BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Price and Index Number Research 

Reconciling User Costs and Rental Equivalence: Evidence from the U.S. Consumer 

Expenditure Survey 

 

Thesia I. Garner, U.S. Bureau of Labor Statistics

Randal Verbrugge, U.S. Bureau of Labor Statistics

Working Paper 427

August 2009

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Reconciling User Costs and Rental Equivalence: 

Evidence from the U.S. Consumer Expenditure Survey 

Thesia I. Garner 

Randal Verbrugge 

Division of Price and Index Number Research, Bureau of Labor Statistics 

2 Massachusetts Ave NE, Washington, DC 20212 

Email: garner.thesia@bls.gov; verbrugge.randal@bls.gov 

First version: June 2008; this version: August 13, 2009 

______________________________________________________________________________ 

Abstract 

Previous research (Verbrugge, 2008a) demonstrated that housing rents and ex ante user costs 

diverge markedly for extended periods of time, a finding with profound implications for income and inflation 

measurement. But the primary data sources in that study were various indexes, based upon largely disjoint 

data sources, constructed using different aggregation techniques, and each subject to various criticisms. 

This raised doubts about the quality of the comparison. The relationship between user costs and rents 

might well be much tighter at the micro level; after all, house prices and rents (and their growth rates) can 

vary dramatically within cities, and rents are notoriously sticky. Furthermore, the use of indexes precludes 

both cross-sectional and dollar cost comparisons. In this study, we use Consumer Expenditure Interview 

Survey (CE) data to examine the relationship between user costs and rents at the individual unit level, in 

dollars, using unit-level information on house value, rent, taxes, and the like. This allows us to accurately 

estimate unit-specific user costs and to control for unobservables like structure and neighborhood quality. 

We also make the point that in theory, after-tax user costs should equal net rent, i.e., expected rental 

income, rather than gross rent. 

 Our findings are striking. In keeping with most previous research, we find tremendous divergence 

between conventional measures of user costs and net rents, thus ruling out index construction errors as a 

possible explanation. This divergence does not result from a faulty rent measure: we find that reported 

rents are sensible, in that they move similarly to official rent indexes, and are not simply out-of-pocket 

expenses. Instead, and most perplexing, we find a surprisingly close correspondence between net rents 

and a particular estimate of user costs, one implicitly assuming zero transactions costs and constructed 

using an appreciation measure that is both theoretically suspect and empirically a poor predictor of actual 

appreciation. 

Keywords: user costs; house price appreciation; forecasting; rental equivalence 

______________________________________________________________________________ 

Acknowledgements: Thanks are due to two anonymous referees and to Alice Nakamura, Andy Baldwin, 

Erwin Diewert, Pierre Duguay, Tim Erickson, Ryan Greenaway-McGrevy, John Greenlees, Arnold Katz, 

Rob McClelland, Leonard Nakamura, Marshall Reinsdorf, Paul Schreyer, Gary Smith, Paul Sullivan, Tony 

Yezer, Steve Henderson, Carolyn Pickering, Jay Ryan and other BLS Division of Consumer Expenditure 

Surveys staff, and participants at the 2008 World Congress on National Accounts and Economic 

Performance Measures for Nations, the 2009 SGE-ASSA meetings, and the 2009 Ottawa Productivity 

Workshop. However, only the authors are responsible for remaining errors. All views expressed in this 

paper are those of the authors and do not reflect the views or policies of the Bureau of Labor Statistics or 

the views of other BLS staff members. 
Reconciling User Costs and Rents 

1. Introduction 

What is the value of the service flow obtained from owned housing? This is an important 

question, not only in the field of real estate economics (which studies the rent-versus-buy 

decision, landlord decisions, and so on), but also in the fields of tax policy, macroeconomics, and 

poverty measurement. In practice, different approaches to the measurement of this service flow 

can lead to rather different conclusions. For example, Frick, Grabka, Smeeding and Tsakloglou 

(2008)1

 and Garner and Short (2001) demonstrated that distributional measures vary 

dramatically depending upon the treatment of housing flows; OECD (2005), Cournède (2005) 

and Eiglsperger (2006) likewise found major impacts on inflation and real output measurement; 

and Garner and Short (2009) found differences in the estimation of aggregate housing flows and 

net income for the U.S. 

In standard frictionless Jorgensonian capital theory with competitive markets, a durable 

good’s rental cost will equal its ex ante user cost, suggesting that these alternative measurements 

of the value of the flow of services should be roughly equivalent.2

 But in a study of the U.S. 

market, Verbrugge (2008a) demonstrated that not only are housing rents far less volatile than ex 

ante user costs, but these measures also diverge markedly for extended periods of time, a 

seeming failure of arbitrage and a puzzle from the perspective of the standard theory. However, 

that study largely relied upon aggregated indexes – in particular, the Bureau of Labor Statistics 

(BLS) rent index, Freddie Mac’s Conventional Mortgage Home Price Index (CMHPI), and the 

Census House Price Index.3

 In this context, the use of indexes has several potentially severe 

drawbacks. First, the use of indexes precludes a comparison in dollars, and precludes a crosssectional comparison. Second, the use of these particular indexes precludes a comparison of like 

with like; for example, only about one-quarter of the BLS rent sample consists of detached 

housing (reflecting the rental housing stock), yet – reflecting the owned housing stock – the vast 

majority of the Freddie Mac and Census samples consists of detached housing. (This could be a 

 

1

 Frick et al. (2008) provide a summary of research in seven European countries that focus on poverty and inequality. 

See also Garner (2005). 

2

 See expositions in Gillingham (1980, 1983), Dougherty and Van Order (1982), and Diewert (2003/2010). 3

 Almost all other related studies have relied upon aggregated data. Perhaps the most prominent is Himmelberg, 

Mayer and Sinai (2005), a study focused on explaining house price dynamics. It did not directly address the issue of 

rents versus user costs, given its crude measure of expected appreciation; see Verbrugge (2008a). The treatment of 

expected appreciation is key, as will be seen below; see also Johannessen (2004) for evidence. When using BLS rent 

indexes before 1995, one should make the adjustments suggested by Crone, Nakamura and Voith (2009). 

2 
Reconciling User Costs and Rents 

severe problem; for example, Chang, Cutts and Green (2005) stress the importance of adjusting 

for quality differentials in rent/value comparisons.) Third, these indexes are constructed in 

different ways, using different weights and index forms. It is well-known that rents and house 

prices have a very strong micro-spatial dimension (see, e.g., Hwang and Quigley 2006), and the 

same is true of house-price and rent inflation (see, e.g., Poole and Verbrugge, 2009). That being 

the case, the choices of index form and weighting could well be consequential in this context, as 

it is in other contexts (see, e.g., Deaton and Heston 2008 and Poole and Verbrugge 2009). It 

would be far superior to begin with a data source in which every variable is available at the unit 

level (i.e., where there is both a user cost and a rent associated with the same unit); this would 

remove concerns related to differential index construction and weights, and would allow one to 

control for unobservables like structure and neighborhood quality. Finally, each of the 

aforementioned indexes has been subject to its own set of criticisms; for example, there is no 

extant U.S. aggregate house price index which is built using a truly representative sample, and 

the utilities adjustment in the Bureau of Labor Statistics (BLS) Owners’ Equivalent Rent (OER) 

index has been criticized (see Verbrugge 2008b). Thus, it is possible that the use of said indexes 

in studies of user costs and rents has masked a much tighter relationship between these measures 

at the micro level. 

This study is one of the first to use micro data to study the relationship between user costs 

and rents.4

 In particular, we use Consumer Expenditure Survey (CE) Interview data collected in 

2004:I – 2007:I to examine the relationship between user costs and rents at the individual unit 

level, in dollars, using unit-level information.5

 These data include a rent measure (reported rental 

equivalence, described below), house value, and almost all of the components of unit-level user 

costs, such as maintenance and repairs and mortgage information, and income and homeowner 

characteristics (from which an estimate of tax rates may be obtained). In some of our analysis, 

we also link these data to Census 2000 data on neighborhood characteristics, in order to more 

 

4

 Several prior studies have investigated the extent to which rents respond to their user cost determinants; see, for 

example, DiPasquale and Wheaton (1992), Follain, Leavens and Velz (1993), Blackley and Follain (1996), Green 

and Malpezzi (2003), and Tian (2008). More realistic user cost expressions are idiosyncratic, and little is known 

about how user costs, rents, and the shadow price of housing are related to one another in realistic environments 

with risk and frictions. Sommers, Sullivan and Verbrugge (2009) study this topic; see also Blow and Nesheim 

(2009), a study which is related but which abstracts from transactions costs. 5

 While both Verbrugge (2008a) and Garner and Verbrugge (2009) used CE data in parts of their analysis, neither 

constructed unit-level user costs; instead, they compared estimates of rents and estimates of user costs pertaining to 

hypothetical median structures. A companion paper to this one (Garner and Verbrugge, 2008) uses CE data to 

investigate under- and over-valuation of houses both before and after the recent real estate boom. 

