The purpose of this study is to analyze determinants of macroeconomic variables which have influence on prices of apartments in Seoul and 6 major metropolitan cities. The macroeconomic variables have been set to be 8 variables including the total amou...
다국어 입력
あ
ぁ
か
が
さ
ざ
た
だ
な
は
ば
ぱ
ま
や
ゃ
ら
わ
ゎ
ん
い
ぃ
き
ぎ
し
じ
ち
ぢ
に
ひ
び
ぴ
み
り
う
ぅ
く
ぐ
す
ず
つ
づ
っ
ぬ
ふ
ぶ
ぷ
む
ゆ
ゅ
る
え
ぇ
け
げ
せ
ぜ
て
で
ね
へ
べ
ぺ
め
れ
お
ぉ
こ
ご
そ
ぞ
と
ど
の
ほ
ぼ
ぽ
も
よ
ょ
ろ
を
ア
ァ
カ
サ
ザ
タ
ダ
ナ
ハ
バ
パ
マ
ヤ
ャ
ラ
ワ
ヮ
ン
イ
ィ
キ
ギ
シ
ジ
チ
ヂ
ニ
ヒ
ビ
ピ
ミ
リ
ウ
ゥ
ク
グ
ス
ズ
ツ
ヅ
ッ
ヌ
フ
ブ
プ
ム
ユ
ュ
ル
エ
ェ
ケ
ゲ
セ
ゼ
テ
デ
ヘ
ベ
ペ
メ
レ
オ
ォ
コ
ゴ
ソ
ゾ
ト
ド
ノ
ホ
ボ
ポ
モ
ヨ
ョ
ロ
ヲ
―
http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
예시)
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
А
Б
В
Г
Д
Е
Ё
Ж
З
И
Й
К
Л
М
Н
О
П
Р
С
Т
У
Ф
Х
Ц
Ч
Ш
Щ
Ъ
Ы
Ь
Э
Ю
Я
а
б
в
г
д
е
ё
ж
з
и
й
к
л
м
н
о
п
р
с
т
у
ф
х
ц
ч
ш
щ
ъ
ы
ь
э
ю
я
′
″
℃
Å
￠
￡
￥
¤
℉
‰
＄
％
Ｆ
￦
㎕
㎖
㎗
ℓ
㎘
㏄
㎣
㎤
㎥
㎦
㎙
㎚
㎛
㎜
㎝
㎞
㎟
㎠
㎡
㎢
㏊
㎍
㎎
㎏
㏏
㎈
㎉
㏈
㎧
㎨
㎰
㎱
㎲
㎳
㎴
㎵
㎶
㎷
㎸
㎹
㎀
㎁
㎂
㎃
㎄
㎺
㎻
㎽
㎾
㎿
㎐
㎑
㎒
㎓
㎔
Ω
㏀
㏁
㎊
㎋
㎌
㏖
㏅
㎭
㎮
㎯
㏛
㎩
㎪
㎫
㎬
㏝
㏐
㏓
㏃
㏉
㏜
㏆
아파트가격에 미치는 거시경제변수에 관한 연구 = A study on Macroeconomic Variables Affecting the Price of Apartments
한글로보기https://www.riss.kr/link?id=T14223847
- 저자
-
발행사항
경산 : 대구대학교 대학원, 2016
-
학위논문사항
학위논문(박사) -- 대구대학교 대학원 , 도시학과 부동산학전공 , 2016. 8
-
발행연도
2016
-
작성언어
한국어
- 주제어
-
KDC
359
-
발행국(도시)
경상북도
-
형태사항
ⅷ, 166 ; 26 cm
-
일반주기명
지도교수: 이성화
-
UCI식별코드
I804:47009-000000023971
-
소장기관
- 국립중앙도서관
- 대구대학교 학술정보원
- 국립중앙도서관
-
0
상세조회 -
0
다운로드
부가정보
다국어 초록 (Multilingual Abstract)
The purpose of this study is to analyze determinants of macroeconomic variables which have influence on prices of apartments in Seoul and 6 major metropolitan cities. The macroeconomic variables have been set to be 8 variables including the total amount of currency(M2), gross national income(GNI), CD rates, consumer price index, producer price index, KOSPI, apartment price index and mortgage loans. In addition, real estate policies of Korea's administrations of Dae-jung Kim, Moo-hyun Roh, Myung-bak Lee and Geun-hye Park have been compared and analyzed and the results have been evaluated with diagrams.
