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U .S Census Bureau . 200 1. X -12-A RIMA R ef erence M anual.
Summary
VAR Mode l in Fo re ca s ting La nd a nd Hous ing Ma rke t J u- hy un Y oon
Th e purp ose of this stu dy is to analyze real estate m arket esp ecially th e land m arket and hou sing m arket to set up th e Vector Auto-Regressive(VAR) Model to forecast the land price and hou sing price in the short-term . Th e advantages of u sing VAR m od el are in that it is sim ple relative to the structural econ om etric m odel, it can reflect the relationships among other affecting variables relative to ARIMA m odel, and it can be applied to analyze the response of a target variable resulting from an impulse of a certain variable(so-called impulse-response analysis).
In advance to establish VAR m odel, land and housing market analysis is accomplished to prom ote understanding of the relationships among concerned variables and to select proper variables to be u sed in the VAR model.
Understanding of real estate market is important in two folds; one is in economic circle of production and consumption, the other is in composing portfolios of agents like individu als, firms, and governments.
In the economic circle, land is an input factor in producing goods in all the other sectors, and house is a final good to be necessarily consum ed in people' s living. In the portfolio composition of each agent, real estate is usually recognized as stable but less liquid investment good compared to the stock or bond in the capital market.
Real estate market is newly enlightened as an emerging one with the introduction of asset securitization and real estate investment companies since 1998, the right after the foreign exchange and financial crisis occurred. Securitization of real estate smoothly connects the real estate sector and the capital market via real estate financing institution like special purpose vehicles(SPC) or real estate investment companies as intermediaries. It alleviates the real estate sector' s lack of liquidity.
Therefore, real estate sector' s role in composing portfolio of agents will be growing.
Actually the relation am ong the national economy, real estate sector including land m arket and housing m arket, and the capital m arket is m ore closely tied than before. We saw a strong interaction between hou sing market and capital market. Housing market strongly affect the national economy that in turn causes land m arket movem ent, thus hou sing m arket is directly and indirectly affect the land market. Thus understanding the m ovement of the real estate sector becom es more important to figure the national economy and vise versa.
From the market analysis, relationships am ong selective variables representing the national economy, land and hou sing market, and capital m arket were derived. Finally five variables such as gross domestic product, consumer price index, corporate bond yield, inclu ding land price and housing price are chosen to be used in establishing VAR model
by testing Granger-Sim s causality. Quarterly data during 1987.1/ 4～
2000.4/ 4 are used for identification, estimation, diagnosis, and prediction of the m odel. Seasonal factors are adju sted using Korean version of X-12-ARIMA m odified by the Bank of Korea.
This study attempted to establish VAR model in two ways. One is to establish the individu al VAR models for land market and housing m arket, separately. And the other is to set up a composed VAR model that includes the interaction between land market and hou sing market.
As a result, composed m odel is identified to be m ore useful to predict the real estate market.
According to the result of the study, we cannot say that land price will increase coming year. The level of land price will be remain as this year. Housing sales price is expected to slightly increase but the increasing rate will be less than that of this year. However, Jonsei price is expected to continuing increase with more than two-digit increasing rate. As an expectation of economic recovery from long-run recess grows recently, housing price will increase a certain degree, however, it will not show temporal boom arisen from stock m arket recess and low-interest policy like this year. And the restructuring of hou sing rental m arket will be continued, that is represented by rapid increase in Jonsei price and by conversion of tenure from Jonsei to monthly rent.
