear to influence the moving decision and are positively associated with saving, suggesting strong heterogeneity in tastes for overall asset holding. 2002 Elsevier Science B.V. All rights reserved. Keywords: Housing equity; Home ownership; Saving; Retirement JEL classification: D12; J26
*Corresponding author. Tel.: 1 44-115-951-5151; fax: 1 44-115-951-4159. E-mail address: [email protected] (R. Disney). 0166-0462 / 02 / $ – see front matter 2002 Elsevier Science B.V. All rights reserved. PII: S0166-0462( 01 )00086-2
608
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
1. Introduction This paper considers the saving behaviour of a sample of older owner occupiers in Britain in the late 1980s and early 1990s, using a short panel of household data. In particular, it focuses on the relationship between changes in household housing wealth, housing costs, and saving in financial assets. The paper makes two broad contributions to the existing literature. The first concerns the impact of exogenous shocks to housing wealth on household saving behaviour. This period in the UK is particularly interesting insofar as real average house prices fell by 30% (11% in nominal terms), albeit with a good deal of regional variation that we exploit as a proxy for expected household housing wealth changes. We look for evidence of life cycle consumption and wealth smoothing — that is, for adjustments of household saving levels to offset housing wealth changes such as to leave total wealth and, therefore, prospective consumption unchanged. Our empirical results show that households that moved home in the period almost completely offset these shocks to housing wealth by adjusting their financial asset holdings. In contrast, households that did not move made no discernible adjustment to their saving behaviour. Of course, the majority of households did not move; moreover, the decision to move is non-random and the estimates of housing wealth-saving offsets are corrected for the selectivity of the mover-stayer decision. Our second finding concerns the relationship between moving behaviour, financial asset holding and the ratio of holdings of housing wealth to current ‘needs’ and income. It has been suggested that households with ‘excess’ costs of housing relative to current income and family size are more likely to move in order to bring housing costs into equilibrium with financial flows. Among older households, this seems a particularly pertinent issue since households in later life typically experience changes in family needs (children becoming independent) and changes in current income (such as a fall after retirement) which might induce them to ‘downsize’ their housing stock. Unlike the first issue, therefore, which considers the overall level of wealth, this finding focuses on the portfolio composition of household wealth and in particular on the possibility that wealth illiquidity may induce ‘financial stress’, especially among older households, leading them to downsize housing. We do indeed find evidence that moving behaviour is associated with family ‘events’, such as inheritance of a house or the spouse retiring. However, we find no evidence of moving behaviour being related to our measure of ‘financial stress’: the ratio of housing costs to current income. Moreover, in a number of specifications, housing expenditure relative to income is positively associated with saving in financial assets, suggesting that we are observing strong heterogeneity in tastes for asset-holding, rather than adjustment of assets to a common wealth– income ratio at this stage of the life cycle.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
609
In all this, however, there is one potential ‘puzzle’ (at least, for the standard consumption smoothing model). Households that move do generally downsize their housing wealth, and increase financial assets to maintain their overall wealth position. But moving behaviour itself is uncorrelated both with the regional change in house prices (the proxy for expected changes in household housing wealth) and with the household ratio of housing costs to income — variables that might be expected to affect the probability of housing wealth adjustment. It may be that this particular period is unusual given that it was dominated by house price falls, which undoubtedly made older households reluctant to move. Indeed, there is some tentative evidence elsewhere of a correlation between moving behaviour and ‘excess’ house size over a slightly longer period for Britain for this age group (Ermisch and Jenkins, 1999). However, we reiterate that this line of argument does not explain the general positive relation between housing costs (conditioned on income) and saving that is observed in our data. Further work on this issue is required in the UK context. Our interpretation of why households move, consistent with our results, is slightly different. We find that actual moving behaviour is related to moving intentions and to residential tenure, as well as to household retirement outcomes and bequests. We interpret this as suggesting that households plan housing moves in relation to life cycle ‘events’ and also exhibit strong heterogeneity in moving behaviour, which may reflect tastes, or housing-specific differences in moving costs. It is impossible to pin this down further given the limited small number of moves in the data [see Venti and Wise (1984), for a fuller treatment]. Thus, overall, we conclude that households in this age group that do move use the opportunity to adjust wealth holding consistent with life cycle consumption smoothing, but that other households simply absorb wealth shocks without changing saving behaviour. The data utilised in the paper comprise the two waves of the UK Retirement and Retirement Plans Survey, conducted in 1988–89 and 1994 [hereafter RS, and described in greater detail in OPCS (1992) and Disney et al. (1997)]. This Survey, carried out by the Office of National Statistics (previously the Office of Population Census and Surveys), is a random sample of over 2500 households that contained at least one person aged 55–69 in 1988–89. The survey contains information on a wide variety of socio-economic characteristics as well as evidence on housing tenure, housing costs and other wealth holdings.1 It also contains a full event history concerning labour market states, and a family history. Furthermore, the survey contains interesting questions concerning peoples’ intentions and expectations about retirement and their economic behaviour, including (dis)saving behaviour since retirement. Although sharing much in common with the US Health and Retirement Survey and AHEAD, the Retirement Survey has only two 1 On the evolution of wealth holdings, including financial and housing wealth, and imputed social security and pension wealth, see Disney et al. (1997) and Disney et al. (1998).