To overcome limits of studies so far, emphasis has been placed on analysis of the interconnectivity between Seoul and local areas.
The method to verify the study is based on Vector Autoregressive Model which is most widely used among multivariate analytical methods. After looking at descriptive statistics quantities of variables such as apartment price index, mortgage loans and economic variables to analyze, the unit root test has been implemented as time series data can have non-stationarity, not stationarity, due to characteristics of each economic variable. Granger Causality analysis has been implemented to determine the influential relation between apartment price index and macroeconomic variables and the order of priority for the variables, and models were built finally. The variance decomposition has been implemented to look at the impulse response and immanent relation for measuring the influence of variables in the established models so that it was possible to analyze the statistical relations of apartment price index, economic index and mortgage loans which are set in this study.
First, data of macroeconomic variables, apartment price index and mortgage loans which are subjects of the study have been stabilized through the unit root test as they have the long-term trend.
Second, when the linear relationship between economic variables and apartment price index and mortgage loans was analyzed through the cointegration test, they were found out to be different according to environments of Seoul and 6 major metropolitan cities. With 5% as a significance level, 7 variables in Seoul, 4 variables in Busan, Daegu and Incheon and 3 variables in Gwangju, Daejeon and Ulsan have been found to be in a public relation. In case of Seoul, the linear relation of economic variables with the apartment price index has been found to be well established. Therefore, it was possible to set the model's formula through the cointegration test.
Third, Granger Causality of variables was analyzed to find out the relations between variables. There were also differences of the variables between Seoul and 6 major metropolitan cities. For instance, in Seoul, variables such as consumer price index, producer price index, GNI and mortgage loans are influential while in Busan, the total amount of currency, GNI and consumer price index are found out to be influential variables, which shows influences of economic indexes are different in each area. Therefore, it has been proven that the application of economic variables should be differentiated according to each region and different real estate strategies should be adopted in each area.
Fourth, the impulse response analysis which evaluates the influence of apartment price index on the economic variables has found that the influence and impulse of same economic indexes have been different according to the region like in Granger Causality. For instance, as for the amount of currency, the impulse started from negative numbers and moved slowly to the positive direction in Seoul and the impulse started from the positive direction and shifted to the negative direction after 3rd times in Busan. As a result of the analysis, it was found that in Seoul, 7 variables are organically and closely affecting each other and the influence of the amount of currency is weak as can be seen in the Granger Causality analysis result, but in Busan, the increase of the amount of currency has immediate positive influence on the price of apartments. Therefore, the meaning of same macroeconomic index can be different according to each region, so real estate policies reflecting the differences have to be established and evaluated.
Fifth, the variance decomposition was implemented in proportion to the degree of the impulse to the variables. So the variables were separated into 10 parts and the impulse response was measured, as a result, differences according to each region have been found.
The limit of this study is that even though the real estate variables are same, they can appear differently according to the region, space and time and external factors couldn't be controlled actively. Therefore, it is necessary to expand the range of the space to provinces and cities in the future study. Furthermore, for this study to have the local representativeness, the economic index should be designed for each region and unique measurement of the variables for each region and meaningful verification have to support the study, but it was not sufficiently done in this study. Therefore, it is necessary to carry out diversified studies on this issue and there should be continuous studies on increasing explainable feature of the models. This study also has a limit in that universal variables such as economic index, apartment price index and mortgage loans were used, so the variables to explain are limited. This is why there should be more verifications on relations related to selection of the economic index. Therefore, it is needed to implement an in-depth study on the usage of variables including the variables used in this study and future macroeconomic variables and diversified simulations of the variables are also essential. This will be the subject for the next study.
To overcome limits of studies so far, emphasis has been placed on analysis of the interconnectivity between Seoul and local areas.
The method to verify the study is based on Vector Autoregressive Model which is most widely used among multivariate analytical methods. After looking at descriptive statistics quantities of variables such as apartment price index, mortgage loans and economic variables to analyze, the unit root test has been implemented as time series data can have non-stationarity, not stationarity, due to characteristics of each economic variable. Granger Causality analysis has been implemented to determine the influential relation between apartment price index and macroeconomic variables and the order of priority for the variables, and models were built finally. The variance decomposition has been implemented to look at the impulse response and immanent relation for measuring the influence of variables in the established models so that it was possible to analyze the statistical relations of apartment price index, economic index and mortgage loans which are set in this study.
