ELSEVIER Regional Science and Urban Economics 26 (1996) 465-480 ECONOMICS
Age, housing demand, and real house prices
Richard Green a, Patric H. Hendersho t t b'*
aSchool of Business, University of Wisconsin, Madison, W1 53706, USA bDepartment of Finance, Ohio State University, Columbus,OH 43410, USA
Received 9 December 1994; final version received 30 October 1995
Abstract
Real house prices are directly determined by the willingness of households to pay for (and willingness of builders to supply) a constant-quality house. Changes in the quantity of housing demanded will affect real prices only to the extent that the long-run housing supply schedule is positively sloped. In this paper we use 1980 census data to measure the impact of the age structure, education and income on the willingness of households to pay for a constant-quality house.
We compute total and partial derivatives for the effect of age on housing demand. The total derivatives--which carry along with age all of the average characteristics (i.e. income, marital status and education) associated with that age- - look much like the Mankiw-Weil age -demand results. But the partial derivatives suggest that holding all else constant, the demand for housing tends to be fiat or rising slightly with age. Since much is in fact held constant over the life-cycle, we believe that our partial derivatives more accurately depict the age-demand relationship and thus that the aging of the population should not be expected to lower real house prices.
Keywords: Age; Demographics; House prices
I. Introduction
Mankiw and Weil (1989) link real per capita housing expenditures directly to age by estimating a cross-sectional expenditures equation on data from the 1970 census. Rea l e x p e n d i t u r e s r ise sharp ly b e t w e e n the ea r ly 1920s and the la te 1930s, a re bas ica l ly fiat t h rough the late 1950s, and then dec l ine .
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McFadden (1994) shows that this basic result also holds for the 1940, 1960 and 1980 censuses. It follows that the shift during the 1970-85 period of the b a b y - b o o m e r bulge from under age 23 to over raised average real per capita housing expenditures significantly. More important , the further shift in the b a b y - b o o m e r bulge over the next three decades from under age 50 to over age 60 would be expected to lower average real per capita housing expenditures significantly.
Both studies conclude that the shifting baby-boomer bulge was a major cause of the 20% 1969-89 surge in real house prices and will lead to a sharp decline in real house prices over the next 30 years. Neither interpretat ion is widely accepted. Peek and Wilcox (1991) find no impact for demographic factors over the crucial 1970-84 period; the combined estimated impact of changes in their real income, household and population variables on real house prices is 0.5% (Peek and Wilcox, p. 377, table 5). Moreover , Poterba (1991) finds no impact of the Mankiw-Weil demographic variable on metropol i tan level real house appreciat ion during the 1980s. Support for the forecast of declining real house prices over the next 30 years is also lacking (see the 1991 issue of Regional Science and Urban Economics); the negative t ime trend in the M-W equation, not the a g e - d e m a n d construct, is responsible for the large forecasted drop in real prices. 1
Nonetheless, we believe that linking real per capita housing expenditures to age by estimating a cross-sectional expenditures equation is a valid and useful exercise. We offer two improvements on estimating that linkage. First, we relate real expenditures to hedonic characteristics, which allows us to separate expenditures into price and quantity components . Second, we link the real prices of characteristics to numerous economic and demo- graphic variables in addition to age. This permits us to compute how the willingness to pay for a constant-quality house varies solely with age (a partial derivative) or with age in conjunction with other demographic and economic characteristics (a total derivative). Thus we can compute how the demand curve, rather than the area below a particular point on the curve, shifts with demographic and economic characteristics.
We obtain an analogue to Mankiw and Weil's principal result in that the willingness to pay for a constant-quality house in 1980 was about 50% lower for households in their late seventies than for those in their early fifties. However , we also show that this decrease is not due to age per se, but to the fact that 70 year olds in 1980 had far less education and income than 50 year olds, owing to the surge in education after World War II. If anything, the
1 Hendershott (1992) is perhaps the severest critic, arguing that the Mankiw-Weil estimation equation does not even explain the observed movement in real house prices in the 1970s and 1980s. For a multi-equation model that leads to forecasts of positive real house price changes through the year 2000, see DiPasquale and Wheaton (1994).
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'partial ' effect of age on housing demand is positive. To extrapolate from a negative 'total age derivative' that the housing demand of today's 50 year olds will decline sharply as they age ignores the fact that the formal education of these households cannot decline and thus their real income is unlikely to fall significantly.
