Chinese Studies
2013. Vol.2, No.4, 149-151
Published Online November 2013 in SciRes (
Open Access 149
Is Urban China’s CPI Underreported?
Sun Wenkai
School of Economics, Renmin University of China, Beijing, Chin a
Email: sunwk@r
Received July 8th, 2013; revised August 20th, 2013; accepted September 2nd, 2013
Copyright © 2013 Sun Wenkai. This is an open access article distributed under the Creative Commons Attribu-
tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Many have doubts about statistics, including the consumer price index (CPI) released by Chinese Gov-
ernment. We re-estimate the CPI in urban China from 1997 to 2009, with two widely accepted approaches
to treating owner-occupied housing (i.e., user cost approach and consumption cost approach). The results
are not considerably different from the NBS statistics, especially for recent years.
Keywords: Consumer Price Index; Owner-Occupied Housing; User Cost Approach; Consumption Cost
Accuracy of economic indicators is of paramount importance
to macroeconomic empirical research. However, many macro
indicators in Mainland China (China, hereafter) are controversial
because the National Bureau of Statistics (NBS, hereafter) usu-
ally releases them without provi ding the methods and procedure s
for obtaining them. The consumer price index (CPI, hereafter) i s
such an example. In recent years, it has just moderately increased
despite dramatically increasing house prices. This has led to a
lot of concerns and controversies. After He (2010) successfully
replicated the NBS calculation of CPI and thus provided sup-
port to the NBS statistics, most concerns now focus on the
weights of the CPI categories, in particular the weight of hous-
ing expenditure. In fact, the NBS has been aware of the under-
estimation of the weight of housing expenditure and announced
a decision to adjust the weights of the CPI categories after 2011,
with a focus on enhancing the weight of housing expenditure.
Research has shown that the weight of housing expenditure
in the NBS-released CPI is lower than it should be. Specifically,
Wang (2008), He (2010) and Xu (2010) estimated it to be
13.6%, 10% - 16% and 16%, respectively, all much lower than
42% in the United States (Wang, 2008) and the mean weight,
20.26%, in OECD countries. Xu (2010) further pinpointed the
cause of the underestimation to be the NBS method for calcu-
lating the owner-occupied housing expenditure. With most
Chinese households having their own housing, the weight of
owner-occupied housing in CPI should be larger in China than
in the US. This, however, is not the case.
There are several widely-accepted approaches to treating
owner-occupied housing; however, the NBS adopts none of
them (Wang, 2006). In this paper, we use two of these ap-
proaches to re-estimate the weights of the CPI categories and
recalculate the CPI in urban China.
Approaches to Treating Owner-Occupied
The International Labor Office endorsed four major ap-
proaches to handling owner-occupied housing. They are: the
acquisitions approach, the rental equivalent approach, the user
cost approach, and the payments approach. In addition, Beatty,
Larsen, and Sommervoll (2010) proposed an improved rental
equivalent approach, termed “consumption cost approach”.
Among the five approaches, we adopt the user cost approach
and the consumption cost approach, because of certain short-
comings of the other approaches. As Beatty et al. (2010) cri-
tiqued, the acquisitions approach ignores the role played by
interest rates and the payments approach (based on observed
interest payments by households with a mortgage) ignores the
role played by households with equity. Meanwhile, the rental
equivalent approach would not apply well in China, because the
rental market in China (as in many other countries) is not de-
veloped during the research period and the rental imputation
would have to be based on an unreliable out-of-sample ex-
trapolation. Therefore, we adopt only the user cost approach
and the consumption cost approach.
User Cost Approach
The user cost approach is based on the pricing theory (Katz,
2009). The house purchase price equals the discounted present
value of its expected future services less the discounted present
value of its expected future operating costs.
 
11 1
tt tmvt
tvvm v
vt t
tmv i
uuu O
rr r
 
where m denotes the remaining service life of a house, the
purchase price of a v periods old house at the beginning of pe-
riod t, the expected value of the period t services of this
house, the
expected period t operating expenses (including
maintenance cost and depreciation, to be paid at the end of
period t) for this v periods old house in period t, and the
expected nominal discount rate.
From (1) we can derive
ttt ttt
 . (2)
This, however, confuses consumption prices with investment
returns because of the price difference between two periods.
Sometimes, the user cost may even be negative when house
prices rise rapidly. When this happens, the overall CPI could
decline even when every price is increasing in an economy.
Therefore, as some suggest (e.g., Poole et al., 2005; Beatty et
al., 2010; Diewert et al., 2009), we adopt this approach without
the appreciation of housing prices component. Furthermore, for
China we can set the maintenance cost to zero. Correspondingly
Equation (2) is now simplified to:
where denotes depreciation in period t.
Consumption Cost Ap p roach
An empirical calculation of the consumption cost can be im-
plemented with
 
