Current Urban Studies
2013. Vol.1, No.4, 110-116
Published Online December 2013 in SciRes (
Open Access
The Estimation of Property Tax Capitalisation in
the Korean Taxation Context
Hyung Min Kim1*, Kyoung-Seok Jang2, Youn-Kyoung Hur3
1Department of Urban Planning and Design, Xianjiaotong-Liverpool University, Suzhou, China
2Office of Economic and Industrial Research, National Assembly Research Service, Seoul, Korea
3Construction Economy Division, Construction and Economy Research Institute of Korea, Seoul, Korea
Email: *,,
Received August 12th, 2013; revised September 15th, 2013; accepted September 23rd, 2013
Copyright © 2013 Hyung Min Kim et al. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
The difference between public services and property tax rates is capitalised into home values. The aim of
this research is to estimate the property tax capitalisation rate under a different taxation context of Korea,
using a repeat sales method with short-term data on housing prices and estimated tax payments. In the
operation of the property taxation, there is complexity that needs to be considered in the estimation of the
property tax capitalisation rate. In this research, 32,101 apartment samples in Seoul are used for the anal-
ysis. Given these unique institutional circumstances, as a result of the analysis, the property tax capitalisa-
tion rate in Seoul was between 73.7% and 82.8% in the analysis periods.
Keywords: Housing Prices; Property Tax; Gross Real Estate Tax (GRET); Capitalisation; Repeat Sales
The difference between public services and property tax rates
is capitalised in home values. This phenomenon is called a
capitalisation effect (Yinger et al., 1988; Rosen & Gayer, 2007).
Higher property tax payments lead to lower house values if all
else being equal. Theoretically, housing prices drop by the dis-
counted present value of property tax payments when the prop-
erty taxes are imposed. This is called full or complete capi-
talisation. However, in the extensive study of Yinger et al.
(1988), property taxes were partially capitalised. In spite of a
simple concept of property tax capitalisation, it is challenging
to estimate the degree of capitalisation (Yinger et al., 1988).
Different taxation policy contexts require different analysis
methods. During the early 2000s, housing prices increased dras-
tically in a selected few areas in Korea. According to a housing
price index, housing prices in Seoul increased approximately
50% over the three years in the early 2000s. In response to the
changes in the housing market, the Korean government utilised
property taxation as a tool to stabilise housing prices and to
improve housing affordability. The government announced
more than 30 policies on property markets during the period
2003-7 (Jang, 2010: pp. 274-355). Nevertheless, little has been
studied about the influence of the reinforced property taxes on
housing prices in Korea.
Thus, the aim of this research is to estimate the property tax
capitalisation rate under a different taxation context of Korea,
using a repeat sales method with short-term data on housing
prices and estimated tax payments. This study sheds light on
the presence of the property tax capitalisation effect and pro-
poses a new method to estimate that effect.
Institutional Context
The Korean government has three hierarchies: the central
government, metropolitan governments (si/do, e.g. Seoul, Bu-
san or Gyeonggi), and local governments (si/gun/gu). Each
government levies property taxes, respectively. In order to cal-
culate property tax rates precisely, this section reviews the
structure of property taxation in Korea currently being operated
in the three levels.
Firstly, there are many types of property taxes as a form of a
surtax imposed by different levels of governments. These taxes
include “Property Tax”1 and “Gross Real Estate Tax (hereafter
GRET)” and there are other taxes, based on the same property,
such as “City Planning Tax”2, “Local Education Tax”, “Facili-
ties Tax”, and “Special Tax for Rural Development”. These
taxes should be regarded as one of the property taxes because
they are all levied to the same property using the same paying
method. For example, when tax payers pay for the “Property
Tax” to the local governments, they also have to pay for the
“Facilities Tax” to the metropolitan government with the same
tax bill. The “Local Education Tax” is additionally imposed to
the owner with 20% of property tax payments. These taxes are
included in one tax bill so that the governments may collect
taxes easily. The collected money is distributed to each gov-
ernment. Likewise, the “Special Tax for Rural Development” is
the surtax of 20% of the GRET. The GRET is imposed to ex-
pensive housing by the central government in order to stabilise
housing prices, to recoup capital gains and to contribute to ba-
1In this study, the property tax as a general term is different from the “Prop-
erty Tax” as one sort of property taxes in Korea; the property tax includes
taxes based on land and property.