The paper is organized as follows. Section 2 states our general f ramework and how we intend to implement it. Section 3 reports the cross-sectional results for 1980 and discusses the sensitivity of average household willing- ness to pay to demographic factors and real income. In addition to education and age, race and marital status matter.
In Section 4 we backcast average willingness to pay to 1950 and forecast it to 2030. We also compute 'demographic driven' aggregate housing demand for the 1950-2030 period, using as our measure the product of average household willingness to pay and the number of households (forecast beyond 1990). We conclude that changes in demographic factors may have contributed significantly to both the decline in real house prices during the 1950s and 1960s and to the rise in the 1970s and 1980s. In contrast, demographic changes over the next three decades are likely to be so modest in magnitude that they almost certainly will not have a significant impact on housing demand and thus real prices.
2. The general framework
Rosen's (1974) well-known model relates the demand for housing charac- teristics to (among other things) demographic factors. The model requires three steps. First, we establish a relationship between the real flow of (real expenditures on) housing services provided by a house and the characteris- tics of the house:
q = f ( Z ) , (1)
where q is the flow of housing services consumed (the 'user cost' times the asset price) and Z is a vector of n hedonic characteristics of the house, z 1 , z 2 . . . . . z n. Second, we obtain the real marginal contributions to housing demand of each hedonic characteristic by taking derivatives of (1):
of qi = ~ (Z) . (2)
Third, we relate these real contributions to the characteristics of the house, the demographic characteristics of the household, and the household's real income net of housing expenditures (Y):
qi = &(Z, A, X, Y ) , (3)
468 R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480
where Z and the qg are as before, A is the household's age and X is a vector of m other demographic characteristics, x 1, x z , . . . , x, , .
To estimate (1), we use Christensen et al.'s (1975) translog function:
l n q = a o + In + 0 . 5 ~ B~jlnz~lnz~.+ E i = l i ~ l j = I
(1 ')
where E is independently and normally distributed. We use the translog because it will approximate any arbitrary functional form and imposes fewer restrictions than many other functional forms that are homogeneous of degree one. 2 We impose homogeneity and symmetry upon the translog through the following restrictions:
n
a i = 1, (4) i = 1
i1
= o (5 ) ij i = 1
Bij = Bji . (6)
That is, we estimate (1') subject to (4)-(6). By restricting (1) to be homogeneous of degree one, Euler 's Theorem allows us to compute the aggregate quantity of housing services from a specific house as
n
q = ~ q i z i . (7) i = 1
The assumption that f ( Z ) is homogeneous of degree one is a strong one, but is justifiable from two perspectives. First, the point of our exercise is to determine the willingness of households to pay for a constant-quality house. Our method for doing this is to measure the willingness to pay for the individual parts of houses (bedrooms, bathrooms, etc.) and then to add up the parts to recreate the whole house. Homogenei ty of degree one allows this addition. Second, the adding up implies that the marginal prices Pz are also average prices, a necessary condition for a long-run competitive equilibrium.
We obtain the hedonic prices, qi, by taking partial derivatives of q with respect to the z i in the estimated (1'):
2 For example, while a linear functional form is homogeneous of degree one, it imposes for each hedonic characteristic a common price for each household. This would defeat our ability to derive a relationship between a household's demographic characteristics and its demand for housing.
R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480 4 6 9
0q ( n )q qi = cgZ---~i = Oti q- ~f l i j In z i . (2')
j = l
Ideally we would estimate the effect of every hedonic characteristic de- manded on every qi. However , in practice we do not have sufficient instrumental variables to do so. We therefore regress the q~ on only their corresponding characteristic and on the demographic and economic vari- ables:
17 17
q~ = a o + aiz ~ + ~ a,Aa + ~OX + ~ ayagA ~ + ['~i ' (3') a = l a = l
where the A a are age dummies for each five-year age class running from 0-5 to 81-85, Y is the income net of housing expenditures of the household, the a's are individual coefficients, ff is a coefficient vector for the other demographic variables denoted by X, and /x is independently and normally distributed.
