BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Prices and Living Conditions 

Explaining the Rent-OER Inflation Divergence, 1999-2006 

Robert Poole, U.S. Bureau of Labor Statistics 

Randal Verbrugge, U.S. Bureau of Labor Statistics 

Working Paper 410

October 2007

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. 
Explaining the Rent-OER Inflation Divergence,

1999-2006

Robert Poole

Statistical Methods Division

Bureau of Labor Statistics

2 Massachusetts Ave. NE

Washington, DC 20212

Email: poole.robert@bls.gov

Randal J. Verbrugge

Division of Price and Index Number Research

Bureau of Labor Statistics

2 Massachusetts Ave. NE

Washington, DC 20212

Email: verbrugge.randal@bls.gov

This version completed on: August 2, 2007

JEL Classification Codes: R31; R21; E31; C81; C82; O47

Keywords: owners’ equivalent rent; utilities adjustment; rental market segmentation; rent control;

inflation measurement; core inflation.

Acknowledgement We thank John Greenlees, Pat Jackman, John Layng, Mike Prell, Frank

Ptacek, Laura Rosner, and Brian Sack for comments; however, this does not imply that they share

the views presented herein. All errors, misinterpretations and omissions are ours. All the analysis,

views, and conclusions expressed in this paper are those of the authors; they do not reflect the views

or policies of the Bureau of Labor Statistics or the views of other BLS staff members.
Investigating the Rent-OER Inflation Divergence

Abstract

Between 1999 and 2006, there were two episodes during which inflation in the Rent index

in the CPI diverged markedly from inflation in the index for Owner’s Equivalent Rent (OER);

early in 2007, these series began to diverge again. Such divergence often prompts many to

question CPI methods. A key difference between these two series is that OER indexes are

based upon rents which have received a utilities adjustment — an adjustment which is necessary

because the OER index is intended to track pure rent-of-shelter, not shelter-plus-utilities. Critics

have claimed that the Rent-OER inflation divergences stem from an inappropriate utilities

adjustment.

This claim is false. In this paper, we decompose the Rent-OER inflation differential into

its various determinants, and explore the multiple causes of this divergence over time. There is

only one divergence episode — of only six months duration — which is primarily attributable to

the utilities adjustment procedure. Indeed, the utilities adjustment sometimes reduced potential

divergence between the two series.

Instead, the main culprit is rental market segmentation; that is, different rent inflation

rates were experienced by different parts of the rental market. Before 2003, the Rent-OER

inflation divergence mainly resulted from divergent rental inflation rates within metropolitan

areas: areas with a higher proportion of renters experienced higher rental inflation. After 2004,

similar divergent inflation across metropolitan areas resulted in higher Rent inflation. Compared

to other units, rent control units experienced higher inflation in 2004 (and, to a lesser extent,

before mid-2001 and in 2006), which increased Rent inflation but not OER inflation. Finally,

in early 2007, there was a sizable divergence between OER and Rent inflation, driven mostly

by divergent rental inflation rates within metropolitan areas; the extent of the divergence only

becomes evident once the effect of the utilities adjustment is accounted for.

2
Investigating the Rent-OER Inflation Divergence

1 Introduction

Housing costs are a substantial part of most American’s monthly outlays. As a result, these costs

account for about one-third of the total weight of the Consumer Price Index (CPI). Hence, accurate

measurement of shelter costs is crucial to obtaining an accurate measurement of the overall inflation

experienced by the average US consumer, and these costs get a lot of attention from the financial

press. Within the CPI, the Bureau of Labor Statistics (BLS) produces two shelter indexes: Rent,

which covers the shelter expenditures of renters, and Owners’ Equivalent Rent (OER), which covers

owners. This latter index is constructed using the rental-equivalence method,1 which equates the

change in a homeowner’s shelter cost to the change in the market rental price of that person’s home.

OER is thus a rent-of-shelter concept which does not include utilities, since utilities are measurable

out-of-pocket expenses for homeowners.

The Rent and OER indexes are produced using essentially the same data — market rents — and

critics in the financial press often wrongly conclude that these measures should move in lockstep

with each other (or, even more commonly and erroneously, that OER should move in lockstep with

house prices). To understand why OER inflation can diverge from Rent inflation, one needs to

know something about how these indexes are produced.

How is OER produced? The exact market rent of an owned home is, of course, unobservable.

However, a well-known and empirically valid rule of thumb in real estate pricing is “location,

location, location.” This principle carries over to rents: internal BLS research — most recently

in Verbrugge et al. (2007) — has consistently supported the notion that, outside of location, it

is difficult to find any reliable predictor of rent inflation. Hence, the BLS estimates inflation in

the homeowner rents using inflation in market rents of nearby rental units. (Furthermore, about

one-quarter of the total BLS sample of rental units consists of detached units, so — even though

there is only weak evidence that rents differ by shelter type — much of the rent sample that is used

for measuring homeowner shelter cost inflation consists of the same structure type.) Ultimately,

1This is a measure of the shelter cost itself, i.e. the cost of obtaining shelter services from this house, which

abstracts from the highly-volatile, difficult-to-measure, financial-asset aspect of homeownership. Most of the

academic research suggests that this method is the best of the available methods for estimating changes in

homeowner shelter costs. See, e.g., the discussion in Diewert (2003), in Poole, Ptacek and Verbrugge (2005),

and the recent findings of Verbrugge (2007a).

3
Investigating the Rent-OER Inflation Divergence

homeowner cost inflation is estimated by re-weighting inflation in market rents, as follows.

