1

Financial and Housing Wealth, Expenditures and the Dividend to Ownership

Sheng Guo, Ph.D. 

Department of Economics 

College of Arts and Sciences 

Florida International University 

11200 SW 8th Street 

Miami, FL 33199 

(305) 348-2735 

sheng.guo@fiu.edu

William G. Hardin III 

Tibor and Sheila Hollo School of Real Estate 

College of Business Administration 

Florida International University 

1101 Brickell Avenue, 1100S 

Miami, FL 33131 

(305) 779-7905 

hardinw@fiu.edu

The authors would like to acknowledge Thesia Garner, Robert McClelland, Jonathan Skinner, 

participants at the 2014 Consumer Expenditure Survey Microdata Users’ Workshop at the 

Bureau of Labor Statistics, seminar participants at the Congressional Budget Office, and session 

participants at the 2014 Global Chinese Real Estate Conference in Nanjing, China. 
2

Financial and Housing Wealth, Expenditures and the Dividend to Ownership 

Abstract 

For a household, home ownership provides necessary shelter, potential investment returns associated with 

property appreciation and a hedge against increased housing related cash outlays. In addition to potential 

appreciation, individual households benefit over time from a housing dividend defined as the difference 

between the market rent for the individual household’s housing unit and the household’s actual house 

ownership costs. The purchase of a house can substantially fix a household’s recurring housing related 

expenditures and generates a hedge (implied housing dividend) that increases with ownership tenure. This 

expenditure hedge (dividend) to home ownership is documented using pooled, cross-year samples from 

the Consumer Expenditure Survey (CEX). The housing dividend delivers a non-trivial effect on 

household non-housing expenditures after controlling for housing value, housing equity, financial assets 

and income. 

Key words: housing, imputed rent, consumption, wealth effect, expenditure, dividend 

JEL codes: D11, D12, G14, R21, R31 
3

Financial and Housing Wealth, Expenditures and the Dividend to Ownership 

Introduction 

Understanding how home ownership and financial wealth impact household level expenditures 

and decision-making is essential. In the U.S., slightly less than two-thirds of households own the 

home in which they live and housing wealth constitutes a substantial portion of total household 

wealth (Poterba et al., 2011, and others). In addition, influential works document a housing 

wealth effect on consumption greater than the more broadly discussed stock-based equity wealth 

effect.1

The housing wealth effect has been shown using macro-level aggregate data (Case et al., 

2005) and using micro-level (household-level) data (Bostic et al., 2009).2 Notwithstanding this

existing literature, however, much remains to be explored regarding the relations between 

housing, macro-level consumption, household level non-housing expenditures and decisionmaking.

3

 

1

 We do not want to get caught up in semantics. There is a difference between consumption and expenditures, 

especially at the household level and for durable goods and durable services. Consumption is a theoretical concept in 

economic models dated at least as early as in Friedman (1957), and expenditure is an empirical measure that is used 

to approximate consumption. At the macro-level the distinction may be less relevant given the specific question of 

interest. It is acknowledged that some consumption items may be purchased once upfront, but their produced utility 

streams will be spread over time. The literature of consumption studies using micro household data usually 

concentrates on expenditures on food or non-durable goods and non-durable services (see the superb summary and 

references listed in Deaton (1992), and more recently, Vissing-Jørgensen (2002), Malloy, et al. (2009), Aguiar and 

Hurst (2013), Aguila, et al. (2011), Attanasio, et al. (2012), Blundell, et al. (2012), among others). In these 

expenditure categories, consumption and expenditures are less likely to diverge drastically from each other. 

Whenever possible, we use “consumption” in a theoretical discussion setting, and “expenditure” in an empirically 

oriented context. We also highlight that at the household level we are modeling expenditures, or where the 

household obtains and spends its resources or cash flows. 

2

 A large body of literature compares the housing wealth effect with the stock wealth effect. Beside what is cited 

elsewhere in this paper, a non-exhaustive list includes Benjamin et al. (2004), Slacalek (2009) and Carroll et al. 

(2011), among others. 

3

 Engelhardt (1996) and Skinner (1996) show that the consumption response to housing capital gains, especially 

positive ones, is rather small. A homeowner can benefit from rising housing wealth by pledging the house as 

collateral to borrow to fund consumption. By doing this, the consumer retains or increases access to cash and liquid 

assets that can be used to pay for expenditures. Cooper (2013) shows that this borrowing channel is significant, 
4

The central issues in the present study are household level expenditures and decisionmaking conditional on home ownership. Extending Sinai and Souleles (2005) who argue that 

ownership provides rent hedging, a new and relevant measure for households, the housing 

dividend (or hedge reflective of potential benefits to fixing housing services expenditures) is 

assessed while controlling for other measures of housing wealth common to the literature,

including house value or equity. The principal interest is the relation between this dividend (or 

hedge benefit) and household expenditures. The housing dividend is defined as the difference 

between equivalent market rent for a household’s housing unit and the household’s actual 

recurring house ownership costs (expenditures such as debt service, property taxes, maintenance 

costs, et cetera). The definition of housing dividend is rooted in the user cost (or imputed rent) 

concept that occupies a central place in the housing economics literature (Poterba, 1984; 

Himmelberg et al., 2005 and others) and provides empirical recognition that the benefit to home 

ownership at the household level is related to fixing actual cash outlays for shelter in addition to 

purely an appreciation return and associated potential increased borrowing capacity. We obtain 

the formula of the housing dividend by recasting the components of user cost into an explicit, 

simple form that resembles the common asset pricing framework from Miller and Modigliani 

(1961) and Flood and Hodrick (1990).4

Housing is an asset that is either bought and owned or rented for use and occupancy. An 

individual or household makes a choice to live somewhere and determines how to pay for that 

choice. The formulization of a housing dividend explicitly recognizes an additional way 

households may benefit from home ownership. Acquisition of an owner-occupied housing unit, 

 

particularly for those households who might be liquidity constrained. Gan (2010) provides similar evidence for 

Hong Kong.

4

 Research of testing for housing market efficiency makes progress without particular needs for a clarified definition 

of the housing dividend. Cho (1996) surveys related empirical studies. 
5

even with debt, hedges payment of the future equivalent market rent (which reflects the then 

current market price for occupancy), while allowing for asset appreciation. When market 

equivalent rents rise, while ownership related costs remain the same or increase less 

proportionately to market rents, homeowners benefit. That is, a homeowner is not forced to 

increase expenditures to acquire the equivalent amount of housing services because he/she has 

substantially hedged current and future expenditures through ownership. The household can 

reallocate money that would have gone to paying more for housing services if these services 

were unhedged. The benefit comes from not having to spend more over time for the same quality 

of housing in contrast to a renter’s situation in a market of rising rents. This is the economic and 

practical mechanism behind the operation of the housing dividend. 

The housing dividend benefit is further distinguished from the benefit of borrowing 

capacity by using the house as collateral (Cooper, 2013). The household retains the right to 

borrow additional funds greater than the existing debt as equity increases over time. The benefit 

to borrowing (a cash-out refinancing or home equity line or loan) is a change in cash available 

for expenditure on non-housing goods, but additional borrowing can also reduce the future 

housing dividend benefit since a new and higher level of housing ownership expenses (primarily 

debt service) may be required. Although more borrowing reduces housing dividends in the 

current and future periods, it allows households to obtain liquidity needed for expenditures. 

Since the focus is the relation between housing and consumption, detailed U.S. household 

data from the Consumer Expenditure Survey (CEX) for homeowners across years is used in the 

analysis. The CEX provides detailed information on housing and non-housing household 

expenditures, along with measures of household wealth, which enables analysis connecting 
6

housing dividends with non-housing expenditures while controlling for wealth. 5 Hence, our 

baseline approach starts with regressions of homeowners’ non-housing expenditures on housing 

dividends while controlling for financial and housing wealth. The housing dividend variable is 

positive and significant, both statistically and economically, in predicting a household’s nonhousing expenditures. This is so even when house value is decomposed into home equity and 

mortgage balance. The results are robust across various specifications and suggest that housing 

ownership and the ability to hedge housing expenditures over the long-term allows households to 

increase non-housing expenditures. The choice to own allows for greater expenditures on nonhousing goods over the long-term while allowing households to also hold an asset with 

appreciation or return potential. 

The results also have implications for the buy-versus-rent literature which requires 

assumptions related to the use of any net cash flow differences with regime (rent or buy) 

switching.6These assumptions typically assume that households exclusively allocate cash flow 

“savings” from correctly renting or buying to financial assets which then earn a market return. 

Our results show that households may tap these cash flow savings for consumption. Finally, 

further decomposition of the data by time period suggests that the housing dividend effect 

declined during the housing boom period when housing assets were seemingly more liquid and 

debt was available for cash out refinancings. During the boom period, households faced a 

reduced housing dividend as the over-priced assets acquired during the period limited any 

hedging benefit as the cost of ownership increased substantially and disproportionately relative 

to rents. 

 

5

In contrast, Gan (2010) uses credit card spending to measure consumption, which presumably includes spending on 

a subset of both nondurable and durable goods and services, but excludes all others where a credit card is not used. 

6

 Beracha & Johnson (2012) provide an example of the constructs related to the buy versus rent literature which 

includes debt assumptions, cash flows assumptions and housing quality assumptions across rental and owner based 

markets.
7

The paper proceeds as follows. Section 2 positions the paper in relation to various strands 

of the literature. Section 3 derives the conceptual framework from the definition of user cost. 

Section 4 provides the empirical specifications for our estimation based upon that conceptual 

framework. Section 5 clarifies the CEX sample selection criteria and addresses measurement 

issues related to several key variables. Sections 6, 7 and 8 present and discuss the estimation 

results. Section 9 concludes. 

Related Literature 

The research is related to several strands of the economics and finance literatures. First, the study 

extends the growing literature devoted to understanding the relationship between housing wealth 

and non-housing consumption. The relations between housing related wealth and non-housing 

consumption have long been a focus of interest to economists and policy makers. The literature 

often focuses on static measures of housing and financial wealth without expliciting addressing 

flows, potential income returns or the ability to hedge expenditures. The goal has been to 

determine how levels (or changes in the levels) of asset values impact expenditures throughout 

the economy. For example, at the aggregate market level, Case, et al. (2005), using international 

country-level plus U.S. state-level data, estimate that the elasticity of consumption to housing 

market wealth is between 0.04 and 0.16.7

Among research using U.S. household-level data, Engelhardt (1996) estimates that the 

Marginal Propensity to Consume (henceforth MPC) out of real housing capital gains is 0.03 (3 

cents for every dollar increase in housing wealth) for the median household. By matching a CEX 

 

7

Case, et al. (2011) update their previous analysis by extending the sample to more recent years, and find that new 

results only reinforce their previous conclusions.
8

sample with Survey of Consumer Finances (SCF) data, Bostic et al. (2009) obtain estimates of 

the elasticity of consumption to home value of 0.04 to 0.06, and estimates of the elasticity of 

consumption to home equity of 0.02 to 0.04. Guo and Hardin (2014) examine a Panel Study of 

Income Dynamics (PSID) sample and obtain the same magnitude of estimates for the elasticity 

of consumption to home equity as in Bostic et al. (2009). They note this estimate varies among 

households depending on wealth composition. Households with different levels of net worth in 

financial and real estate assets show different expenditure patterns. Cooper (2013) employs a 

very different set of regression specifications and estimates the MPC out of housing wealth at 

0.06, with a MPC out of home equity of 0.02 to 0.06.8 None of the noted studies explicitly 

recognizes the consumption benefit from ownership when the actual cash expenditures for 

housing are substantially fixed or hedged. Complementing and extending existing research, this 

paper investigates this additional homeowner benefit.

