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Accounting for owner-occupied dwelling services: Aggregates

and distributions

Thesia I. Garner a,*, Kathleen Short b,1

aDPINR, Bureau of Labor Statistics, U.S. Department of Labor, 2 Mass. Ave., NE, Washington, DC 20212, USA

b Housing and Household Economic Statistics Division, U.S. Census Bureau, Washington, DC 20233, USA

article info

Article history:

Received 12 July 2009

Available online 23 July 2009

JEL classification:

D3

E01

Keywords:

Dwelling services

Macro–micro linkages

Distributional analysis

abstract

Research linking macro and micro statistics of dwelling services is in its infancy in the U.S.

including work by the Bureau of Economic Analysis, Bureau of Labor Statistics, and the Census Bureau. Comparisons of aggregated estimates generated from micro-level data to estimates at the macro-level can inform both levels on the accuracy and precision of methods

and data sources. In this study, the treatments of housing in the macro statistics of the

National Accounts and in the micro statistics of household expenditure and income surveys

are examined. Three approaches to value dwelling services using household survey data

are compared: capitalization rate, hedonic, and rental equivalence. Estimates are produced

using data from the U.S. Consumer Expenditure Survey and American Housing Survey. Estimated aggregates of implicit net rental income from owner-occupied housing are compared to the aggregate value in the National Accounts. Possible sources of differences in

the macro- and micro-based aggregates are discussed. The effects of adding net implicit

rental income on income distributions are examined, particularly on inferences about

the relative well-being by the age of householder. Overall, only marginal reductions in

income equality result when net rental incomes are added to before tax money income;

this only occurs when reported rental equivalence and return to home equity are used

as methods of rent estimation.

2009 Published by Elsevier Inc.

Thanks are due to two anonymous referees and to Alice

Nakamura and Randal Verbrugge for their detailed comments and suggestions. Earlier versions of this paper were

presented at the International Association for Research in

Income and Wealth (IARIW) Thirtieth General Conference

held in Slovenia, August 2008, and at the Southern Economic Association Annual Meetings held in Washington,

DC, November 21–23, 2008. We thank our conference paper discussants, Andrew Grodner and Fabrice Lenglart, as

well as session participants for suggesting improvements

and for commenting on our research.

The views expressed in this research, including those

related to statistical, methodological, technical, or operational issues, are solely those of the authors and do not

necessarily reflect the official positions or policies of the

U.S. Census Bureau or U.S. Bureau of Labor Statistics, or

the views of other staff members within these agencies.

The authors accept responsibility for all errors.

1. Introduction

Dwelling services are produced from the stock of housing

for consumption. Market rents are a good approximation of

the value of dwelling services for most rental housing. However, some households do not pay a market price for the

accommodation that is consumed. These include households living in subsidized rental or rent-controlled units,

1051-1377/$ - see front matter 2009 Published by Elsevier Inc.

doi:10.1016/j.jhe.2009.07.004

* Corresponding author. Fax: +1 202 691 6583.

E-mail addresses: Garner.Thesia@BLS.gov (T.I. Garner), Kathleen.s.

short@census.gov (K. Short). 1 Fax: +1 301 763 3248.

Journal of Housing Economics 18 (2009) 233–248

Contents lists available at ScienceDirect

Journal of Housing Economics

journal homepage: www.elsevier.com/locate/jhec
Author's personal copy

and households living in owner-occupied dwellings. In the

past few years, there has been increased research on valuing

these services with a particular focus on owner-occupied

housing. Implicit rent and net rental income from housing

services are important in the official economic statistics

for the United States (U.S.) and for other countries. For

example, at the macro level, reported rental equivalence

from the U.S. Consumer Expenditure Survey are used in conjunction with market rents in the creation of the owners’

shelter component of the Consumer Price Index (CPI), see

Ptacek and Baskin (1996) and Verbrugge (2008a,b).

2 Imputed rent and net rental income enter the National Income

and Product Accounts (NIPAs), specifically in Personal Consumption Expenditures (PCE) and personal income. At the micro level, implicit owners’ rent and net rental income are

important in assessments of economic well-being. For example, the U.S. Census Bureau adds net implicit rental income to

the household income series to assess distributional differences between owners and renters, and to compare poverty

rates among population subgroups taking account of the flow

of services from owner-occupied housing (U.S. Census

Bureau, 1992; see also Short et al., 2007). Garner (2006) and

Garner and Short (2001) have examined the impact of using

rental equivalence in the production of National Academy of

Sciences-based poverty thresholds for the U.S.

The 1977 United Nations (UN) Provisional Guidelines

on Statistics of Distribution of Income, Consumption and

Accumulations of Households provided guidelines on

defining household income for statistical purposes. Their

recommendations for household income followed the

structure of the System of National Accounts. The United

Nations System of National Accounts (UN, 1993) provides

a uniform basis for reporting national income statistics at

the macro-level across countries.

The Expert Group on Household Income Statistics (Canberra Group, 2001) further developed standards on the

production of income distribution statistics internationally

to improve national income statistics and facilitate international comparability on household income distributions.

Using the structure of the National Accounts to develop a

micro-level income definition, the report encouraged comparing ‘‘grossed-up” micro data with national accounts

aggregates to assess the accuracy of estimates.

In 2003, the International Labor Organization (ILO) issued the Report II: Household Income and Expenditures Statistics on micro-level economic statistics. This report provided

the international guidelines on household expenditures and

income statistics including guidelines for valuing dwelling

services. The ILO Report emphasized consistency with the

System of National Accounts (SNA) and across countries to

improve international comparisons of distributions of economic well-being. The valuation approaches outlined in

the ILO Report include rental equivalence, current market

value approaches, repayment methods and user costs. The

report noted that rental equivalence can be based on imputations derived from rents for rental units or from responses

provided by the interviewees and or interviewers. The

report provided guidance regarding how to obtain a consumption value for dwelling services and how to derive

the implicit rental income from these services.

Within countries3 and cross-nationally there has been an

increasing demand for better coherence between and within

macro and micro statistics, and to examine the distributional impact of imputed rent for dwelling services for which

households do not pay full rent. Macro–micro research focused on the U.S., for example, has attempted to reconcile

differences in household survey and macro national accounts estimates.4 The most coordinated work at the micro

level on imputed rents has been conducted in Europe under

the auspices of AIM-AP (Accurate Income Measurement for

the Assessment of Public Policies) in which Household Budget Survey (HBS) and European Union (EU)-Survey of Income and Living Conditions (SILC) data have been used.

Studies focused on the distributional impact of dwelling services under the auspices of this project have been conducted

for the following countries: Belgium (Verbist and Lefebure,

2007), Germany (Frick et al., 2007), Greece (Koutsambelas

and Tsakloglou, 2007), and Ireland (Callan, 2007), Italy

(D’Ambrosio and Gigliarano, 2007), The Netherlands (De

Vos, 2007), and the United Kingdom (Mullan et al., 2007).

In most of the research to date, little attention has been

given to reconciling micro and macro estimates of owneroccupied housing. In the U.S. developing such linkages is in

its infancy. Of particular interest in this paper are the value

of owner-occupied housing services and estimates of the

implicit rent and net rental income from these services,

and their impact on income distributions.

In general, the valuations of owner’s imputed rents and

net rental income have been made independently across

economic series and research studies using different data

sources and varied methodologies, making it difficult to assess conflicting findings. This paper describes, implements,

and evaluates a number of approaches that have been used

to produce these estimates. In particular, the purpose of

this paper is twofold: first, to scrutinize and compare U.S.

macro and micro estimates of owner-occupied dwelling

services and implicit net rental income from these services; and second, to use the various micro data estimates

to examine the respective implied distributions of net implicit rental incomes across U.S. households, in order to

compare the implied well-being effects of the various approaches. Using 2005 data, micro estimates are derived

from two national household surveys — American Housing

Survey (AHS) and the Consumer Expenditure Interview

Survey (CE) — and then compared to estimates from the

U.S. NIPAs. We briefly review the methods used5 by the

2 For examinations of the difference between rental equivalence and user

costs, see Garner and Verbrugge (2009a).

