House Price Volatility and the Housing Ladder
ABSTRACT This paper investigates the effects of housing price risk on housing choices over the life-cycle. Housing price risk can be substantial but, unlike other risky assets which people can avoid, the fact that most people will eventually own their home creates an insurance demand for housing assets early in life. Our contribution is to focus on the importance of home ownership and housing wealth as a hedge against future house price risk for individuals moving up the ladder – people living in places with higher housing price risk should own their first home at a younger age, should live in larger homes, and should be less likely to refinance. These predictions are tested and shown to hold using panel data from the United States and Great Britain.
House Price Volatility and
the Housing Ladder
JAMES P. SMITH
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House price volatility and the housing ladder
Institute for Fiscal Studies and University of Manchester
Institute for Fiscal Studies and University College London
Institute for Fiscal Studies
James P. Smith
This paper investigates the effects of housing price risk on housing choices over the life-cycle.
Housing price risk can be substantial but, unlike other risky assets which people can avoid, the
fact that most people will eventually own their home creates an insurance demand for housing
assets early in life. Our contribution is to focus on the importance of home ownership and housing
wealth as a hedge against future house price risk for individuals moving up the ladder—people
living in places with higher housing price risk should own their first home at a younger age,
should live in larger homes, and should be less likely to refinance. These predictions are tested
and shown to hold using panel data from the United States and Great Britain.
JEL: D12, D91
Acknowledgements: The authors gratefully acknowledge the financial support of the Economic
and Social Research Council through the research grant RES-000-22-0513. Blundell and Banks
would like to thank the ESRC Centre for the Microeconomic Analysis of Public Policy at IFS for
co-funding. James Smith’s research was supported by grants from the National Institute on Aging
and benefited from the expert programming assistance of Patty St. Clair and David Rumpel.
One of the most critical consumption and investment decisions that individuals
and families make over their life-cycle involves the amount of housing services to
consume and whether or not to combine consumption with ownership. Housing is an
important component of consumption, but not simply because it absorbs a large fraction
of the household budget—which it does. Where we live and how much we decide to
spend on housing is a key ingredient to the amenities and life-style we have chosen for
our families and ourselves. But housing, or more particularly housing wealth, can be even
more critical as an investment as it is typically by far the biggest marketable asset in the
household portfolio for most people.
The contribution of this paper is to bring together two key determinants of
housing consumption and home ownership decisions into an empirical model of housing
outcomes. The first of these is the housing ladder. Rather than modeling home ownership
as a one-time durable purchase, we model it as a series of purchase decisions, or a
housing ladder, where the desired flow of housing services rises with family formation
and growing family size over the life cycle. The second is the acknowledgement of the
role of future house price risk. In some geographic markets, housing can be a risky asset
with high levels of unpredictable price volatility while in other places the prospect of
capital gains or losses in housing are understandably not the subject of much social
Our contribution is to focus on the importance of ownership as a hedge against
future house price risk as individuals move up the ladder. We use a stylized model to
show that increasing house price risk acts as an incentive to become a home owner earlier
in the life-cycle and, once an owner, to move more rapidly up the housing ladder.
Increases in volatility are shown to increase ownership and to increase the quantity of
housing wealth conditional on ownership in earlier periods of the life-cycle. We then
establish that these relationships hold empirically using panel data on families in different
geographic markets in Britain and in the US.
Housing needs change over the life-cycle and the decision of when to buy the first
property and at what point to move up the ladder is a key life-cycle decision. For
example, Ortalo-Magne and Rady (2004, 2006) note the importance of new entrants at
the bottom of the ladder for the determination of housing transactions along the whole
ladder. Ermisch and Pevalin (2004) document the importance of childbearing and family
formation decisions on housing choices. We follow this lead by allowing the demand for
housing consumption and movements up the ladder to depend directly on the
demographic profile of the family. We then add to this the enhanced incentive to own and
to move up the ladder created by more volatile house prices.