Average expenditures on a constant-quality house are obtained by using a variant of Eq. (3') to compute the service flow provided by the ith characteristic for a household in the j th age class,
17 17
qij = ao + aizi + ~a aaAja + q,X + ~ ayaYjAja , (3") a = l a = l
and summing over the age classes and then the housing characteristics,
17 n
q = ~ E wiq i j , (8) i = l j = l
where w i is the proport ion of households in the j th age class. Two comments about Eq. (8) seem pertinent. First, the likely most
important determinant of real house prices is not one of the demographic or income variables on the right-hand side of Eq. (3"), but the user cost of housing. Recall that q is the product of the user cost and the real price of housing. With a perfectly inelastic supply schedule, a doubling of the user cost would halve real house prices. Second, the real house price response to user cost, real income, or demographic variables depends on the supply elasticity of housing. The greater the elasticity, the less will any variable affect real house prices. A final observation: while the average expenditure on a constant-quality house is independent of total population, the real price of housing probably is not, i.e. an increase in population would, other things being equal, be expected to raise real house prices.
470 R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480
3. Cross-sectional results for 1980
We estimate Eq. (1') for the 1980 census year. The hedonic characteristics are house age, number of bedrooms, number of bathrooms, and number of o ther rooms, whether the house is owned, whether it has central air conditioning, gas heating, sewer and water hookup, whether it is a condominium or is in an urban area, and which of nine census regions it is in. All data come from the public use micro-data series. Note that we do not have a direct estimate of either the quality or quantity of land. Whether the household is an owner or renter serves in part as a crude proxy for the land attr ibute.
For renters, q is gross rent as repor ted by renters to the census. For owner-occupiers , q is the product of a user cost measure and the house price as repor ted by owner-occupiers to the census. We estimate the user cost of housing capital in period t for each owning household from
uc, = rt(1 - mr) + p + ~-~(1 - rn,) + 6 - g , , (9)
where r, is the nominal interest rate, p is a risk premium, 7, is the proper ty tax rate, m, is the marginal tax rate, ~ is the rate of depreciation and /o r maintenance , and g, is the expected capital gain. We set r equal to the 1980 ten-year Treasury rate (0.1!46) and set p = 0.04, 6 = 0.03 and g = 0.06. We determine the marginal tax rate for each household on the basis of on 1980 tax law and repor ted household income, marital status and number of dependents , as reported to the census. 3 We take proper ty taxes as repor ted to the 1980 census.
Using ordinary least squares (OLS) to explain the service flow provided by 65,622 houses, we obtain a n R 2 of 0.40. In Table 1 we report the own-quanti ty coefficients on the 18 characteristics, their standard errors, and the partial derivatives with respect to the own quantity. As can be seen, 15 of the 18 own coefficients are statistically different f rom zero at the 5% level, and all partial derivatives, with the possible exception of gas heating, have the expected sign. Of the 139 coefficients on the interactive terms, 95 are statistically different from zero at the 5% level, none with unexpected signs.
We then estimate (3') for each of the 18 characteristics using two-stage least squares as prescribed in Bartik (1987). 4 We use two-stage least squares
3 Itemized deductions are not available in this data set. In effect, households are assumed to be non-itemizers.
4 Following Bartik, we employ non-housing income and regional dummies as instruments. As Bartik notes, the constraint relating the income and expenditure coefficients allows income to be an instrument. Regional dummies probably exogenously shift hedonic functions but are not correlated with unobservable tastes.
b e c a u s e e a c h h o u s e h o l d ' s c h o i c e o f h o u s e c h a r a c t e r i s t i c s is a l m o s t c e r t a i n l y
c o r r e l a t e d w i t h t h e h o u s e h o l d ' s u n o b s e r v e d o r i m p r o p e r l y m e a s u r e d t a s t e c h a r a c t e r i s t i c s . T h u s O L S w o u l d p r o d u c e b i a s e d coe f f i c i en t s . F o r t h e d e m o g r a p h i c v e c t o r , we i n c l u d e t h e age d u m m y for e a c h h o u s e h o l d m e m b e r
472 R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480
(and therefore implicitly, each household's size), the household head's marital status, and the race, gender, and educational attainment (one of five categories ranging from no high school to post college) of the household's highest earner. The age dummies are also interacted with non-housing income (income less housing expenditures). For owners, non-housing income is simply reported income, which already excludes housing expendi- tures (imputed rental income). For renters, measured rents are subtracted from reported income. The combination of current income, income inter- acted with age, and education proxies for permanent income.