2City Planning Tax was incorporated into the part of “Property Tax” in
*Corresponding author.
lanced development between regions. Accordingly, it is easy to
underestimate effective property tax rates when tax payers only
consider the legal tax rate described in the Local Tax Act. To be
precise, all these taxes, thus, need to be regarded as the property
Secondly, the Korean property taxation is progressive by
values. Higher tax rates are applied to expensive houses, while
lower tax rates, inexpensive ones. Progressiveness was streng-
thened due to the enforcement of the “Gross Real Estate Tax
Act” in 2006. For houses below 40 million WON3, their “Prop-
erty Tax” rate is only 0.15%. However, for houses more expen-
sive than 600 million WON, their “Property Tax” rate becomes
1%. If the price is over 2 billion WON, the owner has to pay
2% of assessed housing prices. Ta b le 1 summarises the details
of tax rates by housing price bands.
Thirdly, local governments have a limited influence in the
decision of property tax rates. For the purpose of equity be-
tween regions, the “Local Tax Act” bans local governments
from changing tax rates. The elastic taxation meant that local
governments had the authority to adopt a different tax rate
within 50% of flexibility; the elastic tax rates were only oper-
ated in 2005 and 2006. During these periods, a few local gov-
ernments adopted lower property tax rates than standard tax
rates presented in the “Local Tax Act”. However, equity was
considered significant and the implementation of elastic taxa-
tion ended in 2006. Basically, the central and metropolitan
government support the deficit after local governments finance
local taxes at given property tax rates. So, there is a discrepancy
between burdens of tax payers and benefits from the local gov-
ernments. Even though owners of expensive properties pay for
a large amount of property taxes on a higher tax rate, they ben-
efit from public services similar to those who pay less due to an
inflexible tax rate system discussed above.
Besides, additional complexity exists in the taxation. A tax
payment-cap policy is in operation. The purpose of this policy
is to prevent a rapid increase in tax payments caused from sud-
den changes in the real estate market. For example, a property
tax payment should not exceed over 105% of the last payment
when the price of the property is within 300 million WON,
110% for its price is laid between 300 and 600 million WON,
and 150%, over 600 million WON. In addition, the aggregate
amount of the “Property Tax” and the GRET should not exceed
three times of the property tax payments in relation to the cor-
responding period last year.
This complexity under Korean taxation contexts creates dif-
ficulties in estimating the property capitalisation rate. Thus, the
principle of the repeat sales model is used to estimate the prop-
erty taxation capitalisation.
Data and Method
The Korean central government assesses housing prices of all
housing in Korea for tax purpose on a yearly basis viaa gov-
ernment agency—Korea Appraisal Board (KAB). The ap-
praised prices make it possible to calculate the amount of indi-
vidual property tax payments.
The unit of the analysis in this research is an individual
apartment (high-rise condominium). Extensive data for three
years about housing (appraised) prices between 2006 and 2008
Table 1.
Legal tax rates and tax base in Korea.
Tax title Housing prices
(million WON)
Tax rate
(%) Tax base
Below 40 0.15
40 - 100 0.3
Over 100 0.5
Assessed housing
Below 300 1.0
300 - 1400 1.5
1400 - 10,000 2.0
Gross Real
Estate Tax
10,000 3.0
Assessed housing
prices subtracted
600 million WON
City Planning Tax 0.15 The same to the property tax
Local Education Tax 20 The amount of property tax
Special Tax for Rural
Development 20 The amount of
global real estate tax
Source: The Local Tax Act and Gross Real Estate Tax Act.
was used for this research. A sample consists of 32,101 apart-
ments, which account for 2.6% of total apartment stocks in
Seoul (see Table 2). Apartment is a major housing type in
Seoul, accounting for 54.2% of the housing stock (Census in
2005). Almost new housing construction is composed of apart-
ments, for example, 76.5% in 2006, and 100% of housing re-
newal projects were a form of high-rise apartments (Ha, 2010).
Thus, the sample of this research is large enough to represent
Seoul’s housing market.