First, data of macroeconomic variables, apartment price index and mortgage loans which are subjects of the study have been stabilized through the unit root test as they have the long-term trend.
Second, when the linear relationship between economic variables and apartment price index and mortgage loans was analyzed through the cointegration test, they were found out to be different according to environments of Seoul and 6 major metropolitan cities. With 5% as a significance level, 7 variables in Seoul, 4 variables in Busan, Daegu and Incheon and 3 variables in Gwangju, Daejeon and Ulsan have been found to be in a public relation. In case of Seoul, the linear relation of economic variables with the apartment price index has been found to be well established. Therefore, it was possible to set the model's formula through the cointegration test.
Third, Granger Causality of variables was analyzed to find out the relations between variables. There were also differences of the variables between Seoul and 6 major metropolitan cities. For instance, in Seoul, variables such as consumer price index, producer price index, GNI and mortgage loans are influential while in Busan, the total amount of currency, GNI and consumer price index are found out to be influential variables, which shows influences of economic indexes are different in each area. Therefore, it has been proven that the application of economic variables should be differentiated according to each region and different real estate strategies should be adopted in each area.
Fourth, the impulse response analysis which evaluates the influence of apartment price index on the economic variables has found that the influence and impulse of same economic indexes have been different according to the region like in Granger Causality. For instance, as for the amount of currency, the impulse started from negative numbers and moved slowly to the positive direction in Seoul and the impulse started from the positive direction and shifted to the negative direction after 3rd times in Busan. As a result of the analysis, it was found that in Seoul, 7 variables are organically and closely affecting each other and the influence of the amount of currency is weak as can be seen in the Granger Causality analysis result, but in Busan, the increase of the amount of currency has immediate positive influence on the price of apartments. Therefore, the meaning of same macroeconomic index can be different according to each region, so real estate policies reflecting the differences have to be established and evaluated.
Fifth, the variance decomposition was implemented in proportion to the degree of the impulse to the variables. So the variables were separated into 10 parts and the impulse response was measured, as a result, differences according to each region have been found.
The limit of this study is that even though the real estate variables are same, they can appear differently according to the region, space and time and external factors couldn't be controlled actively. Therefore, it is necessary to expand the range of the space to provinces and cities in the future study. Furthermore, for this study to have the local representativeness, the economic index should be designed for each region and unique measurement of the variables for each region and meaningful verification have to support the study, but it was not sufficiently done in this study. Therefore, it is necessary to carry out diversified studies on this issue and there should be continuous studies on increasing explainable feature of the models. This study also has a limit in that universal variables such as economic index, apartment price index and mortgage loans were used, so the variables to explain are limited. This is why there should be more verifications on relations related to selection of the economic index. Therefore, it is needed to implement an in-depth study on the usage of variables including the variables used in this study and future macroeconomic variables and diversified simulations of the variables are also essential. This will be the subject for the next study.
The purpose of this study is to analyze determinants of macroeconomic variables which have influence on prices of apartments in Seoul and 6 major metropolitan cities. The macroeconomic variables have been set to be 8 variables including the total amount of currency(M2), gross national income(GNI), CD rates, consumer price index, producer price index, KOSPI, apartment price index and mortgage loans. In addition, real estate policies of Korea's administrations of Dae-jung Kim, Moo-hyun Roh, Myung-bak Lee and Geun-hye Park have been compared and analyzed and the results have been evaluated with diagrams.
To overcome limits of studies so far, emphasis has been placed on analysis of the interconnectivity between Seoul and local areas.
The method to verify the study is based on Vector Autoregressive Model which is most widely used among multivariate analytical methods. After looking at descriptive statistics quantities of variables such as apartment price index, mortgage loans and economic variables to analyze, the unit root test has been implemented as time series data can have non-stationarity, not stationarity, due to characteristics of each economic variable. Granger Causality analysis has been implemented to determine the influential relation between apartment price index and macroeconomic variables and the order of priority for the variables, and models were built finally. The variance decomposition has been implemented to look at the impulse response and immanent relation for measuring the influence of variables in the established models so that it was possible to analyze the statistical relations of apartment price index, economic index and mortgage loans which are set in this study.
First, data of macroeconomic variables, apartment price index and mortgage loans which are subjects of the study have been stabilized through the unit root test as they have the long-term trend.