The second link and extension of existing literature is related with user cost. The 

definition of housing dividend is closely tied with the concept of user cost, one of the 

fundamental building blocks in the housing economics literature (Poterba, 1984; Himmelberg et 

al., 2005). We demonstrate in a straightforward manner that the equilibrium condition involving 

user cost can be written as a dividend discount model (DDM) with housing dividend and 

discount rate appropriately defined. While much of the application of user cost has been devoted 

to quantifying the effect of preferential tax treatments to homeowners (Poterba and Sinai, 2008),

housing tenure choice determinants (Green, 1996; Green and White, 1997), and house price-rent 

dynamics (Duca, et al., 2011), we focus on the consumption benefits of the housing dividend. 

 

8

These studies also estimate elasticities of consumption to income and to financial wealth by including them as 

additional regressors. Results vary. The elasticity estimates of income are around 0.10 to 0.30. The elasticity 

estimates of financial wealth are in the range of 0.01 to 0.10. Except Cooper (2013), all of the cited studies above 

find a larger housing wealth elasticity than the financial wealth elasticity.
9

Household recognition of flow attributes inherent in the owner cost equation impacts 

expenditures. 

In equilibrium, user costs should equal rents for dwelling units. In a series of papers, 

however, Verbrugge and Garner (Verbrugge, 2008; Garner and Verbrugge, 2009a, 2009b) 

demonstrate that the estimated user costs and rents diverge persistently from each other to a 

surprising degree. The findings are robust regardless of using aggregate indexes or CEX unitlevel data. We note that part of this gap between the rent and the user cost is related to what we 

define as the housing dividend. 

The third (somewhat more loosely) related literature strand includes studies on 

investors’/consumers’ differentiation between dividends and capital gains. One of the primary 

theoretical foundations of financial economics is the irrelevance of dividend policy as applied to 

firm value (Miller and Modigliani, 1961). Under perfect and complete market conditions, the 

way a firm chooses to distribute returns related to earning power should not matter in its 

valuation. 9 When the distribution of dividends conflicts with consumption needs, 

investors/consumers can always reinvest disbursed dividends or sell shares to create dividend 

equivalents, thus insulating individual consumption from variations in corporate dividend 

disbursement. Recent empirical studies testing this theorem with micro level data of stock 

holdings, however, resoundingly reject the dividend irrelevance tenet (Baker et al., 2007; Dong 

et al., 2005; Graham and Kumar, 2006), although there are lively debates on why the tests fail.10

 

9

 Relaxing assumptions in Miller and Modigliani (1961) would challenge the Miller-Modigliani irrelevance theorem. 

Latest developments along this vein in the field of corporate finance include DeAngelo and DeAngelo (2006), and 

Mori (2010). 

10 Finding different consumption propensities for dividends versus capital gains is potentially justified by the 

duration dependent net return structure generated by capital gains taxation (Balcer and Judd, 1987; Dammon et al., 

2001). Mental accounting is another behavioral finance explanation for investors’ preference for cash dividends 

(Shefrin and Statman, 1984). Meanwhile, it is well-known that equity dividend incomes are concentrated in highincome households. These households’ consumption behavior likely differs from the average household, and, in 
10 

By proposing and clarifying the housing dividend construct related to housing wealth, we open 

the possibility of subjecting housing wealth to the same category of tests with implications 

attached to the distinctive features of housing wealth.

Theoretical Framework 

In this section, we give a precise definition of the housing dividend and expose its role in the 

pricing of house assets through an analogous formula of the dividend discount model (DDM). 

We show that insofar as a current occupant-homeowner can fix the operating cost of residing in 

the house compared with later comers, the resulting housing dividend flow shall be greater than 

that of a new buyer in the market. This housing dividend accruing to the homeowner shall 

increase over time, provided that the difference between market equivalent rent and ownership 

operating cost increases over time. 

Since a housing dividend is an implicit cash flow to current homeowners, its liquidity is 

superior to home equity since access to the latter often involves non-trivial transaction costs. We 

derive the testable hypothesis concerning the relationship between housing dividend and 

household expenditure: homeowners’ consumption expenditures respond more to a housing 

dividend than to home equity. Two particular extensions of this hypothesis are: (1) when the 

liquidity of home equity increases (decreases) due to favorable (unfavorable) market conditions, 

the relative consumption response to the housing dividend versus home equity is decreased 

(increased) accordingly; (2) insofar as the size of housing dividend rises with ownership tenure, 

the consumption response to housing dividend also increases with ownership tenure. 

 

particular, may be less sensitive to cash flow considerations (Poterba and Slemrod, 2007). Hence, it is difficult to 

extrapolate the Baker et al.’s (2007) estimated propensity to consume to the general population.
11 

We highlight a remark from Himmelberg et al. (2005, pp.68): “The ‘dividend’ portion of 

the return from owning a house comes from the rent the owner saves by living in the house rentfree ...” This insight is correct in that the “dividend” indeed includes the rent saved and is an 

implicit cash savings. But, this remark does not adjust for the realistic senario that homeowners 

may have to pay mortgages and maintenance costs, two cash outflow items that renters do not 

have to endure. From a household expenditure or cash flow stand point, ownership alone does 

not signal the actual benefit over renting. 

 We start with the familiar (long-run) equilibrium condition in the housing market that 

annual cost of owning a particular house should equal the annual cost of renting it, which renders 

a potential buyer indifferent to owning versus renting: 

Annual cost of ownershipt = Rentt

. (1) 

The majority of households in the U.S. purchase their primary residence with a mortgage. 

Therefore, the exact formula to compute the annual cost of ownership (e.g., see Himmelberg et al. 

(2005)) in the left-hand side of (1) shall be modified to include the terms related to mortgage 

debt 

Annual cost of ownershipt ≡ (1 − αt

)Pt(rt

f + γt) + Pt(ιt + αtrt

m,p +

αtrt

m,i

) − τtPt(ιt + αtrt

m,i

) + Ptδt − (Et

(Pt+1) − Pt) ,

(2)

where Ptis the house price at time t, αtthe loan-to-value ratio, rt

f

the risk-free interest rate, γt

the risk premium to compensate for the higher risk of owning versus renting, ιtthe property tax 

rate, τt

the effective income tax rate, rt

m,pthe amortized mortgage principal deduction rate, and 

rt

m,iis the mortgage interest rate. The third term in (2) captures the tax exemption benefit of 

mortgage interest and property tax. δtis the fraction of maintenance cost relative to home value. 

The last term is the expected capital gain. 
12 

 User cost, a central concept in the literature of housing economics, is the annual cost of 

ownership expressed as a fraction of the house value, 

User costt ≡ Annual cost of ownershipt/Pt

=(1 − αt

)(rt

f + γt) + (1 − τt

)(lt + αtrt

m,i

) + αtrt

m,p + δt − Etgt+1,

(3)

where Etgt+1 is the expected growth rate of house value. Note that (3) is a definitional equation 

paraphrased from the definitional equation (2). Hence, the equilibrium condition (1) can be 

rewritten into 

User costt × Pt = Rentt.

 Back to the equilibrium equation (1), its simplicity obscures one critical aspect of 

homeownership: once a house is purchased, the value of some components in the annual cost of 

ownership will be more fixed than others for the foreseeable future. More specifically, when a 

homeowner enters into a mortgage contract, the periodic mortgage payment will be largely 

determined, especially for a fixed rate mortgage; the maintenance cost for the specific house 

structure shall not fluctuate in response to its market price or rent; the property tax is levied 

based upon the assessed value, which will be less volatile than the market price; and, various tax 

exemptions will add to the relative stability of the tax bill. 

 To capture this in the model, suppose at time t=0, a buyer purchased a house when the 

cost of ownership was less than the rent, that is, 

(1 − α0)P0(r0

f + γ0) − (E0

(P1) − P0) + P0(l0 + α0r0

m,p + α0r0

m,i

) −

τ0P0(l0 + α0r0

m,i

) + P0δ0 < Rent0 .

(4)
13 

Take the last three terms in the left-hand side of (4) (mortgage payment and property tax, tax 

exemption savings, and maintenance cost) and name them as the operating cost of ownership, or 

OC for short: 

OC0 ≡ P0(l0 + α0r0

m,p + α0r0

m,i

) − τ0P0(l0 + α0r0

m,i

) + P0δ0 . (5)

From our preceding discussion, after purchase, OC will be fixed to this new homeowner. Yet the 

vector of the market price, rent, and risk-free rate and risk premium will continue to fluctuate 

over time (Rentt, Pt, rt

f

, γt)t=1

∞ .

 In the idealized setting, for this specific house, its structure and size cannot be changed, 

and there are always buyers in the market bidding on this house. Sooner or later, at some point of 

time t>0, to potential buyers, the equilibrium condition for the house will again be attained: 

Rentt = (1 − αt

)Pt(rt

f + γt) − (Et

(Pt+1) − Pt) + OCt

. (6)

Equation (6) is restructured from the equilibrium condition (1), singling out OCtfor its tendency 

of being fixed. Equation (6) can be reformatted into 

Pt =

Rentt − OCt

1 + (1 − αt)(rt

f + γt)

+

Et

(Pt+1)

1 + (1 − αt)(rt

f + γt)

, (7)

where the perceived dividend for potential buyers is

DIVt ≡ Rentt − OCt, (8)

which is the present definition of the housing dividend: the difference between market rent and 

operating cost of ownership. If we iterate (7) into future periods, it becomes a version of the 

dividend discount model, with the current price Ptrepresented by the present value of the future 

dividend stream with the corresponding discount rate as (1 − αt)(rt

f + γt) .
14 

 For the homeowner who currently resides in the house, however, the operating cost is 

OC0 rather than OCt. Thus, the actual housing dividend enjoyed by this occupying homeowner is 

Rentt − OC0. Let Pt

o

denote the private value placed by the occupying homeowner, then 

Pt

o =

Rentt − OC0

1 + (1 − αt)(rt

f + γt)

+

Et

(Pt+1)

1 + (1 − αt)(rt

f + γt)

. (9)

In a generally up-trending market, new buyers will face higher mortgage payments and property 

taxes than the current homeowner who has resided in the house for some time. Therefore the 

operating cost of ownership of the current homeowner will be less than that of potential buyers, 

OC0 < OCt, (10)

while other determinants in the market on the right-hand side of (9) will be the same for both. 

For the current homeowner this implies that the housing dividend benefit is higher than what

would accrue to a new owner: 

DIVt

o > DIVt

. (11)

where Divt

o

is the housing dividend enjoyed by the current homeowner who purchased the house 

at period 0. Hence, the market price of the house is below her private value placed on it: 

Pt < Pt

o

. (12)

This suggests continued occupancy of the house and no sale, other things held constant. 

Having established the intrinsic benefit of owning and occupying the house and enjoying 

the housing dividend flow, we demonstrate that given that the liquidity attributes of the housing 

dividend are superior to those of home equity, the consumption response to a change in housing 

dividend is accordingly higher than that to the change of home equity. 

This is best illustrated in a continuous-time stochastic setting for analytical convenience.

We write the house pricing equation (7) in its continuous-time counterpart form in which the 

market price of the house, Pt, faced by the homeowner, follows the Brownian process 
15 

dPt

Pt

= (c + d)dt + σcdZc,t + σddZd,t, (13)

where c is the expected rate of capital gain, and d is the expected housing dividend yield. σc

2

and 

σd

2

are the instantaneous conditional variances correspondingly. There is a (probably positive) 

correlation between housing dividends and capital gains denoted by the instantaneous covariance 

σc,d. From our previous discussion, the current occupant-homeowner enjoys a higher housing 

dividend flow than that expected in the market for a potential buyer. We capture this by adding a 

positive φ to d so that d + φ is the housing dividend yield for the current homeowner. 