The average housing price was 222,375 thousand WON in
2006, 279,042 thousand WON in 2007 (a 25.5% increase in
relation to 2006), and 290,946 thousand WON in 2008 (a 4.3%
increase in relation to 2007). More details are in Appendix.
To calculate the amount of tax payments and effective tax
rates, following four factors were considered in the Korean
context: appraised apartment prices, tax-rate bands, the elastic
tax rates in 2006, and the property tax payment cap4. These
factors made the amount of property tax payments change
every year. The effective tax rate is house’s property tax pay-
ment divided by its market value (Yinger et al., 1988). The
summary of calculated tax payment and their effective tax rates
are presented in Table 3.
Based on aggregated tax payments and appraised prices, ef-
fective property tax rates were 2.059 mill in 2006, 1.990 mill in
2007, and 2.139 mill in 2008, respectively.
This study takes advantage of the principle of a repeat sales
model. The repeat sales model was developed by Bailey et al.
(1963). Then, Case & Shiller (1987) used this model to estimate
variations in housing prices and to create an housing price in-
4The “Local Tax Act” bans local governments from elastic tax rates across
regions for the purpose of equity. The elastic taxation was that local gov-
ernments had the authority to adopt a different tax rate within fifty per cent
of flexibility; the elastic tax rates were only operated in 2005 and 2006.
During this period, a few local governments adopted lower property tax
rates than standard tax rates presented in the “Local Tax Act”. The central
and metropolitan government support the deficit after local governments
finance local taxes.
3US $1 = 956 WON in 2006, 1103 WON in 2008.
Open Access 111
Table 2.
Statistics of summary1.
Variables Mean Standard
deviation Minimum Maximum
The age of house in 2006 11.86 7.23 0.00 39.00
House net area (m2) 72.70 24.29 23.70 244.97
Housing price in 20062 222.38 164.21 38.00 2780.00
Housing price in 20072 279.04 222.08 39.00 3856.00
Housing price in 20082 290.95 208.57 48.00 3480.00
1Number of samples is 32101; 2Million WON.
Table 3.
Estimated effective tax rates and tax payments by districts.
Tax rates (mill) Tax payment per house
(thousand WON)
2006 2007 2008 2006 2007 2008
Gangnam 2.243 2.483 3.085 1360 20842660
Gangdong 2.189 2.078 2.286 616 754 827
Gangbuk 1.814 1.704 1.662 283 314 369
Gangseo 1.830 1.643 1.769 312 386 424
Gwanak 1.968 1.840 1.902 373 417 466
Gwangjin 2.517 2.318 2.535 739 914 1018
Guro 1.841 1.714 1.727 299 338 385
Geumcheon 2.106 1.967 2.014 337 370 409
Nowon 1.743 1.603 1.613 256 291 336
Dobong 2.058 1.944 1.926 325 357 406
Dongdaemun 2.059 1.997 2.020 424 459 517
Dongjak 2.181 1.981 2.091 538 629 714
Mapo 2.184 2.017 2.154 548 638 715
Seodaemun 2.310 2.219 2.250 454 490 546
Seocho 2.643 3.057 3.795 1,439 22852845
Seongdong 2.427 2.274 2.397 643 729 808
Seongbuk 1.993 1.928 1.945 386 419 473
Songpa 2.190 2.314 2.774 969 14141700
Yangcheon 1.991 1.870 2.166 545 825 954
Yeongdeungpo 2.129 2.058 2.210 585 769 890
Yongsan 2.658 2.828 3.196 1248 19182334
Eunpyeong 2.139 2.030 2.056 352 379 421
Jongno 1.999 1.965 2.014 428 490 565
Jung 1.959 1.902 2.079 558 652 719
Jungnang 2.062 1.946 1.974 311 340 378
(Seoul) 2.059 1.990 2.139 534 698 808
Note: Mill is 1/1000.
dex using transaction prices. Since Bailey et al. and Case &
Shiller, the repeat sales model has been widely used in generat-
ing a housing price index and understanding property markets
(McMillen, 2008). The principle of the repeat sales model is
that, when one house is transacted, the price matches to the
previous transaction price of the same house so that the differ-
ence between the two prices can be measured precisely. The
changes in housing prices are estimated by a regression analysis.