부 록
<그림 1> 지역별 토지거래 변동율
그림 2> 용도지역별 토지거래량(필지기준)
<그림 3> 주택유형별・규모별 주택가격변동율
주) SL: 단독대형, SM: 단독중형, SS: 단독소형, AL: 아파트대형,AM:아파트 중 형, AS: 아파트 소형, ML: 연립대형, MS: 연립소형
<그림4>주택유형별・규모별 전세가격변동율
) SL:단독대형, SM: 단독중형, SS: 단독소형, AL:아파트대형, AM: 아파트 중형, AS: 아파트 소형, ML: 연립대형, MS: 연립소형
<그림 5> 지역별・유형별 주택가격변동율
) SS: 단독서울, SG: 단독광역, SC: 단독중소도시, AS: 아파트서울, AG: 아파트광 역, AC: 아파트중소도시, MS: 연립서울, MG: 연립광역, MC: 연립중소도시
<그림 6> 지역별・주택유형별 전세가격변동율
) SS: 단독서울, SG: 단독광역, SC: 단독중소도시, AS: 아파트서울, AG: 아파트광 역, AC: 아파트중소도시, MS: 연립서울, MG: 연립광역, MC: 연립중소도시
<표 1> 시계열 자료의 ADF 단위근 검정결과
GLLP -2.003 -3.694 * GLPH -2.976 -3.898 * GLPHC -3.662 * -3.946 * GLGDP -1.976 -3.954 * GLM2 -2.111 -3.657 * GLCPI -2.127 -4.267 * GLCON -1.898 -4.679 * R -2.874 -5.783 * GLSP -2.750 -3.874 *
수준 차분
DGPH →DGLP 0.000 0.000 0.000 0.000
DGLP → DGPH 0.051 0.006 0.019 0.017
DGPHC → DGLP 0.000 0.000 0.001 0.003
DGLP → DGPHC 0.331 0.030 0.042 0.034
DGPHC → DGPH 0.600 0.494 0.544 0.607
DGPH → DGPHC 0.920 0.094 0.199 0.317
전년동분기 lag 1 lag 2 lag 3 lag 4
DGLPH →DGLLP 0.01 0.03 0.07 0.07
DGLLP →DGLPH 0.45 0.24 0.06 0.03
DGLPHC →DGLLP 0.05 0.24 0.27 0.41
DGLLP →DGLPHC 0.78 0.25 0.27 0.24
DGLPHC →DGLPH 0.04 0.96 0.80 0.88
DGLPH →DGLPHC 0.21 0.85 0.22 0.23
X-12 ARIMA lag 1 lag 2 lag 3 lag 4
DDLPH →DDLLP 0.00 0.00 0.00 0.00
DDLLP →DDLPH 0.25 0.45 0.85 0.54
DDLPHC →DDLLP 0.14 0.10 0.19 0.35
DDLLP →DDLPHC 0.97 0.79 0.85 0.20
DDLPHC →DDLPH 0.43 0.48 0.46 0.71
DDLPH →DDLPHC 0.64 0.43 0.55 0.25
<표 3> 토지가격과 거시경제지표간의 그랜저 인과관계
전기대비 lag 1 lag 2 lag 3 lag 4
DGGDPN → DGLP 0.09 0.19 0.44 0.66
DGLP → DGGDPN 0.79 0.40 0.36 0.46
DGCPI → DGLP 0.085 0.081 0.040 0.000
DGLP → DGCPI 0.335 0.779 0.123 0.190
DGCON → DGLP 0.594 0.829 0.409 0.487
DGLP → DGCON 0.885 0.937 0.023 0.095
DGSP → DGLP 0.113 0.035 0.009 0.007
DGLP → DGSP 0.351 0.883 0.473 0.137
DR → DGLP 0.004 0.005 0.002 0.001
DGLP → DR 0.130 0.415 0.663 0.552
전년동기대비 lag 1 lag 2 lag 3 lag 4
DGLCON →DGLLP 0.48 0.92 0.43 0.16
DGLLP →DGLCON 0.03 0.12 0.10 0.24
DGLCPI →DGLLP 0.01 0.00 0.00 0.01
DGGLP →DGLCPI 0.02 0.06 0.10 0.16
DGLGDP →DGLLP 0.70 0.05 0.11 0.11
DGLLP →DGLGDP 0.50 0.09 0.34 0.13
DGLSP →DGLLP 0.00 0.51 0.36 0.48
DGLLP →DGLSP 0.17 0.17 0.08 0.04