Second, when the linear relationship between economic variables and apartment price index and mortgage loans was analyzed through the cointegration test, they were found out to be different according to environments of Seoul and 6 major metropolitan cities. With 5% as a significance level, 7 variables in Seoul, 4 variables in Busan, Daegu and Incheon and 3 variables in Gwangju, Daejeon and Ulsan have been found to be in a public relation. In case of Seoul, the linear relation of economic variables with the apartment price index has been found to be well established. Therefore, it was possible to set the model's formula through the cointegration test.
Third, Granger Causality of variables was analyzed to find out the relations between variables. There were also differences of the variables between Seoul and 6 major metropolitan cities. For instance, in Seoul, variables such as consumer price index, producer price index, GNI and mortgage loans are influential while in Busan, the total amount of currency, GNI and consumer price index are found out to be influential variables, which shows influences of economic indexes are different in each area. Therefore, it has been proven that the application of economic variables should be differentiated according to each region and different real estate strategies should be adopted in each area.
Fourth, the impulse response analysis which evaluates the influence of apartment price index on the economic variables has found that the influence and impulse of same economic indexes have been different according to the region like in Granger Causality. For instance, as for the amount of currency, the impulse started from negative numbers and moved slowly to the positive direction in Seoul and the impulse started from the positive direction and shifted to the negative direction after 3rd times in Busan. As a result of the analysis, it was found that in Seoul, 7 variables are organically and closely affecting each other and the influence of the amount of currency is weak as can be seen in the Granger Causality analysis result, but in Busan, the increase of the amount of currency has immediate positive influence on the price of apartments. Therefore, the meaning of same macroeconomic index can be different according to each region, so real estate policies reflecting the differences have to be established and evaluated.
Fifth, the variance decomposition was implemented in proportion to the degree of the impulse to the variables. So the variables were separated into 10 parts and the impulse response was measured, as a result, differences according to each region have been found.