The rationale of this matching is to remove any expected biases.
Every single house has its unique locational and structural cha-
racteristics. Accordingly, only when the house is compared to
the same house without any structural alterations, can real
changes of housing prices be correctly measured.
The repeat sales model adds time-variables to the hedonic
price model used for a cross-sectional analysis. As the same
house is compared in calculation of housing price changes, the
repeat sales model rules out the effects of other variables, such
as lot size, bedrooms, toilets, and garages, as long as the house
was not renovated between the two transaction periods.
In the model of the initial repeat sales model by Bailey et al.
(1963), a natural log was taken to calculate the changing rates
of housing prices. The natural log makes left-sides directly a
variation rate in housing prices. Pf is the first transaction and Ps
is the second one in Equations (1) and (2) (Choi et al., 2010).
are the intercept composed of macro-economic
factors, housing preferences and other factors that can influence
housing prices.
In In
isis s s
In In
ifif f f
where Xi is characteristic of the house.
is coefficient.
are error terms.
As Equation (1) subtracts Equation (2), the equation is sim-
plified to conduct a regression analysis. In Equation (3), the
explanatory variables in a general hedonic price model are de-
In v
where, 10, v
The left side is the rate of variations in housing prices; all in-
dividual housing characteristics are removed in the right side.
This is the most advantageous feature in the repeat sales model
simplifying model specification. Dt in Equation (3) is a dummy
variable. It becomes one when transaction occurs, otherwise
zero. βt is the coefficient to be estimated by conducting a re-
gression analysis. βt can be transformed to a housing price in-
McMillen (2003) and Kim & Lee (2004, 2005) modify the
repeat sales model. They divide variables into time-varying
variables and time-fixed variables to discover how influence of
the time-varying variables has changed, by estimating the coef-
ficient of the time-varying variables, where time-fixed variables
are eliminated. Likewise, this paper utilises the principle of the
repeat sales model to estimate the property tax capitalisation
effect under the Korean taxation context6.
exp 100
6Although this research uses appraisal prices, the principle of the analysis
relies on the repeat sales model that use transaction data.
Open Access
When there are no property taxes, the hedonic price model
can be specified like Equation (4).
X (4)
where, is the housing price before imposing the property
X is attributes of the house.
is an intercept.
is a coefficient.
When the effective property tax rate is
, the amount of
property taxes is P
should be divided by the dis-
count rate (r) in order to acquire the total present value of prop-
erty taxes. When the property tax is in operation, the hedonic
price model can be expressed like Equation (5)7.
In Equation (5), 1t
is a net present value of property tax
payments and β is a coefficient that indicates the capitalisation
rate. If β equals one, property taxes are fully capitalised. Hous-
ing prices decrease by the present value of increased property
taxes. On the other hand, ifequals zero, housing prices do not
change regardless of newly added property taxes. In Equation
(5), the period of the left side—the housing price (P)—is t
while, on one of the right side—the amount of property tax (T)
—is t 1. Pt is closely tied to 1t
as effective tax rates and
the amount of total property taxes are decided by housing prices,
causing an auto-correlation problem that creates difficulty in
estimating by OLS. Due to the Korean taxation context dis-
cussed in the Section 2, one more step is required. In order to
remove the auto-correlation problem, the repeat sales method is
modified as suggested by McMillen (2003) and used by Kim &
Lee (2005). In the period t + 1, housing prices including prop-
erty taxes become Equation (6).
11 1
tt kt
 
If Equation (6) is subtracted from Equation (5), the attributes
of the house are eliminated. It is possible when there have been
no substantial physical changes to housing attributes between t
and t + 1. Finally, Equation (7) can be reached to a simplified
form to conduct a regression analysis.
101tt tt
 
is 1tt
, and this intercept signifies changes of hous-
ing prices due to the socio-economic conditions such as income
levels, the demand for or the supply of housing, public services
and employment. These factors are not spatially specific, thus
can be equally applied to all areas in Seoul. 1
is the coeffi-
cient that represents the change in housing prices caused by the
changes of property taxes. 1
equals r
, therefore,
calculated by 1r
In addition to the changes in market trends such as the de-
mand and the supply, local influences caused by location-spe-
cific changes in amenity, infrastructure, and accessibility, can
differ between districts over the analysis period. For example,
the construction of an outer circular highway around Seoul was
completed in 2006. Due to the new highway, accessibility was
improved in the northern part of Seoul, which influenced hous-
ing prices. Housing supply varies between districts. For exam-
ple, while there was no new apartment construction in Nowon,
27,559 new apartments were built in Songpa in 2007-2008.