3 
Reconciling User Costs and Rents 

completely control for unobserved quality variables which may influence rent differentially from 

house price. In our estimates of user costs, we construct measures of expected appreciation using 

a forecasting approach (based upon CMHPIs), and in addition explore a popular ad hoc 

approach – simply using overall price inflation – which amounts to an assumption of no expected 

real capital gains even in the short run. The CE data is described in more detail in Section 2. In 

addition to other advantages listed above, an additional advantage to using CE data is that we are 

able to obtain concurrent rent and user cost estimates; conversely, since market rents typically 

change on an annual basis, market rents inevitably reflect lagged conditions – and thus, perhaps, 

lagged rather than current user costs – potentially making it more difficult to discern a 

relationship between these variables. Given the wealth of information at the individual unit level, 

and given the expansive geographic coverage, CE data appear to be well-suited to a study of this 

type. 

Our two aims in this paper were to compare rents and user costs at the micro level (and 

then at aggregated levels, where aggregation is done in the same way for both variables), and to 

study which factors are related to reported rental equivalence, which may provide clues towards 

understanding how rents relate to user costs. 

Is the relationship between rents and user costs tighter in these micro data? Yes and no. 

When constructing user cost estimates using the expected appreciation measures that most 

naturally follow from theory, we find striking divergence; indeed, in the cross section, the 

correlation appears to be negative. While the evidence in Verbrugge (2008a) suggests that 

constructing user costs using long-horizon forecasts markedly reduces user cost-rent divergence, 

our evidence suggests that such long-horizon user cost measures still diverge conspicuously from 

rents. The cross-sectional dispersion of rents versus these user cost estimates is also surprisingly 

large. In dollar terms over the 2004:I-2007:I period, expected user costs were generally well 

below rents – mainly driven by expectations of real appreciation in the short run – and often 

negative. A priori, we expected concavity in the rent/value relationship6

 to result in reduced 

divergence for higher-valued properties, but – while the user cost/value relationship is also 

concave – we find the divergence to be even greater for more expensive properties. 

 

6

 Others who have noted this concavity include Garner and Short (2001), McBride and Smith (2001), and Heston 

and Nakamura (2009). Diewert and Nakamura (2009) and Diewert, Nakamura and Nakamura (2009) highlight the 

implications of this curvature for rent/user cost comparisons and inflation measurement. Tian (2008) highlights and 

directly studies this phenomenon, using a unique micro data set; this study also examines the relationship of rents to 

a measure of user costs. His findings are very much in keeping with those in the present study. 

4 
Reconciling User Costs and Rents 

However, we find that the use of inflation as the proxy for expected appreciation in the 

user cost estimate results in striking correspondence of user costs and net expected rent earnings, 

with slightly less correspondence for more expensive homes. Both informal (graphical) and 

formal (statistical) evidence suggest that, on average, net expected rents are largely a function of 

user costs (as constructed in this manner). A key to detecting this relationship is to recognize that 

expected rental earnings lie below market rents: market rents must be adjusted downwards to 

account for taxes and the rental vacancy rate (as units do not earn rental income when they are 

vacant). An important area for future research is to explain why a theoretically suspect user cost 

estimate appears to be so useful for explaining rents. 

Since we use CE data, our rent measure is reported rental equivalence, an estimate that 

CE respondents make regarding the rental values of their homes. It is commonly suggested that 

respondents are naïve and simply report the out-of-pocket expenses associated with owning their 

home. However, both informal and formal evidence rule this out decisively. Reported rents 

appear to grow at the same rate as the BLS OER index, lie well above out-of-pocket expenses, 

respond to other market-rent-determining factors, and have a correlation with out-of-pocket 

expenses that is well below unity. Thus, homeowners are doing something other than simply 

reporting out-of-pocket expenses. However, the relationship of rents to expected appreciation is 

tenuous. 

Section 2 describes the data. Section 3 investigates the relationship between rent and 

value, and the relationship between rent, out-of-pocket expenses, and user costs using a series of 

graphs and regressions. Section 4 uses regression analysis to study the relationship between 

homeowner rental estimates, user costs, and other covariates. Section 5 concludes. 

2. Data and Measurement Description 

The primary source of data for this study is the Consumer Expenditure Survey (CE) interview 

data. Freddie Mac Conventional Mortgage Home Price Indexes (CMHPIs) for the U.S. and for 

28 metropolitan areas form the basis of the appreciation forecasts, as described below. We also 

apply the analysis of Kumcu (2009), which uses IRS tax tables and CE homeowner information 

data to impute marginal income taxes to CE consumer units. 7

 

7

 A consumer unit is defined as: (1) all members of a particular housing unit who are related by blood, marriage, 

adoption, or some other legal arrangement, such as foster children; (2) a person living alone or sharing a household 

5 
Reconciling User Costs and Rents 

2.1 Consumer Expenditure Survey Data 

CE Interview data, collected between 2004 calendar quarter one and 2007 calendar quarter one 

(2004:I to 2007:I) from consumer units living in the United States, were used as the basis for 

estimating user costs and rents for the same structure. The CE is a nationwide household survey 

designed to be representative of the U.S. civilian population. The first step in sampling is the 

creation and selection of Primary Sampling Units (PSUs), which consist of counties (or parts 

thereof), groups of counties, or independent cities grouped together into geographic entities (see 

http://stats.bls.gov/cex for details). The current sample of PSUs consists of 91 areas, most of 

which are also used by the Consumer Price Index program. For this study we restricted attention 

to the 28 largest PSUs which were present in the CE sample over the entire 2004-2007:I period; 

these are listed in Appendix Table 1. 

Using personal interviews, CE Interview data are collected by the U.S. Census Bureau 

from consumer units on behalf of the Bureau of Labor Statistics (BLS). The BLS reviews and 

edits the data after collection, imputing values when missing using a variety of variable-specific 

techniques. The CE Interview is designed so that each consumer unit in the sample is 

interviewed over five consecutive quarters, once every three months; every quarter 20 percent of 

the sample is replaced with new households. The first interview is used to bound expenditure 

estimates using one-month recall, and to collect other basic data such as housing unit 

characteristics (e.g., number of rooms). Interviews two through five are used to collect detailed 

expenditures and related information from the three months prior to each interview, and for the 

current month in some cases (e.g., rental equivalence). 

 Among the data collected in the CE Interview are both estimated current market values 

and “rental equivalences” or rental values for owner-occupied and vacation homes. Current 

market value is asked only in the first interview (if the property was currently owned), and is 

subsequently inventoried to the following interviews.8

 Consumer units are asked, “About how 

 with others, or living as a roomer in a private home, lodging house, or in permanent living quarters in a hotel or 

motel, but who is financially independent; or (3) two or more unrelated people living together who share certain 

major expenditures. Financial independence is determined by the three major expense categories: housing, food, and 

other living expenses. To be considered financially independent, at least two of the three major expense categories 

are to be provided entirely, or in part, by the respondent. Students living in university sponsored housing are 

included in the sample as separate consumer units. (See http://stats.bls.gov/CE/csxgloss.htm) 8

 If a property is owned when the bounding interview takes place, the interview respondent is asked to estimate the 

current market value of the property as of the date of the interview. If a property is acquired in a later interview, the 

6 
Reconciling User Costs and Rents 

much do you think this property would sell for on today’s market?” Rental values for owneroccupants are collected each quarter, by asking consumer units, “If someone were to rent your 

home today, how much do you think it would rent for monthly, unfurnished and without 

utilities?” Given the timing and structure of the data, we use only data from the second interview, 

so that each household enters only once, and the value and rent estimates are only three months 

apart. The only exception would be for newly acquired properties and for consumer units 

entering the survey after the bounding interview. 

 Other data collected by the CE include: mortgage information; housing structure type; 

consumer unit income9; property taxes; and expenditures on maintenance and repair and home 

insurance. For this study, a number of restrictions were placed upon the data. Only owneroccupied housing which was not a condo or coop was considered.10 None of the costs of this 

housing could have been paid for by Federal, State, or local government. If property value or 

rental equivalence was missing or imputed during BLS data processing, the observation was 

dropped from the sample. We also restricted the sample by the family type variable; in particular, 

in order to be able to accurately estimate marginal tax rates, we dropped observations where 

family type was coded as “other” by the BLS.11 We then restricted the sample by house value; in 

particular, we dropped 4 percent of the observations corresponding to home values in excess of 

$950,000 within the 2004:I-2007:I survey data period, as these units possess very high leverage 

and distort parameter estimates. Finally, on a PSU-by-PSU basis, we dropped any observations 

whose rent/value ratio was outside of two standard deviations from the mean of this ratio; this 

reduced our sample by about 120 observations. In sum, our restrictions regarding missing and 

imputed data and outliers reduced the sample size to 5,802 observations. See Appendix Table 2 

for sample sizes by PSU. Additional outlier treatment, applied at the regression estimation stage, 

is discussed below. 

 current market value of the property is collected as of the time of the first interview after acquisition of the property. 

Beginning in April 2007, the market value of owner-occupied housing and vacation homes has been asked each 

quarter, rather than only once. 

9

 Starting in 2004:I, the BLS began imputing income data when these were missing. 

10 Condo and coop owners comprise less than 5 percent of the population. Paulin (2005) highlights several reasons, 

including coop and condo fees, which suggest that condo and coop ownership is a distinct form of housing tenure 

that should probably modeled separately. 