Monthly movements in the OER index, and the Rent index, are based upon ratios of weighted

averages of rents. In particular, for a metropolitan area, the BLS constructs its index for Rent (IR

t )

or for OER (IO

t ) using a “rent relative” approach. That is, for index j = {Rent, OER},

Ij

t = Ij

t−1 ∗ Rj

t

where Rj is the rent relative (defined below), and t indexes months. The BLS reprices the housing

units in their sample only every six months. Accordingly, the rent relative — which is used to move

the index in the current month t — is defined as

Rj

t =

⎛

⎝

Pwj

i rentji,t

Pwj

i rentji,t−6e

Fk

i,t

⎞

⎠

1

6

=: Ã

rentj

t

rentj

t−s

!1

6

(1)

where wj

i is the expenditure weight for unit i pertaining to index j, and rentji,t is the period-t

measure of rent from unit i that is applicable to the construction of index j. As discussed in

more detail below, weights and rent measures differ across indexes: weights differ across indexes

because, for example, rent control units do not enter the OER indexes; and rent measures differ

across indexes because, for example, rents used in OER must be adjusted for utilities. Regional or

national Rent and OER indexes are constructed via weighted-averages of area-indexes, with weights

again differing across the two indexes. Period-t rent-measures are quality-adjusted via an age-bias

factor Fk

i,t (which is common across Rent and OER) to ensure that the index is measuring inflation

in constant-quality units.2

In 1999, the BLS introduced a new rental sample. Following this introduction, the rent index and

the OER index began to diverge. Over the next seven years, inflation in these series subsequently

converged, diverged, converged, and diverged again; see Figure 1, which plots 12-month changes in

these indexes.

2The age-bias adjustment is studied in Gallin and Verbrugge (2007). As this factor is common across

Rent and OER, is of second-order size, and has only second-order effects in Rent-OER comparisons, it is

henceforth ignored. For more details on BLS procedures, see Ptacek and Baskin (1996).

4
Investigating the Rent-OER Inflation Divergence

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

199901199904199907199910200001200004200007200010200101

200104200107200110200201200204200207200210

200301

200304

200307200310200401

200404200407200410

200501200504200507

200510

200601200604200607200610

200701200704

RENT

OER

Figure 1: Divergence in 12-Month Inflation of Rent and OER

Divergence often prompts many analysts and commentators to speculate that the BLS is doing

something wrong with the utilities adjustment. But this is only the first of five key differences

between the Rent and OER index.3

The utilities adjustment is indeed a key difference, and we label it factor 1. Although both BLS

indexes are based upon a single rent survey, the rent measures which enter (1) for the OER index

must be pure-shelter-service prices and hence must exclude utilities. (This point is explained more

fully in Section 2 below.) But market rents in the US are rarely pure shelter prices; most rental

contracts include at least one utility. Thus, rents of utilities-included-units must be adjusted downward to take into account the utilities component of those rents. Otherwise, utilities expenditures

3We discuss factors with only second-order effects, namely “non-interview adjustments” and facilities

adjustments, in Section 5 below. Age-bias adjustment was discussed above.

5
Investigating the Rent-OER Inflation Divergence

would be double-counted for homeowners — counted first as a consequence of the resultant inflation

in the rents of utilities-included units, and then again as a consequence of the resultant increase

in their out-of-pocket expenses on utilities. An implication is that, since the Rent index includes

some utilities and the OER index does not, changes in utilities inflation will impact Rent inflation

differently than OER inflation. OER inflation should diverge from Rent inflation if utilities inflation changes. If utilities inflation is rapid, for example, then ceteris paribus, Rent inflation should

exceed OER inflation.

The potential factors 2 through 4 each relate to the weights in (1) interacting with differential

inflation rates across different parts of the rental market. The weights wRent

i and wOERi differ in

three distinct ways. First, wRent

i = 0 for some units, namely those in a special OER augmentation

sample, which are termed “helper segments.” This is factor 2. Second, wOER

i = 0 for rent-controlled

units. This is factor 4. Finally, wRent

i 6= wOERi in general, since (for example) the OER index will

place higher relative weight on units located near heavily-owner-occupied regions. This is factor 3.

The fifth potential factor relates not to weights at the level of Elementary Indexes,4 but rather

to “upper-level” weights, i.e. those relevant for the aggregation of Elementary Indexes into regional

or national averages. Rent inflation in the Northeast — i.e., “average” inflation experienced by

renters in the Northeast — is a weighted average of Rent inflation in Boston, in New York City, and

so on. But since New York City has a greater proportion of renters than does Boston, its weight

in the regional Rent index is larger than its weight in the regional OER index. Thus for example,

if New York City experienced unusually high shelter cost inflation due to a temporary housing

shortage, then this would increase the regional Rent index more than it would the regional OER

index.

In this paper, we study the extent to which each of these factors contributed to the divergence

of Rent and OER inflation depicted in Figure 1. In order to do so, we constructed a CPI Shelter

Index simulator, which estimates official CPI shelter indexes to a high degree of accuracy. This

enables us to change one feature at a time, and thus to construct experimental indexes, such as

4 “Elementary Indexes” are indexes which are created directly from observed price changes. (In contrast,

regional or national indexes are formed by aggregating Elementary Indexes.) In some cases — namely larger

metropolitan areas like Chicago or Miami — Elementary Indexes correspond to a single metropolitan area.

But in other cases, Elementary Indexes are computed by aggregating together data from several different

cities.

6
Investigating the Rent-OER Inflation Divergence

an OER index without any utilities adjustment. Thus, we can separately determine the impact of

each of the factors above.

Our four key findings are surprising. To summarize:

First, the critics are wrong: the utilities adjustment is no smoking gun. It was rarely responsible

for the majority of the divergence, and indeed reduced the divergence almost as frequently as it

increased it.

Second, the main driver of the divergence was differential rent inflation across different segments

of the market.

Third, as of 2007:5, there was a substantial divergence in 12-month rent inflation rates across

different segments of within-metropolitan area markets. In particular, loosely speaking, units in

heavily owner-occupied regions were experiencing far less inflation than other units. This pattern

was observed in many metropolitan areas across the country. But it is only fully evident upon

controlling for the effect of the utilities adjustment.