The idea that home ownership can be seen as a hedge against uncertainty in the
price of housing services has many precedents. For example, Sinai and Souleles (2005)
use this observation to carefully show the increased demand for ownership when rental
price uncertainty is higher. Our contribution instead is to focus on the importance of
ownership and the quantity of housing owned as a hedge against future house price risk
as individuals move up the ladder. We examine the impact of volatility on both
ownership and on measures of the quantity of housing wealth conditional on ownership.
Both are shown to rise with increased house price volatility.
In contrast to other risky assets in which risk-averse individuals can simply
choose to avoid them, everyone must consume housing, and the vast majority of people
desire to and eventually do end up owning their own home. In addition, for most
individuals the demand for housing will rise over the life-cycle as family size increases.
The combination of these factors results in an insurance role for housing wealth in early
life that drives the predictions we investigate in our empirical analysis.
Using panel data from the UK and the US, we test the implications of the ladder
and price volatility on the decision on when to become a homeowner, how much housing
to consume, and whether to refinance out of housing equity. In the presence of volatility
in house prices, housing has three roles—investment, consumption, and insurance against
price fluctuations for future movements up the housing ladder. A simple theoretical
discussion illustrates these effects, and the predictions for home ownership, and housing
wealth accumulation are drawn out.
Because housing price volatility is spatially variable, we test the importance of the
role of volatility in housing decisions empirically using comparable panel data from the
US and the UK. There are significant differences in housing price variability between and
within these two countries. But in addition there are also differences in the tax treatment
of mortgage debt, the nature of mortgage arrangements, and even the level of geographic
mobility of younger households. Consequently a test relying on between country
differences is unlikely to isolate the effects of interest. In our analysis we show that,
while the international differences are indeed in accordance with the predictions of our
model, the model also performs well when estimated from within country variation in
each of the countries we consider, despite their rather wide institutional differences.
The analysis in this paper is in five sections. Section I documents a critical and
salient fact—a steep housing ladder with age which is coincident with changing
demographics over the life-cycle that are common across the two countries. Section II
shows the large special dispersion in house price volatility within and between the UK
and US. Section III then discusses the implications of housing price variability for
housing choices in a simple life-cycle framework. In Section IV the model predictions
concerning the age of initial home ownership, the decision to refinance, and the shape of
housing wealth and the number of rooms are put to the test. In the final section we
summarize our conclusions.
I. The Housing Ladder
Even without credit constraints or income uncertainty, individuals would not choose to
consume the same flow of housing services at all times in their lives. People may start by
moving out of the parental home into a small rented or purchased apartment or flat of
their own. When they marry, they may know that two may well live more cheaply than
one but they generally do not want to live in smaller places and often may want to own a
bigger but still modest first home. Children then appear on the scene and eventually will
age into rooms of their own—all of which requires a bigger if not better home.
A simple way of illustrating this point is to examine how the size of homes people
live in changes with age. Table 1 shows the age profile of mean number of rooms of
household heads for owners and renters alike in the US and UK using the Panel Study of
Income Dynamics (PSID) in the US and the British Household Panel Study (BHPS).1
Note that the number of rooms in the British data excludes kitchens and bathrooms and so
the number of rooms is not strictly comparable across the two countries. In both countries
there is a strong increase in size of house as the head of household grows older, flattening
out around the age 40 but rising steeply from the 20s to the 30s. The general shape of the
ladder is similar in the two countries.2 It is important to note that the steep part of the
ladder is not simply the consequence of changing tenure status from renter to owner.
While owned homes are always larger than rented ones on average, the steep early ladder
characterizes both rented and owned properties.3
Another way of seeing this transition is to examine the increase in home size at
the time of purchase among new and repeat buyers. This is shown in Table 2. New buyers
are defined as those who were previously renters in the prior wave of PSID or BHPS so
that especially at young ages this often will be their first owned home. Repeat buyers
were previously also homeowners so that this change now reflects changes in the size of
owner occupied housing. In the US, while the transition from renter to owner involves a
larger increment in house size, people are also clearly trading up in the early part of the
life cycle when they purchase their second and subsequent homes. This effect is even
stronger in the UK—on average first time buyers purchase houses that are bigger
comparable than their rented house, but bigger movements up the ladder, defined in terms
of increments to the number of rooms, tend to take place for repeat buyers.