11 We included singles, single parents, and husband-and-wife families with and without children. 

7 
Reconciling User Costs and Rents 

2.2 User cost; tax model; and CMHPI data 

While research is progressing on the nature of user costs when owners face frictions of various 

sorts (see, e.g., Diaz and Luengo-Prado 2008; Luengo-Prado et al. 2008), almost all housing 

studies use an annual ex ante user cost formula12 associated with a frictionless model, similar to 

( ()() 1 1 ) h Fed prop Fed h

t t t t t t t tt uc P i = - + - +- t t t gp E (1) 

where is the price of the home; is a nominal mortgage interest rate; h Pt t

i 13 is the sum of 

depreciation, maintenance and repair, and insurance; is the 4-quarter constant-quality home 

price appreciation between now and 1 year from now; and 

t g

h

t p

h Et t p represents the expectation of 

this appreciation at time t. Given the current U.S. tax code, such appreciation will almost 

certainly remain untaxed for the homes we consider. As Diewert (2006/2009) points out, one 

may interpret ( )

h

t

i E t - p as a period-t real interest rate.14 

We use the 30-year fixed mortgage rate as our measure of interest rates, except when 

computing measures of out-of-pocket costs, when we use actual respondent data.15 Homeowner 

marginal income tax rates are computed by applying the analysis of Kumcu (2009) to the CE 

data. Aside from the measure of expected appreciation (discussed below), all the other elements 

in (1) are generally available in CE data. 

 

12 Such user costs are readily derived from the fundamental capital pricing equation. The standard frictionless theory, 

which builds upon Hall and Jorgenson (1967), implies that rents equal user costs, and is exposited in Gillingham 

(1980, 1983), Dougherty and Van Order (1982), and Green and Malpezzi (2003). For more details and extensive 

discussion about user costs and other housing measures, see Diewert (2003/2010). 

13 Sometimes researchers distinguish between the equity in the home and the loan amount, and apply distinct interest 

rates to them. This is controversial; some hold that the mortgage interest rate is the relevant rate to apply even to 

equity, given the riskiness of housing investment (see Wang, Basu and Fernald 2005 and Verbrugge 2008a for a 

brief discussion). Chinloy (1991) emphasizes the risk facing homeowners; he estimates an average risk premium in 

excess of 2% in the California data he examines (conversely, Sarama (2009) estimates premia in the 0.1% range). 

Chinloy also argues that standard mortgage rates are not appropriate in (1), since they include the prices of options, 

in particular options to prepay and to default. In (1), the marginal income tax is applied regardless: for debt cost, 

mortgage interest is deductible, while for the opportunity cost associated with equity, investors only obtain after-tax 

interest earnings. Note that landlord user costs are fairly similar to (1); the main distinction is that expected 

appreciation is taxable for landlords. At least during periods of low inflation, owning is typically less costly than 

renting mainly because landlords pay taxes on rents, while owners obtain shelter services from their housing tax-free. 14 Note that some authors refer to uct/Pt as the user cost. Some authors, e.g. Prescott (1997) and ILO et al. (2004), 

suggest including expected transactions costs in (1). In Diaz and Luengo-Prado (2008), these costs do appear in the 

derived user cost expression in the differentiable case. Equation (1) assumes that all owners itemize, but many tax 

returns are filed using a standard deduction; furthermore some itemizers run up against the alternative minimum tax. 

15 More specifically, we use the series “average contract rate on commitments for 30-year fixed-rate first 

mortgages” from the Federal Reserve Board. This rate includes risk, default, and pre-payment premia. 

8 
Reconciling User Costs and Rents 

However, imputation of these measures is occasionally necessary, either because the data 

are missing for a unit or because they are ex post and hence of the incorrect form for use within 

an ex ante user cost measure. A handful of units did not report property taxes; this variable was 

imputed using CE data on the basis of a simple regression model with PSU, year, home value, 

and Census neighborhood characteristics as the regressors. We use number of rooms as a control 

variable in some of our regressions; this variable, when missing, was imputed on the basis of 

each year’s set of data, based upon region, PSU, dwelling age, and structure type. More 

extensive imputation was necessary for maintenance and repair costs and for home insurance. 

Actual annual maintenance and repair costs are highly variable and seasonal, and CE data 

include only ex post quarterly expenditures, but we are forming expected annual user costs. 

Hence we must construct a prediction of annual maintenance and repair costs for every unit. 

Annual home insurance is often missing, perhaps because many homeowners may pay for their 

insurance less frequently than every quarter,16 which implies that many respondents do not 

report any home insurance expenses in their second interview. Both of these variables were 

imputed in a similar manner. Each used available data from four consecutive interviews (not just 

second interview data) on a year-by-year basis. Regressors consist of home value, PSU, number 

of rooms, dwelling age, structure type, and housing amenities (such as air conditioning). Finally, 

user costs include not only maintenance and repairs, but also depreciation. Depreciation 

encompasses several notions, not only including physical deterioration (which is accounted for in 

maintenance and repair), but also the notions of aging and obsolescence (which is not thus 

accounted for). Thus, to ensure that our estimates line up with comparable measures from the 

Bureau of Economic Analysis (BEA) and from the Census, we add 1 percent of home value to 

our maintenance and repair estimates, which may be interpreted as adding an estimate of 

depreciation, or as an estimate of deferred major maintenance.17

The treatment of expected appreciation is central. Rather than restricting attention to a 

crude proxy, city-by-city forecasts for Eπ

h

 were constructed. This choice is crucial, for at least 

four reasons. First, home price appreciation is quite persistent, so it has a significant forecastable 

 

16 For example, homeowners who no longer possess mortgages do not usually have quarterly home insurance 

expenses. 

17 The BEA estimates for annual depreciation are 1.5% and 1.8% for owner-occupied and renter-occupied housing, 

respectively. In computing owner costs, the BEA also includes various costs of acquisition and disposal, such as 

realtor fees. 

9 
Reconciling User Costs and Rents 

component; market participants are aware of this, and are expected to take this into consideration 

in their decision making. Second, expected home price appreciation is quite variable across time 

and across cities, so it would appear to be inappropriate to use a time- or city-averaged rate. 

Third, this term has an enormous impact on user costs and their divergence from rents. After all, 

one can always assume the theory is valid and solve for the appropriate appreciation term which 

makes user costs equal rents; but the resultant appreciation term can be strongly at odds with the 

data in practice (see Verbrugge 2008a). Finally, there is no agreed-upon model of house price 

dynamics, so it is more conservative to take a more agnostic, statistical viewpoint to these 

expectations. Our forecasts are based upon metro-area house price indexes, namely the CMHPIs 

which are described briefly below. While ex post house price appreciation has a strong 

microspatial element, differences in housing appreciation rates across neighborhoods within a 

given city will be extremely hard to predict. Thus, the approach we take is arguably the best that 

market participants could do. 

Based upon popular conjectures and the findings of previous research, we use four 

alternative measures of h Et t p in (1), which give rise to four different user cost estimates for each 

unit. The first measure is a forecast of expected appreciation over the next year; the resulting 

user cost is defined as uc{1}. The second is an annualized forecast of expected appreciation over 

the next four years; the resulting user cost is defined as uc{4}. (We provide a justification for 

this second measure below.) 

The third expected appreciation measure we investigate is current annual overall inflation 

(“pi”). This measure treats overall inflation as a proxy for expected appreciation – which is 

equivalent to an assumption of zero real capital gains even in the short run. The resulting user 

cost is defined as uc{pi}.18 There are many different inflation estimates which could be used, 

and we explore three alternatives. Our baseline measure, uc{pi(1)}, is very crude and uses the 

previous calendar year’s overall CPI inflation as the inflation estimate. Our second measure, 

 

18 While this measure has little theoretical justification, it is nonetheless popular amongst practitioners; see, e.g., 

Poterba (1992), Blackley and Follain (1996), OECD (2005) and Cournède (2005). A similar user cost measure is 

also used in Iceland’s CPI (see Guðnason, 2004, 2005 and Guðnason and Jónsdóttir 2008), and variants are in use in 

the system of national accounts statistics in several Western Balkan countries such as Croatia and Serbia (see 

Roberts 2008) – though Eurostat guidelines are to make the operational assumption that (i-Eπ

h

)=2.5% (see Katz 

2009). A priori, this no-real-capital-gains-in-the-short-run assumption seems strange since it is both so strongly at 

odds with the U.S. data, and is also so theoretically dubious – in that, at least outside of steady state, there is no 

reason to believe that expected inflation equals expected home price appreciation. 

10 
Reconciling User Costs and Rents 

uc{pi(2)}, is similar but more timely, and uses the average of the current, and lagged, 4-quarter 

inflation rate. Our third measure, uc{pi(3)}, is identical to the second, except that it uses the 

overall CPI-less-shelter to compute 4-quarter inflation rates. 

The fourth and final expected appreciation measure we investigate is zero, at least 

roughly speaking. More specifically, in this case we treat out-of-pocket costs as the measure of 

“user costs.” In this out-of-pocket case, two implicit assumptions are thus made: first, expected 

real capital gains are negative; and second, the opportunity cost of equity in the home is zero. We 

explore two variants: baseline out-of-pocket expenses, which include only interest from first and 

second mortgages; and extended out-of-pocket expenses, which include interest from home 

equity loans and lines of credit. Out-of-pocket expenses refer to after-tax out-of-pocket expenses 

and, for household j, are computed as 

- - ( )(1 ) ( ) . 1 & . ( ) ( )( Fed Fed out of pocket mort. int. j = -+ -+ + j j t t prop taxjj j m r ins j)

where mort. int.j refers to actual annual mortgage interest payments of household j, prop. taxj

refers to annual property taxes paid by household j, m & rj refers to annual maintenance and 

repair costs by household j, ins.j refers to annual home insurance paid by household j, and Fed

j t

refers to the marginal income tax rate of household j. 