Finally, rent-control — which is concentrated in three metropolitan areas, New York, Los Angeles,

and the San Francisco Bay area — periodically had a sizable and upward impact on aggregate Rent

inflation over this period.

Looking at the 2000-2007:05 period in more detail, four distinct episodes stand out. First,

between 2000 and 2002, factor 3 was the main story: during this period there were divergent rental

inflation rates within metropolitan regions, and areas with a higher Rent weight experienced higher

rental inflation than did other areas. The differential impact on Rent inflation was about 0.35%.

(In 2000, factor 5 also contributed: cities with a higher concentration of renter experienced higher

rent inflation, with a differential impact on Rent inflation of about 0.25%.) Second, in 2003, factor 1

(utilities) was the main story: the BLS implemented an improvement to its water/sewer adjustment

(described below), which reduced OER inflation by about 0.3%. Third, in the 2004-2006 period,

three factors were notable: utilities; upper level aggregation (cities with a higher concentration of

renters experienced higher rent inflation); and rent control. In particular, the utilities adjustment

reduced OER inflation by about 0.25%; upper level aggregation caused a divergence of about 0.20%;

and higher inflation in rent-control units in the Northeast and West caused overall Rent inflation to

7
Investigating the Rent-OER Inflation Divergence

rise by nearly 0.13% on average between 2004-2005:06. Finally, in early 2007, factor 3 was mainly

responsible for an enormous wedge between OER and Rent inflation, although factor 2 contributed

as well. Thus, each of the five factors played a significant role, at some point, in explaining 12-month

Rent-OER inflation divergence.

Looking instead at inflation over the 1999-2006 period, Rent grew about 31%, and OER grew

by about 27.5%. In explaining this difference, factors 1 and 2 played only a trivial role. Factors 3

and 5 each explain about 1.2% of the difference, and factor 4 explains about 0.8% of the difference.

The outline of the remainder of this paper is as follows. Sections 2-5 follow the sequence of

adjustments required to move OER to Rent. In particular, Sections 2-4 follow the sequence of

adjustments required to move OER to Rent-less-rent-control: re-incorporate utilities → remove

helper segments → use Rent weights in (1) → use Rent weights in upper-level aggregation; the

last step is to remove rent-regulated units from Rent. Accordingly, Section 2 explores the impact

of the utilities adjustment. Section 3 explores the impact of factors 2 and 3, namely of helper

segments and of OER versus Rent weights in (1). Section 4 explores the impact of factor 5, upperlevel aggregation weights. Section 5 explores the impact of factor 4, rent-control units, and two

remaining minor differences. Section 6 then explores regional divergences in greater detail. Finally,

Section 7 concludes.

2 Factor 1: The Utilities Adjustment

The rental equivalence method implies that, for a homeowner, the inflation in pure shelter costs

equals the inflation in what that home would rent for. As noted above, “location, location, location”

is an empirically valid rule of thumb. Hence, the BLS estimates inflation in homeowner rents using

inflation in market rents of nearby rental units.

The BLS Rent index tracks inflation in actual rents paid, whether or not the contract includes

utilities. In the US, around two-thirds of US rental contracts include at least one utility (that is,

the renter does not pay a separate bill for that utility); see the American Housing Survey, 2002

Metropolitan area survey. Heat, in particular, is included in about one quarter of all US rental

8
Investigating the Rent-OER Inflation Divergence

apartments. The prevalence of utilities-included contracts varies regionally, and also by building

size and age. (See Levinson and Niemann, 2004, for details.) Thus, utilities price inflation is

implicitly embedded in the Rent index.

However, OER is a rent-of-shelter concept; it seeks to track inflation in the rent that a hypothetical landlord would charge for the home. Homeowners typically pay for their own utilities directly.

Thus, for this portion of the population, utilities costs are directly taken into consideration elsewhere in the CPI, and not in the item OER. If the market rent on unit i, rentRent

i,t , includes utilities,

the BLS must apply a utilities adjustment in order to transform it into a pure-shelter rent measure

rentOER

i,t which is admissible for use in (1) when constructing OER. It would be inappropriate to

equate the imputed shelter costs of a homeowner — i.e., what a hypothetical landlord would charge

to rent the unit without utilities — to the utilities-included rent of an otherwise identical unit.5 A

utilities adjustment is necessary for constructing the OER index; the only question is whether the

BLS is doing it properly. (Verbrugge (2007b) studies the utilities adjustment in the CPI, and offers

some recommendations.)

Many commentators have asserted that the utilities adjustment basically accounts for the divergence between OER and Rent inflation that is depicted in Figure 1.

This is false. Figure 2 depicts four series. The first is the published Rent series. The next two

are experimental series. The first of these is a simulated OER series which retroactively applies

the late 2003 water/sewer utilities adjustment improvement.6 The second is a simulated OER-type

series which removes all utilities adjustments from the OER index. (This second index is not a

valid measure of OER, but it is a useful measure in that it allows one to deduce the impact of

utilities adjustment on OER, and provides clues about the impact of utilities on Rent.) The last

series is the published OER series, which includes all the historical utilities adjustments.

5Putting this somewhat differently, if utilities from a utilities-included rent were not removed, then utilities

expenditures would be double-counted for homeowners — counted first as a consequence of the resultant

inflation in the rents of utilities-included units, and counted again as a consequence of the resultant increase

in their out-of-pocket expenses on utilities. 6Prior to 2003, the water and sewer adjustment was a constant value over time. In 2003, the amount was

updated, and afterwards was adjustment monthly to reflect changes.