, (4)
is the holding period, i the interest rate, A the house
the tax deductibility rate, Tc the transaction costs
and government fees (rate), and m the maintenance cost (rate).
For China, we can set the transaction cost, maintenance cost,
and taxes deduct to zero.
With both approaches, we use 3-year moving average inter-
est rates, instead of annual interest rates. That is, we use
smoothed interest rates for in the user cost approach and i
in the consumption cost approach. The adjustment prevents CPI
estimations from being affected by monetary policies and al-
lows it to remain objective.
Empirical Analysis
The data used in this paper are from the following sources:
Dwelling area per capita data, urban households’ consump-
tion categories and their respective price indices, and sales price
of commercial housing are collected from the Chinese Statisti-
cal Yearbook 1998-2010. The loan and deposit interest rates are
from the CEIC database and the website of the People’s Bank
of China, respectively. The depreciation rate is from our own
The analysis is composed of three parts. First, we calculate
owner-occupied housing expenditure, with the user cost ap-
proach and the consumption cost approach, respectively. Next,
we substitute the estimated housing expenditure into the con-
sumption categories of urban residents to calculate the weight
of each category. Finally, we use the weights thus obtained to
re-estimate the CPI in urban China.
With the user cost approach, we use a depreciation rate of
1.9% (50 years service life and residual value 5%). In addition,
with both approaches, we try both loan and deposit interest
rates since it is not clear which Beatty et al. (2010) use.
Housing Expenditure
As we can see from Table 1, NBS tends to underestimate
owner-occupied housing expenditure. No matter which ap-
proach we use and whether we use loan interest rates (column a)
or deposit interest rates (column b), our estimations are much
larger than the NBS statistics (last column).
The weight of housing expenditure, obtained by substituting
the results in Table 1 into the urban households’ consumption
categories, is in the range of 20% to 50% (Table 2). This is
considerably larger than the NBS weight of housing expendi-
ture, estimated by Wang (2008), He (2010) and Xu (2010).
Table 1.
Estimation of o wn er-occupied housing expenditure (RMB Yuan).
User Cost Consumpti on Cost
(a) (b) (a) (b)
19973920.99 2576.863316.29 1972.16 358.64
19983521.84 2099.962864.52 1442.64 408.39
19993097.81 1777.9 2412.61 1092.71 453.99
20003092.64 1637.052343.15 887.56 500.49
20013289.16 1703.322492.04 906.2 547.96
20023666.34 1892.762760.48 986.91 623.675
20033972.86 2067.142983.55 1077.83 699.39
20044935.64 2647.123696.84 1408.32 733.53
20055871.75 3250.164415.31 1793.72 808.66
20066851.29 3926.5 5245.19 2320.39 904.19
20078529.32 5034.426583.15 3088.25 982.28
20088516.86 5287.366545.12 3315.63 1145.41
200910311.886385.297770.25 3843.66 1228.91
Note: (a) moving average loan interest rate; (b) moving average deposit interest
Table 2.
Estimated weight of housing expenditure.
User Cost Consumpti on Cost
(a) (b) (a) (b)
1997 .52 .42 .48 .36
1998 .47 .36 .43 .29
1999 .45 .33 .39 .25
2000 .43 .3 .37 .21
2001 .43 .3 .37 .21
2002 .43 .3 .37 .2
2003 .43 .3 .37 .2
2004 .46 .32 .39 .22
2005 .47 .35 .41 .24
2006 .49 .37 .43 .27
2007 .5 .39 .44 .29
2008 .48 .37 .42 .29
2009 .5 .39 .44 .29
Open Access
Open Access 151
It is noteworthy that we cannot directly use the weights of
the urban households’ consumption categories to calculate CPI,
because the urban households’ consumption categories do not
have an exact one-to-one correspondence relationship to the
CPI categories. Specifically, there is no corresponding CPI
category for a “miscellaneous” term in the consumption expen-
diture. We obtain the weights of the CPI categories by dividing
this miscellaneous term into two equal parts and adding them to
“Tobacco, Liquor and Related Products” and “Medical Health
Care and Personal Articles”, respectively (He, 2010).
Our CPI estimations prove to be not considerably different
from the NBS statistics and are actually smaller than the latter
in many cases (Table 3). This is true whether we use the user
cost approach or the consumption cost approach and whether
we use the loan interest rate (column a) or the deposit interest
rate (column b). Averaged across the years, our estimations are
smaller than the NBS statistics .
In this paper, we re-estimate the CPI in urban China by ad-
dressing owner-occupied housing expenditure with two widely-
accepted approaches instead of the NBS approach. Even though
we enhance the weight of housing expenditure in this way, our
estimations are close to the NBS statistics and actually smaller
Table 3.
Estimation of CPI .
User Cost Consumpti on Cost
(a) (b) (a) (b)
1997 106.2 105.19 105.79 104.58 103.1
1998 101.65 100.68 101.25 100.07 99.4
1999 99.86 98.98 99.44 98.35 98.7
2000 100.66 99.81 100.26 99.2 100.8
2001 100.3 100.37 100.33 100.41 100.7
2002 97.18 97.88 97.5 98.38 99
2003 100.17 100.49 100.32 100.73 100.9
2004 102.43 102.8 102.61 103.09 103.3
2005 103.77 103.04 103.4 102.45 101.6
2006 102.48 102.14 102.31 101.88 101.5
2007 105.85 105.49 105.67 105.19 104.5
2008 104.49 104.87 104.71 105.19 105.6
2009 101.72 101.75 101.74 101.77 101.8
Note: CPI 2009 is for December 2009.
than the latter when averaged across the years. One possible
explanation for our findings is that, while we enhance the
weight of housing in CPI, food prices have increased even more
rapidly than housing prices in the past decade.
Our findings suggest that the NBS has not underreported CPI
and the statistics can be used to guide monetary policies, espe-
cially those depending on the CPI trend over years. However,
this does not mean that the NBS method of calculating CPI is
scientific, since it has no strong theoretical basis. The NBS-
released CPI could be less dependable for studies requiring
higher accuracy of the CPI, such as those trying to decompose
CPI variance.
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