We expect the coefficients on the age- income dummies to be positive for normal goods, i.e. households will demand more as income rises. To conserve space, we report in Table 2 coefficients on only two age- income variables, those for age-classes 20-24 and 40-44. The age- income co- efficients on most of the goods we expect to be normal (bedrooms, bathrooms, other rooms, tenure) are either positive or not significantly different from zero at the 90% level, and the house's age, which we expect to be an inferior good, has a negative income coefficient for 40-44 year olds; the coefficient is not statistically different from zero for 20-24 year olds. (This general pattern holds for all age-class variables.) The only mystery is central air conditioning, which has a coefficent that is negative and significant for 20-24 year olds. We note further that for all the normal goods, for a given level of income, demand rises from ages 20-24 to 40-44. We depict the general age-demand relationship graphically below.
Also shown in Table 2 are coefficients on selected other variables: the age-class dummy variable 35-39, the single and black identifiers, and the educational attainment variables. Once we have controlled for household size (which our regressions do via the age dummies), singles tend not to be very different from married couples in their demands for various attributes. There are three major exceptions to this statement: singles value home- ownership less than marrieds, they value condominiums more, and they value bathrooms more. The first two of these outcomes seem entirely plausible; the third is difficult to explain. The willingness of blacks to pay for housing characteristics is less than the willingness of others (non-bedroom rooms being an exception). This could reflect either different tastes or discrimination. Past literature is ambiguous on this point. 5
The coefficients on the educational status dummies in the demand equations for the structural characteristics of houses indicate that education is proxying for permanent income (the omitted status is post college).
King and Mieszkowski (1973) contend that blacks generally pay a premium for housing, but Follain and Malpezzi (1982) show that after proper quality adjustments, blacks pay less for housing than whites.
R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480
Table 2 Selected second-stage regression results
473
Dep. var. Explanatory variable
Price of: z Age Income Income Single Black 35-39 20-24 40-44
Demand for houses with more bedrooms, more bathrooms, more other rooms, and central air conditioning rises with education. Demand for newer houses, for condominiums and for houses in urban areas also rises with education. These effects are both statistically significant and, in context, large.
Estimates of how the willingness to pay varies with age, income, education, household type and race are obtained by selecting a housing quality mix (specified by Z), computing the implicit prices from Eq. (3') and
474 R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480
Table 2 (continued)
Dep. var. Explanatory variable
Price of: Head not Head Head Head R 2 HS grad. HS grad. Att. coll. coll. grad.
House age 30.4 23.7 17.7 3.17 0.143 (2.7) (2.6) (2.7) (3.08)
solving Eq. (7 ) . 6 Fig. 1 plots two measures of how this willingness to pay for our constant-quality house varied with age in 1980. The first (lower) measure allows all the average characteristics (education, real income, household size, race, gender, and marital status) associated with age to vary with age. That is, the plotted age relationship is a total derivative. The
6 O u r cons t an t -qua l i t y house is 20 years old and owner -occup ied , has t h r ee b e d r o o m s , one and o n e - h a l f b a t h r o o m s , t h r ee o t h e r rooms , cen t ra l a i r cond i t ion ing , and cen t ra l hea t ing . O u r h o u s e is h o o k e d up to c i ty s ewer and wate r , and is in an u r b a n a rea in R e g i o n 9, the Pacific D i v i s i o n of the U n i t e d Sta tes .
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i i iiiiiiiiiiii .......... I I I I d I I I I I I I I
21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61..65 66-70 71-75 76-80 81-85 Age of HH Head
......... Total age derivative - - Partial Age Derivative i
Fig. 1. Willingness to pay for a constant-quality house.
second (higher) measure holds all variables constant, i.e. the plotted relationship is the partial derivative with respect to age. When variables are held constant, they are at the averages for a household age 36-40.
Both curves rise sharply from the early 20s to the late 30s. We conjecture that this increase is due to a relaxation of affordability and downpayment constraints on low-wealth households. The total derivative is basically flat through the early 50s and then declines monotonically through the late 70s, the total decline being nearly 50%. This impact is quite similar to that of Mankiw and Weil. In contrast, the partial age derivative is basically flat, with a slight increase after the late 50s. All else being equal, older households are willing to pay a premium for housing; in spite of empty nests and lower incomes, many retirees do not leave the homestead.
Most of the difference between the partial and total derivatives with respect to age is due to differences in education. In 1980, older households had far lower education than younger households. As shown in Table 3, roughly 15% of those aged 25-34 were college graduates versus 9% for those aged 40-54, vs. less than 5.5% for those over age 64. Put another way, only 12% of those under age 35 were not high school graduates, and this percentage rose almost linearly to 65% for those over age 74.