9
Investigating the Rent-OER Inflation Divergence

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

199901

199904199907

199910200001

200004200007200010

200101200104

200107200110200201

200204200207200210200301200304

200307

200310

200401200404

200407

200410

200501200504

200507

200510200601200604200607

200610200701200704

Rent

OER (no utilities adjustment)

OER (utilities-adjusted, W/S retroactive)

OER (utilities-adjusted, 2003 W/S adj.)

Figure 2: Impact of Utilities Adjustment

The difference between the published OER series, i.e. “OER (utilities adjusted, 2003 W/S

adj.),” and the OER with retroactive water/sewer, i.e. “OER (utilities-adjusted, W/S retroactive),” is the impact of the one-time water-sewer adjustment improvement which was made in

2003. Clearly, this had a major impact on OER in that year. The water/sewer utilities adjustment

was actually fairly modest, but it decreased — by a very small percentage — nearly all utilitiesadjusted (OER) rents for a six-month period. This effectively reduced 12-month OER inflation by

0.3% in 2003. Note from Figure 2 that, had this improvement been continuously implemented since

1999, its impact on the OER index would have been trivial.

The difference between the “OER (utilities-adjusted, W/S retroactive)” series and the “OER

(no utilities adjustment)” is the remaining (non-water/sewer) impact of the utilities adjustment.

How much of the OER-Rent inflation divergence is due to the utilities adjustment? Contrary to

popular opinion, it is no smoking gun. Regarding the impact of the utilities adjustment, five distinct

periods can be discerned: 2000:01-2001:12; 2002:01-2003:02; 2003:03-2004:06; 2004:07-2006:04; and

10
Investigating the Rent-OER Inflation Divergence

2006:05-2007:05. Only during the third period, 2003:03-2004:06 (the water/sewer episode), does

the utilities adjustment “explain” the observed divergence between OER and Rent inflation. The

utilities adjustment does not account for even half of the divergence during the first period, and

only about half during the fourth period. During the second and last of these periods, 2002:01-

2003:02 and 2006:05-2007:05, the utilities adjustment actually reduced the divergence between Rent

inflation and OER inflation — sometimes by very considerable amounts. (The fact that each of the

factors investigated has the potential to increase or decrease divergence also implies that there is

no unambiguous way to attribute a particular percentage divergence between OER inflation and

Rent inflation to different causes.)7

It is easy to misinterpret these divergences, particularly with respect to their timing. We here

remind the reader that we are considering 12-month changes in indexes which are themselves constructed on the basis of 6-month changes. The 6-month inflation in utilities does not translate

directly and immediately into a given “impact of utilities” on 12-month OER inflation. For example, upon examining higher-frequency changes in the above indexes,8 it is evident that the large

utilities adjustments driving the 12-month-inflation divergence between OER and “OER (no utilities adjustment)” in late 2006-early 2007 actually stem from early-to-mid 2006. Furthermore, as

explained in Verbrugge (2007b), the relationship is nonlinear; the influence of utilities inflation

upon divergence depends not only upon the relative importance of utilities in rent, but also upon

its inflation relative to inflation in rent-of-shelter. (For example, if utilities inflation is only slightly

positive but rent inflation is robustly positive, then OER inflation will exceed inflation in “OER

(no utilities adjustment).”)

Thus, the utilities adjustment can have a significant impact on the aggregate (and even more

so in the Northeast and Midwest indexes, as we demonstrate below.) But eliminating the effects of

this adjustment results in an OER inflation series that still displays significant divergence from the

Rent inflation series, for most time periods. There is much more to the story than simply utilities.9

7On average, if one uses the metric 1

T

PT

j=1

|OERno helpers−OERno helpers, no util. adj.|

j

|Rent −OER|j

,the utilities adjustment

accounted for 25% of the inflation divergence. 8We thank Brian Sack of Macroeconomic Advisers, whose feedback and questions encouraged us to report

this. 9Recall that the rent series is not a pure shelter series, since it also includes utilities — due to the presence

of rental contracts which include utilities. Both the rent and OER series include the services of consumer

durables like refrigerators, as these are typically included in all rental contracts.

11
Investigating the Rent-OER Inflation Divergence

And what is the rest of the story? As noted above, differential inflation experienced by different

segments of the rental market. To demonstrate this, we first consider a sequence of adjustments

which move the experimental OER measures closer to Rent; then finally, we remove the Rent units

unique to Rent, in order to elucidate the impact of rent-control units.

3 Factors 2 and 3: Weights on Uncontrolled Units

Recall that OER indexes and Rent indexes are both constructed using (1), but each is constructed

using different weights; some OER units receive zero Rent-weight (and vice versa), and most units

receive differential weight. This section explores the impact of removing the units unique to OER

(factor 3) — while maintaining the OER-Rent weight differential — and then of changing the weights

used in (1) from OER weights to Rent weights (factor 2). (Recall that weights-related factors will

be important only to the extent that different segments of the rental market experience different

inflation rates.) Note that — since we wish to isolate the effect of changing weights — from here until

Section 6, none of the experimental OER-type series have utilities adjustments; in other words, if

utilities are included in the rental contract, they are included in the rent measures used in (1).

Figure 3 below plots four series: Rent, and three experimental OER-type series. In reverse

order, the last of these is the experimental OER series mentioned in the previous section, i.e. OER

without any utilities adjustments. The second is an experimental OER series which is constructed

using only units common to both the OER and Rent indexes; in other words, no “helper segment”

units which are unique to the OER index are included. However, this index continues to use OER

weights in (1) when constructing Elementary Indexes. The first index is identical to the second

one, except that it constructs the Elementary Indexes using Rent rather than OER weights. (For

all the OER series depicted, aggregation of the “OER” indexes to the national index is conducted

using OER weights.)