The standard theory leading to equation (1) and to its equality with rent is derived from a 

riskless frictionless model, in which continuous asset rebalancing occurs. But long-horizonforecast advocates correctly point out that, owing to large transactions costs, the expected tenure 

for homeowners is much longer than one year; indeed, it is actually closer to a decade. Thus, the 

forecasting horizon of the typical owner is far longer than one year. The expected tenure for 

renters is shorter, but is still itself about four years. This suggests that the margin of indifference 

between homeownership and renting has an implied horizon longer than the one-year horizon of 

a rental contract.19 On this basis, one could argue on behalf of a longer horizon forecast in an 

otherwise standard user cost expression.20 A second line of argument in favor of long-horizon 

 

19 The question of the appropriate horizon for comparing renting to homeownership is discussed in Sinai and 

Souleles (2005). 

20 Over extremely large horizons, say decades or longer, one might argue that a no-real-capital-gains assumption is 

not too unrealistic (see, e.g., Eichholtz 1997); furthermore this assumption corresponds to a simple random walk 

view of real house price dynamics. It is also possible that during this period, homeowners had zero-real-appreciation 

forecasts. Schreyer (2008) discusses this assumption in the context of discussing the challenges posed by the 

existence of bubbles. The literature on bubbles in real estate markets is growing rapidly; we mention only a few 

papers here. Case and Shiller (2003) provide survey evidence indicating “irrational exuberance.” Peterson (2009) 

combines a search model with a particular behavioral assumption – namely, that market participants ignore the 

11 
Reconciling User Costs and Rents 

forecasts derives from postulated landlord behavior: landlords might use long-run appreciation 

measures in their own cost calculations, and form rents on that basis.21 However, this 

explanation requires a theoretical justification for rent inflation stickiness. One such justification 

is sketched out in Diewert (2003/2010): landlords, reflecting the preferences of tenants, may 

attempt to minimize volatility in rent changes. (Rent control, which surprisingly turns out to 

impact aggregate rent inflation, may also provide a partial answer; see Poole and Verbrugge, 

2009.) A desire to avoid rent inflation volatility leads directly to the use of long-run appreciation 

rates in landlord user cost calculations. 

Forecasts were constructed as follows. Following best practices in the forecasting 

literature, we use averages of several different forecasting models (see, e.g., Granger and Jeon 

2004, Stock and Watson 2004 or Timmerman 2006); the data, CMHPI indexes, are described 

below. For one-year forecasts, for each city and for every quarter we constructed a weighted 

average of five different models: four distinct forecasting models, and a model which is simply 

the four-year moving average of annual appreciation rates. The dependent variable in each of the 

forecasting models was the latest-available four-quarter appreciation rate, and the independent 

variables included four-quarter city-specific appreciation rates at lags greater than three quarters, 

four-quarter all-US appreciation rates at lags greater than three quarters, and lagged quarterly

appreciation rates.22 In all cases, models were re-estimated every quarter, and forecasts were 

formed using only information available at time t. In particular, the models and weights were: a 

Bayesian Vector Autoregression (VAR) model with four lags of city-specific and aggregate 

annual (four-quarter) appreciation rates, estimated with a tight random walk prior, receiving a 

weight of 0.4; a VAR model with one lag each of city-specific and aggregate annual appreciation 

rates, receiving a weight of 0.1; a univariate model with three lags of quarterly city-specific 

appreciation rates, receiving a weight of 0.3; a naïve unit root model (i.e., simply using the last 

annual appreciation rate as the forecast), receiving a weight of 0.1; and the four-year moving 

 effects of frictions on past prices – and argues that this will generate bubbles. Credit and information frictions (such 

as rational inattention) can amplify the effects of shocks. Using their measures of fundamentals, Himmelberg, Mayer 

and Sinai (2005) found “little evidence of housing bubbles” in 2004; see also Smith and Smith (2006), another study 

comparing prices to fundamentals. Applying a demand/supply analysis and defining bubbles accordingly, over the 

2000-2005 period Goodman and Thibodeau (2008) found bubbles in 30% of the U.S. metropolitan areas they study. 

Ayuso and Restoy (2007) note that many studies do not adequately address frictions which prevent immediate 

adjustment, so that gaps between data and model predictions might be misinterpreted. 

21 This suggestion is due to Tim Erickson (private communication). 

22 We considered other independent variables, such as interest rates, but these did not significantly aid prediction; we 

also considered Autoregressive Moving Average (ARMA) models, but did not find a model which significantly 

improved prediction. If the data existed, one might want to forecast land and structures separately. 

12 
Reconciling User Costs and Rents 

average, receiving a weight of 0.1. For four-year forecasts, we used the simple average of the 

four-year moving average and a model with the (annualized) four-year appreciation rate as the 

dependent variable and with lags 16-18 of this variable as independent variables. 

We end this section with a brief description of CMHPIs. The most widely-used US home 

price data series which are available for most cities are the Federal Housing Finance Agency 

house price indexes and the Freddie Mac CMHPIs, which behave similarly. Each of these 

quarterly indexes uses the same data to construct an index using a weighted repeat-sales method 

(see Case and Shiller, 1987, 1989); CMHPI construction is described in Stephens et al. (1995). 

The common data source consists of repeat mortgage transactions – both purchases and 

refinancings – for single family homes in a database of loans purchased or securitized by Freddie 

Mac or Fannie Mae. Over our data period, these comprised approximately 60 percent of all loan 

originations. These indexes have been subject to various criticisms, which are briefly sketched 

out here.23 These data do not fully represent the housing stock of the U.S., as neither the lower 

end nor the upper end of the market is fully represented. While repeat-sales methods limit the 

extent to which changes in the composition of the sample influence the estimated index – since 

only price changes on the same property are used in estimating the index – still homes which 

turn over more frequently are overrepresented, and major renovations are poorly captured. Since 

we only use these indexes for estimating appreciation rates, and since they are almost certainly 

the best-available data for market participants (imperfect though they may be), we do not believe 

that these criticisms are of major importance for our analysis. 

Because we form our expectation forecasts using a statistical model, from time to time 

our estimated user costs are negative. As noted above, expression (1) derives from a riskless 

model without transactions costs and in which continuous portfolio rebalancing occurs. In reality, 

transactions costs imply a region of inaction, and imply that user costs are idiosyncratic and 

depend upon the agent’s current housing portfolio, idiosyncratic shocks, expectations of 

switching domiciles (and incurring transactions costs), and the like. These considerations will 

greatly alter estimated user costs, and will likely imply that expected user costs are nonnegative, 

at least for prospective homeowners. However, this theory is not yet developed, so measures like 

(1) are the estimates being used by practitioners. Given our use of (1), we believe it preferable to 

be transparent about the implications of our assumptions, rather than apply ad hoc adjustments 

 

23 See Haurin, Hendershott and Kim (1991), Case, Pollakowski and Wachter (1997), Gatzlaff and Haurin (1997), 

Dreiman and Pennington-Cross (2004), McCarthy and Peach (2004), and Leventis (2006) for evidence. 

13 
Reconciling User Costs and Rents 

that would ensure user costs remained nonnegative – since these would potentially change our 

implications. 

3. Rents, Out-of-Pocket Expenses, and User Costs 

We plot the entire 2004:I-2007:I cross-section of reported annual rents against home values in 

Figure 1. We also plot the best-fit curve from a regression of reported annual rents on a constant, 

value, value2

, and value3; for this and other best-fit curves, we trimmed the top and bottom 1 

percent of rents. This regression also received our standard outlier treatment: after the initial 

regression, all observations with externally studentized residuals which were greater than 2.5 in 

absolute value were removed, and the regression re-estimated. (In total, 218 observations were 

dropped.) 

Home Value ($)

Reported Rent ($)

0 200000 400000 600000 800000 1000000

0

10000

20000

30000

40000

50000

Fig. 1. Reported rents against home values 

As can be seen, there is both a fair amount of dispersion (reflecting variation in the 

rent/value ratio within as well as across cities) and considerable rounding in the reported 

14 
Reconciling User Costs and Rents 

numbers. It is evident that the relationship is relatively concave. Average user costs will also 

feature some concavity, as a result of the correlation of higher marginal income tax rates and 

higher home values in conjunction with the federal income tax treatment of interest expenses 

and/or income (see equation (1)). But all homes within a metropolitan area share a common 

appreciation expectation, so it is not a priori obvious how much concavity user costs will possess. 

 We next provide, in Figure 2, a plot of annual user costs as defined in equation (1) with 

the conventional one-year forecast (i.e., uc{1}) against home values. We also plot the best-fit 

curve from a regression of user costs on a constant, value, value2

, and value3, which trimmed the 

top and bottom 1 percent of user costs, and which received our standard outlier treatment. (In this 

case, 178 observations were dropped.) 

Home Value ($)

uc{1} ($)

0 200000 400000 600000 800000 1000000

-150000

-125000

-100000

-75000

-50000

-25000

0

25000

50000

75000

Fig. 2. User costs with conventional annual forecast against home values 

 In Figure 2, note the expanded vertical scale relative to Figure 1; still, we must drop 8 

observations with estimated user costs below -$150,000. As can be seen, there is a tremendous 

amount of cross-sectional dispersion (three times that of reported rents), and uc{1} is estimated 

to be negative for 41percent of the homes. This reflects both the deductibility of mortgage 

15 
Reconciling User Costs and Rents 

interest and property taxes in the federal income tax code, and the fact that expected annual 

house price appreciation exceeds 6 percent for over half of the observations. Furthermore, in 

these data, expected annual house price appreciation is modestly positively correlated with home 

value: more expensive metro areas evidently featured higher expected appreciation during this 

period. 