 Aside from housing, the homeowner can also save in a risk-free security at the constant 

rate rf.

11 Net worth at the end of period t, Wt

, is divided between home equity and savings in the 

risk-free asset, St,

Wt = θ(Pt − Lt

) + St

, (14)

where Ltis the mortgage loan balance. Since the housing dividend is a kind of cash savings, it is 

a component of St. Since cashing out home equity involves some transaction costs, θ (0 < θ <

1) is the liquidity discount coefficient applied to home equity. 

Let ωt be the fraction of wealth held in the home equity. With consumption in period t

denoted by Ct, the dynamic evolution of the homeowner’s net worth over time is 

dWt = [ωt

(θc + d + φ) + (1 − ωt)r

f

]Wtdt − Ctdt − ωtWtr

m,pdt

+ ωtWt(θσcdZc,t + σddZd,t) .

(15)

where r

m,p is the amortized mortgage principal reduction rate. Furthermore, we assume the 

investor/consumer is maximizing the expected discounted future utility 

 

11 The essence of the model remains the same should income and more assets be allowed for, but the derivation will 

be considerably more cumbersome. 
16 

E ∫ e

−ρsU(Cs

)ds ,

T

s=t

(16)

where ρ is the discount rate, and U(. ) is the within-period utility function. The 

investor/consumer’s objective is to maximize (16) subject to (15). The maximized value of the 

function (16) is denoted by the value function J(Wt).

The solution to this problem can be characterized by the standard Hamilton–Jacobi–Bellman 

equation 

ρJ(W) = maxC,ω[D

c

J(W) + U(C)] ,

where 

D

c

J(W) = [(ω(θc + d + φ − r

m,p) + (1 − ω)rf

)W − C]JW(W)

+

1

2

ω

2W2

(θ

2σc2 + σd

2 + 2θσcd)JWW(W),

from which we solve out 

ω

∗ = −

(θc + d + φ − r

m,p − rf

)JW(W)

(θ

2σc2 + σd

2 + 2θσcd)WJWW(W)

.

In order to proceed for an explicit solution of optimal consumption, C

∗

, a particular form of 

utility function for U(C) has to be specified. Let it be the Constant Relative Risk Aversion 

(CRRA) function, 

U(C) =

C

1−γ

1 − γ

,

from which we solve out the optimal consumption as 
17 

C

∗ =

γ − 1

γ

[r

f +

ρ

γ − 1

+

1

2γ

(θc + d + φ − r

m,p − rf

)

2

(θ

2σc2 + σD

2 + 2θσcd)

] W ≡ κ ⋅ W

≡ κ(θ ⋅ HE + S) = κθ ⋅ HE + κ ⋅ S .

(17)

In (17) the Marginal Propensity to Consume (MPC) for home equity is κθ, and for liquid 

savings (a portion of which comes from the housing dividend) it is κ. From its algebraic 

expression, κ increases with c, d or φ, implying a higher MPC when the housing dividend or the 

capital gain is higher. But the increase in κ is larger with respect to the increase in d or φ, as the 

latter two have no liquidity discount, in contrast to the case for c . To the extent that the 

magnitude of φ would rise for the current homeowner because of longer tenure, the MPC would 

be higher, all else equal. Finally, although it is difficult to pin down the direction of change in κ

in response to a change in θ (which depends on the sign of, say, d + φ − rm,p − rf), at least, 

when θ increases — that is, when the liquidity of home equity improves — the relative 

difference between the MPC of home equity and the MPC of housing dividend will shrink. 

Empirical Framework 

Following the theoretical model outlined in the last section, a homeowner’s housing dividend 

(DIV) can be measured by the following equation,

DIV = EquivRent + TaxSavings − MorPayments − MaintCosts, (18) 

where EquivRent is the current market rent of the homeowner’s present house, and the other 

three variables are tax benefits, mortgage payments (including insurance expenses and property 
18 

taxes) and house related maintenance costs, respectively. Constructed in such a way, DIV

represents the housing dividend benefit arising from home ownership. It measures the implied 

cash flow benefit associated with ownership. 

We start with the following specification to estimate the relationship between logs of 

expenditures (C), housing dividend (DIV), home value (H), financial wealth (Fin), and income 

(I),

log(Ci,t) = β0 + β1log(DIVi,t) + β2 log(Hi,t)

+ β3 log(Fini,t) + β4 log(Ii,t) + Xi,t

′ β + ut + εi,t

,

(19)

where i is the household index, and t is the year index. Xi,t is the vector of household-level 

demographic controls (such as age, family size, etc.), ut captures year effects, and εi,t is the 

idiosyncratic disturbance term.12 Equation (19), without the housing dividend variable, is a fairly 

conventional specification in the literature (Bostic et al., 2009). 

The housing dividend includes measures of mortgage debt. To demonstrate that the 

housing dividend effect is not merely a manifestation of a mortgage effect, we break down home 

value into equity and mortgage debt in the second specification. Including home equity (HE) and

mortgage balance (M),

log(Ci,t) = β0 + β1log(DIVi,t) + β2 log(HEi,t) + β3 log(Mi,t) +

β4 log(Fini,t) + β5 log(Ii,t) + Xi,t

′ β + ut + εi,t

. 

(20)

 

12 Due to the fact that the financial wealth information is only available in the fifth interview of CUs in CEX, we 

cannot exploit the quarterly panel nature of CEX.
19 

Housing equity and mortgage debt are joint determinants of consumption at the household level. 

From a cash flow standpoint, the amount of mortgage debt outstanding reflects cash that has 

been withdrawn against the property’s value which may have allowed consumption or 

investment in financial assets at a prior point. This withdrawn cash either decreases the cash 

outlay that would otherwise occur (when a loan is used to purchase the property), increases the 

balance of other assets (when the cash is spent on purchases of other assets or investments which 

likely enhances liquidity) or is channeled directly towards consumption. The cash flow impact of 

mortgage loans on consumption expenditures has found empirical support in recent studies 

(Muellbauer, 2008; Benjamin and Chinloy, 2008; Guo and Hardin, 2014). 

Sample Selection and Measurement Issues 

NBER’s CEX extracts are used to construct the pooled sample for the period 1988 to 

2011.

13 Housing market prices and housing costs of ownership, along with mortgage balances 

outstanding, are extracted from the BLS version of the original CEX sample.14 The criteria for a 

consumer unit (CU) (most likely a household) to be selected for analysis are standard: the CU 

 

13 1995:Q3-Q4 and 2004:Q3-Q4 observations are omitted due to a sample frame change that occurred in 1996 and 

2005, which makes it impossible to track a small fraction of the consumer units for the full year. Observations from 

years prior to 1988 as well as 1991:Q2-Q4 and 1992 are omitted due to unavailability of detailed expenditure files 

on mortgage payments and house operational costs. 

14 NBER CEX files (cleaned and streamlined by Ed Harris and John Sabelhaus) condensed hundreds of expense 

categories into a much smaller set of summary categories (the documentation can easily be accessed at 

http://www.nber.org/data/ces_cbo.html) and annualized the data. The BLS version of the CEX samples also includes 

some summary categories that may overlap with those in the NBER extract version, but they are presented on a 

quarterly basis. Whenever possible, we use what is already available in the NBER version; if a particular variable or 

spending category is opaque, missing, or limited in construction in the NBER version, we turn to the family (FMLY) 

or detailed expenditure files of BLS version. For example, the variable of owned housing principal reduction in the 

NBER version includes the reduction of mortgage principals on vacation properties, whereas the BLS version 

distinguishes between these two. For the post-2007 data for which NBER version is unavailable, we follow Harris 

and Sabelhaus’ procedure to create those summary-level expenditure variables to be consistent. 
20 

has to be interviewed consecutively for four quarters (the maximum number of quarters for 

which a CU could be interviewed), is a “complete income reporter” as defined by the BLS, is not 

a student household, and has unambiguous homeownership status.15 We only use the houserelated records pertaining to the property that the consumer unit occupies and further restrict the 

sample to households reporting positive pre-tax income and positive food expenditures. Financial 

wealth equals the combined balances in checking, savings, and brokerage accounts. All measures 

of expenditures are annualized. All economic variables, if applicable, are deflated to 2005 dollars 

using the CPI-U index. 

Summary statistics of the benchmark analysis sample are presented in Table 1. The 

typical household is headed by a 52 year-old person. 67 percent of the heads of household from 

the sample periods are married. The typical family size is 2.7 members. 89 percent of the 

households are in urban areas. 89 percent of household heads are caucasian. In the sample, the 

median value of home equity is substantially greater than the value of financial wealth, but is 

only slightly greater than the balance of mortgage debt. The financial wealth has the greatest 

variation relative to its mean. Perhaps most important, the median housing dividend, which is a 

recurring flow measure, is very similar in size to the median stock holdings of a household. In 

essence, the typical household receives a flow benefit (implied dividend) on an annual basis that 

is almost equivalent to its stock of financial assets. For example, a household may receive $3,000 

per year by accounting for its housing dividend while holding only $2,500 in stock/financial

assets. The household would need $60,000 in financial assets yielding 5% annually to generate 

an equivalent dividend yield on its financial assets. This highlights the importance and relevance 

of housing dividends. 

 

15 Which means, for instance, a homeowner who rents a property elsewhere to live in is discarded from the sample. 
21 

Expenditure measures are of three primary types: durable goods, non-durable goods, and 

services, in accordance with the latest 2009 comprehensive revision of classifications of Personal 

Consumption Expenditures (PCE) (McCully and Teensma, 2008).16 In the standard classification 

system, services include housing and utilities, healthcare, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. 

However, for our purpose, housing-related maintenance, insurance and other expenses are 

excluded from services expenditures to avoid a confounding bias due to the use of these

expenses to generate the housing dividend (which already includes these expenditures and debt 

service as part of the recurring expenditures related to home ownership). Total consumption 

expenditures are the sum of the above three primary types. Food expenditures include food 

consumed at and outside the home in our analysis.17 These estimates related to food facilitate 

comparisons with previous studies using PSID data to examine wealth effects and food 

expenditures. We also add two broader measures of services and total expenditures that add 

education expenses and charitable giving into the components of their baseline definitions.

The CEX data provide estimates of the market value for each household’s housing unit. 

The BLS version of the CEX is used to extract this value from the household’s initial interview. 

The value estimate is provided by the CU. Correspondingly, the mortgage principal balance is 

 

16 In general, goods are commodities that can be stored and perhaps used over time, and services are commodities 

that cannot be stored and have to be consumed at the time and location of purchase. For an item that has both a 

goods component and a services component, the classification is based on the predominant component (e.g., 

spending on alcoholic beverages consumed away from home is classified as services). Durable goods have an 

average useful life of at least 3 years, including motor vehicles and parts, furnishings and durable household 

equipment, recreational goods and vehicles, and other durable goods. Nondurable goods have an average useful life 

of less than 3 years, including food and beverages purchased for off-premises consumption, clothing and footwear, 

gasoline and other energy goods. 

17 In the 2009 revision of the NIPA Personal Consumption Expenditures classification system, purchased meals and 

beverages at food services and drinking places are counted as services (Bureau of Economic Analysis, 2012), thus 

spending at restaurants is now counted as service expenditures, in contrast to as non-durable goods as before. 