610
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
waves and it is not possible to use panel estimators that require several lags as instruments. The plan of the remainder of the paper is as follows. The next section surveys some contributions to the literature that focus on the relationship between housing wealth, moving behaviour and the change in financial assets. Section 3 provides some empirical description of house price changes, of the Retirement Survey data, and briefly examines household housing expenditure. Section 4 estimates a joint model of moving behaviour and financial asset accumulation. Section 5 summarises the main conclusions of the paper.
2. Previous literature and some hypotheses In the basic life cycle hypothesis of saving (LCH), households in the age category examined here are entering the stage of their life when they should generally be running down their household wealth, either by reducing financial assets, moving in order to ‘downsize’ housing wealth, or through remortgaging or ‘equity release’ schemes. However, two kinds of evidence cast doubt on the stylised LCH model as an adequate description of saving behaviour among this age group. First, evidence from the United States suggests that households continue to accrue net wealth through asset appreciation even late in their life (Merrill, 1984; Feinstein and McFadden, 1989; Venti and Wise, 1989, 1990, 1991; Sheiner and Weil, 1992). Second, households tend to cut consumption sharply at or after retirement, leading to the so-called ‘retirement saving puzzle’ (Banks et al., 1998). There are a number of potential explanations for this finding that older households do not decumulate assets to the extent predicted by a simple LCH model. These include the role of the bequest motive (Bernheim, 1991), shifts in preferences at retirement, precautionary saving against incapacity and disability, ¨ ceilings on consumption imposed by poor health (Borsch-Supan and Stahl, 1991), and so on. A key factor, however, is likely to be that a large fraction of the assets of older households is tied up in housing wealth. Real increases in the value of housing wealth over time therefore lead to what is commonly termed ‘passive’ saving. Nevertheless, many studies in both the UK and the US find evidence that changes in housing wealth do lead to changes in consumption and to reductions in non-housing wealth (dissaving), although the implied marginal propensities to consume from housing wealth are rather small.2 A particularly interesting finding 2 For evidence that consumption may be related to housing wealth, see Skinner (1989, 1993) in the US context, and, for the UK, Acemoglou and Scott (1994), Miles (1992) and Bayoumi (1993). For evidence as to whether housing windfalls affected consumption in the UK, see the debate between Muellbauer and Murphy (1990) and Attanasio and Weber (1994).
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
611
is that of Engelhardt (1996) who finds an asymmetric response of saving behaviour to house price changes, with saving in financial assets much more responsive to house price falls than to rises. As mentioned in the introduction, many of the households in our sample faced house price falls during the period in which their behaviour was observed. Even if housing wealth and financial wealth are imperfect substitutes, there are other potential means of adjusting wealth portfolios, which involve adjusting the stock of housing wealth directly. One possibility is to remortgage, or to utilise reverse annuity mortgages and other equity release schemes, either to finance consumption or to adjust the liquidity of the wealth portfolio. There it little evidence, either from the United States (Venti and Wise, 1991) or the UK (Leather, 1990), of older households using second mortgages as a means of financing consumption. Schemes to release housing equity have also proved relatively unpopular, especially in the UK. First, the very thinness of the market generates the perception of, and the likelihood of, adverse selection. Secondly, the present value of the annuity released through equity release is generally rather small, at least until households near the end of their life (Venti and Wise, 1991; Skinner, 1993; Hancock, 1998).3 The most direct means of adjusting housing wealth is for the household to move, generally ‘downsizing’ the family home, and thereby releasing housing equity. However, a feature of home ownership that distinguishes it from other asset holdings is that housing is both a consumption good as well as a potential form of wealth-holding (Henderson and Ioannides, 1983). Home ownership generates a flow of services, both real and psychic, and decisions concerning optimal consumption of housing services may not easily be separable from optimal asset holdings. Moreover, older households in particular may be reluctant to move house to adjust their lifetime wealth position because of high moving costs or psychic costs from leaving a known domestic environment (Venti and Wise, 1990; Megboulugbe et al., 1999) Consequently, their consumption behaviour may be constrained by asset illiquidity. This may induce risk averse households to reduce other forms of consumption expenditure or lead to an accelerated decline in holdings of other, more liquid, assets. Given this potential disjunction between the twin roles of home ownership as an investment and as a flow of consumption services, we should augment our estimates of moving behaviour and the household wealth trajectory by measures of ‘excess’ housing costs or other physical measures of ‘excess’ housing space relative to ‘needs’.4 But high measured housing costs (budget shares) may also reflect heterogeneity of preferences for housing consumption, and indeed heteroge3 Falling returns in bond markets in the UK in the 1990s also made annuitisation of housing wealth relatively unattractive. 4 See Feinstein and McFadden (1989) and Venti and Wise (1989), for the US; Disney et al. (1994), and Ermisch and Jenkins (1999), for the UK, and Stahl (1989), for Germany.
612
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
neous tastes for saving and for holding wealth. It is hard however to separate out ‘excess’ costs of housing from preferences with only two temporal observations in the panel. Although we do not provide a definitive answer to this difficulty, we believe that exploiting the variation in exogenous price shocks across regions and judicious selection of instruments in our two-step procedure cast light on this issue.
