Transcript Seoul Cube Land Model Building and Its Application: The

SEOUL CUBE LAND MODEL
BUILDING AND ITS APPLICATION:
The Effects of Housing Preference for Apartment
on Housing Market
Myung-Jin Jun
Professor, Chung-Ang University,
Korea
Background
• Households of the Seoul metropolitan area (SMA)
have a strong preference for apartment*, unlike
citizen of the Western cities who likes to live in
single-family detached housing.
• Living in a high-rise apartment is a lifelong dream
for many Korean.
• The dominant housing type for Korean has
dramatically shifted from single family housing to
multi-family housing (especially apartment) in the
nation and the SMA over the last three decades
* Apartment is defined in Korea Housing Law as a housing unit occupying
a 5 or more story multi-family housing. As of 2007, 81.6% of apartments
are in 10 or more story apartment buildings in Korea.
Background (cont’)
• The share of apartments to total housing stock has
significantly increased from 13.6% in 1980 to
63.6% in 2010 for apartments, and decreased
from 77.2% to 15.5% for the single family
housings in the SMA over the last three decades.
• According to the National Statistical Office, as of
2010, 71% of residents in the SMA lives in multifamily housings, and 77.5% of them resides in
houses in the high-rise apartment buildings,
indicating high consumer preference for
apartments in the SMA.
Study Purpose
• To investigate the effects of household apartment
preference on the housing market in the SMA
• To empirically build SEOUL CUBELAND MODEL, a
random utility-based land use simulation model with
bid-rent theory, that represents the housing market with
endogenous prices and a market clearing mechanism.
• To analyze the effects of dwelling preference on the
housing market by comparing two different scenarios:
– The baseline scenario taking the difference in housing
preference by income group into account,
– A counterfactual scenario in which there is no difference in
housing preference among income groups.
BUILDING SEOUL CUBELAND
MODEL
Housing Supply
Housing Supply
• 74 Zones
• 3 Housing Types
1) Single-Family
Housing
2) Apartment
3) Others
• 220 location Options
Households
• Sixteen household
types with four
income levels and
four household size
levels covering 24.5
million inhabitants
Agent Category
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Household
Size
(person)
1
2
3
4 or more
1
2
3
4 or more
1
2
3
4 or more
1
2
3
4 or more
Monthly
Income ($)
1,000 or less
1,000 or less
1,000 or less
1,000 or less
1,000-3,000
1,000-3,000
1,000-3,000
1,000-3,000
3,000-5,000
3,000-5,000
3,000-5,000
3,000-5,000
5000 or more
5000 or more
5000 or more
5000 or more
Data Sources
• The primary data sources for the building of the model are
the 2006 Household Travel Survey and the 2010 real estate
sales data for the SMR from the MLTM (Ministry of Land,
Transport, and Maritime Affairs).
• The Household Travel Survey (HTS) data includes sociodemographic and trip information for individual households
and persons such as monthly income, household size, and
residential and employment locations.
• The HTS also contains information on travel mode and time
by trip purpose.
• The real estate sales data includes the lump-sum deposit
amount (Jeonse), monthly rent, and floor space by housing
type.
• The lump-sum deposits are converted into monthly rent
through amortization in terms of US dollars.
Parameter Estimation and Model
Calibration
• Bid Function
• Rent Function
• Cost Adjustment Parameters
Bid Function
• The MNL model estimates 3 equations for 4
household income categories on the dependent
variable, assigning the lowest income group as the
reference group.
• The explanatory variables for the bid function
includes average zonal income, population and
employment densities, and accessibility as a
location attribute.
• Also included is an apartment dummy variable in
order to capture the difference in housing
preference for apartments by income group.
Residential Bid Function Parameters
Constant
Accessibility
Employment Density
(employment/Km2)
Household Density
(person/Km2)
Seoul Dummy (1 if locate
in Seoul, 0 otherwise)
Apartment (1 if housing is
apartment, 0 otherwise)
Log(Average Zonal
Income: $)
N
Likelihood ratio test
Rho-square
Income Group 2
Income Group 3
Income Group 4
Vs.
Vs.
Vs.
Income Group 1
Income Group 1
Income Group 1
-1.450
-15.800
-32.500
**
(-0.66)
(-6.95)
(-12.30)**
2.2.E-05
3.7.E-05
1.8.E-04
*
**
(2.35)
(3.32)
(4.76)**
-2.4.E-05
-2.6.E-05
-4.0.E-05
*
**
(-2.37)
(-2.49)
(-2.95)**
-1.7.E-05
-4.0.E-05
-5.2.E-05
*
(-0.90)
(-2.03)
(-2.19)*
-0.251
-0.119
0.103
(-2.43)**
(-1.09)
(0.76)
0.603
1.400
1.800
(9.07)**
(20.42)*
(21.87)**
0.154
1.880
3.870
(0.56)
(6.57)**
(11.67)**
20,000
12859.26
0.232
t-value in parenthesis, ** p<0.01, *p<0.05
Rent Function
• The rent function has two components: the
logsum of bids and a hedonic part of the rent.
