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SEOUL CUBE LAND MODEL BUILDING AND ITS APPLICATION: The Effects of Housing Preference for Apartment on Housing Market Myung-Jin Jun Professor, Chung-Ang.

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Presentation on theme: "SEOUL CUBE LAND MODEL BUILDING AND ITS APPLICATION: The Effects of Housing Preference for Apartment on Housing Market Myung-Jin Jun Professor, Chung-Ang."— Presentation transcript:

1 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

2 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.

3 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 multi- family housings, and 77.5% of them resides in houses in the high-rise apartment buildings, indicating high consumer preference for apartments in the SMA.

4 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.


6 Housing Supply 74 Zones 3 Housing Types 1) Single-Family Housing 2) Apartment 3) Others 220 location Options

7 Households Agent Category ID Household Size (person) Monthly Income ($) 11 1,000 or less 22 33 44 or more 1,000 or less 51 1,000-3,000 62 73 84 or more 1,000-3,000 91 3,000-5,000 102 3,000-5,000 113 3,000-5,000 124 or more 3,000-5,000 131 5000 or more 142 5000 or more 153 5000 or more 164 or more 5000 or more Sixteen household types with four income levels and four household size levels covering 24.5 million inhabitants

8 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 socio- demographic 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.

9 Parameter Estimation and Model Calibration Bid Function Rent Function Cost Adjustment Parameters

10 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.

11 Residential Bid Function Parameters Income Group 2 Vs. Income Group 1 Income Group 3 Vs. Income Group 1 Income Group 4 Vs. Income Group 1 Constant -1.450 (-0.66) -15.800 (-6.95) ** -32.500 (-12.30) ** Accessibility 2.2.E-05 (2.35) * 3.7.E-05 (3.32) ** 1.8.E-04 (4.76) ** Employment Density (employment/Km 2 ) -2.4.E-05 (-2.37) * -2.6.E-05 (-2.49) ** -4.0.E-05 (-2.95) ** Household Density (person/Km 2 ) -1.7.E-05 (-0.90) -4.0.E-05 (-2.03) * -5.2.E-05 (-2.19) * Seoul Dummy (1 if locate in Seoul, 0 otherwise) -0.251 (-2.43) ** -0.119 (-1.09) 0.103 (0.76) Apartment (1 if housing is apartment, 0 otherwise) 0.603 (9.07) ** 1.400 (20.42 )* 1.800 (21.87) ** Log(Average Zonal Income: $) 0.154 (0.56) 1.880 (6.57) ** 3.870 (11.67) ** N20,000 Likelihood ratio test12859.26 Rho-square0.232 t-value in parenthesis, ** p<0.01, *p<0.05

12 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.

13 Residential Rent Function Parameter Estimates β t-valuep Intercept-0.78242-9.150.0001 Logsum of Bids0.202423.940.0001 Average Zonal Monthly Income ($)0.000234.630.0001 Average Size of Floor Space (m 2 )0.006145.860.0001 Single Family Housing Dummy-0.14895-5.690.0001 Education Quality (1 if locate in Kangnam 3 Gus, 0 otherwise) 0.135722.280.0236 Seoul Dummy (1 if locate in Seoul, 0 otherwise)0.252079.730.0001 N=222, R 2 =0.84

14 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.

15 Actual and Estimated Housing Units Supplied

16 The Effects of Apartment Preference on Housing Supply and Rent

17 The Effects of Apartment Preference on Housing Supply by Housing Type Housing TypeRegionBaseline (A) No-Preference Scenario (B) Difference (A-B) Apartment Seoul1,257,761 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 Single Family Housing 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 Other Type Housing Seoul 576,522 626,729- 50,207 Incheon 191,801 206,375- 14,573 Kyunggi 476,918 516,690- 39,772 Total 1,245,242 1,349,794- 104,553

18 Spatial Distribution of Apartment Preference Impact by Zone Type Zone Type* ApartmentSingle Family Housing Other Types Units% ShareUnits % ShareUnits% Share Central City (City of Seoul) CBD1,5970.5%- 3,7301.8%- 1,8471.8% SUBCENTER 29,360 9.4%- 19,5369.3%- 10,0669.6% NON- CENTER 84,724 27.0%- 85,37840.8%- 38,29436.6% Suburban Area INNER RING 140,139 44.7%- 69,10333.1%- 42,05540.2% OUTER RING 57,756 18.4%- 31,27515.0%- 12,29011.8% Total 313,576 100.0%- 209,023100.0%- 104,553100.0%

19 Apartment Rent Impacts of Apartment Preference ($/Month)

20 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 high- income 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.

21 Policy Implications Seouls 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 single- family houses is declining, as demographic and economic factors in the housing market are changing, including the aging population, smaller households, rising fuel prices, etc. Seouls case study supports that multi-family dwellings such as apartments can be an alternative to suburban single- family housing if they offer accessibility and amenity advantages, leading to a dense suburban development

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