3 See papers by Braakmann et al. (2008), Coli and Tartamella (2008),

Fesseau et al. (2008), Garner and Short (2008a), and Ho Han (2008). 4 For expenditure comparisons, see, for example, Attanasio et al. (2006),

Garner et al. (2006, 2009a), Houthakker and Taylor (1970), Meyer and

Sullivan (2009), and Slesnick (1992, 1998). For income comparisons, see

Ruser et al. (2004). 5 The BEA method reviewed is the one in effect as of June 2009. The BEA

introduced a new classification system for Personal Consumption Expenditures (PCE) in July 2009 (McCully and Payson, 2009), along with changes

in definitions and statistical improvements. As discussed below, among the

changes is the use of annual data from the CE to estimate the imputed

space rental value of owner-occupied permanent-site non-farm housing.

234 T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248
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Bureau of Economic Analysis (BEA) to produce the NIPA

aggregates for owner-occupied housing as background for

a comparison of aggregate rents, expenses, and implicit net

rental incomes that are entirely micro-based. We find that

the values of imputed rents are quite sensitive to the methods chosen and to the data used. However, a robust distributional finding is that elderly households would appear better

off if account were taken of home ownership.

In the first section of the paper, a brief description of the

treatment of owner-dwelling services in the NIPAs is provided. Next the AHS and CE, the sources of the micro data

for this study, are described along with the methods used

to produce the imputed rent for owner-occupants,

expenses, and implicit net rental income. Aggregate rents,

expenses and net implicit rental incomes resulting from

summations of the micro data are compared to the U.S.

NIPA aggregates. After the macro–micro comparison, a distributional examination of net implicit rental income is

presented. The final section concludes the paper and offers

suggestions for future research.

2. U.S. National Income and Product Accounts6

According to the U.S. Bureau of Economic Analysis,

which produces the U.S. national accounts, the output of

housing represents about 10 percent of total U.S. Gross

Domestic Product (GDP) final expenditures and about 5

percent of total U.S. gross output. The term ‘‘housing” is

used to represent the provision of shelter services by residential housing units and captures both tenant and owneroccupied residential units. As reported by Mayerhauser

and McBride (2008), owner-occupied housing output is

an abstract concept because there is no actual market

transaction between two parties. In the National Accounts,

owners are treated as both renters and as enterprises that

produce services in the form of shelter. In their capacity as

renters they pay a market rent for shelter services, but as

enterprises, they may earn a profit as income or experience

a loss due to housing expenses. This profit is measured as

the difference between imputed rental income and expenditures incurred in operating the rental unit, excluding

capital gains as discussed below. This is referred to as rental income of persons living in owner-occupied housing by the

BEA. In this study we refer to this profit (or loss) as the net

implicit rental income, or net rental income, of owneroccupants or homeowners.

Measuring owner-occupied housing services requires a

measure of rental income. Since this is not an actual transaction, it must be imputed. Imputed rents for owner-occupied housing are the only imputation for household

services included in the NIPA’s production boundary. BEA

makes this imputation so that economic growth is invariant to whether shelter is owned or rented. This imputation

allows international comparability because home ownership rates differ significantly across countries.

The BEA produces estimates of the space or gross rent,

expenses, and imputed rental income for non-farm

owner-occupied permanent-site housing, owner-occupied

non-farm manufactured homes, and farm dwellings owned

by farm operators. In addition to owner-occupied permanent-site housing that is occupied, it also includes dwellings that have been sold and are awaiting occupancy and

dwellings held off the market for occasional use by the

owner, termed ‘‘vacant reserves”; these are treated as if

they have been occupied for the full year. Each year, BEA

produces annual estimates for owner-occupied housing.

The BEA imputes the gross rental value of owneroccupied units using a rent-to-value ratio approach.

Rent-to-value ratios are computed from rental housing

data collected in the decennial Residential Finance Survey

(RFS). The rent-to-value ratios are applied to the mid-point

market value of the owner-occupied units within corresponding value classes as reported in the AHS. In this calculation it is not possible to exclude the amount paid for

utilities or furnishings that might be embedded in rents reported in the RFS data. In between survey estimates, BEA

uses the BLS CPI for owners’ equivalent rent. For owneroccupied manufactured homes, the value is based on

applying rents from renters to owners. For this type of

housing, the ratio of rooms-per-unit of tenant housing to

rooms-per-unit of owner housing from the Census of

Housing is applied to the average rent of tenant-occupied

manufactured homes. For farm owner-occupied housing

owned by farm operators, the BEA uses U.S. Department

of Agriculture’s (USDA’s) estimates of the gross rental value of farm dwellings, which is a combination of tenantand owner-occupied housing. BEA splits the rental value

of operator dwellings between owner-occupied and tenant-occupied (which reflect dwellings for hired labor,

etc.) using tenure splits of farm dwellings from BEA’s fixed

assets accounts. These calculations and estimates are used

to produce an overall estimate of the market value of the

flow of services from owner-occupied housing. The gross

rental value of this space represents implicit spending for

the consumption of owner-occupied housing; it also represents the implicit owner-occupied housing output.

The implicit rental income of persons living in owneroccupied housing is measured as output less expenses plus

subsidies. Expenses include intermediate consumption,

taxes on production, mortgage interest, current transfer

payments and the consumption of fixed capital. Intermediate consumption includes expenditures on maintenance

and repairs, property insurance, mortgage origination fees,

other closing costs (such as title insurance, escrow fees,

attorney fees), broker commissions on land, condo and

co-op fees, and imputed banking services. Taxes on production paid by the housing sector are chiefly property

taxes paid to local governments. During benchmark years,

many of these expenses are based on survey or census

data.

Residential housing subsidies are another element of

housing services (McBride, 2008). These are grants made

by governmental units and include two types: (1) disaster

relief to owners of residential property affected by natural

calamities such as hurricanes and floods (FEMA disaster

assistance); and (2) housing assistance that involves

Federal programs benefiting owner-occupants and

tenant-occupants of residences. Housing subsidies, in a

6 This section draws extensively on Mayerhauser and McBride (2008);

see also Mayerhauser and Reinsdorf (2005).

T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248 235
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national accounting framework, are equivalent to negative

taxes on production because they act to offset some or all

of the effects on gross operating surplus of the below-market rents charged by landlords for public housing or the

operating expenses of property owners.

The aggregate rental income of persons is calculated as

revenue from gross implicit rents and residential housing

subsidies less current operating expenses for the stock of

housing that is owner-occupied. Capital gains (losses)

due to the change in property values are not included in

the NIPA concept of saving because they represent changes

in prices of assets and are not a source of funding for new

investment.

3. Going from macro to micro

Goals of this study are to start with the concepts of

owner-occupied rent and net rental income as outlined

for the NIPA, produce values for these using micro data,

and then distribute the values across households. By distributing the implicit costs and benefits of home ownership across the population, it is possible to examine their

impact on income-based measures of economic wellbeing. Incorporating measures of housing into distributional measures of household economic well-being allows

for more reasonable inter-household comparisons, as well

as international comparisons, of economic well-being.

Regarding the distributional analysis of income, we attempt to reconcile estimates of owned housing net rental

income from the NIPA to a similar concept assigned at

the household level. The Census Bureau currently implements an approximation of net rental income in its household income measures. This calculation is based on a

return to equity approach following Smeeding et al.

(1993). In that study, homeowners were assumed to have

sold their homes and captured equity from the sales. They

invest the equity and earn from this an income based on

some rate of return. This return represents the flow from

the financial asset, one’s home, and results in shifting the

relative standing of homeowners upward in an income distribution compared with renters.

According to Short et al. (2007), this method has weaknesses. The selection of return rates is arbitrary and the result has been that the value of net implicit rental income

from owner-occupied housing generally exceeds the comparable NIPA estimates (Ruser et al., 2004). The Census

Bureau reduces the overestimate by subtracting property

taxes from the return to home equity. But the main expense that homeowners face is not property taxes, but

rather, mortgage interest; this is an expense with a large

life-cycle relationship that is not captured by property

taxes.7 The current Census method, then, overestimates

the net implicit rental income of young families relative to

older homeowners. The return to equity approach is also

weak in capturing changes in housing markets. The NIPA

net implicit rental income from owner-occupied housing

was $5318 million for 2005 (McBride, 2008); this suggests

that expenses incurred by households or on behalf of households for owner-occupied dwellings exceeded the market

rent assumed by the BEA for this housing. Further, this reflects the very low rent-to-value ratios inherent in the housing markets during the mid-2000s in the U.S. and the large

interest expenses that households were assuming in anticipation of future appreciation of home values. These variations are not captured in the return to equity approach.