12
Investigating the Rent-OER Inflation Divergence

2.0

2.5

3.0

3.5

4.0

4.5

5.0

199901199904199907199910200001200004200007200010200101200104200107

200110200201

200204200207200210

200301200304

200307

200310

200401200404200407

200410200501

200504200507200510200601200604

200607200610200701200704

Rent

OER (no util. adj., no helpers), Rent weights

OER (no util. adj., no helpers), OER weights

OER (no utilities adj.)

Figure 3: Helpers, OER vs. Rent Weights

First, how much of the divergence is due to the presence of helper segments? As can be seen

from the change in going from “OER (no utilities adjustment)” to “OER (no util. adj.), OER

weights,” only rarely do helper segment units experience inflation different from other units with

large OER weight. The exceptions: in the first half of 2004, helper segment units experienced

higher inflation, causing a gap of 0.15% on average; and in the first five months of 2007, helper

units experienced lower inflation, causing a gap of 0.21% on average.

Second, how much of the divergence is due to different weights applied to units common to

both Rent and OER?10 Here is one of the big surprises. Rent inflation was often quite different

across different segments of the market within metropolitan areas. In particular, until mid-2003,

the inflation experienced by units with larger OER weight was lower than that experienced by

units with larger Rent weight. Subsequently, this differential reversed, and high-OER-weight units

1 0 In point of fact, there are some units which are not common; in particular, there is a small fraction of

non-rent-control units in Rent which receive zero weight in OER.

13
Investigating the Rent-OER Inflation Divergence

experienced higher inflation for a time. After 2004, these inflation rates became roughly equal until

the end of 2005, when again, units with higher OER weight experienced somewhat higher inflation.

This ended in late 2006, and subsequently high-Rent-weight units experienced considerably higher

inflation.11’

12

In sum, there are three periods during which factor 3 is dominant. First, during the early

part of the period, between 2000:01 and 2002:09, different weights in (1) were responsible for

the vast majority (about two-thirds) of the divergence between 12-month inflation in Rent versus

in OER. In particular, the units weighted more heavily by the Rent index experienced greater

inflation; moving from OER to Rent weights leads to an increase of 0.35% in inflation over this

period. Conversely, starting in 2003, units weighted more heavily by the Rent index experienced

somewhat lower inflation; moving from OER to Rent weights leads to an average decrease of 0.1%

in inflation over this period.13 Finally, in late 2006, moving from OER to Rent weights leads to

a considerable increase in inflation. Indeed, by 2007:5, there is a full percentage point difference

attributable to differential inflation in market rents within metropolitan areas (factors 2 and 3).

(We find that in mid-2007, within-metro-region inflation differentials caused OER-Rent inflation

differentials of this sign and about this magnitude in: three of the four Census regions; practically

every metropolitan area in the Northeast; medium-sized cities in the South; and, in Chicago, for a

stunning inflation differential of about 3% — which rebuts claims that Manhattan was driving the

US differential.) We note that the rental vacancy rate remained essentially flat between 2005:I2007:I, while the homeowner vacancy rate climbed 55% over this period,14 and the homeownership

rate fell modestly.

1 1The goal is to isolate the impact of changing weights in (1). Above, we did this using rent measures

appropriate for constructing Rent, namely measures with utilities included. But we could equally well have,

instead, used rent measures appropriate for constructing OER, namely measures with utilities removed. As

a robustness check, we thus applied an experimental utilities-adjustment procedure suggested by Verbrugge

(2007b) to rents, and used these utilities-adjusted rents in (1), changing only the weights. Results are

basically unchanged. 1 2Looking at 12-month inflation rates obscures the timing of the divergence; inflation in high-OER-weight

units actually began to decline in the third quarter of 2006, while inflation in high-Rent-weight units did not

begin to decline until the first quarter of 2007. 1 3Verbrugge et al. (2007) found that the influence of block-group-percent-renter was statistically-significant

and generally negative when considering 2001-2004 rent growth (a span which crosses two of these periods).

That study is not directly comparable, however, because it did not consider aggregation weights (i.e., it

implicitly assigned an equal weight to each rental unit). Furthermore, despite high correlation, there is not

a direct mapping between percent-renter and Rent weight. 1 4Wheaton and Nechayev (2006) note that the share of house purchases for second home or investment

purchases has increased sharply since 1999, and that small movements in the homeowner vacancy rate have

a major impact on housing prices.

14
Investigating the Rent-OER Inflation Divergence

4 Factor Five: Upper-Level Aggregation Weights

Regional and National Indexes are weighted averages of Elementary Indexes. Different metropolitan

areas can experience different shelter inflation rates. This can affect the OER and Rent indexes

differently, because some cities are much more renter-intensive than others, and thus receive higher

weight in regional or national Rent indexes. In this Section, we demonstrate the impact of this

factor.

Figure 4 below plots three series: Rent, and two experimental OER-type series. In reverse

order, the last of these is the aforementioned experimental OER series with no utilities adjustment, no helper-units, constructed with Rent weights, but using OER weights for aggregating the

“OER” indexes into a national index. The second is identical, except that it uses Rent weights for

aggregating the “OER” indexes into a national index.

2.0

2.5

3.0

3.5

4.0

4.5

5.0

199901199904199907199910200001200004200007

200010

200101

200104200107200110200201200204

200207

200210

200301

200304200307

200310200401200404

200407

200410200501200504200507200510200601200604

200607

200610

200701200704

Rent

OER - Rent w, Rent Upper Agg.

OER - Rent w, OER Upper Agg.

Figure 4: Upper-Level Aggregation Weights

15
Investigating the Rent-OER Inflation Divergence

How much of the divergence is due to the different upper-level aggregation weights? There are

two periods during which this had a fairly significant impact on the aggregate index. In 2000, upperlevel aggregation weights accounted for a difference of about 0.25%, representing about one-third

of the divergence between Rent inflation and OER inflation. Similarly, after 2003, this accounted

for an average increase of about 0.20%, which again puts it in the ballpark of about one-third of

the divergence.