We view the shape of the ladder as demographically determined as individuals
marry, form families with children growing, eventually complete their family building
with the by now older children leaving home to go off on their own. Figures 1a and 1b
plot the cumulative distribution of individuals who have completed their fertility by age.4
The steepness of this cumulative distribution mimics closely the overall shape of the
housing ladder—a steep incline during the 20s and 30s with a flattening out during the
40s. In fact, between ages 25 and the late 30s, this cumulative distribution of competed
fertility is almost linear, with each year of age increasing the fraction that has finished
childbearing by 5 percentage points. For example, around age 31, half of all American
individuals have completed their fertility with three out of every four doing so by age 36.
The shape, and level, of the profile corresponds extremely closely to that observed in the
UK over the same ages.
Children turning 5 years old may be at a critical stage for housing decisions since
parents may choose places to live with the quality of schools in mind and may want to
stay longer in the same place. This could be another indicator of reaching the top of the
housing ladder and arrival in the ‘family home.’ With this in mind, Figures 1a and 1b also
plot the cumulative fraction of individuals who ever had child at least 5 years old. Not
surprisingly, compared to the cumulative completed fertility, this figure is shifted out to
the right so that if age 5 is a useful marker, reaching the top of the ladder takes place for
the median family in the mid to late thirties. Nevertheless, as with the completed family
size profile, the proportion rises steeply over the life cycle up to age 40 in parallel to the
sharp rise in the number of rooms demonstrated over the same ages. Finally, Figures 1a
and 1b also plot the proportion with their own children aged 5 or over currently in the
household, as a measure of contemporaneous housing needs. Again the similarities
between US and UK are striking—in both countries after age 40 there is a sharp decline
in young children at home, an indication of an eventual demographic rationale for
downsizing in later life.
II House Price Volatility
Figure 2 shows real indices of country-wide average house prices for the US and
UK over the period 1974 to 1998 with both series normalized to take a value of one in
1980. Immediately apparent is the much larger volatility of housing prices in the UK,
with real prices rising by 50% over the period 1980 to 1989 and then falling back to its
previous value by 1992. Over the period as a whole, however, real returns were similar
across the two countries.
Although such difference will be instructive when looking at differences in
housing choices across the two countries, the majority of our testing will rely instead on
within-country differences in house price volatility in each of the two countries. The UK
and the US indexes both hide considerable differences across regions with some places
being much more volatile in housing prices than others. In Figures 3a and 3b we present
house prices from regional sub indices, grouped to show house price trends in the more
and less volatile areas.
The variation across American states in housing price volatility is large. Using the
standard deviation in real prices (relative to a 1980 base) as the index, Massachusetts
ranks at the top with price swings between peak and trough over this period of more than
two to one. At the other extreme lies South Carolina where the peak price exceeds the
trough by only 15%. The most volatile states are concentrated in New England and along
the North Eastern seaboard (Massachusetts, New York, New Jersey, Rhode Island,
Connecticut, New Hampshire, and Maine) and in California and Hawaii. While we will
use a continuous measure of volatility in our analyses below, for descriptive purposes we
label these the volatile states.
To exploit regional and time series differences in volatility in house prices we
construct indices of volatility by computing the standard deviation of the change in the
log real house price index over the previous five years for each of the 50 US states and 12
UK regions for which we have house price indices. These indices, which measure percent
volatility over the sample are plotted in Figures 4a and 4b, grouped by the same two
‘volatile’ and ‘non-volatile’ areas as before. Two things are important to note. First, the
higher levels of volatility in the UK (even in the ‘non-volatile’ regions) are apparent.
Second, in both countries, it will be the state/regional level volatility index, not an
average across groups of regions that enters our empirical specifications.
III. Housing Choices in the Presence of House Price Risk and the Housing Ladder
In order to think about how the housing ladder might affect housing demand in
the presence of house price risk we use the concept of a minimum housing ‘need’ that
changes with family size. This need can then be thought of as increasing over the life-
cycle as individuals form into couples, have children and reach their maximum family
size. Central to our empirical modeling is the idea that these increasing housing needs
over the life-cycle interact with future house price risks to generate an insurance role for
housing consumption early in life.5 In this section we discuss the intuition behind this
idea, before moving on to testing the predictions of such a framework empirically.