For this study, we produce gross rents, expenses and

implicit net rental income from owner-occupied dwellings

living in the U.S. We apply different methods to value owner-dwelling services and compare the results to U.S. NIPA

estimates. In the NIPAs, owner housing is divided into

three groups: non-farm permanent site, owner-occupied

and vacant reserves; manufactured, owner-occupied nonfarm; and farm properties, owner-occupied. In the analysis,

the household survey data are restricted to owner-occupied housing, on and off farms; there is no distinction between manufactured homes and permanent-site

dwellings. Also not accounted for in the study are the values for vacant reserves. Based on BEA estimates (McBride,

2008), vacant reserves accounted for approximately 3 percent of all non-farm permanent site owner housing in

2005.

Net implicit rental incomes across the various methods

are compared. The distributional impact of accounting for

owner-occupied housing on economic well-being is examined in terms of income deciles. The decile analysis focuses

separately on older and younger homeowners. We focus on

these two groups as there is a general understanding that

the benefits of homeownership change over time and in

conjunction with stage in the life-cycle.

3.1. Valuation methods

There are several approaches presented in the literature

to value imputed rents.8 The approaches that we use in this

study are a capitalization rate approach, a hedonic model

with sample selection correction, and reported rental equivalence. Once we have estimated imputed rents, we subtract

operating expenses that are based on the same household

survey data, to derive implicit net rental income for each

owner-occupied dwelling. The micro-level estimates are

aggregated over all households to represent the gross rent

and implicit net rental income concepts in the NIPAs. Also

presented for the income distributional analysis are results

based on the return to equity approach that the Census

Bureau currently uses in its income calculations to value implicit net rental income from owner-occupied dwellings in

household income statistics.

In the following, we review the valuation methods followed by descriptions of the data used. The results of the

valuation methods are presented and contrasted to each

7 Distributional effects across age groups will differ for many reasons

that reflect market conditions and consumer choices. For example, Asberg

(1999) and Boehm (1993) have examined the tenure choice decisions of

young adults. VanderHart (1998) studied housing choices for the older

households (50 years and older). Recent dynamic general equilibrium

studies of the tenure decision include Díaz and Luengo-Prado (2008),

Chambers et al. (2009), and Sommers et al. (2009).

8 See also Diewert and Nakamura (2009) for additional methods used to

account for owner-occupied housing in the CPI.

236 T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248
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other and to the NIPA values. Data and estimates for the

year 2005 are used.

3.1.1. Capitalization rate approach

Several authors have suggested estimating a=Rg/V, the

capitalization rate (Yates, 1994; Phillips, 1988a,b; Crone

et al., 2004; Frick and Grabka, 2003) to represent the tradeoff between investing in one’s own home or placing the

capital in other investments that would yield a return in

income flow over time. The variable a represents the rate

of return, Rg is the implicit gross rent and V is the market

value of the home. This method is used to calculate a

rent-to-value ratio (or rate of return) to transform the

value of an owned home into a market rent. The capitalization rate approach for this study uses the average rent-tovalue ratio implicit in the national product accounts to

derive imputed rent and employed micro data to compute

and subtract associated costs. Using U.S. NIPA data published by BEA for assets and space rents, this rate would

be 5.4 percent in 2005. The rate of 5.4 percent is applied

to each owner-occupied housing unit in the AHS and CE.

One shortcoming of this approach is that it assumes a common capitalization rate for the entire U. S. In practice, the

BEA uses multiple capitalization rates. Applying the single

rate to each property value of owner-occupied dwellings,

as reported in the AHS and the CE, should result in correct

aggregates; however, the distributional impact of net imputed rents based on the single rate approach will mask

important differences in property values along the income

distribution.

3.1.2. Selection/hedonic approach

Another way of directly calculating net implicit rental

income is by estimating gross rent with a hedonic regression model. The basic hedonic equation follows:

Y 1⁄4 Xb þ u1 ð1Þ

where Y is the natural log of gross monthly rent, and X is a

vector of housing unit characteristics. This model is estimated using data from a sample of renters9

; estimated

model coefficients are then used to predict market rents

for homeowners in similar types of homes. We control for

selectivity bias in the renter regression model using the

Heckman two-step approach; thus we add an explicit selection equation to the model. The selection model is specified

as follows:

s 1⁄4 1; if renter; ð2Þ

or

s 1⁄4 0; if owner

probability of being a renter 1⁄4 Zd þ u2

corrðu1; u2Þ 1⁄4 q

In the selection model, Z is the vector that contains variables relevant to the selection process. Some variables

are common to both the rent determination equation and

the selection equation.

Imputed rents for owners are estimated by applying the

renter coefficients to owner characteristics. The predicted

log rent value for each owner is transformed into a level

variable by multiplying each predicted value by an adjustment factor. Algebraically the rent is:

b

Y 1⁄4 expðrb 2=2Þ  expðln byÞ ð3Þ

Hedonic models with selection are developed using

both the AHS and the CE data. The goal is to include variables deemed relevant to housing status selection and rent

levels, drawing from the details about housing available in

the separate surveys; some variables overlapped while

others did not. Table 1 includes a list of the explanatory

variables used and the weighted means for both surveys

and for the selection and rent models. One advantage of

this method over the first one is that one can control for

rent differences by geographic area. We do this by including variables that capture housing price variation. In both

the AHS and CE specifications, Fair Market Rents (FMRs)

were sorted into deciles and each household was assigned

the appropriate rent category.10 The CE specification included, in addition, the median value of homes in the area

where the household lived. Region and metro status were

also included in the model.

Various housing amenities and household income variables were included in both models. The AHS specifications

included neighborhood characteristics not available in the

CE. The CE models included percentage of renters and percent poor in the Census tract where the housing unit was

located based on 2000 decennial census data matched to

the CE using census tract. The selection models included

characteristics of the households, such as education and

marital status that may affect the decision to own or rent.

Estimated coefficients for these models are presented in

Table 2.

3.1.3. Reported rental equivalence

Reported rental equivalence is only available from the

CE Interview. During each of four quarterly interviews,

owner-occupants are asked the following question:

If someone were to rent your home today, how much do

you think it would rent for monthly, unfurnished and

without utilities?

If owned housing and rental housing are the same in

terms of characteristics and quality, this approach should

yield estimates of imputed rents that are similar to those

from the hedonic model since both approaches are used

to produce imputed rents at the individual housing unit

9 Renters living in subsidized housing or in rent-controlled units are not

included in the AHS calculations. Renters in subsidized housing are not

included in the CE calculations; renters living in rent-controlled units could

not be identified so they are by default included in the CE sample.

10 The Department of Housing and Urban Developments (HUD) calculates

FMRs to administer housing programs. Housing assistance payments are

limited by FMRs established by HUD for different areas. In the Housing

Choice Voucher program, the FMR is the basis for determining the

‘‘payment standard amount” used to calculate the maximum monthly

subsidy for an assisted family. In general, the FMR for an area is the amount

that would be needed to pay the gross rent (shelter rent plus utilities) of

privately owned, decent, and safe rental housing of a modest (non-luxury)

nature with suitable amenities.

T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248 237
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Table 1

Weighted means of characteristics of owners and renters living in the U.S.: 2005.