However, the food expenditure used in this paper covers both food purchased for home production as well as 

consumed at restaurants, therefore is a mix of components from non-durable goods and services categories. 
22 

also the amount reported by the household. Home equity is calculated as the difference between 

these two. 

Recall that the housing dividend for homeowners is defined as the equivalent market rent 

(what a homeowner would receive should he or she rent the house) plus tax savings related to 

interest payments and property taxes, then subtracted by the mortgage payments (inclusive of 

principal reduction, 18 mortgage interest and property taxes), house maintenance, repairs, 

insurance, and other expenses. Two data issues related to this variable are worth noting. First, the 

variables used to derive the “housing dividend” are all available in CEX, except individual 

federal tax rates, without which a homeowner’s tax savings cannot be exactly imputed. While it 

may be possible to impute household-level tax benefits related to homeownership, we, however, 

have chosen not to do so, which likely understates housing dividends. The present results would 

likely be strengthened should tax benefit estimates be incorporated. 

Second, the equivalent market rent for the house of a homeowner is self-assessed. These 

self-reported rents could be noisy relative to some benchmark, either professionally assessed 

rents or the predicted values of a hedonic regression model.19 The benchmark is difficult to 

define, however, if the owner-occupied dwelling units fundamentally differ from rental units by 

location and zoning codes restrict actions that can be taken by homeowners. In addition and more 

important in the context of CEX data, a benchmark or general index does not tie each household 

observation (concerning the household’s mortgage debt, financial assets, household expenditures, 

et cetera) directly to a property. A homeowner has both advantages and disadvantages in 

 

18 This variable is to be distinguished from another variable — special or lump sum mortgage payments, which may 

or may not be related with refinancing or property transactions. As a robustness check, we exclude all observations 

that report non-zero values for lump sum mortgage payments, or non-zero values in purchase or selling prices of 

properties, and find virtually no difference in results. 

19 Francois (1989) lists a number of reasons why a hedonic model using market rents would underestimate the true 

underlying rents.
23 

providing an estimate of equivalent rent when compared with professional assessors. On one 

hand, a homeowner has to stay alert to rental market conditions to be able to offer an accurate 

estimate. On the other hand, it is possible that a homeowner possesses superior information that 

is unavailable to outside assessors in estimating equivalent rent.20 Specifically, for the present 

case, Garner and Verbrugge (2009a) find that in CEX data the self-reported equivalent rents are 

sensibly related to the limited set of housing characteristics, such as home value, number of 

rooms, structural type, age of dwelling, poverty rate in the region, et cetera. This suggests 

homeowners are reasonably informed concerning the housing market when estimating market 

rents. In any case, if there is any potential classical measurement error in the self-reported 

equivalent market rent that enters into the housing dividend variable, the textbook conclusion for 

errors-in-variables holds that the coefficient estimates of household dividend are biased towards 

zero. The implication is that the reported estimates of the housing dividend variable could be in

the low-end range of its true parameter value which strengthens the results presented.21, 22

Likewise, some concerns with respect to the accuracy of the wealth variables (financial 

wealth in particular) in CEX data need to be addressed. For instance, Bostic et al. (2009) argue 

 

20 Heston and Nakamura (2009) note the possibility that homeowners might place above market values when 

assessing equivalent rents if units have special features. 

21 It is possible that the noise in equivalent rents is non-random, correlated with household characteristics or even 

expenditures in one way or the other. Explicit evidence on this, especially for the CEX self-reported rent data, is 

elusive. If the literature on the quality of homeowners’ self-assessed house value is of any guidance, no consensus 

has been attained. Goodman and Ittner (1992) and the earlier studies cited therein, and more recently, Kiel and Zabel 

(1999) and Agarwal (2007), find homeowners mostly overestimated their house value by several percentage points 

relative to an outside benchmark. Ihlanfeldt and Martinez-Vazquez (1986) find the discrepancy between 

homeowners’ self-assessed house value and appraisal value is related to owners’ race and age. Agarwal (2007) finds 

that whether a homeowner is an overestimator or an underestimator depends on a host of household socioeconomic 

characteristics. In contrast, Kain and Quigley (1972), Goodman and Ittner (1992) and Kiel and Zabel (1999) find the 

errors in these estimates are not statistically significantly related to characteristics of the owners. The discrepancy in 

the findings of studies cited above may well rest on the different data sources these authors use. Regardless, owners’ 

assessed house value is heavily employed in empirical work. In theory, how a consumer plans his or her 

consumption depends justifiably on the perception of wealth and income level he or she has given the information 

set at that point of time. Agarwal (2007) provides evidence supporting this view.

22 In addition, the limitations of CEX data on housing characteristics variables prevent us from taking a crosshedonic estimation to obtain predicted equivalent rents for these households, as done in Goodman (1988). To assess 

the impact of over-estimating rents, we discount each household’s reported rent value by as large as 10% and still 

find the housing dividend effect is comparable to the home value wealth effect in terms of MPCs. 
24 

that the CEX financial wealth variables may be noisy and of limited scope. This is why they 

resort to a statistical matching algorithm to combine CEX with Survey of Consumer Finances 

(SCF) samples to obtain balance sheet-related measures. This view, however, does not represent 

the consensus on the issue, as a number of studies have endorsed the validity of available CEX 

balance sheet information (Attanasio, 1994; Johnson and Li, 2009, 2010; Maki, 2001). Johnson 

and Li (2009, Table 3) show that the mean levels of primary mortgage, vehicle loans, and credit 

card balances of consumer units in the CEX are fairly close to those in the SCF, while the CEX 

displays lower variation in these variables relative to the SCF.23 

To shed light on measurement concerns, we present in the appended Table A1 the 

estimated coefficients of home value and financial wealth without controlling for the housing 

dividend. This is the baseline specifiction used in Bostic et al. (2009, Table 3). Compared with 

their results, the present estimates show a somewhat larger effect for home value (0.10 for 

durables and 0.12 for total expenditures) and for income, but a similar effect for financial wealth 

in terms of elasticity.24 This is consistent with the view that wealth variables in CEX are not 

necessarily noisier in the classical sense of errors-in-variables violations. Again, recognition is 

made with regard to the overall data associated with this and other papers related to the area of 

interest, which remains central to policy and household decision-making.

Life-cycle Patterns 

 

23 This probably reflects the top-coding of some of the variables in CEX. In Table A2, we provide the estimates 

from the median regressions, which are less likely to be affected by top-coding, to show that they are similar to 

those yielded by linear regressions. 

24 These estimates are comparable to other studies using different sources of micro data and different estimation 

schemes. For example, by exploring a large panel data set of Hong Kong households with various econometric 

specifications, Gan (2010) estimates a housing wealth effect of about 0.10-0.19 measured by the elasticity of 

consumption. 
25 

Figure 1 depicts housing dividend life-cycle patterns. The sample is divided into eight 5-year age 

groups based on the head of household’s age. The last group is with age equal to or above 65. 

The mean of housing dividends (in 2005 dollars) is computed for each group from all years, from 

1988 to 1996, from 1997 to 2006, and from 2007 to 2011, respectively. The period of the late 

1990s to 2005 or 2006 represents the housing booming period preceding the latest housing 

market crash (Glaeser, et al., 2010; Ferreira and Gyourko, 2011). The age related measures are 

meaningful in several ways. It is likely that households headed by older members have longer

house tenure while the life-cycle pattern should follow a typical housing choice or housing 

quality pattern.

Figure 1 clearly demonstrates that inflation adjusted housing dividends increase across 

the age groups for all three time periods. For the entire sample, the mean real housing dividend 

increases from $860 for the 25-29 age group to over $6,700 for the 65+ age group. This trend 

suggests that the housing dividend (housing expenditure hedge) accrues more to households 

headed by older persons, likely due to the fact that many of them have resided longer in their 

current housing units. This fact is confirmed by Figure 3, showing the rise of housing dividend 

with housing tenure. Interestingly, Garner and Short (2009) also find that older households’ (age 

65 and above) implicit rental incomes (akin to our housing dividends) are greater than younger 

households’ across all deciles of income distribution, no matter what method is used for 

computing the equivalent rents of owner-occupied house units. The life-cycle profile of housing 

dividends contrasts with the hump-shaped profiles of earnings and consumption expenditures 

documented in Ghez and Becker (1975) and Gourinchas and Parker (2002). 

To better understand the rise in housing dividend with age, the dividend is broken into 

three components (equivalent rent, mortgage payments, and maintenance, insurance and other 
26 

expenditures) to study the relative importance of each in driving the observed trend. Since the 

BLS data does not disaggregate information for mortgage and maintenance prior to 1994, and 

there was a sample frame change for the 1995 survey, we focus on the 1996-2011 period. Figure 

2 presents the decomposition results underlying the pattern in Figure 1. 

The most striking observation from this figure is the steep decline in real mortgage 

payments (the red, solid curve) after age 35-39. The inflation adjusted rental value of the housing 

unit (the blue, dashed curve) increases slightly and peaks at age 40-44 and then gradually levels 

off. This is consistent with the life-cycle variation in family composition, which in turn leads to 

the variation in the demand for housing services over the life-cycle. The last component, 

maintenance costs (the green, dotted curve), again inflation adjusted, is stable as expected, 

slowly increasing with age until about age 60 and then leveling off. Taken together, the market 

equivalent rent contributes to a larger share in the housing dividend over time, as real rents 

increase over time and mortgage payments become a smaller component of expenditure over 

time. 

Primary Empirical Results 

Since expenditure and wealth variables are in logarithms in the specifications, their associated 

coefficients are interpreted as elasticities. To compute the marginal propensity to consume (MPC) 

estimate, each of the estimated coefficients is multiplied by the ratio of the respective 

expenditure relative to the wealth/income variable.25 For example, to obtain the MPCs for food 

expenditure, the food elasticities from the regressions are multiplied with food expenditure 

 

25 For aggregate data, within the representative agent framework, the coefficients from the regression of the log 

consumption on the log of wealth components may be interpreted as wealth shares, given the co-integration 

relationship between these variables arising from the budget constraint (Ludvigson, 2007).
27 

relative to the value of each of the wealth component variables and income. Since the ratios 

differ across households, a benchmark is needed. The median of a ratio measure in the sample, a 

common benchmark statistic, is used to calculate the corresponding MPC.

Table 2 provides initial results using home value as the housing measure for households 

with heads aged 26 to 55, the primary wage-earning age group. In these regressions, home value 

is used without decomposition into equity and debt components. The results offer initial support 

for the importance of the housing dividend, as the coefficients are statistically significant across 

expenditure categories. The MPCs also have practical significance. While the estimated 

elasticities of the housing dividend variable are smaller than all the ones for home value and 

most for financial wealth, the implied MPCs of the housing dividend are meaningful. For total 

expenditure, the estimated elasticity of the housing dividend is 0.0115 with a MPC of over 4 

cents for every dollar. This MPC rises to above 5 cents if education expenses and charitable 

giving are counted. The housing dividend MPC is greater than the home value MPC.26 The 

control variables have coefficients in line with expectations.

In addition, Table 2 shows that the housing dividend effect differs by expenditure type.

The housing dividend has its greatest impact on food and service expenditures. 27 For food 

expenditure, the elasticity estimate of housing dividend is 0.024, statistically significant at the 1% 

level. Its implied MPC is 0.02, virtually identical to the MPC for after-tax income and larger than 

the MPC of home value. For service expenditures, the elasticity is 0.01, again statistically 

significant at the 1% level. The implied MPC is 0.022, greater than the MPC of home value, but 

 

26 The estimated elasticities for the conventional wealth components are generally similar to those in the literature 

(see the referred studies in Section 2). For example, when we look at the total expenditure, the estimates are in line 

with existing studies: the home value elasticity is 0.0962 with a MPC of 0.02, the financial wealth elasticity is 

0.0178 with a MPC of 0.09 and the log of after-tax income coefficient is 0.2484 with a MPC of 0.12. All are 

statistically significant at the 0.01 level.