Conditions of supply and demand in each region create a dis-
crepancy in housing price movements. It is necessary to con-
sider these effects are different in each district. Thus, district
dummy variables are added to Equation (4) to measure the lo-
cation-specific influence over time. For example, for Seochogu,
only the Seochogu’s district dummy variable equals one, other-
wise zero. District dummy variables explain time-varying ef-
fects between districts.
Result of Analysis
As a result of a regression analysis8, almost all variables are
statistically significant at the 95% confidence level (see Table
4). The intercept—17,537—plays an important role in inter-
preting the trend in the housing market. With taxation effects
and location-specific differences controlled, the intercept ex-
plains that housing prices increased by 17,537 thousand WON
on average between 2007 and 2008. When referring to the av-
erage housing price in 2007, the average change in housing
prices over the one year was a 6.3% increase (=17.537/279.04).
This is attributable to the provision of public goods, macro-
economic factors, and the demand for and the supply of hous-
Only four district dummy variables are not significant at the
5% significance level, but two of them are statistically signifi-
cant at the 90% confidence level (see Ta b l e 5 )9. Two districts,
Eunpyeong and Gwanak, show that the dummy variable is not
significant at the 10% of the significance level. This is due to
variations in housing prices from house to house in Eunpyeong
and Gwanak are so large that the statistics cannot conclude that
the change in housing prices is different from the base district,
Gangnam. For example, there have been redevelopment pro-
jects, called New Town Development, in Eunpyeo ng, since
2002. Thus the infrastructure provided concentrated on specific
areas. This caused larger variations in housing prices in Eun-
pyeong. Housing prices in the new residential development in
Eunpyeong increased much more than outside of the develop-
7Theoretically, capitalisation takes place at the time of the announcement o
the tax change. However, one year time lag is necessary given the policy
context. Property taxation is complicated enough to recognise because they
consist of different kinds of taxes in Korea. In addition, the amount of prop-
erty taxes is billed to home owners in June and September whereas apprais-
ing is done in January and noticing, in April. Thus, this study assumes that
property taxes in year t have an influence on housing prices in year t + 1,
taking a one-year time-lag in Equation (5). The assumption about the
one-year time-lag is reasonable as far as processes of appraisal, notice, and
billing are concerned.
Jan Apr Jun Sep Jan
AppraisalNotice landlords
of housing prices
Billing property taxesBilling property taxesAppraisal
Year tYear t+1
8This model has 48.3% of explanatory power. The low R-square is the
weakness of repeat sales model because the repeat sales model does not
have other independent variables that can explain the variation in the de-
pendent variable.
9The regression is based on housing prices changes from 2007 to 2008. Due
to the one-year time-lag, changes from 2006 to 2007 were not analysed in
this regression analysis.
Open Access 113
Table 4.
Descriptive statistics of dependent and independent variables.
Variables Mean Standard
Deviation Minimum Maximum
Tax payment
(independent variable)
Tt Tt1 163.7 502.0 1,785.7 13,819.2
Housing price
(dependent variable)
Pt+1 Pt 11,904 25,801 376,000 239,000
Note: Unit is thousand WON.
Table 5.
Result of a regression analysis.