DR →DLLP 0.00 0.01 0.01 0.01
DLLP →DR 0.05 0.02 0.10 0.14
X-12 ARIMA lag 1 lag 2 lag 3 lag 4
DLCON →DLLP 0.15 0.16 0.44 0.62
DLLP →DLCON 0.00 0.09 0.12 0.29
DLCPI →DLLP 0.12 0.18 0.05 0.04
DLLP →DLCPI 0.04 0.12 0.18 0.15
DLGDP →DLLP 0.45 0.14 0.08 0.22
DLLP →DLGDP 0.10 0.24 0.51 0.90
DLSP →DLLP 0.67 0.50 0.48 0.39
DLLP →DLSP 0.99 0.18 0.29 0.66
DR →DLLP 0.01 0.00 0.01 0.01
DLLP →DR 0.01 0.05 0.09 0.11
<표 4> 주택가격과 거시경제지표간의 그랜저 인과관계
전년동기비 lag 1 lag 2 lag 3 lag 4
DGPH → DGGDPN 0.69 0.94 0.02 0.01
DGGDPN → DGPH 0.04 0.02 0.00 0.01
DGCPI → DGPH 0.01 0.04 0.00 0.02
DGPH → DGCPI 0.13 0.51 0.56 0.02
DGCON → DGPH 0.20 0.00 0.00 0.01
DGPH → DGCON 0.01 0.01 0.05 0.21
DGPH → DGSP 0.15 0.27 0.48 0.54
DGSP → DGPH 0.99 0.07 0.04 0.04
DGPH → DR 0.38 0.65 0.67 0.60
DR → DGPH 0.00 0.00 0.00 0.00
전년동기비 lag 1 lag 2 lag 3 lag 4
DLCON →DLPH 0.68 0.69 0.82 0.57
DLPH →DLCON 0.06 0.19 0.15 0.28
DLCPI →DLPH 0.02 0.34 0.48 0.61
DLPH →DLCPI 0.00 0.01 0.01 0.04
DLGDP →DLPH 0.15 0.62 0.79 0.51
DLPH →DLGDP 0.12 0.00 0.02 0.03
DLSP →DLPH 0.04 0.89 0.78 0.02
DLPH →DLSP 0.30 0.00 0.00 0.00
DR →DLPH 0.00 0.00 0.00 0.00
DLPH →DR 0.00 0.01 0.03 0.01
X-12 ARIMA lag 1 lag 2 lag 3 lag 4
DLCON →DLPH 0.94 0.10 0.20 0.24
DLPH →DLCON 0.00 0.19 0.10 0.21
DLCPI →DLPH 0.38 0.35 0.45 0.31
DLPH →DLCPI 0.02 0.03 0.05 0.06
DLGDP →DLPH 0.54 0.55 0.50 0.61
DLPH →DLGDP 0.00 0.01 0.05 0.10
DLSP →DLPH 0.22 0.14 0.04 0.08
DLPH →DLSP 0.08 0.01 0.00 0.01
DR →DLPH 0.00 0.00 0.00 0.00
DLPH →DR 0.00 0.02 0.00 0.01
<표 5> 전세가격과 거시경제지표간의 그랜저 인과관계
전년동기비 lag 1 lag 2 lag 3 lag 4
DLCON →DLPHC 0.16 0.98 0.96 0.95
DLPHC →DLCON 0.04 0.32 0.28 0.48
DLCPI →DLPHC 0.01 0.04 0.05 0.05
DLPHC →DLCPI 0.00 0.03 0.01 0.02
DLGDP →DLPHC 0.09 0.86 0.91 0.65
DLPHC →DLGDP 0.16 0.01 0.04 0.02
DLSP →DLPHC 0.00 0.12 0.12 0.00
DLPHC →DLSP 0.08 0.00 0.00 0.00
DR →DLPHC 0.00 0.00 0.00 0.00
DLPHC →DR 0.00 0.09 0.19 0.20
X-12 ARIMA lag 1 lag 2 lag 3 lag 4
DLCON →DLPHC 0.98 0.21 0.67 0.81
DLPHC →DLCON 0.03 0.48 0.28 0.48
DLCPI →DLPHC 0.06 0.17 0.10 0.12
DLPHC →DLCPI 0.06 0.10 0.11 0.02
DLGDP →DLPHC 0.52 0.57 0.42 0.61
DLPHC →DLGDP 0.00 0.01 0.05 0.08
DLM2 →DLPHC 0.12 0.26 0.40 0.47