 Previous work (Verbrugge 2008a) explored the use of longer-horizon forecasts in 

equation (1), and found much closer coherence of rents and user costs dynamics when these were 

used. Figure 3 below accordingly plots uc{4} – user costs with an annualized four-year inflation 

forecast – against home values. As before, we plot the best-fit curve from a regression of user 

costs on a constant, value, value2

, and value3, again trimming the top and bottom 1 percent of 

user costs; thus a total of 344 observations were dropped. 

Home Value ($)

uc{4} ($)

0 200000 400000 600000 800000 1000000

-50000

-25000

0

25000

50000

Fig. 3. User costs with annualized four-year forecast against home values 

 Notice that the scale of Figure 3 is expanded relative to Figure 1, but contracted relative 

to Figure 2. The dispersion of uc{4}is roughly equal to that of reported rents (at $9,000), but on 

average uc{4} lies well below average rent ($1,700 versus $18,700). While expected annual 

appreciation derived from longer-horizon forecasts is 2 percent lower on average, still uc{4} is 

negative for 31 percent of the sample; this reflects the fact that even this measure of expected 

16 
Reconciling User Costs and Rents 

annual house price appreciation exceeds 6 percent for over half of the observations. Clearly the 

use of a long-horizon forecast does not guarantee that the corresponding user cost will be close to 

rent. 

 As noted above, there are two other measures of owner costs which have been considered 

in the literature: user costs with inflation as the measure of expected appreciation, and out-ofpocket costs. Neither of these is completely defensible on theoretical grounds: the first assumes 

zero expected real capital gains, while the second – by implicitly assuming a nominal 

appreciation rate of 0 –assumes negative expected real capital gains. Nonetheless, it is of interest 

to investigate the correspondence of these measures to reported rents. 

Figure 4 below accordingly plots our baseline version of uc{pi} against home values. As 

before, we plot the best-fit curve from a regression of user costs on a constant, value, value2

, and 

value3

, which received our standard trimming and outlier treatment; altogether a total of 340 

observations were dropped. Unlike the previous user cost measures, these unit-specific uc{pi} 

measures are all positive, and we plot this on the same scale as that for Figure 1. Some concavity 

is evident in the relationship. Both the mean ($15,053) and the standard deviation ($7,546) are 

below that of reported rents, facts to which we return below. 

Home Value ($)

uc{pi} ($)

0 200000 400000 600000 800000 1000000

0

10000

20000

30000

40000

50000

Fig. 4. User costs with inflation as forecast against Home Values 

17 
Reconciling User Costs and Rents 

 Given the degree of dispersion of reported rents for a given house value, it is of interest to 

see how, on a unit-by-unit basis, uc{4} and uc{pi} correspond to rent. This is particularly 

interesting since, as shown in Figure 1, higher rent does not correspond perfectly to higher value. 

We do not, however, compare the (after-tax) user cost to the reported rent. This is 

because the net (after-tax) rental earnings that a unit would provide to an owner, in expectation, 

are given by ( ) 1 1( ) Fed

t t - - vac rent t t , where vact is the vacancy rate in the city. Put differently, 

for a landlord to break even, rent would need to exceed user costs by the markup factor 

( )( ) Fed

tt vac 11 −− τ . Accordingly, we construct an estimate of this net measure for each unit, using 

the marginal tax rate for the household and the region’s vacancy rate.24 

In Figure 5, we plot uc{4}, and our three versions uc{pi}, against net, vacancy-corrected, 

reported rent (in the first column), and then against net, vacancy-corrected, predicted rent (in the 

second column). “Predicted rent” corresponds to the fitted values from a regression of reported 

rent on the regressors described in Section 4; we use this measure to reduce the level of noise in 

reported rents. We have included a 45o line, and also the best-fit curve from a regression of each 

user cost measure against a constant, rent, and rent2 which received our standard 1 percent 

trimming (of both variables) and outlier treatment. 

 

24 PSU-by-PSU vacancy rates are not reported by the Census. Note that the user costs here considered are 

homeowner user costs, which differ from landlord user costs in that landlords may deduct essentially all the 

expenses of ownership (including interest and maintenance), but may face higher maintenance and depreciation 

costs due to moral hazard, and must treat capital gains as income (so their user costs are less sensitive to expected 

appreciation). Landlord user costs usually exceed homeowner user costs. 

18 
Reconciling User Costs and Rents 

Net , Vacancy-Correct ed Report ed Rent ($)

uc{ 4} ( $)

0 10000 20000 30000 40000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

Net , Vacancy-Correct ed Report ed Rent ($)

uc{ pi ( 1) } ( $)

0 10000 20000 30000 40000

0

5000

10000

15000

20000

25000

30000

35000

40000

Net , Vacancy-Correct ed Report ed Rent ($)

uc{ pi ( 2) } ( $)

0 10000 20000 30000 40000

0

5000

10000

15000

20000

25000

30000

35000

40000

Net , Vacancy-Correct ed Report ed Rent ($)

uc{ pi ( 3) } ( $)

0 10000 20000 30000 40000

0

5000

10000

15000

20000

25000

30000

35000

40000

Net , Vacancy-Correct ed Predict ed Rent ($)

uc{ 4} ( $)

0 10000 20000 30000 40000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

Net , Vacancy-Correct ed Predict ed Rent ($)

uc{ pi ( 1) } ( $)

0 10000 20000 30000 40000

0

5000

10000

15000

20000

25000

30000

35000

40000

Net , Vacancy-Correct ed Predict ed Rent ($)

uc{ pi ( 2) } ( $)

0 10000 20000 30000 40000

0

5000

10000

15000

20000

25000

30000

35000

40000

Net , Vacancy-Correct ed Predict ed Rent ($)

uc{ pi ( 3) } ( $)

0 10000 20000 30000 40000

0

5000

10000

15000

20000

25000

30000

35000

40000

Fig. 5: User costs with inflation as forecast against net, vacancy-corrected, 

reported and predicted rent 

 The top two panels in Figure 5 compare uc{4} to reported and predicted rent. The 

divergence of these user cost measures and rents in the cross-section is remarkable; indeed these 

appear to be inversely correlated. Noise in reported rents cannot resolve this puzzle, as the 

19 
Reconciling User Costs and Rents 

divergence between uc{pi} and predicted rents is even more striking than its divergence with 

reported rents, as evidenced by the best-fit curves and by the respective amounts of dispersion. 

Evidently, the aggregation implicit in previous studies understated the degree of divergence 

between rents and user costs ... when these are defined using a conventional long-horizon 

forecast. 

 However, this is not the end of the story. The remainder of the panels in Figure 5 

compare our three alternative uc{pi} measures to reported and predicted rent; uc{pi(1)} is our 

baseline measure, uc{pi(2)} is the measure using the current and lagged 4-quarter inflation rate, 

and uc{pi(3)} is the measure using the inflation in the CPI-less-shelter series. (As the 

conclusions are similar, henceforth we consider only uc{pi(1)}, and refer to this measure as 

uc{pi}.) Using uc{pi} as the measure of user costs, one reaches the opposite conclusion. In 

particular, the correspondence between this measure of user costs and net, vacancy-corrected, 

predicted rent is remarkable. We also note the dispersion of uc{pi} for a given reported rent, 

indicating noise in reported rents. Perhaps uc{pi} represents a steady-state user cost notion 

which anchors rents, despite the fact that it evidently does not guide house purchase decisions. 

Findings like these cannot be discovered using index data. 

 In Figure 6 below, we simply plot several best-fit curves against house value, including 

two which correspond to out-of-pocket expenses. Each curve was constructed in the manner 

described above: trimming, removing outliers, using a third-degree polynomial, etc. 

20 
Reconciling User Costs and Rents 

-20000

-10000

0

10000

20000

30000

40000

5000 95000 185000 275000 365000 455000 545000 635000 725000 815000 905000

Home Value ($)

Alternative Shelter Cost Measures ($)

Net, Vacancy-Adjusted Reported Rent uc{pi} Extended Out-of-Pocket Expenses

Baseline Out-of-Pocket Expenses uc{4} uc{1}

Fig. 6. Best-fit Curves of Cost Measures against Home Value 

 Several key findings are evident. First, vacancy-corrected, after-tax rents are relatively 

closely related to uc{pi} – at least by the metric of similar cost/value structure over this time 

period – but not to other user cost measures. Second, on average, uc{pi} lies above the rent 

measure for homes exceeding $230,000, a finding which corresponds to assertions of Diewert 

(2003/2010) and Diewert and Nakamura (2009) and to assertions and empirical evidence in 

Heston and Nakamura (2009); these authors, on this basis, argue that statistical agencies should 

consider an opportunity-cost OOH-services measure which is the maximum of rental equivalence 

and user cost. Third, vacancy-corrected, after-tax rents lie distinctly above out-of-pocket 

expenses for all home values. Furthermore, the relationship of house value to out-of-pocket 

expenses is about the same regardless of the measure of out-of-pocket expenses used. As we 

found this to be true of their relationships with other variables as well, henceforth we consider 

only the simpler baseline out-of-pocket expenses. 