About half of the decline in the total derivative after age 50 is directly due to a decline in education and the other half is due to a decline in income, a significant part of which must be due to lower education levels, given the high estimates of the returns to education (Card and Krueger, 1992, and Blackburn and Neumark, 1993). To project sharply lower future real house prices on the basis of the estimated negative total derivative is equivalent to
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Table 3 Educational attainment by age of head (1980 census, in percent)
Age Not HS grad. HS grad. Some college College grad.
projecting a sharp decline in the formal education level of today's 50 year olds as they age.
The effects of marital status and race on the willingness of owning households with $25,000 real income to pay for a constant-quality house are significant: blacks are willing to pay only 70% as much for housing as whites and singles 80% as much as marrieds. (Single males and females of the same race are willing to pay similar amounts.)
4. Real house prices, 1950-2030: Some tentative observations
We begin by calculating changes in average housing demand per house- hold using both total and partial age derivatives for the period 1950-2030. To obtain estimates of the demand per household, we take the dot product of the age distribution (actual from 1950 to 1990, middle series census forecast from 2000 to 2030) of the population and amount of housing demanded by each age group according to our total and partial derivatives, as repor ted in Fig. 1. These estimates are reported in the first and second columns of Table 4.4. We obtain estimates of national aggregate demand for housing by multiplying our two measures of per household demand by the total number of households in each decade (observed through 1990 and as forecasted by Masnick and McArdle, 1993, thereafter). These estimates are repor ted in the third and fourth columns.
Movement from our 'demographic demand' to real house prices is a considerable leap. First, the impact of demand on price depends on the supply elasticity of houses. With an infinite elasticity of supply, demand is
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Table 4 Measures of change in the demand for housing
477
Years Average demand per household Aggregate housing demand
Total age Partial age Total age Partial age Real house derivative (%) derivative (%) derivative (%) derivative (%) prices a (%)
a We use Peek and Wilcox's (1991) Adjusted Freddie Mac Real House Price Index. Peek and Wilcox only compute the index through 1989. We assume the 1990 growth rate to equal the 1989 rate of 3.8%.
i r r e l evan t ; pr ice is d e t e r m i n e d sole ly by supp ly condi t ions . Se c ond , even wi th a s ignif icant ly less than pe r fec t ly e las t ic supply , fac tors o t h e r than ou r d e m a n d va r i ab le affect real pr ices . N u m e r o u s inves t iga tors have a t t r i b u t e d a m a j o r ro le to changes in rea l a f te r - t ax in te res t ra tes ( A b r a h a m and H e n d e r s h o t t , 1995; H e n d e r s h o t t , 1992; P o t e r b a , 1991; and P e e k and Wi lcox , 1991), and t echno log ica l changes in hous ing cons t ruc t ion could be i m p o r t a n t . Th i rd , if m a r k e t s a re efficient , only changes in m a r k e t d e m o - g r aph i c d e m a n d forecas t s shou ld effect real p r i c e s ] O f course , the unde r ly - ing p r e m i s e of M a n k i w and Weil (1989) is tha t hous ing m a r k e t s a re no t eff ic ient and , in pa r t i cu la r , tha t m a r k e t s d id not eff ic ient ly p rocess the k n o w n aging o f the b a b y - b o o m e r s .
In wha t fol lows we ask wha t impac t the d e m o g r a p h i c fac tor might have h a d on rea l house pr ices ove r the 1950-90 p e r i o d and wha t it might have d u r i n g the 1990-2030 pe r iod . The ex is tence of these impac t s p r e s u m e s bo th inef f ic ient p rocess ing o f i n f o r m a t i o n on the aging of the b a b y - b o o m e r s and a pos i t ive ly s l oped supp ly curve . O b s e r v e d de c a da l real house pr ice changes a r e shown in co lumn five of T a b l e 4. T h e y are b a s e d on P e a k and Wi lcox (1991).
T h e shif t in the b a b y - b o o m e r bu lge in the 1970s and mos t of the 1980s f r o m u n d e r age 25 to ove r is cons i s t en t wi th the o b s e r v e d 20% increase in rea l pr ice . N o t e tha t the ave rage wi l l ingness to pay m e a s u r e s r e p o r t e d in the first and s econd co lumns o f T a b l e 4 rise much fas ter in these two d e c a d e s (pa r t i cu l a r ly in the 1970s) than in any o t h e r post -1950 d e c a d e , pas t o r
7 Case and Shiller (1989) present evidence of 'inefficiencies' and Shiller (1990) provides an explanation as to why such inefficiencies are 'rational'. For a general discussion of bubbles in asset markets, see Stiglitz (1990).