American Housing Survey Consumer Expenditure Survey

Renter (n = 10,005) Owner (n = 29,507) Renter (n = 7672) Owner (n = 20,610)

Monthly rent $725 $638

Monthly rental equivalence $1243

Monthly capitalization rate-based rent $1070 $1088

Property value $237,845 $241,827

Rooms including bedrooms 4.381 6.581

Rooms not including bedrooms 2.452 3.433

Bedrooms 2.006 3.096

Bathrooms 1.226 1.733 1.253 1.765

Halfbaths 0.148 0.398 0.153 0.382

Dwelling age (in years) 45 38 43 36

Dwelling age imputed 0.504 0.107

Rent includes fuel 0.276 0.302

Rent includes water/trash 0.750 0.731

Tenure duration 3.759 13.780

Off street parking 0.874 0.969 0.769 0.835

Central air-conditioning 0.487 0.687 0.453 0.664

Housing includes major appliancesa 0.340 0.474 0.333 0.459

Housing includes porch, balcony, patio 0.593 0.829

Not detached housing 0.696 0.100 0.672 0.100

Mobile home 0.046 0.074 0.039 0.068

In MSA 0.842 0.752 0.880 0.845

South 0.354 0.374 0.347 0.369

Midwest 0.199 0.245 0.199 0.242

West 0.254 0.205 0.262 0.205

Public transport good 0.541 0.342

Police good 0.882 0.893

Schools good 0.221 0.223

At least one housing problem 0.095 0.049

Crime bothers 0.205 0.123

Traffic bothers 0.334 0.226

Fair Market Rent deciles

Decile 2 0.082 0.107 0.077 0.110

Decile 3 0.097 0.104 0.102 0.100

Decile 4 0.098 0.100 0.097 0.101

Decile 5 0.098 0.103 0.092 0.104

Decile 6 0.102 0.095 0.100 0.098

Decile 7 0.104 0.105 0.096 0.104

Decile 8 0.098 0.095 0.099 0.102

Decile 9 0.119 0.076 0.128 0.087

Decile 10 0.137 0.101 0.130 0.090

Median value of owned homes within the primary sampling unit $237,237 $206,802

Crowd (number of people per room) 0.563 0.413

Percentage of renters in Census tract (2000 decennial census) 0.443 0.279

Poverty rate of people in Census tract (2000 decennial census) 0.150 0.107

Worked 0.611 0.551 0.783 0.690

Black 0.191 0.089 0.169 0.086

Hispanic 0.177 0.077 0.177 0.078

Household income $38,167 $70,591 $38,509 $71,023

Received property income 0.149 0.316 0.143 0.362

Received transfer income 0.039 0.008 0.095 0.024

Age of householder (years) 41 52 40 52

High school graduate to AA degree 0.586 0.566

Bachelors college degree 0.159 0.197 0.218 0.311

Not married 0.318 0.262

Never married 0.390 0.112

Widowed 0.068 0.115

Divorced or separated 0.223 0.136

Children present 0.362 0.352 0.342 0.344

Number of adults 18+ 1.683 1.981 1.682 1.967

Time dummies for CE interview quarters

yr20053 0.251 0.249

yr20054 0.243 0.251

yr20061 0.254 0.250

Mills 0.627 2.065 0.562 2.107

a Major appliances include stove, refrigerator, dishwasher, and garbage disposal. For the CE, the appliances refer to those that were included in the owned

home or rental unit at the first interview.

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Table 2

Results from selection and rent regression models: 2005.

American Housing Survey Consumer Expenditure Survey

Selection model

(probability

of being a renter)

Rent

model

Selection model

(probability

of being a renter)

Rent

model

Intercept 2.250** 5.421** 1.878** 5.033**

Rooms including bedrooms 0.504** 0.086**

Rooms squared including bedrooms 0.026** 0.006**

Rooms not including bedrooms 0.356** 0.010

Rooms squared not including bedrooms 0.027** 0.001

Bedrooms 0.826** 0.096**

Bedrooms squared 0.098** 0.006

Bathrooms 0.300** 0.235** 0.182* 0.247**

Bathrooms squared 0.015 0.040** 0.017 0.055*

Half-baths 0.360** 0.057** 0.296** 0.064*

Half-baths squared 0.051** 0.014** 0.081** 0.013

Dwelling age (in years/10) 0.052** 0.038** 0.113** 0.021**

Dwelling age (/10) squared 0.002 0.002** 0.005** 0.001*

Dwelling age imputed 0.718** 0.038**

Rent includes fuel 0.076** 0.025

Rent includes water/trash 0.037** 0.028

Tenure duration 0.014**

Off street parking 0.066 0.021 0.004 0.010

Central air-conditioning 0.133** 0.076** 0.107** 0.073**

Housing includes major appliancesa 0.057* 0.082** 0.085** 0.071**

Housing includes porch, balcony, patio 0.049**

Not detached housing 1.458** 0.095** 1.281** 0.061**

Mobile home 0.073 0.278** 0.234** 0.246**

In MSA 0.003 0.024 0.027 0.057*

South 0.370** 0.156** 0.372** 0.164**

Midwest 0.147** 0.092** 0.126** 0.114**

West 0.509** 0.041* 0.623** 0.175**

Public transport good 0.062** 0.046**

Police good 0.071* 0.036*

Schools good 0.040 0.009

At least one housing problem 0.147** 0.022

Crime bothers 0.044 0.044**

Traffic bothers 0.052* 0.015

Fair Market Rent deciles

Decile 2 0.083* 0.174** 0.102 0.034

Decile 3 0.172** 0.310** 0.072 0.100**

Decile 4 0.111* 0.304** 0.137* 0.181**

Decile 5 0.116** 0.413** 0.091 0.220**

Decile 6 0.123* 0.442** 0.253** 0.242**

Decile 7 0.090 0.469** 0.405** 0.269**

Decile 8 0.230** 0.544** 0.392** 0.346**

Decile 9 0.112* 0.738** 0.416** 0.372**

Decile 10 0.040 0.833** 0.386** 0.336**

Median value (/100,000) of owned homes within the primary

sampling unit

0.004 0.103**

Crowd (number of people per room) 0.060 0.188**

Percentage of renters in Census tract (2000 decennial census) 1.033** 0.165**

Poverty rate of people in Census tract (2000 decennial census) 1.665** 0.630**

Worked 0.028 0.005

Black 0.207** 0.067

Hispanic 0.001 0.078*

Household before tax income (/100,000) 0.646** 0.346** 0.397** 0.487**

Household before tax income (/100,000) squared 0.073** 0.045** 0.009** 0.013**

Received property income 0.180** 0.253**

Received transfer income 0.380** 0.442**

Age of householder (years) 0.026** 0.025**

High school graduate to AA degree 0.147**

Bachelors college degree 0.056* 0.225**

Not married 0.254**

Never married 0.228**

Widowed 0.197**

Divorced or separated 0.359**

(continued on next page)

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level. The CE is the only U.S. federal survey used for statistical purposes in which a rental equivalence question is

asked.

3.1.4. Subtractions

Operating costs, based on the survey data, are subtracted from each of the owner rents to arrive at the net

imputed rent that we require for the macro–micro comparison and for the distributional analysis. Property taxes,

maintenance costs, and other expenses are subtracted

from the gross rent at the housing unit or household level

to arrive at an estimate of net implicit rental income.

3.2. Data

The two household survey data sets used for this study

are the American Housing Survey (AHS) and the Consumer

Expenditure Interview Survey (CE). These two surveys

provide the most comprehensive data on renter and

owner-occupied housing in the U.S. and thus were natural

candidates for the micro-level analysis.11 Since the surveys

have different purposes, samples, and data collected, differences in estimated net rental income could result. For the

micro-analysis, AHS data are from interviews conducted in

2005 while the CE data are from interviews conducted in

2005 calendar quarter two through 2006 calendar quarter

one. For the CE, an interview refers to the previous threemonths. For example, an interview conducted in January

2006 refers to the October through December 2005 period.

3.2.1. American Housing Survey

The 2005 AHS is a housing unit survey designed to collect data on the quality of housing in the United States.

This cross-section of the housing inventory provides a picture of houses and households as they change over long

periods of time. The AHS actually consists of two surveys,

a national survey and a metropolitan area survey. Both surveys are conducted during a 3 to 7 month period. This

study only uses the national survey.

The national survey is conducted once every 2 years, in

odd-numbered years. Samples of housing units in all survey areas were selected from the 1980 decennial census.

These were updated by a sample of addresses obtained

from building permits (for new construction) to include

housing units added since the sample was selected. The

survey goes back to the same housing units on a regular

basis, recording changes in characteristics, adding and

deleting units when applicable. The current sample of

housing units has been interviewed since 1985.