3. Data sources and description This section provides evidence concerning holdings of both housing wealth and financial assets by age group at each wave of the survey, and measures of housing consumption for homeowners and renters. Our basic unit of analysis, the household, is defined as the administrative ‘benefit unit’ in the United Kingdom, which is a single person or couple plus dependents. We focus only on households where the head was aged between 55 and 69 in the 1988–89 wave of the survey, and who reported data in both waves of the survey.5 Just under 1400 households who survived and who provided all the requisite information are utilised in the ensuing analysis. As background information, we note that between the peak of the housing market in 1989 Q3 and 1994 Q4, real average house prices for the UK fell by 30%, and by over 11% in nominal terms. As Fig. 1 suggests, this episode was not unique in the recent history of fluctuations in real UK house prices, although unusual in that nominal prices also fell.6 However, these price falls were uneven across the country, with the largest falls concentrated in London and the South East where the boom of the mid-1980s in asset values had been most pronounced, whereas in the northern regions of England and Scotland, real house prices were more stable (Table 1).7 We exploit this regional variation to show that the rate of accumulation or decumulation of financial assets among older households is sensitive to the change in the value of housing assets. In contrast to housing wealth, however, there were real increases in the value of the equity market over the period, arising from the recovery from the ‘crash’ in 1987.8
5 There is very strong evidence that attrition of the sample, both through death and non-response, is highly non-random. Survivors are more likely to be owner-occupiers, to own more valuable houses and to have larger holdings of financial assets (Disney et al., 1998). 6 These figures were computed from the mix-adjusted house price series published by the UK Office for National Statistics, deflated in the case of the real series by the consumer price index. 7 Quarterly average house price data by UK region are from Halifax plc, deflated by the consumer price index (change over the period 25.5%). 8 The ‘shakeout’ in house prices was in part a lagged response to the bursting of the bubble in asset markets that occurred in the late 1980s, but we do not consider the behaviour of asset prices further here. On house price changes, see Henley (1998).
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
613
Fig. 1. Annual change in real and nominal average UK house prices 1957–1998. Source: Office for National Statistics.
Table 1 Real house price change 1989 Q3 to 1994 Q4 by UK region Percent Greater London South East outside London East Anglia South West West Midlands East Midlands Yorkshire and Humberside North North West Wales Scotland Northern Ireland
247.7 248.2 250.3 247.2 236.1 242.2 229.5 223.5 225.0 237.9 29.3 20.1
614
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
Table 2 Average housing and financial wealth by age and tenure status of head of household in 1988 Age, tenure No.
1988 Mean
1994
Mean change
% Change
Median
Mean
Median
Housing wealth Owning stayer 100,199 Owning mover 116,063 Owner to renting 59,545
92,256 101,305 52,233
74,163 80,079 0
68,884 76,278 0
226,036 235,984 259,545
226.0 231.0 2100.0
Financial wealth Owning stayer 20,072 Owning mover 29,069 Owner to renting 2,407
7,967 10,315 1,672
27,779 45,052 6,194
11,796 23,241 4,085
7,707 15,983 3,787
38.4 55.0 2157.3
Note: Constant January 1996 prices.
Table 2 depicts predicted mean and median holdings of housing and financial wealth among owner-occupier households observed in both waves of the Retirement Survey, by (changes in) tenure status in 1988–89 and 1994 (all values are in January 1996 prices). Household housing wealth values are derived from banded information on house values provided by the respondents themselves. Predictions are then estimated using a grouped dependent variable (GDV) estimator.9 There are some striking statistics in Table 2. Owner occupiers in the survey on average saw a sharp decrease in real housing wealth between 1988–89 and 1994, mirroring the national decline across all households. This is wholly due to house price deflation dominating the offsetting effects of the net decline in the mortgage liability of households in this age range. Those households who moved typically had a slightly higher value of housing wealth in 1988–89 than non-movers, and the average decline in the value of their housing wealth between 1988–89 and 1994 was greater than that of non-movers. Only a small number of owner occupiers in 1988–89 had moved to rented status by 1994, and indeed they were outweighed by a few moves in the opposite direction (not shown in Table 2). In contrast, average real financial wealth of households in the survey increased sharply between the two dates, although the absolute magnitude of the change was
9
The GDV estimator treats each value of the banded variable as a latent variable within known limits, and finds a best prediction of that value given exogenous information and an assumed overall underlying distribution. The additional regressors comprised a quadratic in age, and gender, marital and employment status of the head of household, socio-economic group, regional dummies, and whether the property was connected to mains drainage and water supplies. The assumed underlying distribution is log-normal. The estimator is described in Stewart (1983), although here the estimates were obtained directly by Maximum Likelihood. For further details of the method used, see Disney et al. (1997) and Disney et al. (1998). To move from a measure of house value to housing wealth, the value of outstanding mortgages was estimated and deducted from each household’s calculated house value.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
615