27 Recall that service expenditure includes food at restaurants. See Section 4 for related discussion.
28 

less than that of financial wealth or income. For both expenditures, the housing dividend is 

meaningful, both statistically and practically. 

For durable expenditure, the MPC of housing dividend is negative at -0.035 and is 

significant at the 5% level. 28 This is likely due to the fact that the housing dividend is implicit 

and is more fungible with non-durable expenditures, while durable expenditures (such as the 

purchase of a new vehicle) are usually financed through collateral based loans. This could occur 

if, say, other things equal, the growth of durable goods loans positively correlates with that of 

mortgage loans, for this would increase the purchasing power of households for the durable 

goods. Although not of key interest in our investigations, the finding certainly deserves further 

research. 

Overall, the housing dividend is closely associated with increased food and service 

expenditures as well as non-durables. The economic and statistical significance of the housing 

dividend in predicting non-housing expenditures is apparent, even after controling for home 

value, which implies that the dividend irrelevance argument (Miller & Modigliani, 1961; Baker, 

et al., 2007) is not stringently applicable to housing as an asset and that homeowners benefit 

from fixing or hedging their ownership expenses via purchase. Households can subsequently 

reallocate the underlying income to food, services and other types of expenditures, independent 

of whether households have taken on additional housing related debt or housing disposition.

The regressions in Table 3 have home value decomposed into home equity and mortgage 

debt as separate regressors, corresponding to specification (20).29 This specification isolates the 

effect of the housing dividend from the effects of home equity and mortgage debt. With this 

 

28 We also note that the goodness of fit for the regression of durable expenditure is substantially lower than other 

expenditures. 

29 In addition, to address the concerns of top-coding for wealth and income variables in the CEX (Poterba & 

Slemrod, 2007), we conduct median regressions with the same set of dependent and independent variables and 

obtain similar results, as are presented in Table A2. 
29 

specification, the estimated elasticities for home equity and financial wealth related to total 

expenditure from the current CEX sample are comparable to those found in Guo and Hardin 

(2014) which uses PSID data. The elasticities for mortgage debt are much smaller in comparison, 

but this smaller magnitude masks the variations evidenced during different housing market 

phases, as is shown by Table 4 and will be discussed in the next section. 

The results in Table 3 extend those in Table 2, and highlight a more meaningful role for 

the housing dividend. For total expenditure, the estimated elasticity for the housing dividend, 

0.0133, is once again statistically significant at the 1% level and is larger than its counterpart in 

Table 2 (0.0115). More important, the MPC for the housing dividend (0.05) is more than double 

the MPC for the home equity variable (0.02). 

Comparisons across expenditure types continue to exhibit heterogeneity in the impact of 

the housing dividend. The corresponding estimated coefficients and implied MPCs for the 

housing dividend are very similar to those in Table 2. More noteworthy is the fact that the MPCs 

for the housing dividend are all larger than those of home equity and mortgage debt across all 

expenditure categories except durables. The housing dividend coefficient for the durable 

expenditure again remains negative. The housing dividend MPC for the broadly defined total 

expenditures category is the greatest when compared to those for other expenditure types, is 

slightly stronger than that in Table 2, and is substantially greater than those for housing equity 

and debt. 

Overall, the results in Table 2 and Table 3 show that the housing dividend is of practical 

significance. Households benefit by the fixation of home ownership costs and the hedging of 

changes in housing service costs as reflected by inflation adjusted changes in the market value of 

rents after controlling for housing equity, financial wealth and income. Households also benefit 
30 

from home ownership by paying down their mortgages which provides an opportunity to 

generate cash via refinancing or sale. These two benefits are separate and on top of the 

accumulation of housing wealth as measured by house equity. Our results differentiate between 

an investment return in housing (appreciation) and an ownership gain from actually owning and 

occupying the property. 

Empirical Results Segmented by Real Estate Cycle and by Housing Tenure 

Beginning in the late 1990s, but especially during the early 2000s up until 2006, the United 

States saw a change in its residential housing markets characterized by excess price appreciation 

amid easy credit and subprime lending (Ferreira and Gyourko, 2011; Mayer, 2011). During the 

residential boom, housing morphed from a long-term consumption good with potential for longterm appreciation to a perceived investment option with substantial liquidity. In addition, 

subprime mortgages, low documentation loans and other aggressive lending options were not as 

widespread or accessible in the housing market until the late 1990s and early 2000s when the 

housing market expanded faster than fundamentals.30 These changing market conditions and 

their impact on housing dividends warrant separate examination of the three periods of the 

sample (1988-1996, 1997-2006, and 2007-2011) when market conditions arguably differ. The 

analysis below is restricted to households in the 26-55 age group.

Table 4 presents the results associated with the three sub-periods. The models 

substantially retain the statistical relations over the periods with regard to the food expenditure, 

service expenditure, and the broader measures of service and total expenditures (inclusive of 

 

30 We provide only the description and do not investigate causal relations related to mortgage lending changes and 

price appreciation. 
31 

educational and charitable spending). A couple of meaningful and interesting observations, 

however, emerge from the cross-period comparison, especially when the housing market crash 

period is compared with the rest. 

First and foremost, the housing dividend effects are statistically significant and larger, in 

terms of both the elasticities and the MPCs in the last period than in the earlier periods. For the 

period 2007-2011, the implied MPC of the housing dividend on the narrower measure of total 

expenditures is almost 9 cents per dollar (9.5 cents for the broader measure of total expenditure). 

In contrast, the counterpart MPCs for home equity are 1.3 and 1.6 cents, respectively, 

dramatically lower than those of the housing dividend. For the period 1997-2006, the implied 

MPC of the housing dividend on total expenditure is 2 cents per dollar (2.9 cents for the broader 

measure) and is not statistically significant at a conventional level. However, the corresponding 

home equity MPCs (2.1 and 2.5 cents, respectively) are now comparable and statistically 

significant. These results accord well with the general hypothesis that as the liquidity of home 

equity declines, as was the case in 2007-2011, the gap between the impact of housing dividend 

and that of home equity expands. The housing dividend’s relevance is underscored. 

The results further suggest that the willingness on the part of home buyers to purchase 

housing or take on debt to buy real estate above intrinsic value eliminates the ability to 

meaningfully hedge housing expenditures. During the boom period, buyers were willing to pay 

more for housing than the economic benefit that accrues to housing as measured by rental or 

occupancy costs. In addition, some existing homeowners refinanced existing debt for immediate 

cash, at the cost of diminishing or eliminating housing dividend altogether. This resulted in a 

large number of transactions in this period that truncated or failed to generate cash flow benefits.

In other words, many market participants fixed their ownership costs at such an elevated level 
32 

that generation of a dividend benefit in the future is limited.31 As the market reverts back to one 

more in line with the long-term trend, not only do these homeowners lose from the housing value 

correction, but they also fail to gain from lower housing costs that accompanied the market 

correction due to leverage taking. These households have little or no equity (as has been noted in 

the default literature), but also have higher housing costs (due to higher debt service needs, for 

example) relative to rental options. Only through default and foreclosure and/or debt 

restructuring, coupled with increases in rents, can the housing dividend increase for these 

households.32 

The evidence presented in Table 4 is further consistent with the popular belief that during 

the boom period a house was used more like an “ATM” as homeowners took on mortgage debt 

to fund consumption, especially for durables and service expenditures. For the 1997-2006 period, 

the elasticity of durables to mortgage balance is 0.15, and that of services to mortgage balance is 

0.08. This is in sharp contrast to 0.0037 (which is not statistically significant) and 0.0086 for 

these two elasticities in the period 2007-2011. The results are confirmatory of the general 

narrative for the boom period. 

Concerning our second hypothesis that homeowners with longer housing tenure enjoy a 

larger housing dividend benefit and thus additional consumption expenditures, Table 5 presents 

the results for our sample segmented by housing tenure. The first subgroup is comprised by 

households with housing tenure of less than 5 years, the second, between 5 and 15 years, and the 

third, between 15 and 25 years. The estimated elasticities of housing dividend in response to total 

 

31 Existing literature highlights a mismatch in the house price-to-income ratio and price-to-rent ratio for the boom 

period. For example, see Beracha and Johnson (2012). 

32 While full investigations are beyond the scope of this paper, the results suggest that a differential between 

housing-service cost and rent could be a factor in mortgage default. A household might continue to service debt even 

with little or no equity if rental options for similar properties are priced such that the cost of the rental option 

(inclusive of moving costs) from a cash flow standpoint is equal or higher. 
33 

expenditure increase slightly from the first to the third subgroup, but they are still less than 0.02 

in magnitude. However, the implied MPCs for total expenditure increase notably along with the 

housing tenure. Beyond 15 years of tenure in the same house, the more narrowly defined total 

expenditure increases 6.6 cents (7.9 cents for the broader measure) in response to one dollar 

increase of housing dividend. In contrast, for those whose tenure was less than 15 years, these 

MPCs are 4.0 and 5.0 cents, respectively. These patterns affirm our proposed hypothesis that the 

impact of housing dividend will rise with housing tenure. 

Conclusion 

The user cost concept widely adopted in the housing economics literature is reformulated into a 

discounted dividends model which serves as the basis for this research. The model links market 

rent, ownership costs and expected capital gains unequivocally within a discounted cash flow or 

dividends model. While the derivation of the model is of some interest, the primary focus is the 

introduction and application of the housing dividend construct to existing empirical models and 

presenting evidence showing that the housing dividend impacts household level non-housing 

expenditures. No studies, to our knowledge, have attempted to discern the effects of a housing 

ownership related implicit dividend on consumption expenditures. This gap in the literature is 

likely due to the literature’s historical focus on macro-level issues and a dearth of large-scale 

data sufficiently detailed to capture differences in both market equivalent rents and actual 

occupancy expenditures at the household level. 

The empirical results based upon CEX data show that this housing related dividend is 

important in determining household expenditures. This is in addition to other wealth effects 
34 

documented in the literature such as house value, home equity, mortgage debt and financial 

assets. The vital role of the housing dividend on expenditures, even after controlling for housing 

wealth, implies that pure application of the dividend irrelevance tenet to consumption is unlikely 

in practice, probably due to the distinctive illiquidity of the housing market, as our model 

conjectures and our empirical results verify. 

Overall, this research strongly suggests that the benefit to home ownership as related to 

consumption comes not only from the accumulation of housing equity and potential cash-outs 

from mortgage loans. An additional benefit to home ownership, related to hedging or fixing 

housing costs, is provided. A supplementary extension is related to the buy versus rent literature 

where the assumption is that households have the ability and willingness to segment housing 

related cash flows based solely as investment related flows. The present research challenges the 

assumption that households will save/invest all cash flow generated from regime switching 

between house renting and buying. Households likely spend part of any benefit and perhaps also 

lose out on the forced savings related to ownership. 

At the policy level, the results suggest that a better understanding of household level 

economic decision-making is needed. Also, a deeper understanding of how household wealth 

accumulation and expenditures change when housing prices departure from their historic value 

relative to rent is required. Households vary in their individual circumstances and these impact 

their economic decisions. In the long run, households should have the correct incentives and 

education or knowledge to make sound choices. These choices may also be related to the local 

markets within which they reside. We also need to recognize that ownership can reduce 

uncertainty in required household level expenditures which will impact consumption choices. 