The population weights applied to the AHS result in the

total number of housing units in the United States based on

the 2000 Census. A demographic adjustment ensures comparability among the surveys for people in occupied units.

Adjustments include those for Hispanic groups, region, race,

housing tenure (rent versus own), husband-wife, other male

householder or other female householder, age of householder, and central city, suburb, or non-metropolitan area.

The national survey, conducted in 2005, collected data

on about 59,581 housing units. For the sample analysis,

unoccupied units and units occupied without payment of

cash rent were dropped resulting in a sample size of

42,457 housing units. Of these, about 69 percent were

owner-occupied dwellings. The rental hedonic model in

this study was estimated with housing units rented for

cash; rental units that are subsidized or rent-controlled

were not included in the analysis sample. All owners were

included in the selection model and received imputed

rents. Outliers were excluded in estimating the hedonic

part of the selection/hedonic model. In order to control

for variations in housing prices by geographic area, the 2-

bedroom Fair Market Rents (FMRs) for counties were attached to each housing unit record in the AHS; these rents

were categorized by decile across all housing units in the

original weighted selection sample of owners and renters.

The weighted mean characteristics of owners receiving imputed rents and renters for whom the rent regression

Table 2 (continued)

American Housing Survey Consumer Expenditure Survey

Selection model

(probability

of being a renter)

Rent

model

Selection model

(probability

of being a renter)

Rent

model

Children present 0.175** 0.103**

Number of adults 18+ 0.151** 0.057**

Interview period

2005Q3 0.009 0.034*

2005Q4 0.030 0.051**

2006Q1 0.018 0.076**

Mills 0.107** 0.087**

Likelihood ratio/adjusted R square 21,698.96 0.387 17,379.11 0.411

a Major appliances include stove, refrigerator, dishwasher, and garbage disposal. For the CE, the appliances refer to those that were included in the owned

home or rental unit at the first interview. * Statistically significant at the 0.05 level. ** Statistically significant at the 0.01 level.

11 The new American Community Survey (ACS) contains most of the same

housing unit data traditionally collected as part of the Census of Population

and Housing. The ACS contains information on acreage, number of

bedrooms, condominium status and fees, contract rent, gross rent, heating

fuel, insurance for fire, hazard, and flood, kitchen facilities, meals included

in rent, mobile home costs, monthly housing costs, mortgage payment,

mortgage status, occupants per room, plumbing facilities, real estate taxes,

number of rooms, second or junior mortgage payment or home equity loan,

tenure, units in structure, utilities, value, year householder moved into unit,

and year structure was built.

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estimates were produced are presented in Table 1. The table shows means separately for owners and renters.

3.2.2. U.S. Consumer Expenditure Survey

CE-based estimates use data collected in 2005 quarter

two through 2006 quarter one (2005Q2–2006Q1). The CE

is composed of two components, the Interview and Diary.

Detailed housing unit information and associated expenditures are only collected in the Interview; thus this study is

restricted to the Interview sample.

The CE collects information from households and families living in the United States on their buying habits

(expenditures), income, and household characteristics.

The samples for the CE are national probability samples of

households designed to be representative of the total U.S.

civilian population. The population eligible for the sample

includes all civilian non-institutional persons. The CE Interview is a panel rotation survey. Each panel is interviewed

for five consecutive quarters. Through the use of population

weights and information from the most recent Current Population Survey (CPS) and the Census Bureau’s official population estimates, CE data are made to represent the U.S.

non-institutional population. CE’s non-response adjustments are done using region, race, housing tenure (own

versus rent), family size; CE calibration adjustments are

made considering region, race, housing tenure, age of the

reference person, and urban versus rural location.

The CE Interview is designed to collect data from a consumer unit in five different time periods. Approximately

7500 consumer units are interviewed each quarter of the

calendar year. The first interview is a bounding interview

and includes the collection of housing unit characteristics,

property values,12 and a list of the major appliances in a housing unit when the consumer unit moved in. These questions

are not asked again. The second interview is the first time consumer units are asked to report rental equivalence values and

rents. Homeowners are asked to report rental equivalences as

of the day of the interview. Renters are asked to report the

rents paid in each of the last 3 months. The property value,

rental equivalence, and monthly rent questions all refer to different time periods, thus differences in imputed rents based

on monthly rents and property values can differ from reported rental equivalence in volatile markets.

The original number of interviews conducted over the

time period is 30,544. Of these, 67 percent were for owners

living in the sampled units with rental equivalence and

home property values. Vacation homes are not included

in the analysis. Vacation homes are part of vacant reserves

as defined by the BEA. The renter sample was restricted to

consumer units who were paying rent but not receiving

rent as pay and not living in subsidized housing. The number of renter consumer units was 7793; this number was

further reduced when outliers were dropped from the rent

regression. In the 2005 CE, rent-controlled housing units

could not be identified in the CE and are thus included in

the analysis sample.13 The selection model for the first stage

of the hedonic modeling was estimated using the owner and

full renter sample. See Table 1 for a comparison of AHS and

CE sample statistics.

For this study, all interviews meeting the requirements

of the study and occurring over the 2005Q2–2006Q1 time

period were included in the study sample; in the earlier

studies (Garner and Short, 2008a,b), only the fifth interviews were used. The reason to restrict the earlier samples

to fifth interviews was to include information about assets

and liabilities in the selection model; these data are only

collected in the fifth interviews. Assets and liabilities are

expected to influence whether someone can afford to purchase a dwelling or not. Unpublished results suggest that

assets and liabilities are important; however, since data

on these variables were not available in the AHS, they were

dropped from the model specification. In addition, restricting the same to fifth interviews only resulted in a sample

size that was 18 percent of the sample size of the AHS. By

including all interviews during the time period, the sample

size of the CE increased to 72 percent of that of the AHS.

Mullan et al. (2007), in their selection/hedonic model of

U.K. imputed rents, noted that the differences they found

from using two different household survey samples were

due to sample size.14 We suspected that this might be the

case in the earlier studies, leading us to include all interviews

conduction during the study time period.

3.3. Selection model results

Results from the selection model are presented in Table

2 for the AHS and CE.15 Both the selection and rent models

fit the data well. The coefficients of the Mills ratio are positive and statistically significant, suggesting that factors related to being an owner or a renter differ. This selection

should be accounted for in the estimation of the model to reduce bias in the estimates.

Unlike in the AHS, rent-controlled housing units could

not be identified in the CE data for the study time period.

Thus, it was expected that the selection/hedonic approach

would result in lower imputed rents for owner-occupants

in the CE as compared to the AHS. Also, another statistically significant variable in the AHS model is tenure duration, or the amount of time that the renter has lived in the

dwelling. The AHS results, as well as the results of

Verbrugge et al. (2009),

16 reveal that tenure duration is

related to lower average rents. Not controlling for tenure

duration in the CE estimation was expected to result in lower imputed rents for owners using renter hedonic results.

3.4. Comparison of imputation and aggregations

Average annual rents, housing expenses, and net rental

income for owner-occupied housing are presented in Table

12 Property values of owned properties are collected quarterly beginning

with the 2007Q2 Interview.

13 A question about rent-controlled units was added to the Interview

Survey in 2007 quarter two.

14 However, when they applied a stratification model to the U.K. data sets,

the imputed rents were nearly the same for the two survey samples.

15 One might wonder about the extent to which model specification

differences impact the results. Garner and Short (2008a) applied a common

specification to the AHS and CE samples; however, they found that this

specification fit the data poorly. For this reason, we chose in this study to

use models that were statistically adequate representations of the data.

16 See also Crone et al. (in press), who review earlier studies.

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Table 3

Comparison of 2005 imputed rents and implicit rental income for owners using the U.S. Income and Product Accounts, American Housing Survey, and the Consumer Expenditure Interview Survey.