less than the average decline in real housing wealth.10 As suggested above, this partly reflected the ‘passive saving’ effect of a rising equity market. Overall, movers saw a greater real increase in their financial wealth than non-movers, suggesting that some ‘downsizing’ took place for portfolio reallocation purposes. Specifically, it can be noted that the housing wealth of movers fell, on average, by just under £10,000 more than non-movers, while the financial assets of movers grew by just over £8000 more than non-movers, between 1988–89 and 1994. Next, we examine housing costs to the household, which might also influence moving and saving behaviour. To do this, we calculate the housing budget share (HBS), for which we need a measure of household income and a flow measure of expenditure on housing. Our income measure for the household unit is defined as the sum of earnings, investment income, private pensions, social security and other income, aggregated up from the individual level. Welfare payments, including payments of housing benefit, council tax benefit (paid to offset local property taxes), rent and rate rebates and rent contributions from other resident individuals are also included in gross income.11 It is of course not possible to measure directly the flow of housing services provided by home ownership but one can measure expenditure on housing services and maintenance, and this measure of ‘user cost’ is feasible given data available in the Retirement Survey. Our measure of the ‘user cost’ of housing is defined as the sum of mortgage payments, rates, water rates, maintenance and insurance costs, and other costs.12 The maintenance costs are imputed to equal 1.0% of nominal house value. Insurance costs are assumed to be 0.25% of nominal house value in 1988–89 and 0.325% of house value in 1994, reflecting the relative increase in insurance costs to house values (because of static or falling nominal house prices) between the two periods. We define the ratio of out-of-pocket housing costs to current after-tax income as the predicted housing budget share (HBS). Table 3 examines mean and median HBS in 1988–89 and 1994 for the same household types as Table 2, separating those who stayed in the same house between waves from those who moved, and differentiated by whether they thereby
10
The financial asset estimates were obtained by a similar procedure to those for household housing wealth. After aggregating individual reported assets to form an overlapping set of household asset bands, a GDV estimator was used. The additional regressors comprised information on individual assets held, an estimate of overall income from assets, and a similar set of household characteristics. After a certain amount of experimentation, it was found that a gamma distribution best fitted the overall distribution of the variable (in the sense that the predicted values of wealth were successfully located in the reported band). Further details are obtained in Disney et al. (1998). 11 There are two ways in which welfare payments for housing costs can be treated: either the way chosen here, or as income net of payments with the sum of payments also deducted from housing costs. Either is a consistent treatment of the problem of state support provided to low income renters and home owners. 12 Other costs include service charges, ground rent, Scottish feu duty and compulsory maintenance charges.
616
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
Table 3 Housing budget shares by tenure type and tenure transitions Tenure type
No. in sample
Owner of which: Owning stayer Owning mover Owner to renter
1988
1994
Mean
Median
Mean
Median
958
0.201
0.169
0.201
0.176
889 61 8
0.202 0.181 0.184
0.170 0.167 0.187
0.200 0.186 0.445
0.167 0.159 0.485
changed tenure type. Interestingly, there is very little change by tenure transition other than a sharp rise for the small number who moved from owner occupation to rented accommodation (we can speculate as to the reasons for this but the small sample size limits any inferences). In particular, there is no evidence of significant changes in mean HBS arising from ‘downsizing’, although the median HBS of owners who moved between the sample dates is slightly lower in 1994. The average HBS for owners is very similar to the averages for the United States described by Feinstein and McFadden (1989), Table 2.4. In the analysis of the next section, we utilise an auxiliary ‘residual HBS’ variable to examine the impact on moving behaviour and financial asset accumulation of ‘excess’ (or deficient) expenditure on housing conditioned on current income and other characteristics. Table 4 therefore presents a household expenditure regression for 1988–89 for those owner occupiers who were also present in the 1994 wave of the Survey. Log housing expenditure is strongly related to log income (although the coefficient is significantly different from one), household demographics and other characteristics, and region.
4. Modelling the evolution of financial assets This section examines the impact of the house price ‘shock’ and of potential ‘excess’ consumption of housing services on the evolution of household saving. Given that the trajectory of financial wealth should be conditioned on whether the household moved or not, a simultaneous model of the moving decision and of the evolution of financial assets is constructed, based on Lee’s (1978) empirical methodology.13 Although other motives will also underpin the decision to move home, the two step model structure permits the household to select into a state where housing equity can be released which may be used to restore the wealth 13
Lee used the model to assess the impact of union status on wages, where union status and wages are modelled in a two-stage process to allow for selection into union membership / non-membership to be determined by the difference in wages in the two sectors.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
617
Table 4 Housing expenditure regression (1988–89) — Dependent variable: Log(monthly housing expenditure) Variable
Coefficient
S.E.
Constant Log(monthly gross income) Age Age squared / 100
2.684 0.302 20.004 20.000006
(0.517)* (0.023)* (0.014) (0.0001)
Head of household: Single Widowed Divorced Female Working part time Unemployed Retired Housewife Self-employed Disabled Spouse employed Has private pension rights Owns outright
20.139 20.164 20.142 20.100 0.136 0.049 0.089 0.195 0.183 0.032 20.073 20.047 20.371
(0.047)* (0.047)* (0.058)* (0.039)* (0.045)* (0.067) (0.037)* (0.105)1 (0.066)* (0.049) (0.041)1 (0.028)1 (0.028)*
Region: Scotland Northern England York & Humber North West East Midlands East Anglia Greater London South East South West Wales
20.146 20.220 20.155 20.101 0.082 0.271 0.343 0.388 0.271 20.307
(0.070)* (0.065)* (0.052)* (0.052)1 (0.057) (0.067)* (0.054)* (0.046)* (0.054)* (0.062)*
Number in sample R2 R.M.S.E.