Policies that reduce uncertainty in housing costs and concurrent household level volatility in cash 
35 

flow allocated to housing are likely to be beneficial and need assessment. Policies that reduce 

uncertainty in costs may be as useful as policies that address price uncertainty and volatility. 

Additional research is needed to shed more light on these topics. 
36 

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41 

Figure 1. Life-cycle patterns of the housing dividends for homeowners 

Notes: The graph demonstrates the means of annual housing dividends for each age group. Values in various years are adjusted 

by CPI-U to 2005 dollars. "All years" series are calculated based on all CEX household observations for the entire period. "1988-

1996", "1997-2006" and "2007-2011" are based on household observations from 1988 to 1996, from 1997 to 2006, and from 

2007 to 2011, respectively.

-1500

-500

500

1500

2500

3500

4500

5500

6500

7500

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Housing dividend (in 2005 $)

Age group

All years 1988-1996 1997-2006 2007-2011
42 

Figure 2. Decomposition of the Housing Dividend for Homeowners

Notes: The graph demonstrates the means of housing dividend components for each age group from 1996 to 2011 for which the 

data for decomposition variables are available in CEX. Values of various years are adjusted by CPI-U to 2005 dollars. Mortgage 

payments include payments to reduce mortgage principal, mortgage interests, and property taxes. Maintenance costs include costs 

of house maintenance and repairs, insurance costs, and other expenses.

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Components of housing dividend (in 2005 $)

Age group

market equivalent rent of home mortgage payments maintenance costs
43 

Figure 3. Housing Dividend by Tenure 

Notes: The graph demonstrates the median of housing dividends by tenure of home ownership based on CEX household 

observations from 1996 to 2011. Values in various years are adjusted by CPI-U to 2005 dollars. 

0

1000

2000

3000

4000

5000

6000

7000

8000

123456789

1011121314151617181920

[21,25][26,30][31,35][36,40][41,45][46,50][51,55][56,60]

60+

housing dividends (in 2005 $)

home residence tenure (in years)
44 

Table 1. Summary statistics for CEX sample data 

All Years Year 1988-1996 Year 1997-2006 Year 2007-2011

Median Mean Std. 

Dev

Median Mean Std. 

Dev

Median Mean Std. 

Dev

Median Mean Std. 

Dev

Housing dividend 3,309 3,170 8,892 3,322 3,227 6,844 3,421 3,331 9,362 3,062 2,829 9,748

Home value 161,324 216,959 206,873 131,109 161,358 110,183 160,085 216,404 209,044 177,907 242,133 230,921

Home equity 73,322 124,947 182,188 55,529 78,740 89,909 60,039 108,130 171,744 104,481 164,240 213,612

Mort. bal. 69,739 92,012 96,456 66,128 82,618 68,548 87,455 108,274 92,915 40,544 77,892 107,499

Stockholdings 2,511 56,663 234,609 4,498 33,375 85,322 2,311 66,068 269,244 1,492 62,608 293,171

After-tax income 52,829 65,285 54,411 48,855 56,948 41,968 53,477 66,219 56,286 56,440 71,740 60,406

Age 52 53.6 15.9 50 52.6 16.2 52 53.7 15.9 54 54.4 15.7

Family size 2 2.7 1.4 2 2.8 1.5 2 2.7 1.4 2 2.6 1.5

Marital status 1 67.0% 47.0% 1 69.5% 46.1% 1 66.9% 47.1% 1 64.7% 47.8%

Urban residence 1 89.5% 30.6% 1 86.3% 34.3% 1 89.0% 31.2% 1 93.6% 24.6%

Race - white 1 88.6% 31.8% 1 90.5% 29.3% 1 88.2% 32.3% 1 87.5% 33.1%

Notes: All observations from all years are included in calculating these summary statistics. Wealth variables include both positive and 

negative values. All values are in 2005 dollars. 
45 

Table 2 Housing dividend and household expenditures of homeowners (for the age group 26-55) 

Food Nondurables Durables Services Total Services Total 

(broader definition)

Log housing dividend 0.0241*** 0.0107*** -0.0354** 0.0124*** 0.0115*** 0.0143*** 0.0127***

(0.003) (0.003) (0.013) (0.003) (0.003) (0.003) (0.003)

Log home value 0.0806*** 0.0501*** 0.1063*** 0.1332*** 0.0962*** 0.1461*** 0.1049***

(0.006) (0.003) (0.023) (0.008) (0.007) (0.009) (0.007)

Log financial wealth 0.0070*** 0.0056*** 0.0851*** 0.0213*** 0.0178*** 0.0246*** 0.0198***

(0.001) (0.001) (0.004) (0.001) (0.001) (0.001) (0.001)

Log after-tax income 0.1912*** 0.1504*** 0.5328*** 0.2619*** 0.2484*** 0.2884*** 0.2639***

(0.012) (0.009) (0.037) (0.015) (0.014) (0.017) (0.015)

Implied MPC

 Housing dividend 0.0206 0.0131 -0.0075 0.0221 0.0428 0.0280 0.0505

 Home value 0.0036 0.0031 0.0022 0.0131 0.0196 0.0164 0.0229

 Financial wealth 0.0078 0.0089 0.0494 0.0525 0.0947 0.0691 0.1119

 Income 0.0209 0.0231 0.0276 0.0610 0.1226 0.0757 0.1392

Other variables

 Age 0.0352*** 0.0234*** 0.0064 0.0166*** 0.0120** 0.0111** 0.0097*

(0.005) (0.004) (0.022) (0.005) (0.005) (0.005) (0.005)

 Age Squared -0.0004*** -0.0002*** -0.0002 -0.0001** -0.0001* -0.0001 -0.0001

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

 Family size 0.1155*** 0.1239*** 0.1061*** 0.0393*** 0.0691*** 0.0504*** 0.0737***

(0.003) (0.003) (0.013) (0.003) (0.003) (0.003) (0.003)

 Marital status 0.1047*** 0.1482*** 0.2562*** 0.0843*** 0.1266*** 0.0955*** 0.1308***

(0.010) (0.009) (0.044) (0.010) (0.010) (0.011) (0.010)

 Urban residence 0.0476*** -0.0230* -02315*** 0.0827*** -0.0156 0.0925*** -0.0070

(0.013) (0.012) (0.063) (0.014) (0.014) (0.015) (0.014)

 Race-caucasian 0.1158*** 0.1099*** 0.4122*** 0.0502*** 0.1013*** 0.0309*** 0.0877***

(0.011) (0.009) (0.053) (0.011) (0.011) (0.012) (0.011)

 Year 1993-1996 -0.0907*** -0.1051*** -0.0244 0.0301*** -0.0163 0.0337*** -0.0128

 Year 1997-2000 -0.1145*** -0.1498*** -0.1039* 0.0133** -0.0397** 0.0144 -0.0377**

 Year 2001-2004 -0.1768*** -0.2143*** -0.3320*** -0.0384** -0.0953*** -0.0196 -0.0829***

 Year 2005-2007 -0.1553*** -0.1460*** -0.6110*** -0.0425** -0.1212*** -0.0173 -0.1033***

 Year 2007-2011 -0.1021*** -0.1816*** -0.8649*** 0.0273* -0.1551*** -0.0133 -0.1408***

(Constant) 4.2987*** 5.8112*** 0.2983 4.1541*** 5.6599*** 3.8592*** 5.4489***

Observations 13,603 13,602 13,601 13,601 13,602 13,601 13,602

Adjusted R-squared 0.421 0.449 0.184 0.424 0.406 0.445 0.431

Notes: 

(a) This is the pooled sample 1988-2011, for households whose head aged 26-55. The sample does not include observations 

from 1995:Q3-Q4 and 2004:Q3-Q4, due to sampling frame change starting in 1996 and 2005, which leads to a portion of 

families not able to be tracked across a full year. Neither does the sample include observations from 1991:Q2-Q4 and 1992, 

due to missing detailed expenditure files on mortgage payments and house ownership costs. 

(b) MPC indicates the calculated Marginal Propensity to Consume. Transforming coefficients into MPCs involves multiplying 

them with a benchmark (in our case, the median) of the corresponding expenditure-wealth (income) ratios. 

(c) Dependent variables are the logs of expenditure of each type (food, nondurables, durables, services, and total). The broader 

definition of services and total expenditures includes education and charity expenses. *, **, *** indicate statistical 

significance at 10%, 5%, and 1%, respectively. Robust standard errors are presented in parentheses. 

(d) Food expenditure includes spending on food consumed at home, at work, and at places outside home. Nondurable goods 

have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, 

clothing and footwear, gasoline and other energy goods, and other nondurable goods. Durable goods have an average useful 

life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods 
46 

and vehicles, and other durable goods. Services include house utilities, health care, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. The total expenditure is 

the sum of the above three primary types. 
47 

Table 3. Housing dividend and household expenditures of homeowners: decomposition of home value 

(for the age group 26-55) 

Food Nondurables Durables Services Total Services Total 

 (broader definition)

Log housing dividend 0.0240*** 0.0095*** -0.0263** 0.0153*** 0.0133*** 0.0172*** 0.0146***

(0.003) (0.003) (0.013) (0.003) (0.003) (0.004) (0.003)

Log home equity 0.0441*** 0.0310*** 0.0188 0.0642*** 0.0452*** 0.0709*** 0.0497***

(0.003) (0.003) (0.014) (0.004) (0.004) (0.004) (0.004)

Log mortgage bal. 0.0069*** 0.0039*** 0.0176*** 0.0135*** 0.0086*** 0.0144*** 0.0093***

(0.001) (0.001) (0.006) (0.002) (0.001) (0.002) (0.002)

Log financial wealth 0.0074*** 0.0058*** 0.0850*** 0.0220*** 0.0184*** 0.0254*** 0.0203***

(0.001) (0.001) (0.004) (0.001) (0.001) (0.001) (0.001)

Log after-tax income 0.1999*** 0.1535*** 0.5447*** 0.2780*** 0.2604*** 0.3056*** 0.2768***

(0.013) (0.009) (0.037) (0.016) (0.015) (0.018) (0.016)

Implied MPC

 Housing dividend 0.0205 0.0116 -0.0056 0.0273 0.0496 0.0338 0.0581

 Home equity 0.0044 0.0043 0.0009 0.0139 0.0203 0.0174 0.0240

 Mortgage bal. 0.0005 0.0004 0.0007 0.0023 0.0031 0.0028 0.0036

 Financial wealth 0.0083 0.0093 0.0494 0.0542 0.0975 0.0713 0.1152

 Income 0.0218 0.0236 0.0282 0.0648 0.1286 0.0802 0.1460

Other variables

 Age 0.0341*** 0.0219*** 0.0098 0.0140*** 0.0100* 0.0084 0.0076

(0.005) (0.004) (0.023) (0.005) (0.005) (0.005) (0.005)

 Age Squared -0.0004*** -0.0002*** -0.0002 -0.0001* -0.0001 -0.000 -0.0001

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

 Family size 0.1155*** 0.1242*** 0.1062*** 0.0394*** 0.0694*** 0.0510*** 0.0743***

(0.003) (0.003) (0.013) (0.003) (0.003) (0.003) (0.003)

 Marital status 0.1047*** 0.1487*** 0.2720*** 0.0849*** 0.1264*** 0.0966*** 0.1309***

(0.010) (0.009) (0.045) (0.011) (0.011) (0.012) (0.011)