5 Factor Four: Rent Control

The experimental OER index “OER - Rent w, Rent Upper Agg.” and the Rent index are nearly

identical, except for factor 4: there are some units — namely, rent-regulated units — which are used

in (1) for constructing Rent indexes, but are excluded from OER indexes. Surprisingly, these units

had a sizable impact on the aggregate.

2.0

2.5

3.0

3.5

4.0

4.5

5.0

199901199904199907

199910200001200004200007200010200101200104200107200110200201200204200207200210200301

200304200307200310200401

200404200407200410

200501

200504

200507200510

200601200604200607

200610

200701200704

Rent

Rent (no rent control)

OER - Rent w, Rent Upper Agg.

Figure 5: Rent Control

16
Investigating the Rent-OER Inflation Divergence

During the latter part of the period — and in particular, during 2004 (and, to a lesser extent,

between 2000:06-2001:06, and after 2005) — this regulated sector of the economy experienced greater

rent inflation, contributing almost 0.2% to inflation in the Rent index. This is somewhat of an

empirical puzzle. While most rent-control regimes in the US are so-called “second-generation”

regimes (see Arnott, 1998) which are more moderate than the textbook price ceiling version, they

do nonetheless place restrictions upon rent inflation (generally based upon aggregate CPI inflation).

Furthermore, most rent-control regimes allow landlords to set nominal rents freely when taking on

a new tenant, so that these units will experience rapid inflation upon turnover. But this would

likely lead to rent inflation which — over long periods — would equal market rent inflation.15 This is

consistent with most of the available evidence (see Turner and Malpezzi (2003) for a survey of the

empirical research). For example, Olsen (1997) and Pollakowski (1997) studied rent-control in NYC,

and Schneider et al. (1999) studied rent-control in DC; each study came to the conclusion that rents

on most rent-controlled units are similar to other rents. We tentatively hypothesize that aboveaverage rent inflation in rent-control units reflects “catching up to market rents.” Alternatively, a

steady flow of units coming off of rent control might also explain this: if a unit comes off rentcontrol between t−6 and t, it remains flagged as a rent-control unit for the purposes of constructing

period-t rent relatives.

The fact that rent control in only three metropolitan areas can impact aggregate US rent

inflation is quite surprising, and perhaps is partly responsible for the puzzling unresponsiveness of

rents that has been highlighted in, e.g., DiPasquale and Wheaton (1992) and Blackley and Follain

(1996). A shock to user costs or to shelter demand in a city would presumably impact market-based

rents as soon as contracts are renewed, but would presumably only influence rent-controlled rents

after a longer lag, which would partly explain the rent inflation stickiness highlighted by Verbrugge

(2007a,c).

Before proceeding to the investigation of the Rent-OER divergence in the four Census regions,

we discuss the impact of two remaining minor differences between Rent and OER.

1 5Most of the rent control regimes in place in the US are termed “tenancy rent control” by Basu and

Emerson (2000), which have the character described above: they allow landlords to set a nominal rent freely

when taking on a new tenant, but place restrictions on rent inflation for existing contracts. In particular,

these authors argue that the rent control regimes in Los Angeles, Berkeley, and New York City all have this

character.

17
Investigating the Rent-OER Inflation Divergence

Two remaining minor differences

1. Imputation of missing rents. When a rent quote is not collected from a particular unit in a given

time period, there is an imputation procedure which “fills in” a rent estimate that can be used later

on, if a rent quote does become available six months hence. This procedure estimates an inflation

rate for the unit, based upon the inflation experienced by similar units. This imputation is done

using all the data available to the BLS, but since OER inflation and Rent inflation can differ, the

imputed “Rent” value for a particular unit can differ from the imputed “OER” value. Consistency

requires that when we use Rent weights in (1), we use Rent imputes. (Note that imputations of

this sort can only have a short-run impact on the index; overall index movements are driven by

actual rent quotes.)

2. Changes in the provision of utilities for Rent. If there are changes in the provision of utilities

in a rental contract, utilities-provision adjustments are made for the rent measures entering Rent

indexes. (These have no impact on OER, since utilities are always removed in any case.) The

utilities included or excluded from the contract do not often change, and hence the effect of these

changes are small. We found that making these utilities-provision adjustments explained nearly all

of the remaining differences.

6 Regional Decompositions

In each region, within-metro-area rental market segmentation played a key role, but the remainder

of the story differs by region. Accordingly, in order to limit the number of Figures and enhance

their clarity, in each region we omit plotting one or more of the series studied above, whenever the

corresponding factor’s role is limited.

18
Investigating the Rent-OER Inflation Divergence

6.1 Northeast

Figure 6a plots four series for the Northeast region. The first is Rent without rent control units, and

the remaining three are OER-based indexes. In reverse order, the last of these is OER (with the

retroactively-applied water/sewer improvement); the second is OER without any utilities adjustment; and the first is OER without utilities, using Rent weights but OER upper-level aggregation.

In this region, helper-unit inflation was only occasionally very different from other units with large

OER weight, hence we omit the series “OER (no util. adj., no hlpr, OER w, OER agg.)” to avoid

cluttering up the Figure.

1.0

2.0

3.0

4.0

5.0

6.0

7.0

200001

200004200007

200010

200101200104200107200110

200201200204200207200210

200301

200304

200307

200310200401200404

200407

200410200501200504200507

200510

200601200604

200607

200610

200701200704

Rent (no rent control)

OER (no util., no hlpr., Rent w, OER agg.)

OER (no utilities adjustment)

OER

Figure 6a: Northeast, Utilities and Aggregation Weights

Several factors played a noticeable role in the Northeast: utilities, within-metro-area rental

market segmentation, across-metro-area shelter inflation divergence, and rent control.