958 0.5662 0.3534
Notes: Default categories are: married couple, headed by a man, employed with inactive spouse, with no private pension, with a mortgage, living in the West Midlands. Coefficients (standard errors in brackets); significance at 5% level indicated by *, 10% by 1 .
portfolio (allowing for moving costs). Thus the conditional mover / stayer decision is determined by the difference in the size of financial asset holdings in the two states. The model structure is as follows (see Maddala, 1983, pp. 236ff): A Mi 5 uM 0 1 XMiuM 1 1 ´Mi
(1)
A Si 5 uS 0 1 XSiuS 1 1 ´Si
(2)
618
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
I *i 5 d0 1 d1 (A Mi 2 A Si ) 1 d2 Zi 2 yi
(3)
A Mi and A Si are (log) financial asset holdings in 1994 under each state (mover or stayer), although only observed for each household i in whichever transition occurs between 1988–89 and 1994. I *i is a latent indicator variable capturing the net benefits of moving, such that moving occurs if I *i . 0 or not otherwise. X and Z are vectors of covariates, with identification of the model obtained by elements of Z not in X, uM 0 , uS 0 and u0 are intercepts, uM 1 , uS 1 and u1 are coefficient vectors and ´Mi , ´Si and ni are independently normally distributed disturbance terms. Substitution of the asset Eqs. (1) and (2) into the selection equation gives a reduced form equation: I *i 5 g0 1 g1 Ki 2 y *i
(4)
where Ki contains all the exogenous variables in X and Z. With the disturbance normalised to have unit variance, Eq. (4) can be estimated as a conventional probit, and the results used to provide a selectivity correction. Conditional on mover / stayer status the financial asset equations are thus given by:
F G
f (ci ) A Mi 5 uM 0 1 XMiuM 1 2 s1 y * ]] 1 hMi F(ci )
F
G
f (ci ) A Si 5 uS 0 1 XSiuS 1 2 s2 y * ]]] 1 hSi 1 2 F(ci )
(5) (6)
where ci 5 g0 1 g1Wi and sj y * 5 cov(´j , y * ), j 5 M, S. The right hand side term in brackets is a selectivity term, expressed in terms of the density (f ) and cumulative distribution (F) of a standard normal distribution. Given this formulation of the model we predict a negative selectivity coefficient in Eq. (5) (movers) and a positive one in Eq. (6) (stayers). Assuming valid identifying restrictions, consistent estimates can be obtained through staged estimation of Eqs. (4), (5) and (6). We hypothesise that the decision to move and the (log) level of financial assets at the point of the second wave will depend on three categories of variables: household characteristics, state variables in 1988–89, and changes in circumstances between 1988–89 and 1994. Identification is obtained by excluding from the financial asset equation 1988–89 state variables. This identification strategy is somewhat ad hoc, but we note that two of the state variables that turn out to be individually significant are years of tenure in the residence ( in 1988 – 89) and would like to move within 5 years ( in 1988 – 89). Both of these might reflect individual preferences for (not) moving per se. A positive and significant relationship between the propensity to move and this ‘residual HBS’ variable, described in the previous section, would suggest that households are able and willing to move in response to housing budget disequilibrium. Any other sign suggests that we are merely observing preferences or ‘taste’ for high housing consumption.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
619
Among the variables capturing changes in circumstances between 1988–89 and 1994, we are particularly interested in the impact of the house price ‘shock’. Since observed house wealth is endogenous, we proxy this by the regional house price change. There are however at least two factors at work here. Where the house price decline was disproportionately large and expected to continue, the household might be inclined to shift its portfolio towards financial assets by downsizing. Alternatively, if the household expected the price change to reverse, or was unwilling to taking a nominal loss in housing wealth, it might stay put through the downturn. This would be apparent in a lower level of mobility per se. There is some evidence that this latter strategy predominated: less than 1% of households moved from owner occupation into renting over the 5-year period and 7% in all moved.14 In consequence, there are only 69 movers who were in owner-occupation in 1988–89 in the selection equation. Ermisch and Jenkins (1999), using the British Household Panel Study, found a rate of movement among all households headed by a person aged 55 and over of 3.3% per annum between 1991 and 1995. However these rates are not strictly comparable with ours for two reasons. Firstly, Ermisch and Jenkins include renters, among whom mobility is generally higher, and secondly the period in question is overall a period of slow recovery in nominal house prices after the dramatic collapse in the 1989–1991 period (Fig. 1).15 Nevertheless we would still expect movers to reallocate their portfolio towards financial assets, while non-movers might respond by increasing their saving. On the relationship between financial assets (relative to 1988–89) and the adverse house price ‘shock’, for movers a negative sign would suggest some attempt to release housing equity, and for stayers an attempt to increase saving to compensate for the decline in the overall asset value. The relationship between housing budget share (HBS) in 1988–89 and financial asset changes is harder to predict a priori. If ‘high’ HBS in 1988–89 indicates an ‘unaffordable’ level of housing, then, conditioned on not moving, we might observe either a lower level of consumption or a reduction in financial assets over that period, and thus the potential for a negative correlation between HBS and the change in financial assets. A second possibility is that a ‘high’ HBS reflects a preference for housing 14
Those that did move reported an even greater reduction in housing equity, which may have arisen arise from explicit ‘downsizing’ of housing equity. However, given house values are self-reported for movers and non-movers alike, the decline may arise simply from a comparison of an over-inflated valuation with a market value, see Disney et al. (1997). It is of interest that 182 individuals in the original 1988–89 sample reported that they intended to move house in the next five years in order to withdraw equity. Of this number, just over 40% reported the motive ‘to make other investments’ whereas almost all others reported as their motive some form of consumption decision. 15 There is however a significant rate of attrition in the RS, not all of which is related to mortality (see Disney et al., 1998). If movers are more likely to attrite, relative to, say, the BHPS, this would also explain some of the discrepancy between the two surveys other than the different time periods involved.