 Urban residence 0.0476*** -0.0124 -0.2035*** 0.1100*** 0.0060 0.1238*** 0.0172***

(0.013) (0.012) (0.064) (0.015) (0.015) (0.015) (0.015)

 Race-caucasian 0.1195*** 0.1128*** 0.4345*** 0.0557*** 0.1079*** 0.0348*** 0.0933***

(0.011) (0.009) (0.054) (0.011) (0.011) (0.012) (0.012)

 Year 1993-1996 -0.0807*** -0.0987*** -0.0384 0.0408** -0.0100 0.0465*** -0.0054

 Year 1997-2000 -0.0965*** -0.1359*** -0.1073* 0.0360** -0.0234 0.0399** -0.0198

 Year 2001-2004 -0.1562*** -0.2018*** -0.3254*** -0.0069 -0.0758*** 0.0168 -0.0603***

 Year 2005-2007 -0.1376*** -0.1361*** -0.6001*** -0.0113 -0.1009*** 0.0176 -0.0808***

 Year 2007-2011 -0.0724*** -0.1658*** -0.8050*** 0.0255 -0.1216*** 0.0446** -0.1036***

(Constant) 4.6037*** 6.0212*** 0.8064 4.7036*** 6.0785*** 4.4631*** 5.9025***

Observations 12,996 12,995 12,994 12,994 12,995 12,994 12,995

Adjusted R-squared 0.416 0.449 0.183 0.409 0.396 0.430 0.420

Notes: 

(a) This is the pooled sample 1988-2011, for households whose head aged 26-55. The sample does not include observations 

from 1995:Q3-Q4 and 2004:Q3-Q4, due to sampling frame change starting in 1996 and 2005, which leads to a portion of 

families not able to be tracked across a full year. Neither does the sample include observations from 1991:Q2-Q4 and 1992, 

due to missing detailed expenditure files on mortgage payments and house ownership costs. 

(b) MPC indicates the calculated Marginal Propensity to Consume. Transforming coefficients into MPCs involves multiplying 

them with a benchmark (in our case, the median) of the corresponding expenditure-wealth(income) ratios. 

(c) Dependent variables are the logs of expenditure of each type (food, nondurables, durables, services, and total). The broader 

definition of services and total expenditures includes education and charity expenses. *, **, *** indicate statistical 

significance at 10%, 5%, and 1%, respectively. Robust standard errors are presented in parentheses. 
48 

(d) Food expenditure includes spending on food consumed at home, at work, and at places outside home. Nondurable goods 

have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, 

clothing and footwear, gasoline and other energy goods, and other nondurable goods. Durable goods have an average useful 

life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods 

and vehicles, and other durable goods. Services include house utilities, health care, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. The total expenditure is 

the sum of the above three primary types. 
49 

Table 4. Housing dividend and household expenditures of homeowners: real estate cycle sub-periods: 

1988-1996, 1997-2006, vs. 2007-2011 (for the age group 26-55) 

Food Nondurables Durables Services Total Services Total 

(broader definition)

1988-1996 Sub-period

Log housing dividend 0.0196*** 0.0008 0.0018 0.0149** 0.0110 0.0173*** 0.0127***

(0.006) (0.006) (0.023) (0.007) (0.007) (0.007) (0.007)

Log home equity 0.0440*** 0.0388*** 0.0502** 0.0731*** 0.0592*** 0.0798*** 0.0631***

(0.007) (0.006) (0.024) (0.009) (0.009) (0.009) (0.009)

Log mortgage bal. 0.0352*** 0.0205*** 0.0994*** 0.0604*** 0.0473*** 0.0630*** 0.0498***

(0.008) (0.001) (0.031) (0.009) (0.010) (0.009) (0.010)

Log financial wealth 0.0116*** 0.0077*** 0.0431*** 0.0236*** 0.0163*** 0.0270*** 0.0182***

(0.002) (0.002) (0.010) (0.003) (0.003) (0.003) (0.003)

Log after-tax income 0.1688*** 0.1571*** 0.4336*** 0.2469*** 0.2395*** 0.2658*** 0.2487***

(0.025) (0.022) (0.092) (0.036) (0.041) (0.037) (0.042)

Implied MPC

 Housing dividend 0.0213 0.0013 0.0008 0.0316 0.0525 0.0404 0.0637

 Home equity 0.0057 0.0071 0.0041 0.0197 0.0353 0.0243 0.0402

 Mortgage bal. 0.0035 0.0030 0.0067 0.0130 0.0223 0.0151 0.0249

 Financial wealth 0.0131 0.0126 0.0304 0.0544 0.0858 0.0701 0.1020

 Income 0.0208 0.0281 0.0313 0.0612 0.1327 0.0732 0.1453

Observations 2,647 2,647 2,647 2,645 2,647 2,645 2,647

Adjusted R-squared 0.419 0.449 0.133 0.395 0.370 0.418 0.395

1997-2006 Sub-period

Log housing dividend 0.0160*** 0.0038 -0.0455** 0.0102** 0.0053 0.0130*** 0.0072

(0.004) (0.004) (0.019) (0.004) (0.005) (0.005) (0.005)

Log home equity 0.0429*** 0.0317*** 0.0216 0.0571*** 0.0400*** 0.0635*** 0.0444***

(0.004) (0.004) (0.020) (0.005) (0.005) (0.006) (0.005)

Log mortgage bal. 0.0488*** 0.0375*** 0.1546*** 0.0841*** 0.0707*** 0.0949*** 0.0775***

(0.006) (0.005) (0.027) (0.007) (0.007) (0.008) (0.007)

Log financial wealth 0.0067*** 0.0057*** 0.0833*** 0.0206*** 0.0181*** 0.0241*** 0.0200***

(0.001) (0.001) (0.006) (0.001) (0.001) (0.001) (0.001)

Log after-tax income 0.1748*** 0.1470*** 0.4709*** 0.2493*** 0.2466*** 0.2739*** 0.2605***

(0.016) (0.014) (0.048) (0.021) (0.020) (0.024) (0.022)

Implied MPC

 Housing dividend 0.0130 0.0045 -0.0101 0.0181 0.0193 0.0253 0.0285

 Home equity 0.0047 0.0049 0.0013 0.0143 0.0212 0.0180 0.0250

 Mortgage bal. 0.0036 0.0039 0.0066 0.0141 0.0254 0.0182 0.0298

 Financial wealth 0.0068 0.0081 0.0481 0.0480 0.0909 0.0635 0.1065

 Income 0.0179 0.0215 0.0252 0.0568 0.1200 0.0708 0.1359

Observations 6,378 6,378 6,377 6,378 6,378 6,378 6,378

Adjusted R-squared 0.424 0.462 0.155 0.404 0.388 0.427 0.415

2007-2011 Sub-period

Log housing dividend 0.0436*** 0.0276*** -0.0102 0.0280*** 0.0314*** 0.0281*** 0.0314***

(0.006) (0.006) (0.027) (0.007) (0.006) (0.007) (0.006)

Log home equity 0.0437*** 0.0250*** -0.0013 0.0723*** 0.0470*** 0.0799*** 0.0527***

(0.008) (0.006) (0.031) (0.009) (0.008) (0.010) (0.008)

Log mortgage bal. 0.0038*** 0.0022*** 0.0037 0.0086*** 0.0048*** 0.0086*** 0.0050***
50 

(0.002) (0.001) (0.007) (0.009) (0.002) (0.002) (0.002)

Log financial wealth 0.0053*** 0.0037*** 0.0980*** 0.0201*** 0.0166*** 0.0231*** 0.0184***

(0.001) (0.001) (0.008) (0.002) (0.002) (0.002) (0.002)

Log after-tax income 0.2267*** 0.1487*** 0.6433*** 0.2961*** 0.2624*** 0.3279*** 0.2825***

(0.025) (0.015) (0.067) (0.030) (0.025) (0.032) (0.027)

Implied MPC

 Housing dividend 0.0306 0.0255 -0.0007 0.0398 0.0875 0.0435 0.0945

 Home equity 0.0030 0.0023 0.0000 0.0106 0.0134 0.0134 0.0163

 Mortgage bal. 0.0003 0.0002 0.0001 0.0013 0.0014 0.0015 0.0016

 Financial wealth 0.0071 0.0068 0.0432 0.0603 0.0983 0.0790 0.1184

 Income 0.0238 0.0207 0.0197 0.0668 0.1141 0.0839 0.1331

Observations 3.971 3,970 3,970 3.971 3,970 3,971 3,970

Adjusted R-squared 0.427 0.439 0.165 0.458 0.436 0.476 0.461

Notes: 

(a) This is the pooled sample 1988-1996, 1997-2006, and 2007-2011, respectively, for households whose head aged 26-55. The 

sample does not include observations from 1995:Q3-Q4 and 2004:Q3-Q4, due to sampling frame change starting in 1996 

and 2005, which leads to a portion of families not able to be tracked across a full year. Neither does the sample include 

observations from 1991:Q2-Q4 and 1992, due to missing detailed expenditure files on mortgage payments and house 

ownership costs. 

(b) MPC indicates the calculated Marginal Propensity to Consume. Transforming coefficients into MPCs involves multiplying 

them with a benchmark (in our case, the median) of the corresponding expenditure-wealth(income) ratios. 

(c) Dependent variables are the logs of expenditure of each type (food, nondurables, durables, services, and total). The broader 

definition of services and total expenditures includes education and charity expenses. *, **, *** indicate statistical 

significance at 10%, 5%, and 1%, respectively. Robust standard errors are presented in parentheses. 

(d) Food expenditure includes spending on food consumed at home, at work, and at places outside home. Nondurable goods 

have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, 

clothing and footwear, gasoline and other energy goods, and other nondurable goods. Durable goods have an average useful 

life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods 

and vehicles, and other durable goods. Services include house utilities, health care, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. The total expenditure is 

the sum of the above three primary types. 
51 

Table 5. Housing dividend and household expenditures of homeowners: separate results by housing 

tenure (for the age group 26-55) 

Food Nondurables Durables Services Total Services Total 

(broader definition)

Tenure <= 5 years

Log housing dividend 0.0234*** 0.0126*** -0.0425** 0.0164** 0.0121** 0.0159*** 0.0121***

(0.004) (0.006) (0.020) (0.007) (0.005) (0.005) (0.005)

Log of home value 0.0713*** 0.0493*** 0.0744** 0.1352*** 0.0931*** 0.1485*** 0.1014***

(0.009) (0.007) (0.024) (0.012) (0.011) (0.014) (0.011)

Log financial wealth 0.0088*** 0.0063*** 0.0889*** 0.0237*** 0.0202*** 0.0279*** 0.0226***

(0.001) (0.001) (0.007) (0.002) (0.002) (0.003) (0.002)

Log after-tax income 0.1586*** 0.1306*** 0.4795*** 0.2243*** 0.2227*** 0.2388*** 0.2305***

(0.016) (0.014) (0.049) (0.023) (0.021) (0.025) (0.022)

Implied MPC

 Housing dividend 0.0200 0.0154 -0.0091 0.0291 0.0451 0.0311 0.0481

 Home value 0.0032 0.0031 0.0016 0.0133 0.0189 0.0166 0.0222

 Financial wealth 0.0098 0.0100 0.0516 0.0584 0.1074 0.0784 0.1282

 Income 0.0173 0.0201 0.0248 0.0523 0.10998 0.0627 0.1216

Observations 6,032 6,032 6,032 6,030 6,032 6,030 6,032

Adjusted R-squared 0.393 0.435 0.174 0.391 0.373 0.411 0.397

Tenure > 5 years to Tenure <= 15 years

Log housing dividend 0.0250*** 0.0099** -0.0237 0.0110** 0.0106*** 0.0140*** 0.0128***