DLPHC →DLM2 0.37 0.74 0.23 0.60
DLSP →DLPHC 0.08 0.03 0.06 0.12
DLPHC →DLSP 0.12 0.05 0.07 0.26
DR →DLPHC 0.00 0.00 0.00 0.00
DLPHC →DR 0.00 0.06 0.04 0.08
<표 6> 거시경제 지표간의 그랜저 인과
X-12 ARIMA lag 1 lag 2 lag 3 lag 4 lag 5 lag 6 lag 7 lag 8 DLCPI →DLCON 0.09 0.65 0.76 0.64 0.41 0.60 0.60 0.53 DLCON →DLCPI 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 DLGDP →DLCON 0.43 0.25 0.39 0.35 0.51 0.42 0.26 0.24 DLCON →DLGDP 0.01 0.03 0.02 0.01 0.05 0.13 0.19 0.33 DLM2 →DLCON 0.24 0.87 0.90 0.49 0.83 0.86 0.81 0.88 DLCON →DLM2 0.47 0.60 0.59 0.42 0.51 0.63 0.54 0.70 DLSP →DLCON 0.07 0.12 0.28 0.23 0.01 0.00 0.01 0.02 DLCON →DLSP 0.89 0.79 0.52 0.78 0.96 0.78 0.19 0.29 DR →DLCON 0.22 0.01 0.01 0.03 0.10 0.18 0.17 0.39 DLCON →DR 0.01 0.03 0.05 0.06 0.10 0.10 0.03 0.03 DLGDP →DLCPI 0.00 0.00 0.00 0.00 0.01 0.02 0.03 0.02 DLCPI →DLGDP 0.41 0.45 0.01 0.00 0.00 0.01 0.02 0.03 DLM2 →DLCPI 0.07 0.03 0.04 0.09 0.07 0.06 0.19 0.19 DLCPI →DLM2 0.71 0.83 0.52 0.55 0.22 0.36 0.47 0.60 DLSP →DLCPI 0.01 0.03 0.05 0.10 0.08 0.15 0.05 0.09 DLCPI →DLSP 0.04 0.16 0.09 0.11 0.09 0.16 0.41 0.43 DR →DLCPI 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 DLCPI →DR 0.61 0.79 0.88 0.95 0.93 0.93 0.93 0.97 DLM2 →DLGDP 0.06 0.02 0.06 0.01 0.02 0.06 0.01 0.01 DLGDP →DLM2 0.67 0.40 0.54 0.79 0.78 0.65 0.65 0.72 DLSP →DLGDP 0.11 0.14 0.47 0.14 0.13 0.09 0.03 0.06 DLGDP →DLSP 0.13 0.01 0.01 0.03 0.13 0.09 0.27 0.12 DR →DLGDP 0.03 0.01 0.01 0.01 0.01 0.03 0.06 0.10 DLGDP →DR 0.00 0.00 0.02 0.02 0.06 0.12 0.21 0.15 DLSP →DLM2 0.04 0.03 0.06 0.00 0.00 0.00 0.01 0.01 DLM2 →DLSP 0.24 0.74 0.87 0.24 0.16 0.34 0.45 0.47 DR →DLM2 0.13 0.16 0.29 0.03 0.14 0.25 0.30 0.30 DLM2 →DR 0.01 0.05 0.12 0.18 0.37 0.58 0.67 0.83 DR →DLSP 0.17 0.01 0.01 0.11 0.30 0.17 0.18 0.13 DLSP →DR 0.34 0.28 0.31 0.47 0.62 0.60 0.50 0.25
<표 7> 벡터오차수정모형결과(토지: 계절더미변수)
토지 D(R) D(GGDPN) D(GLP)
CointEq1 -0.101 0.400 -0.030
(-2.751) (3.876) (-0.440)
D(R(-1)) 0.597 -0.374 -0.654
(3.962) (-0.887) (-2.383)
D(R(-2)) -0.031 -0.230 -0.256
(-0.201) (-0.531) (-0.907)
D(GGDPN(-1)) -0.228 -0.129 -0.337
(-2.979) (-0.600) (-2.420)
D(GGDPN(-2)) -0.012 -0.063 -0.153
(-0.182) (-0.337) (-1.264)