19
Investigating the Rent-OER Inflation Divergence

The difference between “OER” and “OER (no utilities adjustment)” is due to the utilities

adjustment. Utilities adjustment obviously plays a large role in this region, a region in which the

majority of rental units have heat included in the rental contract. But surprisingly, even in this

region, most of the time the utilities adjustment does not account for the majority of the divergence

between Rent inflation and OER inflation. The difference between “OER (no utilities adjustment)”

and “OER (no util. adj., no hlpr, Rent w, OER agg.)” is due to changing from OER weights to Rent

weights in (1). This frequently has a sizable impact (positive or negative), especially after 2005:03;

again, this reflects different inflation rates experienced by different segments of the rental market

within a city. The difference between “OER (no util. adj., no hlpr, Rent w, OER agg.)” and “Rent

(no rent control)” is due to switching from OER weights to Rent weights in upper level aggregation.

This also plays a significant role for many periods; New York City has a disproportionate impact

owing to its high intensity of renters.

As noted above, in 2007:05 the divergence between 12-month OER and Rent inflation, once the

effect of utilities is accounted for, is substantial.

What remains to be depicted is the impact of rent-control units. Figure 6b accordingly plots

two series, Rent without rent-control units, and Rent.

20
Investigating the Rent-OER Inflation Divergence

1.0

2.0

3.0

4.0

5.0

6.0

7.0

200001200004200007200010200101200104

200107

200110

200201

200204200207

200210

200301200304

200307200310200401200404

200407

200410

200501

200504200507200510

200601

200604

200607

200610200701200704

Rent (no rent control)

Rent

Figure 6b: Northeast, Rent Control

It is clear that rent control had a noticeable impact on Rent inflation in the Northeast over this

period. During the 9-month period between 2000:07-2001:03, inclusion of rent-controlled units increased annual Rent inflation by nearly 0.4%. Between 2004:01-2005:09 and in 2006, their inclusion

raised annual Rent inflation by about 0.2%.

In greater New York City, the impact was obviously even greater: their inclusion contributed

almost 0.7% to Rent inflation between 2000:07 and 2001:03; and on average over the entire period,

it contributed slightly more than 0.2% per year to this metropolitan region’s Rent inflation. The

positive average impact of rent control on Rent inflation was mostly due to a positive average impact

in New York City itself; inclusion of rent-controlled units had an average impact of roughly 0% in

its New York-Connecticut suburbs, and an average impact of -0.1% in its New Jersey suburbs.

21
Investigating the Rent-OER Inflation Divergence

6.2 Midwest

Figure 7 plots four series for the Midwest region: OER (with the retroactively-applied water/sewer

improvement); OER without any utilities adjustment; OER without utilities, using OER weights

and OER upper-level aggregation; and Rent. For this region, we do not plot the series “OER (no

util., no hlpr., Rent w, OER agg.).” Prior to 2002, moving from OER to Rent upper aggregation

weights reduced inflation by an average of 0.15%; but afterward, this factor played only a very

modest role. Accordingly, factor 2’s impact is more-or-less seen in moving from “OER (no util., no

hlpr., OER w, OER agg.)” to “Rent.”

1.0

2.0

3.0

4.0

5.0

200001200004200007200010200101200104200107200110200201200204200207200210200301200304200307200310200401200404200407200410

200501200504

200507200510

200601

200604

200607

200610200701

200704

OER

OER (no utilities adjustment)

OER (no util, no hlpr, OER w, OER agg.)

Rent

Figure 7: Midwest

The story in the Midwest is utilities plus within-metro-area rental market segmentation.

The difference between “OER” and “OER (no utilities adjustment)” is due to the utilities

adjustment. Its impact was often quite large in the Midwest during this period, reflecting the large

22
Investigating the Rent-OER Inflation Divergence

percentage of rental units which have heat included in the rental contract. (Notice that later in

the period, the utilities adjustment reduced rather than increased the divergence between Rent and

OER inflation.) The difference between “OER (no utilities adjustment)” and “OER (no util., no

hlpr., OER w, OER agg.)” is due to factor 2, removing helper segments; and the difference between

“OER (no util., no hlpr., OER w, OER agg.)” and Rent is essentially attributable to factor 3. Thus,

after considering the effect of utilities, within-metro-area rental market segmentation — i.e. moving

from OER weights to Rent weights in (1), and addressing helper-unit inflation — is essentially

the rest of the story. After 2004, inflation in helper units began to fall below inflation in other

high-OER-weight units, particularly after mid-2006. By 2007:5, it accounted for fully half of the

extremely large (1.5%) gap between inflation in utilities-included OER and inflation in Rent.

6.3 South

Figure 8 plots four series for the South region: Rent, OER without utilities, using Rent weights

and OER upper-level aggregation, OER without utilities, using OER weights and OER upper-level

aggregation, and OER (with the retroactively-applied water/sewer improvement). We do not plot

the series “OER (no utilities adjustment);” in this region, the utilities adjustment plays very little

role, which is not surprising since only a small percentage of rental units include energy utilities in

the contract.

23
Investigating the Rent-OER Inflation Divergence

1.0

2.0

3.0

4.0

5.0

6.0

200001200004200007

200010200101200104200107200110

200201

200204200207

200210

200301200304200307200310200401

200404200407200410200501

200504200507200510200601200604

200607200610200701

200704

Rent

OER (no util., no hlpr., Rent w, OER agg.)

OER (no util., no hlpr, OER w, OER agg.)