(0.004) (0.004) (0.019) (0.005) (0.005) (0.005) (0.005)

Log of home value 0.0836*** 0.0479*** 0.1280*** 0.1312*** 0.0959*** 0.01434*** 0.1045***

(0.010) (0.008) (0.039) (0.012) (0.011) (0.013) (0.012)

Log financial wealth 0.0054*** 0.0044*** 0.0752*** 0.0193*** 0.0149*** 0.0222*** 0.0167***

(0.001) (0.001) (0.007) (0.001) (0.001) (0.001) (0.001)

Log after-tax income 0.2028*** 0.1611*** 0.5264*** 0.2792*** 0.2550*** 0.3158*** 0.2767***

(0.016) (0.013) (0.064) (0.023) (0.023) (0.025) (0.024)

Implied MPC

 Housing dividend 0.0214 0.0121 -0.0050 0.0196 0.0397 0.0274 0.0506

 Home value 0.0037 0.0030 0.0027 0.0129 0.0195 0.0161 0.0229

 Financial wealth 0.0060 0.0070 0.0437 0.0476 0.0791 0.0623 0.0944

 Income 0.0221 0.0247 0.0272 0.0651 0.1259 0.0829 0.1460

Observations 5,354 5,353 5,352 5,354 5,353 5,354 5,353

Adjusted R-squared 0.438 0.446 0.176 0.436 0.411 0.461 0.438

Tenure > 15 years and Tenure <= 25 years

Log housing dividend 0.0221*** 0.0099 -0.0218 0.0022 0.0177** 0.0081 0.0198**

(0.011) (0.008) (0.037) (0.009) (0.009) (0.010) (0.009)

Log home equity 0.1098*** 0.0608*** 0.1423* 0.1373*** 0.1060*** 0.1538*** 0.1181***

(0.020) (0.014) (0.073) (0.019) (0.017) (0.021) (0.017)

Log financial wealth 0.0053*** 0.0070*** 0.0921*** 0.0209*** 0.0191*** 0.0227*** 0.0200***

(0.002) (0.002) (0.011) (0.003) (0.002) (0.003) (0.003)

Log after-tax income 0.2601*** 0.1801*** 0.6550*** 0.3361*** 0.3005*** 0.3749*** 0.3252***

(0.048) (0.022) (0.088) (0.031) (0.027) (0.033) (0.028)

Implied MPC

 Housing dividend 0.0190 0.0120 -0.0046 0.0039 0.0661 0.0158 0.0787
52 

 Home equity 0.0049 0.0038 0.0030 0.0135 0.0216 0.0172 0.0258

 Financial wealth 0.0088 0.0111 0.0535 0.0515 0.1012 0.0636 0.1130

 Income 0.0284 0.0277 0.0339 0.0783 0.1483 0.0984 0.1716

Observations 1,824 1,824 1,824 1,824 1,824 1,824 1,824

Adjusted R-squared 0.433 0.473 0.222 0.470 0.472 0.488 0.495

Notes: 

(a) This is the pooled sample for households whose head aged 26-55 segmented by their housing tenure. The sample does not 

include observations from 1995:Q3-Q4 and 2004:Q3-Q4, due to sampling frame change starting in 1996 and 2005, which 

leads to a portion of families not able to be tracked across a full year. Neither does the sample include observations from 

1991:Q2-Q4 and 1992, due to missing detailed expenditure files on mortgage payments and house ownership costs. 

(b) MPC indicates the calculated Marginal Propensity to Consume. Transforming coefficients into MPCs involves multiplying 

them with a benchmark (in our case, the median) of the corresponding expenditure-wealth(income) ratios. 

(c) Dependent variables are the logs of expenditure of each type (food, nondurables, durables, services, and total). The broader 

definition of services and total expenditures includes education and charity expenses. *, **, *** indicate statistical 

significance at 10%, 5%, and 1%, respectively. Robust standard errors are presented in parentheses. 

(d) Food expenditure includes spending on food consumed at home, at work, and at places outside home. Nondurable goods 

have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, 

clothing and footwear, gasoline and other energy goods, and other nondurable goods. Durable goods have an average useful 

life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods 

and vehicles, and other durable goods. Services include house utilities, health care, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. The total expenditure is 

the sum of the above three primary types. 
53 

Table A1. Housing wealth, financial wealth and household expenditures (for the age group 26-55)

Food Implied Nondurables Implied Durables Implied Services Implied Total Implied

MPC MPC MPC MPC MPC

Log home value 0.0960*** 0.0039 0.0587*** 0.0033 0.1083*** 0.0020 0.1430*** 0.0127 0.1079*** 0.0198

(0.005) (0.004) (0.018) (0.006) (0.006)

Log financial wealth 0.0065*** 0.0072 0.0057*** 0.0091 0.0840*** 0.0488 0.0210*** 0.0517 0.0180*** 0.0955

(0.001) (0.001) (0.003) (0.001) (0.001)

Log after-tax income 0.1933*** 0.0211 0.1502*** 0.0231 0.5080*** 0.0263 0.2710*** 0.0631 0.2488*** 0.1228

(0.009) (0.007) (0.029) (0.012) (0.012)

Age 0.0360*** 0.0260*** -0.0024 0.0212*** 0.0110***

(0.004) (0.003) (0.017) (0.004) (0.004)

 

Age (squared) -0.0004*** -0.0002*** -0.0001 -0.0002*** -0.0001

(0.000) (0.000) (0.000) (0.000) (0.000)

Family size 0.1103*** 0.1194*** 0.0998*** 0.0374*** 0.0663***

(0.002) (0.002) (0.009) (0.002) (0.002)

Marital status 0.1101*** 0.1538*** 0.2736*** 0.0874*** 0.1310***

(0.007) (0.007) (0.034) (0.008) (0.008)

Urban Residence 0.0534*** -0.0075 -0.1519*** 0.0794*** 0.0018

(0.010) (0.0009) (0.046) (0.010) (0.011)

Race-caucasian 0.1160*** 0.1137*** 0.4609*** 0.0655*** 0.1138***

(0.008) (0.007) (0.041) (0.009) (0.009)

 Year 1993-1996 -0.0829*** -0.0967*** -0.0127 0.0547*** 0.0001

 Year 1997-2000 -0.1048*** -0.1403*** -0.0720* 0.0409*** -0.0163

 Year 2001-2004 -0.1517*** -0.1917*** -0.2559*** 0.0145 -0.0500***

 Year 2005-2007 -0.1212*** -0.1260*** -0.5524*** 0.0147 -0.0777***

 Year 2007-2011 -0.0667*** -0.1591*** -0.8046*** 0.0430*** -0.1069***

(Constant) 4.2109*** 5.6980*** 0.2359 3.8681*** 5.5361***

Observations 22,954 22,953 22,952 22,952 22,953

Adjusted R-squared 0.413 0.440 0.178 0.428 0.402

Notes: 

(a) This is the pooled sample 1988-2011, for households whose head aged 26-55. The sample does not include observations 

from 1995:Q3-Q4 and 2004:Q3-Q4, due to sampling frame change starting in 1996 and 2005, which leads to a portion of 

families not able to be tracked across a full year. Neither does the sample include observations from 1991:Q2-Q4 and 1992, 

due to missing detailed expenditure files on mortgage payments and house maintenance costs. 

(b) MPC indicates the calculated Marginal Propensity to Consume. Transforming coefficients into MPCs involves multiplying 

them with a benchmark (in our case, the median) of the corresponding expenditure-wealth(income) ratios. 

(c) Dependent variables are the logs of expenditure of each type (food, nondurables, durables, services, and total). *, **, *** 

indicate statistical significance at 10%, 5%, and 1%, respectively. Robust standard errors are presented in parentheses. 

(d) Food expenditure includes spending on food consumed at home, at work, and at places outside home. Nondurable goods 

have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, 

clothing and footwear, gasoline and other energy goods, and other nondurable goods. Durable goods have an average useful 

life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods 

and vehicles, and other durable goods. Services include house utilities, health care, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. The total expenditure is 

the sum of the above three primary types. 
54 

Table A2. Median regressions of housing dividend and household expenditures of homeowners 

(for the age group 26-55) 

Food Nondurables Durables Services Total Services Total 

(broader definition)

Log housing dividend 0.0232*** 0.0091*** -0.0082 0.0127*** 0.0118*** 0.0146*** 0.0157***

(0.004) (0.003) (0.013) (0.004) (0.004) (0.003) (0.004)

Log home equity 0.0353*** 0.0246*** 0.0272** 0.0519*** 0.0373*** 0.0548*** 0.0403***

(0.003) (0.003) (0.013) (0.004) (0.004) (0.003) (0.004)

Log mortgage bal. 0.0045*** 0.0021* 0.0101* 0.0084*** 0.0037*** 0.0085*** 0.0050***

(0.001) (0.001) (0.005) (0.001) (0.001) (0.001) (0.002)

Log financial wealth 0.0069*** 0.0048*** 0.0510*** 0.0186*** 0.0166*** 0.0224*** 0.0182***

(0.001) (0.001) (0.004) (0.001) (0.001) (0.001) (0.001)

Log after-tax income 0.2364*** 0.1869*** 0.5927*** 0.3685*** 0.3333*** 0.4079*** 0.3585***

(0.006) (0.005) (0.022) (0.006) (0.006) (0.006) (0.006)

Implied MPC

 Housing dividend 0.0199 0.0111 -0.0017 0.0227 0.0440 0.0286 0.0624

 Home equity 0.0035 0.0034 0.0013 0.0112 0.0168 0.0134 0.0195

 Mortgage balance 0.0004 0.0002 0.0004 0.0014 0.0014 0.0017 0.0019

 Financial wealth 0.0076 0.0076 0.0296 0.0458 0.0883 0.0629 0.1030

 Income 0.0258 0.0287 0.0307 0.0859 0.1645 0.1071 0.1891

Observations 12,996 12,995 12,994 12,994 12,995 12,994 12,995

Pseudo R-squared 0.246 0.266 0.099 0.251 0.244 0.268 0.261

Notes: 

(a) This is the pooled sample 1988-2011, for households whose head aged 26-55. The sample does not include observations 

from 1995:Q3-Q4 and 2004:Q3-Q4, due to sampling frame change starting in 1996 and 2005, which leads to a portion of 

families not able to be tracked across a full year. Neither does the sample include observations from 1991:Q2-Q4 and 1992, 

due to missing detailed expenditure files on mortgage payments and house maintenance costs. 

(b) MPC indicates the calculated Marginal Propensity to Consume. Transforming coefficients into MPCs involves multiplying 

them with a benchmark (in our case, the median) of the corresponding expenditure-wealth (income) ratios. 

(c) Dependent variables are the log of expenditure of each type (food, nondurables, durables, services, and total). The broader 

definition of services and total expenditures includes education and charity expenses. *, **, *** indicate statistical 

significance at 10%, 5%, and 1%, respectively. 

(d) Food expenditure includes spending on food consumed at home, at work, and at places outside home. Nondurable goods 

have an average useful life of less than 3 years, including food and beverages purchased for off-premises consumption, 

clothing and footwear, gasoline and other energy goods, and other nondurable goods. Durable goods have an average useful 

life of at least 3 years, including motor vehicles and parts, furnishings and durable household equipment, recreational goods 

and vehicles, and other durable goods. Services include house utilities, health care, transportation services, recreation 

services, food services and accommodations, financial services and insurance, and other services. The total expenditure is 

the sum of the above three primary types.