• Five independent variables explaining residential
rents were included: the average of floor space
and single family housing dummy variable for the
dwelling factor, the average zonal monthly
income, education quality, and the Seoul dummy
variable representing location factors.
• We employed the OLS method to calibrate the
rent function for the SMA.
Residential Rent Function Parameter Estimates
β
t-value
p
-0.78242
-9.15
0.0001
Logsum of Bids
0.20242
3.94
0.0001
Average Zonal Monthly Income ($)
0.00023
4.63
0.0001
Average Size of Floor Space (m2)
0.00614
5.86
0.0001
-0.14895
-5.69
0.0001
Education Quality (1 if locate in Kangnam 3 Gus, 0
otherwise)
0.13572
2.28
0.0236
Seoul Dummy (1 if locate in Seoul, 0 otherwise)
0.25207
9.73
0.0001
Intercept
Single Family Housing Dummy
N=222, R2=0.84
,
Model Calibration
• To match the number of estimated housing
units by housing type to the observed units.
• We estimate supply cost adjustment factors
using an iterative method as follows:
• Iteration continues until the difference between
the estimated and observed real estate units is
within the tolerance level.
Actual and Estimated Housing Units
Supplied
250000.00
y = 0.9785x + 2103.9
R² = 0.991
200000.00
Estimated
150000.00
100000.00
50000.00
0.00
0
50000
100000
150000
Actual
200000
250000
The Effects of Apartment Preference
on Housing Supply and Rent
The Effects of Apartment Preference on Housing
Supply by Housing Type
Housing Type
Region
Seoul
Apartment
Single Family Housing
Other Type Housing
Baseline (A)
No-Preference
Scenario (B)
Difference
(A-B)
1,257,761
1,142,080
115,681
Incheon
411,159
377,660
33,499
Kyunggi
1,827,759
1,663,363
164,396
Total
3,496,680
3,183,104
313,576
Seoul
1,270,029
1,378,673
- 108,644
Incheon
200,729
215,864
-
15,135
Kyunggi
1,024,687
1,109,931
-
85,244
Total
2,495,445
2,704,468
- 209,023
Seoul
576,522
626,729
-
50,207
Incheon
191,801
206,375
-
14,573
Kyunggi
476,918
516,690
-
39,772
1,245,242
1,349,794
Total
- 104,553
Spatial Distribution of Apartment
Preference Impact by Zone Type
Apartment
Single Family Housing
Zone Type*
Units
CBD
Central City
SUBCENTER
(City of
Seoul)
NONCENTER
Suburban
Area
Total
INNER RING
OUTER RING
1,597
29,360
84,724
140,139
57,756
313,576
% Share
Units
% Share
Other Types
Units
% Share
0.5% -
3,730
1.8% -
1,847
1.8%
9.4% -
19,536
9.3% -
10,066
9.6%
27.0% -
85,378
40.8% -
38,294
36.6%
44.7% -
69,103
33.1% -
42,055
40.2%
18.4% -
31,275
15.0% -
12,290
11.8%
100.0% - 104,553
100.0%
100.0% - 209,023
Apartment Rent Impacts of Apartment
Preference ($/Month)
Conclusions
• The higher apartment preference of the medium- and
high-income group has contributed to the addition of
substantial apartment units (140,000 units) in the
suburban inner ring zones and to the reduction of single
family units (85,000 units) in the residential zones of
the central city, leading to population suburbanization
with dense suburban development.
• Higher apartment preference of the medium- and highincome group has the largest rent impact in the
wealthiest communities in Seoul, including Seocho,
Kangnam, and Yongsan, demonstrating the high income
group's willingness to pay for apartments in these areas.
Policy Implications
• Seoul’s experience presents significant policy implications
for the Smart Growth policy
• Some claim that Americans’ preference for single-family
homes is so strong that smart growth strategies supporting
higher residential density cannot be implemented
successfully
• However, consumer preference for large suburban singlefamily houses is declining, as demographic and economic
factors in the housing market are changing, including the
aging population, smaller households, rising fuel prices, etc.
• Seoul’s case study supports that multi-family dwellings
such as apartments can be an alternative to suburban singlefamily housing if they offer accessibility and amenity
advantages, leading to a dense suburban development