The limit of this study is that even though the real estate variables are same, they can appear differently according to the region, space and time and external factors couldn't be controlled actively. Therefore, it is necessary to expand the range of the space to provinces and cities in the future study. Furthermore, for this study to have the local representativeness, the economic index should be designed for each region and unique measurement of the variables for each region and meaningful verification have to support the study, but it was not sufficiently done in this study. Therefore, it is necessary to carry out diversified studies on this issue and there should be continuous studies on increasing explainable feature of the models. This study also has a limit in that universal variables such as economic index, apartment price index and mortgage loans were used, so the variables to explain are limited. This is why there should be more verifications on relations related to selection of the economic index. Therefore, it is needed to implement an in-depth study on the usage of variables including the variables used in this study and future macroeconomic variables and diversified simulations of the variables are also essential. This will be the subject for the next study.
목차 (Table of Contents)
- Ⅰ. 서론 1
- 제1절 연구의 배경 및 목적 1
- 제2절 연구의 방법 및 범위 4
- 제3절 연구구성 및 흐름도 5
- Ⅱ. 이론배경 및 선행연구 고찰 8
- Ⅰ. 서론 1
- 제1절 연구의 배경 및 목적 1
- 제2절 연구의 방법 및 범위 4
- 제3절 연구구성 및 흐름도 5
- Ⅱ. 이론배경 및 선행연구 고찰 8
- 제1절 부동산 주거 특성 및 결정 이론 8
- 1. 주거 현황 특성 8
- 2. 부동산 가격 결정 이론 12
- 제2절 부동산 가격 결정 요인 19
- 1. 물리적 특성 19
- 2. 경제적 특성 20
- 3. 제도적 특성 21
- 4. 아파트 개별 특성 21
- 5. 각 정부 별 부동산 정책 25
- 6. 정권의 변동에 따른 아파트 가격 변화 29
- 제3절 선행연구 고찰 32
- 1. 주택가격과 거시경제 변수에 관한 선행연구 32
- 2. 부동산 정책에 따른 주택가격 변동에 관한 선행 연구 44
- 3. 선행연구와의 차별성 50
- Ⅲ. 연구모형 및 방법 52
- 제1절 시계열분석 방법론 52
- 1. 시계열의 정상성 52
- 2. 공적분 검정 56
- 3. 벡터자기회귀모형(VAR모형) 58
- 4. 그랜저인과관계 및 충격반응 분석 60
- 5. 예측오차의 분산분해 62
- 제2절 연구 분석모형 설정 63
- 1. 연구 모형 및 가설 설정 63
- 2. 연구 분석 절차 65
- Ⅳ. 실증분석 67
- 제1절 기초자료 분석 67
- 제2절 단위근 검정 76
- 제3절 그랜저인과관계 분석 84
- 제4절 공적분 검정분석 89
- 제5절 충격반응분석 및 예측오차 분산분해 분석 101
- 1. 결정모형 추정 101
- 2. 충격반응분석 102
- 3. 예측오차 분산분해 분석 107
- 제6절 종합적 검토 118
- V. 결론 120
- 제1절 연구결과 요약 120
- 제2절 연구 시사점 및 연구 한계 121
- 참고 문헌 124
- Abstract 129
- 부록 1 132
- 부록 2 154
참고문헌 (Reference)
1. 계량경제학, 박범조, 시그마프레스, 박영사, , 2007
2. 도시경제론, 홍기용, 박영사, 홍문사, , 2004
3. 주택경제론, 이중희, 형설출판사, , 2002
4. 경제 시계열 분석, 한상범, 장윤순, 박준용, 경문사, , 2002
5. 「금융계량분석」, 조담, 청람, 청람, , 2006
6. 현대부동산학. 제3판, 안정근, 법문사, , 2006
7. 부동산학 개론. 제2판, 서진형, 김경한, 이수겸, 부연사, , 2011
8. 부동산 정책론 개정판, 김용민, 형설 출판사, , 2005
9. 서울시의 회고와 전망, 주종원, 대한건축학회, 대한건축학회, 35권, 2호, pp74-75, , 1991
10. 『다변량시계열분석』, 최병선, 세경사, 세경사, , 1995
1. 계량경제학, 박범조, 시그마프레스, 박영사, , 2007
2. 도시경제론, 홍기용, 박영사, 홍문사, , 2004
3. 주택경제론, 이중희, 형설출판사, , 2002
4. 경제 시계열 분석, 한상범, 장윤순, 박준용, 경문사, , 2002
5. 「금융계량분석」, 조담, 청람, 청람, , 2006
6. 현대부동산학. 제3판, 안정근, 법문사, , 2006
7. 부동산학 개론. 제2판, 서진형, 김경한, 이수겸, 부연사, , 2011