OER

Figure 8: South

In the South, the story is particularly simple: within-metro-area rental market segmentation

(and in particular, factor 2: weights on common units). The difference between “OER” and “OER

(no util., no hlpr., OER w, OER agg.)” is due to removing helper segments. This exerts a small

influence from time to time, but whenever there is a significant gap between OER and Rent inflation,

factor 2 — which drives the gap between “OER (no util., no hlpr., OER w, OER agg.)” and “OER

(no util., no hlpr., Rent w, OER agg.)” — is the basic explanation.(Upper level aggregation, which

is responsible for the gap between “OER (no util., no hlpr., Rent w, OER agg.)” and “Rent,” exerts

a small influence from time to time.) As noted above, in 2007:05 the divergence between 12-month

OER and Rent inflation is substantial.

24
Investigating the Rent-OER Inflation Divergence

6.4 West

Figure 9 plots four series for the West region: Rent, Rent without rent control units, OER without

utilities or helpers, using Rent weights but OER upper-level aggregation, and OER (with the

retroactively-applied water/sewer improvement). As for the South, we do not plot the series “OER

(no utilities adjustment);” in the West, the utilities adjustment plays very little role, because only

a small percentage of rental units include energy utilities in the contract. As helper unit inflation

is very similar to the inflation in other high-OER-weight units in this region, we also do not plot

the series “OER (no util., no hlpr, OER w, OER agg.).”

1.0

2.0

3.0

4.0

5.0

6.0

7.0

200001200004200007200010200101200104200107200110200201200204

200207

200210200301

200304200307200310

200401

200404200407200410200501

200504

200507

200510200601200604200607200610200701

200704

Rent

Rent (no rent control)

OER (no util., no hlpr., Rent w, OER agg.)

OER

Figure 9: West

The the West, three factors played a role in explaining Rent-OER inflation divergence: rent

control, across-metro-area shelter inflation divergence, and within-metro-area rental market segmentation.

25
Investigating the Rent-OER Inflation Divergence

First, rent control — responsible for the gap between “Rent” and “Rent (no rent control)” — had

an important impact on Rent inflation in this region. Inflation in rent-regulated units exceeded that

in other units, and hence their inclusion increased rent inflation fairly significantly between 2002:10-

2005:6. (In Los Angeles and in the greater San Francisco region, rent control had an enormous

effect. In Los Angeles, between 2004:04 and 2005:06, inflation in rent-control units increased Rent

inflation by nearly 1%; on average over the entire period, inflation in these units increased Rent

inflation by 0.4%. In fact, rent-control only decreased Rent inflation in Los Angeles for one short

period, between 2005:08 and 2006:02. In the greater San Francisco region (which includes Oakland

and San Jose), rent control had equally large effects, but these oscillated between ±1%, with an

overall average of 0.3%.)

Second, switching from Rent to OER weights in the upper level aggregation — i.e., moving from

“Rent” to “OER (no util., no hlpr, Rent w, OER agg.)” — also plays a significant role between

2003 and 2006. This undoubtedly reflects the fact that beginning in 2002, greater Los Angeles,

a renter-intensive region, experienced much higher rent inflation than did the average city in the

West.

Finally, as in each of the other regions, within-metro-area rental market segmentation played

an important role. Moving from Rent weights to OER weights in (1) — the difference between

“OER (no util., no hlpr, Rent w, OER agg.)” and “OER” — frequently has a sizable impact; this is

particularly notable prior to 2002:06 (during which time this factor is essentially the entire story),

and in 2004. (Conversely, in the period 2005:08-2006:06, using Rent weights would have increased

the divergence between OER and Rent inflation.)

7 Conclusion

While the utilities adjustment sometimes has a quantitatively-significant impact on the OER indexes — as it should — it is by no means the entire explanation of the divergence between OER

inflation and Rent inflation between 1999 and 2006. (Indeed, this adjustment reduced divergence

between these two series almost as often as it increased it.) Instead, rental market segmentation —

different rent inflation rates were experienced by different parts of the rental market — also played

26
Investigating the Rent-OER Inflation Divergence

a key role.

In particular, between 2000:1 and 2002:09, the Rent-OER inflation divergence mainly resulted

from divergent rental inflation rates within metropolitan areas: areas with a higher proportion

of renters experienced higher rental inflation. The differential impact on Rent inflation was about

0.35%. (For more evidence on this issue, see the study of Verbrugge et al., 2007). Differential shelter

inflation across metropolitan areas played a key role in the Rent-OER divergence in 2000 and after

2004, accounting for almost one-third of the divergence; in 2007:05, this factor’s importance is

overwhelming. And — most surprisingly — during 2004, higher inflation in rent-control units in the

Northeast and West caused overall Rent inflation to rise by about 0.2%.

The BLS constructs separate indexes for Rent and for OER. An alternative approach — one

which is already in implicit use in many real estate studies — would be to control for the effect of

utilities and rent control, but otherwise use identical weights to construct Rent and OER indexes.

The analysis above demonstrates the shortcomings of this alternative. Clearly, rental markets

are segmented to an appreciable extent along the Rent weight-OER weight dimension, and can

experience distinctly different inflation rates. The observed divergence between OER and Rent

inflation — particularly once this effect is identified as above — highlights this segmentation, and is

a key empirical finding which requires explanation. We do not attempt such an explanation here,

but leave it for future research.

Finally, there is no reason to expect that Rent and OER inflation will not diverge again in the

future.

27
Investigating the Rent-OER Inflation Divergence

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the Law, vol 3., Ed. Peter Newman (New York: Stockton Press), 305-310.

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Blackley, Dixie M., and James R. Follain (1996) “In search of empirical evidence that links rent

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Calhoun, Charles A. (1996) “OFHEO House Price Indexes: HPI Technical Description.” Manuscript, OFHEO.

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Investigating the Rent-OER Inflation Divergence

Olsen, Edgar O. (1997) “The Impact of Vacancy Decontrol in New York City: The First Estimates from the 1996 Housing and Vacancy Survey.” Report, New York City Rent Guidelines

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29