 Up until this point, we have focused attention on cross-sectional comparisons. But for 

inflation measurement, what matters more is the similarity of evolution over time. Figure 7 

21 
Reconciling User Costs and Rents 

accordingly plots these measures over time. These estimates were obtained by regressions of the 

measure in question on time and PSU dummy variables; each regression is estimated once, then 

outliers specific to that regression are identified. Finally, all regressions are re-estimated after the 

union of all the outliers from each regression is removed. Thus, each resulting “index” has the 

character of a simple average, and each is estimated over the same data. In Figure 7, we also plot 

an index whose initial value matches that of our initial average rent estimate and which is 

subsequently adjusted by the movements in the CPI’s OER index. Movements in the OER index 

are based upon changes in the market rents of about 25,000 rental properties located in 87 PSUs. 

-15000

-10000

-5000

0

5000

10000

15000

20000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

Net, Vacancy-Corrected Reported Rent uc{1}

uc{4} Baseline Out-of-Pocket Expenses

Scaled OER index uc{pi}

Fig. 7. Alternative Shelter Cost Measures Over Time, National 

 The evolution of the average of owners’ net, vacancy-corrected, self-reported rents fairly 

closely matches that of the Scaled OER index25 – meaning that reported rents grow, on average, 

at the same rate as do market rents, a finding that is reassuring to users of these CE data. The 

 

25 Recall that over 300 observations have been dropped, due to our outlier treatment. If these are included in the 

estimation, the evolution of the estimated measure even more closely matches that of the OER index. Arguably, 

including all the observations results in a more appropriate comparison, since BLS rent indexes are constructed 

using every observation, even an “outlier,” as long as its accuracy has been verified by BLS commodity analysts. 

22 
Reconciling User Costs and Rents 

correspondence of these measures with uc{pi} is also noteworthy. Over the entire time period, 

reported rents remained well above the other alternative measures of shelter costs. The volatility 

of the growth rate of the average net, vacancy-corrected rent was considerably lower than other 

cost measures (out-of-pocket expenses and uc{pi} were both about three times as volatile). Over 

quite long horizons, each cost measure, except the out-of-pocket expenses measure, is likely to 

grow at the same rate as rents; but the measures can evidently diverge substantially even over the 

medium term (see Verbrugge (2008a) for more evidence on this topic). 

 Figures 8a-8e plot our estimated measures for 27 of the 28 the metro areas in our sample; 

Houston, omitted in order to limit each graph to 6 panels, features dynamics very similar to those 

of Dallas/Forth Worth. 

23 
Reconciling User Costs and Rents 

Boston

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

New York City - Central

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

New York City - Connecticut Suburbs

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

New York City - New Jersey Suburbs 

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Philadelphia

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Pittsburgh

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Fig. 8a. Alternative Shelter Cost Measures Over Time, Northeast Metro Areas 

24 
Reconciling User Costs and Rents 

Chicago

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Cleveland

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Detroit

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Minneapolis

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

St. Louis

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Fig. 8b. Alternative Shelter Cost Measures Over Time, Midwest Metro Areas 

25 
Reconciling User Costs and Rents 

Atlanta

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Baltimore

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Dallas/Fort Worth

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Miami

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Tampa

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Washington, D.C.

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Fig. 8c. Alternative Shelter Cost Measures Over Time, Southern Metro Areas 

26 
Reconciling User Costs and Rents 

Los Angeles - Central

-15000

-10000

-5000

0

5000

10000

15000

20000

25000

2004I

2004II

2004III

2004IV

2005I

2005II

2005III

2005IV

2006I

2006II

2006III

2006IV

2007I

uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Los Angeles - Suburbs

-15000

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Portland

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San Diego

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San Francisco Bay Area

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Seattle

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uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Fig. 8d. Alternative Shelter Cost Measures Over Time, West-Coast Metro Areas 

27 
Reconciling User Costs and Rents 

Anchorage

-15000

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Denver

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Honolulu

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Phoenix

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uc{pi} Net, Vacancy-Corrected Rep. Rent Baseline Out-of-Pocket uc{4} uc{1}

Fig. 8e. Alternative Shelter Cost Measures Over Time, Other Western Metro Areas 

 At the level of the metro area, more sampling variation is evident; however, the general 

conclusions regarding the cost measure dynamics remain unchanged, and an examination of the 

cross-metro variation yields interesting insights. Net rents generally adhere closely to uc{pi}, 

though there is a tendency for uc{pi} to lie above net rents in more expensive metro areas, 

perhaps reflecting smaller depreciation rates where land is a bigger proportion of value. In some 

metro regions – regions which did not experience large house price inflation – all the alternative 

measures of housing costs move together fairly closely. Out-of-pocket expenses lie distinctly 

below net rents; the remaining user cost measures, uc{4} and uc{1}, almost always lie below the 

other measures (and sometimes below zero) early in the period. Late in the period, collapsing 

house prices drove uc{1} far above net rents in several metro areas, which illustrates the extent 

to which expected appreciation drives user costs. The examples of Phoenix and San Diego are 

noteworthy. These markets experienced strong appreciation prior to the middle of the period, 

driving uc{1} well below 0, which reversed later on. Examination of the uc{4} measure across 

metro areas illustrates a key weakness in user costs with long-horizon forecasts: they will tend to 

28 
Reconciling User Costs and Rents 

respond sluggishly, even to sharp and obvious changes in house price dynamics. We suspect few 

market participants in early 2007 would have expected the low (or negative) user costs indicated 

by uc{4} in some regions. 

 We now turn to using regression analysis to study the relationship between rents and the 

alternative shelter cost measures. 

4. Regression Analysis 

We begin with the most basic comparison: that of tax- and vacancy-corrected reported rents to 

the various alternative measures of costs, with a minimum of other control variables. As our user 

cost estimates are often negative, we cannot take logs and compute elasticities; accordingly, in 

Table 1, we present results from simple linear regressions in levels. Each model received our 

standard outlier treatment, and we report the number of observations which remain after outliers 

are removed. Estimated t-statistics are reported in parentheses. The null hypothesis in each case 

corresponds to a coefficient estimate of one on the cost variable (or variables), and zero on the 

constant. 

29 
Reconciling User Costs and Rents 

Table 1 

Linear regression of tax- and vacancy-adjusted reported rents on user costs and cost measuresa

Model Name

uc{1} uc{4} uc{pi} 

I

uc{pi} 

II 

Out-ofPocket 

I

b

Out-ofPocket 

II

Out-ofPocket 

IIIc

uc{0}d

I 

uc{0}d 

II 

Variable 

Constant 12,941 

(174) 

13,564 

(185) 

5,087 

(42.6) 

2,277 

(10.9) 

8,418 

(63.3) 

7,034 

(34.7) 

7,377 

(63.9) 

5,795 

(44.5) 

5,432 

(42.2)

uc{1} -0.05 

(-17.4) 

uc{4} -0.18 

(-20.5) 

uc{pi} 0.55 

(63.2) 

0.94 

(32.4) 

uc{pi}2 -1.1E-5 

(-13.1) 

Out-of-pocket 

expenses 

0.46 

(37.8) 

0.71 

(21.6) 

0.24 

(19.2) 

Out-of-pocket 

expenses2

-8.6E-6 

(-8.00) 

uc{0} 0.32 

(27.6) 

0.43 

(22.5)

PtEπ4,t e 0.16 

(38.5) 

-0.01 

(-0.69) 

PtEπpi,t 

f -0.26 

(-6.16)

N 5671 5665 5643 5653 5654 5653 5656 5651 5651

Adjusted R2 0.06 0.08 0.50 0.50 0.29 0.29 0.46 0.50 0.50

a Coefficient estimates are reported; associated t-statistics are reported underneath, in parentheses.

b Using a more inclusive measure of out-of-pocket expenses yields essentially identical results. c

 Using P Eπ yields an even larger positive coefficient on expected dollar appreciation. Including home value 

 as a control variable results in an economically and statistically insignificant coefficient estimate on P Eπ. 

t pi,t

t

d This term refers to user costs computed as uc{pi}, but with Eπt removed (i.e., set to 0). e This term is expected appreciation in dollars, using the annualized 4-yr. forecast. 

f

This term is expected appreciation in dollars, using expected inflation (pi) as expected appreciation.

 

Regression analysis confirms the conclusions above: uc{pi} is much more closely related 

to rents than are uc{1} and uc{4}; indeed both of these latter measures are estimated to have an 

inverse relationship to rents, a vivid rejection of the theory. Model uc{pi} II, in particular, 

suggests a very close relationship between net rents and uc{pi} – a finding hinted at in Figure 5 – 

30 
Reconciling User Costs and Rents 

although the fit of this model is nearly identical to that of the simpler linear model, Model uc{pi} 

I. Reported rents are not simply out-of-pocket expenses either;26 net rents are much more closely 

related to uc{pi} than to out-of-pocket expenses.27 Adding an expected appreciation term (Model 

Out-of-pocket III) improves the fit of the model, but the coefficient estimate has the wrong sign. 