478 R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480
forecast through 2030. The contrast of the 1970s and 1980s with the 1950s, when average demand per household (and real house prices) fell owing to a surge in young households, is marked.
The movement of baby-boomers into adulthood had a second, possibly equally important, impact: the ratio of household formations to population growth surged, especially in the 1970s, as boomers left the homes they were reared in for their own houses. Consequently, the 1970s saw an explosion in aggregate housing demand quite unlike any other post-World War II decade past or likely to come. As the third and fourth columns indicate, growth in aggregate demand was 50% greater in the 1970s than in the 1980s and over double that of any other decade. ~ Real house prices increased sharply. 9
Looking forward, a repeat of the real price surges of the 1970s and 1980s seems most unlikely, but so is the cataclysm of Mankiw and Weil. Demand per household is effectively flat. Demand grows by 1-2% annually after 2000 if we use the partial, rather than total, derivative (if we permit households to retain their education and the higher income accompanying it as they age), but these changes are trivial relative to either the surges in the 1970s and 1980s or the plunge of the 1950s. Aggregate demand, on the other hand, grows by roughly 15% a decade, far less than the 39% and 23% increases in the 1970s and 1980s, respectively. 1°
If substantial changes in real house prices occur in the next 30 years, they are likely to be due to shifts in non-demographic variables such as real construction costs and real after-tax interest rates. Demographic changes of the magnitude deemed to be likely are simply not large enough to matter a great deal.
5. Conclusion
Using data from the 1980 census, we have linked real per capita demand for a constant-quality house to age, other demographic characteristics, education, and real income. In this cross-section relationship we find that the willingness to pay for a constant-quality house in 1980 was about 50% lower for households in their late 70s than for those in their early 50s. However , we also show that this decrease is not due to age per se, but to the fact that 70 year olds in 1980 had far less education and real income than 50
s The 1970s increase in demand could well have been too rapid for a long-run equilibrium to have existed at any time during this decade; builders were constantly running out of capacity.
9 Peek and Wilcox (1991) provide evidence that housing demand and real house prices would have risen significantly more except for the extraordinarily high real after-tax interest rates in the first half of the 1980s (with concomitantly higher user costs).
~°Masnick and McArdle (1994) forecast that households will grow 2.8% faster than population during the 1990s. We continue that assumption through 2030.
R. Green, P.H. Hendershott / Reg. Sci. and Urban Econ. 26 (1996) 465-480 479
y e a r o lds , owing to the surge in educa t i on af te r W o r l d W a r II . If any th ing , the ' p a r t i a l ' effect of age on hous ing d e m a n d is pos i t ive . To conc lude f rom the c ross - sec t iona l nega t ive ' t o ta l age de r iva t ive ' tha t the hous ing d e m a n d of t o d a y ' s 50 yea r o lds will dec l ine sha rp ly as they age ignores the fact tha t , un l i ke the c ross -sec t ion , the fo rmal educa t i on of these p e o p l e will no t dec l ine sha rp ly as they age and thus the dec l ine in the i r real i ncome is also l ike ly to be far less d r a m a t i c than in the cross-sec t ion . G i v e n official p r o j e c t i o n s of p o p u l a t i o n and househo lds , aggrega te hous ing d e m a n d in the U n i t e d S ta tes will a lmos t cer ta in ly r ise ove r the next t h r ee decades .
This does not m e a n , of course , tha t real pr ices will no t decl ine . Cha nge s in o t h e r fac tors tha t nega t ive ly affect wi l l ingness to pay , e.g. i nc reased real a f t e r - t ax in te res t ra tes , cou ld lower real pr ices , and we shou ld not fo rge t t ha t the wi l l ingness of bu i lde r s to supply houses ma t t e r s as much as the wi l l ingness of househo ld s to d e m a n d . F ina l ly , even if d e m o g r a p h i c changes do no t r e d u c e agg rega t e hous ing d e m a n d , these changes could s ignif icant ly a l t e r the c o m p o s i t i o n o f tha t d e m a n d . Signif icant changes in real pr ices of d i f f e ren t types and conf igura t ions of houses in d i f fe ren t loca t ions cou ld resul t .
Acknowledgements
W e t h a n k D o n H a u r i n and two rev iewers for useful c o m m e n t s on ea r l i e r d ra f t s of the p a p e r .
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