D(GLP(-1)) 0.240 0.131 -0.428
(2.852) (0.553) (-2.789)
D(GLP(-2)) 0.058 0.587 -0.207
(0.743) (2.681) (-1.452)
C -0.536 -17.213 1.931
(-0.683) (-7.836) (1.353)
SD2 -1.533 19.541 -8.878
(-0.851) (3.870) (-2.706)
SD3 4.021 18.671 0.384
(3.065) (5.081) (0.161)
SD4 -0.417 29.556 -0.089
(-0.237) (5.991) (-0.028)
R2 0.524 0.982 0.530
Adj . R2 0.395 0.978 0.404
<표 8> 벡터오차수정모형결과(주택: 계절더미변수)
주택 D(R) D(GGDPN) D(GPH)
CointEq1 -0.239 0.296 -0.053
(-4.003) (1.884) (-0.533)
CointEq2 0.111 -1.580 0.324
(1.086) (-5.860) (1.898)
D(R(-1)) 0.444 -0.280 -0.796
(2.842) (-0.680) (-3.052)
D(R(-2)) 0.022 -0.341 0.119
(0.138) (-0.819) (0.450)
D(GGDPN(-1)) -0.153 0.340 -0.206
(-1.776) (1.498) (-1.435)
D(GGDPN(-2)) -0.048 0.261 -0.093
(-0.796) (1.643) (-0.928)
D(GPH(-1)) -0.149 -0.584 -0.250
(-1.143) (-1.704) (-1.150)
D(GPH(-2)) -0.033 -0.054 -0.333
(-0.292) (-0.179) (-1.755)
C 0.071 -13.524 -0.390
(0.085) (-6.137) (0.279)
SD2 -1.833 15.288 -0.280
(-0.995) (3.149) (0.091)
SD3 1.263 16.572 1.265
(0.917) (4.566) (0.550)
SD4 0.100 21.170 -0.009
(0.065) (5.223) (0.004)
R2 0.538 0.985 0.601
Adj . R2 0.397 0.980 0.480
<표 9> 벡터오차수정모형결과(통합: 계절더미변수)
부동산 D(R) D(GGDPN) D(GPH) D(GLP)
CointEq1 0.041 -0.464 0.012 0.054
(2.910) (-11.502) (0.560) (2.925)
CointEq2 0.066 -1.701 0.067 0.051
(1.554) (-14.107) (1.051) (0.926) D(R(-1)) 0.494 -0.294 -0.733 -0.671 (2.918) (-0.611) (-2.905) (-3.076)
D(R(-2)) -0.307 0.496 0.043 -0.072
(-1.862) (1.058) (0.174) (-0.336) D(GGDPN(-1)) -0.139 0.340 0.046 -0.154
(-1.850) (1.594) (0.408) (-1.585) D(GGDPN(-2)) 0.042 0.095 -0.004 -0.211
(0.673) (0.540) (-0.040) (-2.630) D(GPH(-1)) -0.476 -0.023 -0.075 0.229
(-2.376) (-0.041) (-0.251) (0.886) D(GPH(-2)) -0.252 0.016 0.077 -0.437 (-1.559) (0.035) (0.320) (-2.098) D(GLP(-1)) 0.418 -0.459 -0.568 -0.356
(3.207) (-1.240) (-2.919) (-2.117)
D(GLP(-2)) 0.005 0.213 -0.221 0.174
(0.046) (0.662) (-1.306) (1.192)
SD2 -3.777 13.369 0.730 -3.659
(-2.450) (3.057) (0.318) (-1.843)
SD3 3.010 11.653 -1.646 -2.708
(2.078) (2.835) (-0.762) (-1.451)
SD4 -2.587 23.260 -1.195 2.189
(-2.061) (6.532) (-0.638) (1.354)
R-squared 0.515 0.982 0.667 0.761
Adj . R-squared 0.349 0.975 0.552 0.679