Coeffici ent Standard Eerror t-value p-value
Intercept 17537.0 475.5 36.88 <.0001
T 21.9 0.2 94.70 <.0001
Gangdong 20226.0 647.7 31.23 <.0001
Gangbuk 19625.0 846.6 23.18 <.0001
Gangseo 15055.0 680.5 22.12 <.0001
Gwanak 354.5 753.9 0.47 .6381
Gwangjin 12842.0 886.5 14.49 <.0001
Guro 6217.5 656.3 9.47 <.0001
Geumcheon 2891.6 818.5 3.53 .0004
Nowon 6328.7 531.9 11.90 <.0001
Dobong 7193.4 607.1 11.85 <.0001
Dongdaemun 8000.8 740.9 10.80 <.0001
Dongjak 2530.7 771.5 3.28 .0010
Mapo 5103.7 810.4 6.30 <.0001
Seodaemun 3538.1 783.6 4.52 <.0001
Seocho 13922.0 724.6 19.21 <.0001
Seongdong 2322.8 700.2 3.32 .0009
Seongbuk 7708.3 690.7 11.16 <.0001
Songpa 24975.0 613.9 40.68 <.0001
Yangcheon 31276.0 684.4 45.70 <.0001
Yeongdeungpo 1195.8 691.5 1.73 .0838
Yongsan 31316.0 1023.7 30.59 <.0001
Eunpyeong 483.0 958.8 0.50 .6144
Jongno 8174.7 1490.7 5.48 <.0001
Jung 16302.0 1184.0 13.77 <.0001
Jungnang 1398.6 735.6 1.90 .0573
*Dependent variable: Pt+1 Pt; **adj-R square: 0.4831; ***Notes: The unit is
thousand WON, and Gangnam is the basement of district-dummy-variables.
ment area10. Yongsan has the highest dummy-variable coeffi-
cient. Yongsan is in the middle of the three central business
districts in Seoul, but the quality of houses is in general not as
good as other residential areas. Recently, there have been rede-
velopment projects all over the Yongsan area. The dummy va-
riable reflects the effect of the projects on housing price
changes in Yongsan. Yangcheon and Songpa show the biggest
decrease in housing prices. These two districts including
Gangnam belonged to the most expensive areas in Seoul.
Housing prices on the expensive areas decreased the most be-
tween 2007 and 2008.
The coefficient 1
is –21.868. This figure represents that
housing prices decreased by 21.868 thousand WON when the
property tax increased by one thousand WON. If there was no
change in property tax payments, housing prices would increase
by 17,537 (the intercept, 0
) thousand WON. Housing prices
decreased by 17,515 WON (17,537 – 21.868 × 163.7) on aver-
age due to the increase in property taxes.
The property tax capitalisation rate, β is estimated using es-
timated coefficient 1
. The capitalisation rate can be acquired
by multiplying 1
by the discount rate. The discount rate is
an opportunity cost that the home-owners can accrue when they
invest in other options instead of housing. Simply, the long-
term interest rate could be used as the discount rate (DiPasquale
& Wheaton, 1996: p. 207). However, deciding on a discount
rate has been problematic in estimating the property tax capi-
talisation rate (Yinger et al., 1988). Some studies adopt a nomi-
nal discount rate instead of a real discount rate and some use
higher rates than the real rates, thus making the capitalisation
rate higher. A real discount rate needs to be used and income
tax on interest and tax deduction should be considered when
measuring the real discount rate (Yinger et al., 1988). Accord-
ing to the Bank of Korea, the long-term interest rate was 3.98%
in 2006 and 4.47% in 2007. The income tax rate on interest
income was 15.4%. Considering the income tax rate on the
interest, the real discount rate becomes 3.37% and 3.78%, re-
spectively. Accordingly, the capitalisation rate of the property
taxes becomes 73.7% and 82.8%. Thus, it is reasonable to con-
clude that the degree of property tax capitalisation was some-
where between 73.7% and 82.8% (see Ta bl e 6 ). As studied by
Yinger et al. (1988), the result shows partial capitalisation that
means the effect could transfer to the future owner (Palmon &
Smith, 1998) or to tenants thus resulting in an increase in rents.
This paper focuses on the property tax capitalisation effect
under a unique Korean taxation context. The model employed
in this research is simple but based on reasonable assumption in
which the time-fixed variables can be eliminated. While a con-
ventional hedonic price model includes locational and structural
variables, the modified repeat sales model utilises variations in
property tax payments, which simplifies model specification.
The model in this research used three years data on housing
prices and tax payments.
Given these unique institutional circumstances, as a result of
the analysis, the property tax capitalisation rate in Seoul was
between 73.7% and 82.8% in the period 2007-8. This is partial
10There are two residential new development plans in Eunpyeong. An in-
crease in housing prices on average in the areas within the development is
11.2 (million WON) while outside of the development is 10.1 (million
WON). This shows that there exists a heterogeneous housing market even
in the same district.