BEAa American Housing Survey CE (2005 with data from 2005Q2–2006Q1)

Population Capitalization

rate

Selection/

hedonic

Population Capitalization

rate

Selection/

hedonic

Reported rental

equivalence

Non-farm owner-occupied permanent-site housing

Owner-occupied (including vacant reserves)

Non-farm manufactured homes, owner-occupied

Farm dwellings owned by farm operators

Total number of owner-occupied housing units (thousands) 74,690 79,085

Average rent for all owner-occupied housing units (millions of dollars) $12,862 $12,164 $13,059 $12,389 $14,916

Non-farm owner-occupied permanent-site housing

Owner-occupied (including vacant reserves)

Non-farm manufactured homes, owner-occupied

Farm dwellings owned by farm operators

Total owner-occupied housing units multiplied by average ‘‘rent” $960,663 $908,529 $1,032,746 $979,780 $1,179,634

Aggregate annual rent for all owner-occupied housing units $960,696 $908,595 $1,032,746 $979,780 $1,179,631

Less: rental of durables $0 $0 $0 $0 $0

Space rent for all farm and non-farm owner-occupied housing units (including

vacant reserves) exclusive of utilities

$959,454

Space rent for all farm and non-farm owner-occupied housing units (does not

include vacant reserves) exclusive of utilities

$960,696 $908,595 $1,032,746 $979,780 $1,179,631

Less expenses Running subtotals Running subtotals

Intermediate inputs (maintenance and repairs (includes ground rent)) $202,138 $69,682 $891,014 $838,913 $100,378 $932,368 $879,402 $1,079,252

Taxes on production and imports (property taxes) $139,213 $175,152 $715,862 $663,761 $181,541 $750,827 $697,861 $897,711

Net Interest (mortgage interest) $408,732 $330,722 $385,140 $333,039 $393,293 $357,533 $304,568 $504,418

Property insurance $71,072c $314,068 $261,967 $38,043 $319,490 $266,525 $466,375

Current transfer payments $10,980

Consumption of fixed capital $229,710 $177,647d $136,421 $84,320 $191,249d $128,241 $75,275 $275,126

Closing costs, total $13,550 $122,871 $70,770 $23,181 $105,060 $52,095 $251,945

Origination fees $32,992b $4263e $415 $127,826 $74,860 $274,710

Other closing costs $22,374b $9287f $22,766 $105,060 $52,095 $251,945

Title insurance

Title abstract and escrow fees

Attorney fees

Credit report

Surveys

Adjustment and collection services

Engineering services

Real Estate Brokers’ commissions $34,124 $88,747 $36,646 $48,973 $56,087 $3121 $202,971

Condominiums and co-operative fees Included Included

Mortgage guaranty insurance (private administration) Included na

Swimming pool maintenance Included

State and local documentary stamp tax Included

242 T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248
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3. Annual average rent estimates vary widely. Using AHS

data, the capitalization rate method yielded an annual rent

of $12,862 as compared to a slightly lower estimate from

the selection/hedonic model ($12,164). The selection/hedonic method also yielded the lowest annual rent for the CE,

$12,389, followed by a higher rent of $13,059 using the capitalization method. For both surveys, the capitalization

method resulted in rents that are about 5 percent higher

than those based on the selection/hedonic models. In contrast, annual rental equivalence is 14 percent higher than

the capitalization rents for the CE. As noted by Garner and

Verbrugge (2009b), higher rental equivalence values can

be expected in the U.S. housing market due to differences

in owner and renter user costs and housing conditions.17

The relative average rents based on the capitalization

approach for the AHS and CE are consistent with relative

average property values in the two surveys; AHS average

property values are slightly higher than those from the

CE (see Table 1). Differences in the estimates for the AHS

and CE resulting from using the selection/hedonic model

are related to differences in the underlying samples and

model specifications (see Table 2 again).

Table 3 includes the aggregations of imputed rents, expenses, and implicit net rental incomes using the survey

data with a comparison to the NIPAs. The gross aggregate

space or implicit rent for owner-occupied dwellings most

similar to NIPAs is the AHS capitalization rate estimate:

$959,345 million versus $960,696 million. This is expected

since the rent-to-value ratios applied to total housing

wealth from the AHS are from the NIPA figures. However, given that vacant reserves are not included in the AHS figures,

it is surprising that the AHS aggregate is higher. The CE capitalization rate estimates exceed those of the AHS and NIPA

by less than 8 percent and the CE selection/hedonic estimate

was higher by only 2 percent. Gross rent using the rental

equivalence data from the CE was the highest, at

$1,179,631 million, exceeding the AHS and NIPA capitalization rate estimates by 23 percent, and the CE capitalization

rate estimate by 14 percent.18

In order to derive implicit net rental income from owner-occupied housing, operating and other expenses were

subtracted from gross rent. For the AHS and CE, some of

these expenses were reported while others were derived

using various assumptions. These costs include those for

maintenance and repairs (referred to in the NIPA as ‘‘intermediate inputs”), mortgage interest (‘‘net interest” in the

NIPA), property insurance, the consumption of fixed capital,

and closing costs. For the AHS and CE, expenses reported by

survey respondents for maintenance and repairs, property

taxes, and mortgage interest were aggregated. Property

insurance premiums reported in the CE were also aggregated. The entry for the AHS for property insurance includes

property insurance, homeowner association fees, condominium fees, and other miscellaneous expenses. The NIPA

separates condominium and co-operative fees, swimming

pool maintenance, and state and local documentary stamp

OthersTotal expenses $968,813 $871,949 $976,659Plus: subsidies $4041Equals: rental income of persons with capital consumption adjustment (net rental

income from all owner-occupied housing)

$5318 $88,747 $36,646 $56,087 $3121 $202,971

Counter factual using BEA expenses and subsidies $4076 $56,177 $67,974 $15,009 $214,859

a Space rent, expenses, and rental income are from the National Income and Produce Accounts (NIPA) Table 7.12. Imputations in National Income and Product Accounts. The rental income of persons with capital

consumption adjustment is equal to the rental income in NIPA Table 7.9. Rental income of persons by legal form of organization and by type of income.

b Unpublished aggregates supplied by Denise McBride from the BEA, August 12, 2008. c Includes property insurance, homeowners association fees, condominium fees, and other miscellaneous expenses. d Estimated to be 1.0 percent of market value of owned home. e Estimated to be 1.0 percent of the value of the original mortgage or home equity loan.

f Estimated to be 3.0 percent of the value of the original mortgage or home equity loan.

17 See also Iwata and Yamaga (2008) for Japanese evidence. 18 While it is tempting to conclude that CE rental equivalence estimates

are upward-biased, Garner and Verbrugge (2009b) argue that these

estimates are in line with a homeowner user cost measure.

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tax, and other expenses. Expenses for these are included in

the previously noted categories for the CE.

We estimated values for the consumption of fixed capital (depreciation) and real estate brokers’ commissions for

the AHS and CE. Origination fees are also estimated for the

AHS; for the CE, the entry reflects loan origination fees.

Other closing costs, not including origination fees, are also

collected in the CE and are presented in Table 2. For the

AHS and CE, the consumption of fixed capital was assumed

to be one percent of the current market values of owneroccupied dwellings for the year. The origination fees for

new mortgages and home equity loans were assumed to

be one percent of the original value of the mortgage or

home equity loan. Loan values were reported in the AHS

and CE. Brokers’ commissions were estimated to be 4 percent of the value of new home sales; new homes were defined to be not older than 2 years. Brokers’ commissions for

older homes were estimated to be 6 percent of home sales.

These percentages underlie the estimates of brokers’ commissions in non-benchmark years (Meyerhauser and

McBride, 2008).19 Included in NIPA estimates, but not the

survey estimates, are current transfer payments as a negative expense and subsidies as additional rent. Although premiums for mortgage guaranty insurance are collected in the

CE, they are grouped with other types of insurance and thus

are not included among the CE expenses.

Table 3 shows the aggregate rents and expenses from

the NIPA accounts compared to those based on the AHS

and CE data. These differ significantly. The aggregate expenses reported for the AHS are lower than those in the

NIPA and in the CE. Property taxes are highest from the

AHS. The only aggregate expenses that are close to those

in the NIPA are those for mortgage interest and other closing costs as reported in the CE.

The gross rent figures from the capitalization rate method applied to the AHS closely replicate the NIPA figures;

however, the resulting aggregate value of net rental income from the NIPA using the BEA calculations is

$5318 million, below the aggregate value from the estimates using survey data. Since operating costs computed

at the household level are well below the NIPA figures,

the end results of net rental income are much higher. Results from a counterfactual using survey gross rents but

NIPA expenses are presented on the first page, last row,

of Table 3. Not surprisingly, this exercise results in the

AHS-based estimates being negative, but more positive

when the CE-based imputed gross rents are assumed.