620
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
wealth over other kinds of wealth or a preference for wealth accumulation per se, which would induce a positive correlation between HBS (via the ratio of housing wealth to income) and financial wealth. Finally, a possibility is that measurement error both in HBS and financial assets rules out any correlation. Table 5 reports our preferred estimates of Eqs. (4)–(6), with a specification Table 5 Determination of household financial assets: selectivity-corrected estimates Variable
(1) Selection equation
(2) Asset equation: movers
(3) Asset equation: stayers
Coefficient
Standard error
Coefficient
Standard error
Coefficient
Standard error
Constant Log financial assets 1988–89 Housing wealth/10000 1988–89 Head of household female Age (in years — 54 in 1994) Age squared Single Disability severity score
21.479 20.016 0.022 20.326 0.006 0.0002 20.057 0.012
(0.798)1 (0.036) (0.023) (0.214) (0.118) (0.004) (0.225) (0.049)
9.008 0.220 20.032 20.408 0.082 20.004 20.273 20.083
(2.060)* (0.089)* (0.054) (0.513) (0.310) (0.012) (0.527) (0.114)
6.317 0.425 0.068 0.031 20.179 0.006 20.272 20.178
(0.618)* (0.027)* (0.017)* (0.157) (0.087)* (0.003)* (0.155)1 (0.036)*
1988–89 status Residential tenure (years) Moved after retirement Like to move within 5 years Residual HBS
20.014 20.241 1.169 20.075
(0.007)* (0.259) (0.153)* (0.182)
0.097
Change in status 1988–89 to 1994 D log(monthly gross income) 0.070 Spouse retired 0.521 Disability score worsened 20.171 Inherited property 0.630 Received pension lump sum 20.068 Regional house price change 0.660 1988–89 to 1994
(0.163) (0.171)* (0.256) (0.238)* (0.183) (0.572)
1.391 20.599 1.342 0.216 0.847 24.775
(0.366)* (0.380) (0.704)1 (0.488) (0.387)* (1.475)*
0.545 20.033 20.522 20.194 0.759 0.405
20.787
(0.347)*
0.496
Selectivity term Number in sample Log Likelihood x 2 (18) F(15,53) F(15,873) x 2 (3) Wald test joint significance of additional instruments: x 2 (3) Wald test of exclusion restrictions:
958 2205.40 85.15*
(0.334)
69 296.76
0.191
(0.165)
(0.118)* (0.148) (0.170)* (0.257) (0.142)* (0.430)
(0.449) 873 21613.02
4.19* 33.72*
61.26* 0.705
3.223
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
621
sensitivity analysis reported in Table 6. A Wald test reported beneath the selection equation in column 1 of Table 5 shows that the three 1988–89 status variables that appear as additional identifying instruments are highly jointly significant. Further Wald tests reported beneath each equation accept the exclusion of these variables from the two financial asset equations in columns 2 and 3. The preferred specification of the moving selection equation fails to find a significant impact on the likelihood of moving from 1988–89 housing ‘disequilibrium’ (a high residual HBS), or from regional price changes. Moving behaviour is related to length of tenure in the house — the longer the tenure, the lower the probability, and to the previous intention to move. Events that significantly affect the probability of moving include retirement of the spouse and receipt of an inherited property. These results are consistent with a model of moving where households plan moves in advance to coincide with family and work-related transitions, and where there is significant ‘duration dependence’ in housing tenure. Households do not appear to move primarily in order to adjust wealth portfolios although, as column 2 reveals, they do use moves to smooth wealth. The results in column 2 reveal that for movers assets depend significantly and Table 6 Alternative asset equation specifications (1) (a) Movers Regional house price change Residual HBS Selectivity term Log L (b) Stayers Regional house price change Residual HBS Selectivity term Log L
(2)
24.338 (1.197)* – 20.714 (0.356)* 2119.6
20.790 (0.376)* – 0.861 (0.429)* 21680.9
(3)
(4)
24.006 (1.379)* 0.199 (0.352) 20.780 (0.371)* 2115.1
23.995 (1.266)* 0.119 (0.333) 20.761 (0.376)* 2106.1
24.217 (1.136)* 0.013 (0.301) 20.744 (0.338)* 297.6
20.563 (0.366) 0.488 (0.161)* 0.792 (0.422)1 21648.9
20.541 (0.361) 0.486 (0.159)* 0.832 (0.453)1 21630.5
20.567 (0.356) 0.400 (0.158)* 0.729 (0.449) 21621.1
(5)
24.775 (1.475)* 0.097 (0.334) 20.787 (0.347)* 296.8
0.405 (0.430) 0.191 (0.165) 0.496 (0.449) 21613.0
(1): lagged assets only (2): lagged assets and 1988–89 status variables (3): lagged assets, 1988–89 status variables and change in status variables (4): lagged assets, 1988–89 status variables, change in status variables and change in income (5): lagged assets, 1988–89 status variables, change in status variables, change in income and 1988–89 housing wealth (as in Table 5) Note: Coefficients (standard errors in brackets); significance at 5% level indicated *, 10% by 1 .