Open Access
Open Access 115
Table 6.
Interest rates and capitalisation rate.
2006 2007
+Long term interest rate (%) 3.98 4.47
Discount rate (%) 3.37 3.78
Capitalisation rate (%) 73.7 82.8
+The bank of Korea (
capitalisation but larger than Yinger et al.’s estimation (1988).
The inevitability of property taxes might result in higher prop-
erty tax capitalisation. As there are few exceptions in imple-
menting property taxation, home owners in Seoul are unable to
avoid increased property taxes. Thus, home owners have to
accept an increase in property taxes.
In terms of policy objectives, the increase in property tax
rates is conducive to changes in housing prices and increases in
public funds. However, there is a fundamental issue about the
rationale for property taxes. The purpose of the property tax is
to ensure public goods which enhance property values, not to
depreciate home values as an intervention tool in the housing
market. Bruekner (1979) suggested that an efficiency is in the
point that a property tax locally financed supports house values
sufficiently in that region. Although Korean property taxes
finance public expenditure, it is difficult to regard that the Ko-
rean property taxation is efficient due to the lack of a close
connection between property taxes and public expenditure.
Equity concerns have been more emphasised than taxation effi-
ciency in Korea.
This study contributes to evidence of property tax capitalisa-
tion in a different policy context using the principle of the re-
peat sales model. As a result of the analysis, it is obvious that
additional property taxes are reflected in home values. Property
tax capitalisation can be understood as a natural result in re-
sponse to policy changes.
The early idea of this paper was presented at the 2009 Hous-
ing Researchers Conference, Sydney, Australia, in 2009.
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Appendix. Summary of Statistics by Districts in Seoul
Average appraisal housing price (thousand WON)
District Area (km2) Population Density
(persons/km2) Sample
2006 2007 2008
Gangnam 39.54 560,958 14,187 1737 456,353 603,216 604,914
Gangdong 24.58 464,546 18,899 1694 267,385 336,663 330,941
Gangbuk 23.60 345,478 14,639 698 149,592 178,074 214,552
Gangseo 41.42 560,424 13,530 1420 153,425 208,951 209,827
Gwanak 29.57 535,571 18,112 996 181,043 218,169 235,098
Gwangjin 17.05 376,572 22,086 602 271,824 348,909 349,781
Guro 20.11 420,542 20,912 1658 153,732 188,551 211,452
Geumcheon 13.01 249,108 19,147 771 151,669 179,289 193,218
Nowon 35.42 616,753 17,413 5780 132,584 165,231 188,325
Dobong 20.70 375,975 18,163 2374 141,795 165,311 189,337
Dongdaemun 14.20 376,421 26,509 1057 197,463 221,254 246,017
Dongjak 16.35 405,967 24,830 916 234,609 301,467 319,546
Mapo 23.87 392,650 16,450 787 237,512 298,539 308,995
Seodaemun 17.61 348,575 19,794 881 186,579 210,430 230,711
Seocho 47.00 405,969 8638 1055 482,405 622,950 608,045
Seongdong 16.85 333,535 19,794 1272 251,875 302,894 316,216
Seongbuk 24.57 469,973 19,128 1358 185,801 209,062 233,591
Songpa 33.88 623,876 18,414 1971 382,054 485,901 468,718
Yangcheon 17.40 503,650 28,945 1325 241,695 350,082 330,229
Yeongdeungpo 24.57 408,178 16,613 1301 240,287 303,849 318,542
Yongsan 21.87 235,832 10,783 405 400,306 530,141 564,346
Eunpyeong 29.71 459,196 15,456 495 158,012 180,048 197,485
Jongno 23.91 165,846 6936 172 197,849 224,000 248,343
Jung 9.96 130,044 13,057 291 276,117 330,732 329,893
Jungnang 18.50 427,071 23,085 1085 142,528 166,378 181,882
Total(Seoul) 605.25 10,192,710 16,840 32,101 222,375 279,042 290,946
Note: For appraisal housing price and sample, the sample of this study; Source: Ministry of Land, Transport and Marine Affairs.
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