Models uc{0} I and II are obtained by taking different versions of (1) and splitting these into two 

parts, the term, and everything else. Model uc{0} I results from starting with uc{4}, and 

undertaking this split; this improves the model fit dramatically. It also suggests that rents do not 

respond at all to expected appreciation, which is a puzzle, since even professional landlords 

receive the bulk of the benefits of appreciation. Conversely, splitting uc{pi} into two parts – i.e., 

moving from Model uc{pi} to Model uc{0} II – does not improve the fit, and reduces the size of 

the estimated coefficient(s), although these coefficients do have the expected sign. 

h PEp t tt

 We next investigate how reported rents are related to a wider variety of covariates. In 

these log-linear specifications, log(net rent) becomes the dependent variable, with levels of each 

variable as independent variables. We are most interested in the relationship of rents to the 

various components of user costs, including expected appreciation. The hypothesis that 

respondents merely report out-of-pocket expenses formally corresponds to the hypothesis that 

the estimated coefficients on these expenses are non-zero (and is consistent with a one-for-one 

transmission of costs into rents) and that the estimated coefficients on all other regressors are 

zero. Regression results are reported in Table 2. 

 

26 In Tables 1 and 2, we use baseline out-of-pocket expenses in the specification. As alluded to above, using 

extended out-of-pocket expenses yields essentially the same results. 

27 A log-log specification yields coefficient estimates of 0.65 for uc{pi}, and 0.40 for out-of-pocket expenses. 

31 
Reconciling User Costs and Rents 

Table 2 

Log-linear regression: Linear regression of log (tax- and vacancy-adjusted reported rents) on 

shelter cost measures and other covariates 

Model 1: 

Out-of-Pocket 

Model 2: 

Components of (1) 

Variable Estimate (t-stat.) Estimate (t-stat.)

Constant 8.896 (97.1) 8.867 (97.1) 

Valuea 0.153 (6.34) 0.211 (5.78) 

Value2a -0.010 (-6.95) -0.007 (-4.06) 

User cost components 

Out-of-pocket expenses b 0.089 (7.92) 

(1-τ)(mortgage payments)b,c 0.102 (7.22) 

(1-τ)(property tax payments)b,c 0.287 (5.90) 

Home insurance residuald 0.608 (4.14) 

Maintenance and repairs b 0.441 (1.72) 

PtEπpi,t a,e -0.147 (-0.20) -0.489 (-0.65) 

Other covariates 

Rooms 0.071 (4.90) 0.045 (2.94) 

Rooms2 -0.003 (-3.01) -0.002 (-2.38) 

Bathrooms f 0.413 (1.16) 0.153 (0.42) 

Bathrooms2f 0.024 (0.03) -1.082 (-1.19) 

Single detached 0.136 (5.81) 0.155 (6.55) 

Mobile home -0.329 (-6.84) -0.318 (-6.63) 

Age of dwelling f -0.033 (-5.89) -0.042 (-6.30) 

Age of dwelling2f 0.001 (3.48) 0.001 (3.10) 

Central City 0.030 (1.59) 0.005 (0.28) 

Block % renter in 2000 -0.125 (-0.92) -0.148 (-1.09) 

% renter2 0.447 (2.42) 0.450 (2.45) 

Block % poverty in 2000 -1.01 (-6.94) -0.86 (-5.87) 

CU Education mediumg 0.006 (0.26) 0.008 (0.32) 

CU Education highh 0.041 (1.65) 0.040 (1.61) 

F 0.000 0.000 -test p-value: PSUs 

F-test p-value: dates 0.565 0.040 

N 5793 5795 

a We divided value and expected appreciation by 100,000.

Adjusted R2 0.37 0.38 

b We divided components of user costs by 10,000.

c Mortgage interest payments and property tax payments are tax-deductible in the federal income tax code; 

see (1) and discussion in Section 2.2. 

d As home insurance is typically a fixed percentage of value, we instead include the residual of a regression 

of home insurance on value. 

32 
Reconciling User Costs and Rents 

e This term is expected dollar appreciation, using expected inflation (pi) as the appreciation forecast. f We divided bathrooms and age of dwelling by 10.

g This variable equals 1 when the consumer unit reference person has education level between that of high 

school graduate and of bachelors degree.

h This variable equals 1 when the consumer unit reference person has education level with a bachelors degree 

or higher.

There are several things to note. First, and most important, we can easily reject the 

hypothesis that respondents merely report out-of-pocket expenses. Variables such as home value, 

number of rooms, structure type, and so on each have an impact on reported rent over and above 

their indirect impact on expenses. Of course, higher costs of ownership – in particular, interest 

rates, property taxes conditional on value, home insurance, and expected maintenance and repair 

costs, do translate into higher rents. However, the semi-elasticity with respect to out-of-pocket 

cost is modest. Clearly, homeowners are not simply reporting their out-of-pocket expenses. This 

low semi-elasticity must reflect the influence of market conditions; these potential landlords are 

not ignorant of the market and recognize that their costs might well diverge from the rents their 

properties would likely command (see Tian 2008). 

Second, as noted previously, the rent/value relationship is concave. Third, expected 

appreciation does not appear to exert a statistically-significant influence on net, vacancycorrected reported rent, once time- and PSU-dummies and the separate components of user costs 

are included as regressors. This is not surprising: in these data, expected dollar appreciation is 

highly collinear with out-of-pocket expenses and time- and PSU-dummies. Fourth, most other 

coefficient estimates are intuitively plausible. Several unit characteristics influence reported rent 

as one would expect: rooms (more rooms means a higher quantity of housing, given house price); 

single detached housing and mobile home (detached being higher quality, and mobile home 

being lower quality, given house price); and age (increased age leading to lower rents conditional 

on house price). We conjectured that value above the metro-region median might have a separate 

influence on rent, but this does not appear to be the case, and we eliminated this variable. 

Similarly, the national vacancy rate was not estimated to be statistically significant in an earlier 

specification of the model, and thus was eliminated as a covariate. This lack of statistical 

significance is not that surprising, since it is a national measure, its variability is not terribly high 

in these data, and we include time dummy variables. Several other variables seem to be 

functioning as proxies for neighborhood quality – although it is worth keeping in mind that this 

33 
Reconciling User Costs and Rents 

refers to an increased desirability (or increased cost of production) of rental properties 

conditional on house value, so that these effects influence rent in a way that is not fully reflected 

in the price of the home. Variables in this category include: percent of renters in the 

neighborhood (more renters leads to higher rents conditional on house price – either reflecting 

more demand for rental housing in the neighborhood, given house prices, or reflecting depressed 

house prices in high renter neighborhoods); income and the percentage of homeowners with high 

education (more of these variables perhaps point to higher quality of housing, given house price); 

and the percentage of the population in poverty (with an increased percentage of those living in 

poverty reducing quality conditional on house price). 

5. Conclusion 

The comparison between alternative measures of housing services consumption is a topic of key 

interest in many fields of economics. In the standard frictionless theory, rents should equal ex 

ante user costs. But prior research, most notably Verbrugge (2008a) and Garner and Verbrugge 

(2009), highlighted the dramatic divergence between these measures. However, such prior 

research has mostly used aggregated (and dissimilar) index data; when micro data were used, 

these relied exclusively upon crude proxies for expected appreciation. Thus, the relationship 

between these measures at the micro level is an important issue that has not been adequately 

explored. 

 Herein, we use data from the Consumer Expenditure Survey to examine the relationship 

between user costs and rents at the individual unit level, in dollars, using unit-level information 

on house value, rent, taxes, and the like. This allows us to accurately estimate unit-specific user 

costs, compare rents and user costs at a point in time, and to control for unobservables like 

neighborhood quality. 

 There are three key findings. First, at the unit level, rents diverge significantly from user 

costs – at least as these costs are conventionally estimated using house price appreciation 

forecasts. Second, reported rents in the CE appear to be noisy but sensible; over this time period, 

they evolve similarly to OER, and the hypothesis that respondents simply report out-of-pocket 

expenses is rejected. Third, while noisy, reported rents are ‘‘well-explained” inasmuch as 

34 
Reconciling User Costs and Rents 

expected net rental earnings correspond closely to an ad hoc estimate of user costs, namely one 

which counterfactually imposes an assumption of no real capital gains in the short run. 

 These three findings jointly constitute an important but puzzling set of facts regarding the 

relationship between rents and user costs. The first finding, considered alone, could have been 

easily explained had not the other two findings been made. This first finding is consistent with 

those from, e.g., Cournède (2005) and Verbrugge (2008a), which point to marked divergence 

between market rents and user costs. Indeed, the evidence here suggests that the divergence is 

even more striking at the micro level: even with long-horizon forecasts, the dispersion of rents 

about the user cost estimate is large, user costs lie well below rents, and the estimated 

relationship is actually inverse. Since we find this divergence at the micro level, we have 

basically ruled out index construction errors as the cause of rent-user cost divergence – though 

there remain numerous other potential explanations related to deficiencies in the theories of rent 

determination, of user costs, and of house price dynamics (see Verbrugge 2008a for a more 

thorough discussion).28

The fact that the rent measures in CE data are respondent estimates rather than actual 

arms-length transaction prices would have seemed to offer an additional and promising 

explanation for the striking divergence of conventional user costs and rents that is present in 

these data. However, the second finding appears to rule this explanation out. Reported rents are 

not simply chosen at random, nor are they simply out-of-pocket expenses. Rather, they seem to 

be quite sensible, and move similarly to OER, suggesting that respondents (on average) have a 

reasonably good idea about what their homes would rent for. Still, without the third finding, the 

first finding could potentially have been explained using the standard explanations alluded to 

above. 