622
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
positively on both assets in the earlier wave and on the change in income between the waves. However for stayers in column 3 the coefficient on assets in the earlier wave is nearly twice as large as for movers. This stronger degree of persistence in assets is consistent with stayers being less able to adjust their financial assets in the face of the housing wealth shock through the release of housing equity. For this group higher housing wealth in 1988–89 is associated with significantly higher financial wealth in 1994. No significant effect arising from the residual HBS is found for either group of households in this specification (although see Table 6). For movers the selectivity term has the expected negative, significant coefficient. This indicates that these have a comparative advantage in the accumulation of financial assets over non-movers with the same control characteristics. For stayers no significant selectivity effect is found in this specification (but again see Table 6). For movers the regional house price change between 1988–89 and 1994 has a strong and statistically well-determined impact on 1994 financial assets, consistent with a strong ‘offset’ effect on saving from an adverse housing wealth shock. For stayers the regional house price attracts a positive coefficient but one that is not statistically significant in our preferred specification. Table 6 reports the key model coefficients, including the selectivity term, for alternative asset equation specifications. The size of the saving ‘offset’ for movers is robust to the broadening of the set of covariates, rising slightly between specifications (4) and (5) with the addition of 1988–89 housing wealth. A positive, though not well-determined, housing ‘disequilibrium’ effect declines as the specification is broadened. For stayers the exclusion of 1988–89 housing wealth yields a significant positive coefficient for the residual HBS variable. The inclusion of housing wealth halves the HBS effect. As the accumulation of housing wealth and financial wealth positively correlate, this supports the conclusion that both residual HBS and housing wealth are capturing a general ‘taste’ for housing amongst the more wealthy, perhaps driven by a strong bequest motive. The inclusion of 1988–89 housing wealth in the stayer equation also appears to be capturing a substantial element of the apparently significant selection effect in specifications (1)–(4). So for stayers falling house prices do not lead to a saving ‘offset’ to counteract the fall in the individual’s overall wealth, whereas by contrast for movers the ‘offset’ is pronounced. Assuming typical levels of housing wealth and financial wealth for this sample of £100,000 and £20,000, respectively (see Table 2), then a 10% adverse movement in house prices would require a 50% increase in financial assets in order to leave the total value of the individual’s portfolio unchanged. This elasticity of 5 is very close to the estimated elasticity of 4.775 for movers, comfortably within a 95% confidence interval. Individuals who are willing and able to move appear to offset almost fully the adverse house price shock of the early 1990s through increased accumulation of financial wealth. At these typical asset levels the implied marginal propensity to save to offset housing wealth loss for movers is, ceteris paribus, 0.955.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
623
5. Conclusion This paper has examined the trajectory of housing wealth and financial assets for households at, and subsequent to, retirement age. Using data from the two waves of the Retirement Survey, empirical results broadly confirm the US evidence, showing that substantial housing wealth is often retained into old age, whether for bequest or precautionary motives, or because transactions costs outweigh the gain from downsizing. But a significant minority of households thereby incurs large user costs of housing (Housing Budget Shares). Homeowners also have significant financial assets, unlike renters (in the latter case, see Disney et al., 1994, 1998). The period between 1988–89 and 1994 is an unusual one, in that real house prices fell sharply over that period, although differentially across regions, while financial asset markets remained buoyant. We examine both moving behaviour and financial asset acquisition in the light of this experience, conditioning the latter on the former. Subject to the caveat that our sample of movers is rather small, we show that the rate of house mobility among owner occupiers was rather low, but that nevertheless movers used moving to accumulate financial wealth that almost exactly offset the locally-specific adverse change in housing wealth. In contrast, stayers did not raise saving to offset the decrease in housing wealth. There is also firm evidence from the significant sample selection effect that moving and financial asset determination are interdependent. In contrast, there is no evidence that households with high HBS in 1988–89 were either more likely to move or to run down financial assets. Indeed particularly for stayers we find evidence that higher housing wealth (conditioned on other characteristics) is associated with greater saving. This suggests that what is being observed in the housing budget share is heterogeneity of household tastes for overall wealth accumulation rather than any ‘excess’ housing relative to demand, planned portfolio reallocations, or noise in the data. With strong evidence of heterogeneity of tastes for wealth, and low rates of moving home, we conclude that the relative thinness of the ‘equity release’ market may simply stem from low demand, rather than from any failure of the financial markets themselves.