3.5. Distributional outcomes

A recent study of the Organization for Economic Cooperation and Development (OECD, 2008) ranked the income distribution of the U.S. as fourth most unequal

among 30 OECD countries. The OECD comparison, along

with most other international comparisons of income

inequality, is based on comparisons of disposable cash

income. However, many authors have stressed the

importance of taking account of non-cash income and inkind transfers for comparing income distributions (see,

e.g., Smeeding et al., 1993) and one of the most important

sources of non-cash income is housing.

Several studies have incorporated a measure of housing

benefits in just such comparisons. Ritakallio (2003) compared Australia and Finland income distributions and concluded that there is an important redistributive effect from

taking account of housing in countries such as the U.S.,

where ownership is common and ‘imputed income’ from

housing is greatest for the elderly who also have the lowest

disposable incomes, on average. Other studies have shown

similar results. Smeeding et al. (1993) found more equal

incomes for Germany, Sweden, Canada, and the Netherlands. Frick and Grabka (2003) showed lower inequality

for Germany, the U.S., and the U.K., with the inclusion of

imputed rents as income.20

For the AIM-AP project in Europe, Frick et al. (2009) reported that almost all seven European countries examined

had lower measured income inequality as a result of

accounting for housing benefits. They noted, however, the

variation in results due to the use of different methodologies

and data sources, suggesting that it was an open question

whether any cross-country differences identified actually

reflected true variations among the countries studied. Italy

was an exception to the general findings; D’Ambrosio and

Gagliarano (2007) reported increases in inequality for Italy

when imputed rents were added to income.

This section describes the empirical results of accounting

for implicit net rental income when measuring household

well-being, something that can only be done with micro

data. The analysis is restricted to owners and does not account for total household disposable income across all owners and renters in the U.S. population. The analysis is

conducted for older and younger households separately as

it is often assumed that that older households benefit more

as owners who have paid off their mortgage obligations

have lower expenses than those who have not. Examining

mean net rental income for age and income groups suggests

that the method employed to estimate implicit rents for

owner-occupied dwellings can affect resulting comparisons

of distributions of implicit net rental income.21

Tables 4 and 5 show mean amounts of net rental income for households by income decile (the means are

those of all owner households within ranges of before tax

money income) and age of householder using data from

the AHS and CE, respectively.22 For the AHS, Table 4 includes mean net rental income using the capitalization rate

19 Our choice of 2 years as the break point between younger and older

dwellings was based on our own judgment.

20 See also Onrubia et al. (2009) for a recent study focused upon Spain. 21 Examinations of distributions for other population subgroups are also

important. Gyourko et al. (1999) analyzed housing tenure differences by

race and found these reflected differences in wealth holdings. Hendershott

et al. (2009) examined differences in home ownership by marital status and

marital history.

22 When examining the results in Tables 4 and 5 it is important to note

that the rankings of income were calculated for each survey sample

independently (see mean values of before tax money income for the AHS

and CE in the last columns of the tables). Thus the level implicit net rental

incomes across methods within a survey can be compared but levels across

surveys cannot, as the cutoffs for the income groups vary by survey. For

comparisons across surveys, trends are reported.

244 T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248
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approach in the first column, followed by the rent selection/

hedonic model in the second. The third distribution is based

on a return to home equity minus property taxes, the method currently employed by the Census Bureau in household

income series. It is clear that there are different outcomes

across these methods. Imputed rental income, subtractions,

property values, and before tax money income are shown

as these underlie the imputed net rents shown in the first

three columns. Table 5 includes results for the CE. Instead

of return to equity as in the AHS, the CE results include estimates based on reported rental equivalence.

Unlike the situation for the capitalization rate and

selection/hedonic approaches, the AHS results using the return to home equity results are based on subtracting only

property taxes (Table 4). Property taxes are generally

proportional to income, reflecting the correlation between

Table 4

Annual weighted means of rental income and subtractions by deciles of before tax money income for all owner households in the American Housing Survey.

Decile Imputed net rental income Imputed rental income Subtractions Property

value

Before tax

money income Capitalization rate Selection/

hedonic

Return to equity

minus property tax

Capitalization rate Selection/

hedonic

Reference person age 65 or over (n = 7209)

1 $2968 $2757 $4567 $8292 $8081 $5324 $153,332 $3477

2 $3196 $2831 $4520 $8431 $8067 $5236 $155,909 $10,146

3 $3063 $3530 $4361 $8013 $8480 $4950 $148,177 $14,119

4 $3494 $3570 $4923 $8938 $9013 $5443 $165,269 $18,610

5 $3514 $3083 $5276 $9836 $9405 $6322 $181,880 $23,719

6 $3946 $3672 $5578 $10,267 $9994 $6322 $189,854 $29,884

7 $4989 $3914 $6664 $12,002 $10,927 $7013 $221,930 $38,028

8 $4212 $3405 $6255 $12,347 $11,540 $8135 $228,310 $49,595

9 $5492 $3712 $7699 $14,818 $13,039 $9327 $274,013 $67,104

10 $9903 $3386 $13,425 $25,021 $18,503 $15,118 $462,671 $150,200

Reference person age 65 years of age or younger (n = 22,298)

1 $664 $733 $2958 $7686 $7754 $7021 $142,117 $10,083

2 $92 $786 $2602 $7789 $8483 $7697 $144,028 $26,953

3 $541 $44 $2377 $8743 $9328 $9284 $161,671 $38,255

4 $345 $175 $2775 $9695 $10,215 $10,040 $179,273 $48,447

5 $681 $256 $2890 $10,424 $10,849 $11,104 $192,750 $58,571

6 $364 $189 $3208 $11,768 $11,943 $12,132 $217,608 $69,444

7 $517 $1040 $3708 $13,603 $13,080 $14,120 $251,542 $82,343

8 $96 $530 $4145 $15,197 $14,572 $15,101 $281,024 $98,630

9 $256 $1603 $5460 $18,885 $17,026 $18,629 $349,211 $123,949

10 $2957 $2247 $9033 $28152 $22,948 $25,195 $520,567 $243,081

Table 5

Annual weighted means of rental income and subtractions by deciles of before tax money income for all owners consumer units: Consumer Expenditure Survey

Interview 2005Q2–2006Q1.

Decile Imputed net rental income Imputed rental income Subtractions Property

value

Before tax

money income Capitalization

rate

Selection/

hedonic

Reported rental

equivalence

Capitalization

rate

Selection/

hedonic

Reported rental

equivalence

Reference person age 65 or over (n = 4898)

1 $3389 $1629 $6010 $8105 $6345 $10,726 $4716 $150,090 $10,045

2 $2907 $1794 $5708 $8611 $7497 $11,411 $5704 $159,464 $19,957

3 $3014 $1706 $5989 $9588 $8280 $12,563 $6574 $177,559 $29,468

4 $4165 $1487 $6942 $11,524 $8845 $14,300 $7358 $213,399 $38,706

5 $4475 $1870 $6104 $12,494 $9889 $14,122 $8019 $231,373 $48,763

6 $5685 $1711 $7361 $14,978 $11,003 $16,653 $9293 $277,372 $59,962

7 $5098 $1225 $6500 $15,059 $11,185 $16,461 $9961 $278,872 $73,490

8 $3084 $749 $4902 $15,193 $12,858 $17,011 $12,109 $281,353 $90,391

9 $6530 $2803 $6318 $18617 $14,889 $18,405 $12,087 $344,754 $114,357

10 $8183 $17,576 $7166 $29,946 $39,340 $28,929 $21,763 $554,558 $250,455

Reference person less than 65 years of age (n = 15,712)

1 $1435 $477 $3910 $7865 $5953 $10,340 $6430 $145,656 $6510

2 $782 $342 $2976 $7430 $6306 $9624 $6648 $137,598 $20,475

3 $395 $372 $2837 $7877 $7111 $10,320 $7482 $145,873 $29,805

4 $804 $1685 $1699 $8744 $7862 $11,247 $9547 $161,919 $38,783

5 $1210 $2258 $1578 $9796 $8748 $12,583 $11,006 $181,408 $48,959

6 $881 $2228 $1821 $11,303 $9957 $14,006 $12,185 $209,322 $60,594

7 $803 $2454 $1640 $12,879 $11,228 $15,322 $13,682 $238,503 $73,881

8 $1276 $2224 $1742 $13,720 $12,772 $16,738 $14,996 $254,074 $90,216

9 $133 $2881 $1122 $18,361 $15,613 $19,616 $18,494 $340,015 $115,058

10 $1366 $8558 $2008 $29,168 $36,360 $25,793 $27,802 $540,139 $222,983

T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248 245
Author's personal copy

incomes and property values by geographic area. This subtraction has the effect of lowering values for higher income

households relative to lower income households but do not

reflect any age differences in householders that typically

arise from mortgage payments.