 The third finding is a conundrum. Current-generation user costs measures are constructed 

on the basis of frictionless Hall-Jorgenson theory. But there are significant frictions in real estate: 

pricing frictions, perhaps relating to asymmetric information; construction lags, associated with 

land acquisition, permits, and the construction process itself; information frictions, relating to 

search and to distinguishing permanent from transitory movements, and prompting delay; and, of 

 

28 Of course, an alternative argument is that perceived user costs were indeed very low, much lower than rents, and 

that such divergence is simply reflecting disequilibrium in the market, so one should not try to figure out a technique 

that manages to make estimated user costs equal to rents. 

35 
Reconciling User Costs and Rents 

course, the sizable transactions costs associated with buying and selling properties.29 Adding 

transactions costs renders user cost formulas more complicated and, indeed, there is uncertainty 

even about the form that user costs take in a more realistic framework (though Diaz and LuengoPrado 2008 have made some progress; see also Luengo-Prado et al. 2008 and Sommer, Sullivan 

and Verbrugge 2009, who study the relationship between rents, user costs, and the shadow price 

of housing in a more realistic framework with transactions costs). We would certainly expect, 

though, that given the significant frictions associated with real estate, a formula premised upon a 

frictionless environment would be a decidedly weak foundation to start from. Hence one would 

have thought that making a second dubious assumption, one decidedly at odds with the data – i.e., 

zero expected real capital gains in the short run – would have made things much worse, rather 

than better. Adding to the puzzle is the fact that previous research has largely failed to find a 

tight linkage between rents and this ad hoc user cost measure; see, e.g., Blackley and Follain 

(1996). Even those studies which did find some evidence for a significant relationship 

nonetheless found it to be deficient along some dimensions; for example, Verbrugge (2008a) 

found that while this measure evolved more similarly to rents than did more conventional 

alternatives, nevertheless significant divergence remained;30 and while Green and Malpezzi 

(2003) located a statistically significant relationship between rents and lagged user costs, the 

coefficient estimates were well below their theoretical magnitudes. 

It will be quite important to determine whether these three findings carry over to other 

data sets, as few micro data sets contain information on value and rent simultaneously. Similarly, 

these findings motivate further research into rent determination, house price dynamics, and user 

costs. An important area for future research in particular is explaining why user costs constructed 

using the ad hoc proxy for expected appreciation – a proxy which is suspect theoretically, and 

poor predictor of actual appreciation in practice – appear to be so useful for explaining rents. 

 

29 Quigley (2002) lists many transactions costs associated with housing markets. Smith and Smith (2006) emphasize 

the weakness of the mechanisms which would correct inefficiency in the housing market. In a principal-agent 

framework, Bruce and Santore (2006) study optimal real estate commissions. 30 This point was also made by Cournède (2005) and Eiglsperger (2006). 

36 
Reconciling User Costs and Rents 

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42 
Appendix Table 1. Analysis Based on Data from Homeowners Liviing in the Following Primary Sampling Units (PSUs)

Northeast Region Midwest Region South Region West Region

psu1102 Philadelphia-Wilmington-Atlantic psu1207 Chicago-Gary-Kenosha, IL-IN-WI psu1312 Washington, DC-MD-VA-WV psu1419 Los Angeles

City, PA-NJ-DE-MD CMSA CMSA DC portion: PMSA

NJ portion: IL portion: District of Columbia Los AngelesAtlantic, Burlington, Camden, Cook, DeKalb, DuPage, MD portion:Cape May, Cumberland, Grundy, Kane, Kankakee, Calvert, Charles, Frederick, psu1420 Los Angeles 

Gloucester, Salem Kendall, Lake, McHenry, Will Montgomery, Prince George’s, Orange, Riv

DE portion: IN portion: Washington Bernardino, 

New Castle Lake, Porter VA portion:MD portion: WI portion: Arlington, Clarke, Culpeper, psu1422 San Francis

Cecil Kenosha Fairfax, Fauquier, King George, CA CMSA

PA portion: Loudoun, Prince William, Alameda, C

Bucks, Chester, Delaware, psu1208 Detroit-Ann Arbor-Flint, MI Spotsylvania, Stafford, Napa, Santa Montgomery, Philadelphia CMSA Warren, Alexandria city, Fairfax Cruz, San F

Genesee, Lapeer, Lenawee, city, Falls Church city, San Mateo, 

psu1103 Boston-Brockton-Nashua, MA- Livingston, Macomb, Monroe, Fredericksburg city, Manassas

NH-ME-CT CMSA Oakland, St. Clair, Washtenaw, city, Manassas Park city psu1423 Seattle-Taco

CT portion: Wayne WV portion: CMSA

Windham (part) Berkeley, Jefferson Island, KingMA portion: psu1209 St. Louis, MO-IL MSA Snohomish, Bristol (part), Essex, Hampden IL portion: psu1313 Baltimore, MD PMSA(part), Middlesex, Norfolk, Clinton, Jersey, Madison, Anne Arundel, Baltimore, psu1424 San Diego, 

Plymouth, Suffolk, Worcester Monroe, St. Clair Carroll, Harford, Howard, San Diego

(part) MO portion: Queen Anne’s, Baltimore cityME portion: Franklin, Jefferson, psu1425 Portland-Sa

York (part) Lincoln, St. Charles, psu1316 Dallas-Fort Worth, TX CMSA OR portion:

NH portion: St. Louis, Warren, St. Louis Collin, Dallas, Denton, Ellis, Clackamas, 

Hillsborough (part), Merrimack city Henderson, Hood, Hunt, Multnomah, (part), Rockingham (part), Johnson, Kaufman, Parker, YamhillStrafford (part) psu1210 Cleveland-Akron, OH CMSA Rockwall, Tarrant WA portion:

Ashtabula, Cuyahoga, Geauga, Clark

psu1104 Pittsburgh, PA MSA Lake, Lorain, Medina, Portage, psu1318 Houston-Galveston-Brazoria, TX 

Allegheny, Beaver, Butler Summit CMSA psu1426 Honolulu, H

Brazoria, Chambers, Fort Honolulu

psu1109 New York City psu1211 Minneapolis-St. Paul, MN-WI Bend, Galveston, Harris,

Bronx, Kings, New York MSA Liberty, Montgomery, Waller psu1427 Anchorage, 

Queens, Richmond MN portion: Anchorage

Anoka, Carver, Chisago, psu1319 Atlanta, GA MSA 

psu1110 New York-Connecticut Suburbs Dakota, Hennepin, Isanti, Barrow, Bartow, Carroll, psu1429 Phoenix-Me

NY portion: Ramsey, Scott, Sherburne, Cherokee, Clayton, Cobb, Maricopa, PDutchess, Nassau, Orange, Washington, Wright Coweta, DeKalb, Douglas,

Putnam, Rockland, Suffolk, WI portion: Fayette, Forsyth, Fulton, psu1433 Denver-Bou

Westchester Pierce, St. Croix

Gwinnett, Henry, Newton, CMSA

CT portion: Paulding, Pickens, Rockdale, Adams, Ara

Fairfield, Litchfield (part), Spalding, Walton Denver, DouMiddlesex (part), New WeldHaven (part)
Appendix Table 1. Analysis Based on Data from Homeowners Liviing in the Following Primary Sampling Units (PSUs) (continued)

Northeast Region Midwest Region South Region West Region

psu1111 New Jersey-Pennsylvania psu1320 Miami-Fort Lauderdale, FL CMSA 

Suburbs Broward, DadeNJ portion:Bergen, Essex, Hudson, psu1321 Tampa-St. Petersburg-Clearwater, Hunterdon, Mercer, FL MSAMiddlesex, Monmouth, Hernando, Hillsborough,Morris, Ocean, Passaic, Pasco, PinellasSomerset, Sussex, Union,WarrenPA portion:Pike

Reference: BLS Handbook of Methods, Chapter 17. Consumer Price Index (Updated 06/2007), Appendix 5. Sample areas, population weights, and pricing cycles. 

Available at: http://stats.bls.gov/opub/hom/pdf/homch17.pdf

44 

Reconciling User Costs and Rents 
Appendix Table 2. Sample Sizes of Homeowners by Primary Sampling Unit (PSU)

PSU Number Abbreviated PSU Area Description Number of Observations

 psu1102 Philadelphia 368

 psu1103 Boston 271

 psu1104 Pittsburgh PA 199

 psu1109 New York City 93

 psu1110 New York-Connecticut suburbs 329

 psu1111 New Jersey Suburbs, NJ 249

 psu1207 Chicago 411

 psu1208 Detroit 271

 psu1209 St. Louis MO-IL 180

 psu1210 Cleveland 177

 psu1211 Minneapolis 185

 psu1312 Washington, DC 264

 psu1313 Baltimore 163

 psu1316 Dallas 234

 psu1318 Houston 197

 psu1319 Atlanta 239

 psu1320 Miami 133

 psu1321 Tampa-St. Petersburg-Clearwate 113

 psu1419 Los Angeles 295

 psu1420 Los Angeles suburbs 188

 psu1422 San Francisco 212

 psu1423 Seattle 181

 psu1424 San Diego 106

 psu1425 Portland, OR 163

 psu1426 Honolulu HI 92

 psu1427 Anchorage AK 166

 psu1429 Phoenix AZ 163

 psu1433 Denver CO 160

TOTAL 5802

Source: Authors' own calculations based on the U.S. Consumer Expenditure Interview Survey Data

2004 quarter one through 2007 quarter 1.