In a standard model without house price risk housing demand would increase with
wealth but would also adjust to reflect the minimum necessary level of consumption. In
such a framework one could write housing demands in each period as a function of
adjusted lifetime wealth (i.e. the present discounted value of lifetime wealth net of the
discounted sum of minimum necessary levels of housing over the lifetime6), the real user
cost of housing services, and the minimum level of housing needs in that period. Any
future change in household demographic composition would simply act through its effect
on adjusted wealth. While the consumption of housing services may involve the purchase
of a house and an asset accumulation decision, the assumption of perfect credit markets
and certainty would yield this aspect of housing consumption unimportant in such a
setting. We need to generalize this model in order to incorporate house price risk and
consider the additional role of housing as a durable asset.
For ease of exposition we will assume the life-cycle profile can be represented by
the following sequence of three discrete life-stages: At stage D = 1 the individual is
living with his or her parents, at stage D = 2 he or she partners to form an independent
family unit, and at stage D = 3 the couple has had children and completed its family size.
This is a simplified demographic profile but represents effectively the upside of the
housing ‘ladder’ that we wish to capture in our model.7 For further simplicity we will
assume that the leaving home decision D=1 → D=2 simply concerns a decision over
whether to rent or own in the light of the possible increase in family size associated with
the arrival of children between D=2 and D=3.
Without price uncertainty the rent/own decision will be driven by transaction
costs of ownership as well as the desire for mobility, the potential tax advantage of a
mortgage, and any down payment rules or constraints on the multiples of income that
may be borrowed. For a household that expects to remain in their house for a reasonable
length of time, for example at D=3 (the top stage of the demographic ladder) owning is
the most efficient way of achieving a desired level (and type) of housing service – with
idiosyncratic tastes a renter can never commit to stay long enough to make it in the
landlord’s interest invest in the renter’s idiosyncratic tastes. Hence we will assume for
simplicity that all households will be owner-occupiers at D=3 and that this is known to
them at D=2.8
Before turning to the introduction of house price risk, there are two aspects of the
supply of housing services, which are relevant to our discussion. First, a more inelastic
supply will induce a larger sensitivity of house prices to changes in demand and, in
particular, to fluctuations in incomes of young first-time buyers. The second aspect
relates to the rental market —imperfections and/or regulation of the private rental market
may make it difficult for the young to use rental housing as the step between leaving the
parental home and acquiring a house.
The introduction of house price uncertainty into the model adds an important
distinction between ownership and renting which will enhance the desire to accumulate
housing wealth and thus the need to become an owner earlier in the life cycle—house
price risk generates an incentive to accumulate housing equity at D=2 before the family
is complete. At first sight this may seem a puzzle since accumulation of a risky asset
might normally be expected to decrease with the level of price volatility for a household
with risk-averse preferences. That usual result does not hold because of the vital
insurance role played by housing in early life in our framework. We argue this intuitively
below, but to back up this intuition, in Appendix II we simulate the predictions of a
simple three-period model with constant relative risk aversion preferences that allows us
to demonstrate more formally the effects on housing consumption profiles of changing
volatility, the changing steepness of the housing ladder and changing degrees of risk
At D = 2 there are two choices: how much housing to consume, and whether to
own or to rent. If house prices are variable and uncertain then, given the expected
increase demand as the household moves up the demographic ladder from D=2 to D=3,
housing equity will be an important source of insurance against future house price risk.
Indeed, in the absence of a financial instrument that could insure this house price risk
(which may well be defined at a very local level), holding housing early in life may be
the only insurance mechanism. The larger the uncertainty in house prices and the steeper
the increase in minimum housing needs over the life cycle, the more important is the
insurance aspect of housing equity.
Thus the key mechanism for these effects is the insurance role of housing in
period 2. If prices turn out to fall or stay the same then ownership will not, ex post,
dominate renting. Indeed if house prices fall there will be some loss to ownership.