The main difference between the elderly and non-elderly net rental incomes is that those for the elderly are

higher. One observation from this comparison is that the

return to home equity method results in values that are

similar to, although higher than, values based on the other

two methods for older households. The return to equity

method appears to overestimate net imputed rent for

younger households; this reflects the fact that younger

households are expected to have very large mortgage

interest costs compared with older households. Also only

property taxes are subtracted from rent for the return to

home equity approach.

For the AHS-based results (Table 4), the distribution of

net imputed rent using the selection/hedonic method is

different from the return to home equity approach in the

same way as the capitalization approach for younger

householders, but there are other differences too. For older

households there is a fairly uniform distribution of net implicit rental incomes above the second income deciles, suggesting that rents may be under-predicted for higher value

homes by this method. However, for younger households,

net implicit rental income based on the selection/hedonic

model becomes negative around the fifth income decile

for younger households. A similar pattern results for net

implicit rental incomes when using the capitalization approach, with net losses beginning with the third decile.

The CE-based distribution results are reported in Table

5, which displays average imputed net rents resulting from

the simple capitalization rate approach, the selection/hedonic model, and reported rental equivalence. As with

the AHS, for most of the distribution for older households,

the capitalization method results in a fairly uniform distribution with positive incomes. Both survey samples imply

higher net rents using the capitalization method for the

higher income groups. In contrast, for younger households,

net implicit rental incomes based on the capitalization rate

method are negative from the fourth to the ninth deciles,

similar to the pattern from the AHS. Comparing selection/

hedonic based net rents of older households with net rents

of younger ones, we see again that older households gain

more, from owner-occupied dwelling services being valued, than do younger households. For older households,

net rents are fairly uniform through the 7th decile, fall,

and then climb at the 9th with income climbing. For younger households, net rents are negative through the 9th decile, with negative net rents fairly uniform from the 5th

through the 8th deciles. Only for the 10th decile are rents

positive. This pattern is the opposite of that found using

the AHS selection/hedonic model and data, with net rents

through the 4th decile being positive and those thereafter

being negative.

Reported rental equivalence, from the CE, leads to the

highest net rents for older owners for most of the income

percentiles (through the 8th decile) when compared to results from the other methods and to results from the AHS.

Net rental equivalence incomes for households between

the 9th and 10th deciles are lower than those from the capitalization rate method for older households. Net rents

based on rental equivalence follow approximately the

same pattern as net rents based on the capitalization

method over much of the income distribution for older

households. For younger households, net rents based on

reported rental equivalence are highest for owners in the

lower three income deciles. These rents fall, flatten, and

then drop again over the income distribution. Net rents

from rental equivalence are higher on average than those

based on the selection/hedonic method.

While there are considerable differences across data

sources and methods, all methods from both sources imply

much larger flows from owned homes for older households

than for younger households, resulting from the lower

operating costs elderly householders face due to having

paid off mortgages. This implies that comparisons of economic well-being will underestimate the relative position

of elderly households if the benefits of home ownership

are not accounted for.23

The impact of net rental income of owner-occupied

housing on income inequality was examined using the Gini

index.24 For this analysis, net rental income using each of

the three approaches was added to before tax money income. Only owners as a subgroup of the population were

considered. Incomes were equivalized across all people living in owner-occupied housing by dividing income by the

square root of family size and then weighting this income

by the number of people living in each owner-occupied

dwelling. Income inequality falls 1.3 percent when net rents

based on reported rental equivalence are included for CE

owners. For the AHS, income inequality also falls when the

return to equity method is used, but the fall is less than

one percent. Net rents added to income leads to increases

in inequality when the capitalization rate and selection/hedonic approaches are used. As noted earlier, most of the

AIM-AP researchers reported decreases in income inequality

when rents were imputed for owners. Yet, increases

in inequality were reported for Italy (D’Ambrosio and

Gagliarano, 2007). The U.S. results, like those for Italy, are

not surprising given the different structure of the population

with the majority of U.S. (and Italian versus those in other

European countries studied) households being owner-occupants and the fact that owner and renter housing appears to

differ more in the U.S. and Italy than in the other European

counties.

4. Conclusions

This paper has examined different methods for valuing

net rental income for owner-occupiers at the household level. These methods were compared to aggregate estimates

included in the NIPAs for the purpose of understanding differences in concepts and measurement. In general, it appears that imputed rent methodologies can replicate the

23 This result supports the arguments of Buckley et al. (2003) who

discussed the relative well-being of Russian elderly households in Moscow.

24 The Theil index and mean log deviation were also produced; however,

samples were reduced for the estimation of these indexes due to zero and

negative income and thus are not reported here.

246 T.I. Garner, K. Short / Journal of Housing Economics 18 (2009) 233–248
Author's personal copy

national account estimates, but that reported expenses for

homeowners differ considerably across methods and data

sources.

In the future, we expect some of the differences uncovered here to be reduced. Differences in rents and net rental

income for the CE and NIPA are expected to be reduced due

to changes in the NIPA resulting from a comprehensive

revision of the accounts, to be published in July 2009. CE

rental equivalence data will henceforth be used in the NIPA

estimation of the imputed space rental value of owneroccupied permanent-site non-farm housing. Estimates affected by the change are the PCE, rental income of persons

with capital consumption adjustment, and gross housing

value added (see McCully and Payson, 2009). Improvements in the CE are expected to reduce differences as well.

For example, beginning with 2007 quarter two, data on

rent-controlled housing is collected; including this variable

in the selection/hedonic model is expected to produce better estimates of imputed rents.

Differences in methods and the data used to estimate

implicit net rental income for dwelling services of owneroccupied housing are important for distributional analyses

and their interpretation. It was shown that some measures

have a more equalizing effect on extended income distributions that include net rental income than others. For

all methods it was shown that elderly households, in

general, benefit more than younger households from net

implicit rental income. In comparisons of economic wellbeing, elderly households would appear better off if

account were taken of home ownership. However, overall,

income inequality either increases or decreases only

marginally when net rents are added to before tax money

income.

Overall, we have shown that care must be used in the

application of methods to estimate imputed rents and in

income distribution measures for populations and comparisons of economic well-being. We find that values of imputed rents are quite sensitive to the methods chosen

and to the data used; however, the trends for younger versus older households seem to hold across surveys.

Acknowledgements

Thanks are due to two anonymous referees and to Alice

Nakamura and Randal Verbrugge for their detailed comments and suggestions. Earlier versions of this paper were

presented at the International Association for Research in

Income and Wealth (IARIW) Thirtieth General Conference

held in Slovenia, August 2008, and at the Southern Economic Association Annual Meetings held in Washington,

DC, November 21-23, 2008. We thank out conference paper discussants, Fabrice Lenglart and Andrew Grodner, as

well as session participants for suggesting improvements

and for commenting on our research. We also thank our

colleagues at the BLS and Census Bureau for comments

and suggestions. Special thanks are extended to Denise

McBride of the BEA for helping us with questions about

the NIPA. The views expressed in this research, including

those related to statistical, methodological, technical, or

operational issues, are solely those of the authors and do

not necessarily reflect the official positions or policies of

the U.S. Census Bureau or U.S. Bureau of Labor Statistics,

or the views of other staff members within these agencies.

The authors accept responsibility for all errors.

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