References Acemoglou, D., Scott, A., 1994. Consumer confidence and rational expectations: Are agents’ beliefs consistent with theory? Economic Journal 104, 1–19. Attanasio, O.P., Weber, G., 1994. The UK consumption boom of the late 1980s: aggregate implications of macroeconomic evidence. Economic Journal 104, 1269–1302. Banks, J., Blundell, R., Tanner, S., 1998. Is there a retirement saving puzzle? American Economic Review 88 (4), 769–788. Bayoumi, T., 1993. Financial deregulation and housing saving. Economic Journal 103, 1432–1443.
624
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
Bernheim, B.D., 1991. How strong are bequest motives? Evidence based on estimates of the demand for life insurance and annuities. Journal of Political Economy 99 (5), 899–927. ¨ Borsch-Supan, A., Stahl, K., 1991. Life cycle saving and consumption constraints. Journal of Population Economics 4, 233–255. Disney, R., Gallagher, T., Henley, A., 1994. Excess housing costs and the behaviour of elderly households. University of Kent at Canterbury Studies in Economics: 94 / 8. Disney, R., Grundy, E., Johnson, P. (Eds.), 1997. The Dynamics of Retirement. Department of Social Security Research Report No. 72. The Stationery Office, London. Disney, R., Johnson, P., Stears, G., 1998. Asset wealth and asset decumulation among households in the Retirement Survey. Fiscal Studies 19, 153–174. Engelhardt, G.V., 1996. House prices and home owner saving behaviour. Regional Science and Urban Economics 26, 313–336. Ermisch, J., Jenkins, S., 1999. Retirement and housing adjustment in later life: evidence from the British Household Panel Survey. Labour Economics 6, 311–333. Feinstein, J., McFadden, D., 1989. The dynamics of housing demand by the elderly: wealth, cash flow and demographic effects. In: Wise, D.A. (Ed.), The Economics of Aging. Chicago University Press for NBER, Chicago, pp. 55–86. Hancock, R., 1998. Can housing wealth alleviate poverty among Britain’s older population? Fiscal Studies 19 (3), 249–272. Henderson, J.V., Ioannides, Y.M., 1983. A model of housing tenure choice. American Economic Review 73, 98–113. Henley, A., 1998. Changes in the distribution of housing wealth in Great Britain, 1985–91. Economica 65, 363–380. Leather, P., 1990. The potential and implication of home equity release in old age. Housing Studies 5, 3–13. Lee, L.-F., 1978. Unionism and wage rates: a simultaneous equation model with qualitative and limited dependent variables. International Economic Review 19, 415–433. Maddala, G.S., 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge. Megboulugbe, I., Sa-Aadu, J., Shilling, J.D., 1999. Elderly female-headed households and the decision to trade down. Journal of Housing Economics 8, 285–300. Merrill, S., 1984. Home equity and the elderly. In: Aaron, H.J., Burtless, G. (Eds.), Retirement and Economic Behaviour. The Brookings Institution, Washington, DC. Miles, D., 1992. Housing markets, consumption and financial liberalisation in the major economies. European Economic Review 36, 1093–1136. Muellbauer, J., Murphy, A., 1990. Is the UK balance of payments sustainable? Economic Policy 11, 345–383. OPCS, 1992. Retirement and Retirement Plans Survey 1988–1989. HMSO for Office of Population Censuses and Surveys, London. Sheiner, L.M., Weil, D., 1992. The Housing Wealth of the Aged. National Bureau of Economic Research Working Paper No. 4115, July. Skinner, J., 1989. Housing wealth and aggregate saving. Regional Science and Urban Economics 19, 305–324. Skinner, J., 1993. Is Housing Wealth A Sideshow. National Bureau of Economic Research Working Paper No. 4552, November. Stahl, K., 1989. Housing patterns and the mobility of the aged: the United States and West Germany. In: Wise, D.A. (Ed.), The Economics of Aging. Chicago University Press for NBER, Chicago, pp. 93–115. Stewart, M., 1983. On least squares estimation when the dependent variable is grouped. Review of Economic Studies 50, 53–73. Venti, S.F., Wise, D.A., 1984. Moving and housing expenditure: Transactions costs and disequilibrium. Journal of Public Economics 23, 207–243.
R. Disney et al. / Regional Science and Urban Economics 32 (2002) 607 – 625
625
Venti, S.F., Wise, D.A., 1989. Aging, moving and housing wealth. In: Wise, D.A. (Ed.), The Economics of Aging. Chicago University Press for NBER, Chicago, pp. 9–48. Venti, S.F., Wise, D.A., 1990. But they don’t want to reduce housing equity. In: Wise, D.A. (Ed.), Issues in the Economics of Aging. Chicago University Press for NBER, Chicago, pp. 13–29. Venti, S.F., Wise, D.A., 1991. Aging and the income value of housing wealth. Journal of Public Economics 44, 371–397.