However, because of the strongly declining marginal utility of consumption associated
with housing consumption in period 3 approaching the minimum necessary requirement,
insuring the risk of house price rises is more important than avoiding the risk of a house
price fall. To achieve this, the consumer needs to hold an asset whose return is correlated
with (local) housing prices. If such an asset is not available on the financial market the
insurance can only be achieved by purchasing the asset itself. Consequently, other things
equal, the higher the level of house price uncertainty the higher the incentive to become
an owner-occupier. In this context increasing minimum housing requirements or
increases in risk aversion are acting in a similar way to an increase in volatility. By a
straightforward extension of these arguments, individuals will also stay away from
endowment mortgages and refinancing of housing equity for non-housing consumption or
In summary, the decision to accumulate housing equity early in the life cycle will
be an increasing function of house price volatility for risk-averse households who expect
an increase in family size. In the absence of an equity market in local housing assets, this
demand for housing equity also enhances the decision to own.10
One further extension that needs to be discussed, since we endeavor to control for
it in the empirical analysis that follows, is geographic mobility. If individuals anticipate
residing in less volatile areas in period 3 then their demand for insurance is reduced (and
the insurance value of their housing equity in period 2 will be reduced also to the extent
that house prices are not perfectly correlated across regions). It is expected volatility at
D=3 (from the point of view of D=2) that drives the insurance motive. In the case of
individuals in D=2 anticipating moving to a ‘safe’ area at D=3, both these factors are
likely to play a reduced role, although they could still be important to some extent.
IV. The Empirical Relationship between Housing Choices and Risk
On the basis of our discussions in the previous section, and the numerical model solutions
presented in Appendix II, there are three principal predictions that we will test
empirically in this paper: (1) other things being equal, individuals should buy homes
earlier in more volatile areas; (2) young homeowners are less likely to consume capital
gains on housing through refinancing in more volatile areas; and (3) young homeowners
will consume ‘more’ housing in more volatile areas than their counterparts in less volatile
areas. In the following subsections we deal with each of the above predictions in turn.
IV.A. Age of Home Ownership
In the presence of a housing ladder, individuals living in places with more volatile
housing prices need to self-insure by buying their first home at a younger age. In the final
column of Table 3, we list for both the UK and US the proportion of individuals who are
homeowners, by age for a typical year—1994. These patterns do not depend critically on
the year chosen. The data are also presented separately for the volatile and non-volatile
areas in both countries. While average rates of home ownership are similar, there are
striking differences by age between the two countries. Home ownership rates amongst
young households are far higher in the UK than in the US, with differences of 10
percentage points for householders between ages 20-29 and 13 percentage points those
between ages 30-39. However, through middle age, homeownership rates converge so
quickly that US rates actually exceed those in the UK among older households.
Since prices are far more variable in the UK, these cross-country differences in
home ownership rates are consistent with our theoretical implication that ownership
should occur at a younger age in more price volatile housing markets. However, when we
compare home ownership rates between the volatile and non-volatile areas within each
country, the challenge to our theory becomes more apparent. In both countries, owning a
home is somewhat less common among younger households in the volatile market.
However, there are other significant differences between these two markets in
each country that will presumably strongly affect the decision to own. Tables 4a and 4b
lists some of the more salient ones. Perhaps, most important, housing prices are much
higher in the volatile markets. For example, the average price of a home in the more
volatile states is almost twice that in the less volatile ones, which should certainly
discourage home ownership among the young. While rental prices are also higher in the
more volatile states, the percentage difference is 46% compared to 68% for housing
prices. Young individuals living in the volatile states also have more education,
household income, and are less likely to be married and to have children. All of these
factors are obviously relevant to the housing tenure decision so the final verdict on the
theory requires multivariate modeling.
In our multivariate analysis, we estimate a probit model of whether or not one is a
homeowner using a sample of individuals who are between the ages of 21 and 35. Results
are similar if one uses a somewhat younger or somewhat older age band that corresponds
to the rising part of the housing ladder. In addition to our measure of housing price
volatility described above, this model includes several relevant demographic attributes—