8. 부동산 정책론 개정판, 김용민, 형설 출판사, , 2005
9. 서울시의 회고와 전망, 주종원, 대한건축학회, 대한건축학회, 35권, 2호, pp74-75, , 1991
10. 『다변량시계열분석』, 최병선, 세경사, 세경사, , 1995
11. SAS를 이용한 시계열분석, 신기일, 교우사, , 2002
12. 주택가격변동에 관한 연구, 김대우, 최승남, 용인대학교 산업경영연구소, 산업경영논총, 11집, pp1-18 용인대학교 산업경영 연구소, , 2004
13. 아파트 선호의 문화 사회학, 전상인, 서울대학교 환경대학원, 환경노총, 제45권, pp11-32 서울대학교 환경대학원, , 2007
14. 「시계열자료의 분석과 예측」, 김현철, 교육과학사, 교육과학사, , 2005
15. 주택시장 분석과 정책과제 분석, 차문중, 제3장 주택가격의 결정요인과 정책적 시사점에 관한 연구. pp85-209 한국개발연구원, , 2004
16. 부동산 정책기조 변화의 영향 분석, 이영범, 광운대학교 박사학위논문, , 2011
17. “아파트경매 매각가율 변동요인”, 김성숙, 전주대학교 박사학위논문, , 2014
18. 지역별 주택 가격 변동에 관한 연구, 한기호, 한양대학교 박사학위논문, , 2010
19. 교육 환경이 주택가격에 미치는 영향, 엄근용, 한양대학교 석사학위논문, , 2005
20. 한국 주택정책의 유효성에 관한 연구, 박홍희, 중앙대학교 박사학위논문, , 2012
21. 대구 주택 가격변동 패턴에 관한 연구, 주용성, 대구대학교 박사학위논문, , 2012
22. “토지가격의 변동요인에 관한 연구”, 박 철, 서울 시립대학교 박사학위논문, , 2002
23. “리츠지수의 영향요인에 관한 연구.”, 황숙희, 전주대학교 박사학위논문, , 2013
24. 주택 가격 변동의 선행지표에 관한연구, 김종선, 인천대학교 박사학위논문, , 2011
25. 단위근 검정의 실증적 분석에 관한 연구, 이석원, 전주대학교 석사학위논문, , 2000
26. VECM를 이용한 아파트 가격 예측에 관한 연구, 이수용, 강남대학교 박사학위 논문, , 2013
27. 1960-1970년대 한국 아파트의 변천에 관한 연구, 장성수, 서울대학교 박사학위 논문, , 1994
28. 거시경제 요인이 부동산 시장에 미치는 영향, 백홍기, 고려대학교 박사 학위논문, , 2007
29. 부동산 정책 변화가 주택가격에 미치는 영향, 양이정, 한양대학교 석사논문, , 2009
30. 우리나라 아파트 특성이 가격에 미치는 영향, 김진희, 경기대학교 박사학위논문, , 2014
31. 아파트 주거 문화의 특성에 관한 사회적 연구, 천현숙, 연세대학교 박사학위 논문, , 2002
32. 주택 가격에 대한 거시경제 요인의 영향 분석, 성용림, 단국대학교 석사학위 논문, , 2012
33. 거시경제 변수를 이용한 부동산효과 실증 분석, 김진수, 서울시립대학교 박사학위 논문, , 2013
34. 우리나라 부동산 가격과 물가에 관한 실증분석, 김경환, 韓國銀行 金融經濟硏究所, 한국은행 금융 경제연구소 일반 단행본 39p, , 1991
35. 부동산 가격과 부동산 정책. 주택금융. 152, pp1-14, 서승환, 한국 주택은행, , 1993
36. “경기변동에 따른 주택시장 변화에 관한 연구”, 류현옥, 건국대학교 대학원, 건국대학교 박사학위논문, , 2013
37. 주택 가격과 거시경제 변수간의 동태적 관계분석, 전혜정, 중앙대학교 박사학위 논문, , 2012
38. 「주택가격과 거시경제변수의 경기동행성 분석」, 윤재호, 한양대학교 박사학위논문, , 2015
39. 주택 문제 해소를 위한 새 정부의 정책 과제. 국토, 조주현, 통권 256호 국토연구원, , 2003
40. 주택 정책이 주택가격에 미치는 영향에 관한 연구, 김종호, 경기대학교 석사학위 논문, , 2011
41. 참여정부 와 이명박 정부의 부동산 정책기조 분석, 이영범, 한국주거환경학회, 한국 주거환경학회, 8권, 1호, pp85-99, , 2010
42. “한국 역대정부의 주택정책에 대한 만족도 연구”, 전광섭, 한국외국어대학교 대학원, 한국외국어대학교 박사학위 논문, , 2007
43. VAR모형을 이용한 부동산가격 변동 요인에 관한 연구, 안지아, 박헌수, 한국부동산연구원, 부동산연구. 제19집, 제1호, p29-49. 한국 부동산연구원, , 2009
44. 거시경제 변수가 오피스 빌딩 공실률에 미치는 영향, 정성원, 단국대학교 석사학위 논문, , 2012
45. 붓스트랩 방법을 이용한 단위근 검정 통계량의 개선, 박신형, 이화여자대학교 대학원, 이화여자대학교 석사학위논문, , 2010
46. “The CausalRelationship between RealEstate and stock Marketsinkorea”, 장명기, 동북아경제연구. 제19권, 제3호, pp117-140 한국동북아경제학회, , 2007
47. 거시경제 변수가 보증부 월세 주택시장에 미치는 영향, 김준식, 전주대학교 박사학위논문, , 2014
48. 우리나라 주택형식 으로의 아파트의 일반화 요인 분석, 강인호, 한국 건축 학회논문집, 107, pp101-112. 한국건축학회, , 1997
49. 주택시장 안정화 정책이 대도시 아파트에 미치는 영향, 최혜경, 전주대학교 박사학위 논문, , 2008
50. 한국 주택시장의 불안요인과 안정화 정책에 관한 연구, 문상현, 동아대학교 박사학위논문, , 2007
51. 거시경제 변수가 주택 매매 및 전세지수에 미치는 영향, 이희석, 경원대학교 박사학위 논문, , 2007
52. 주택 담보 대출이 아파트 가격과 거래량에 미치는 영향, 이선우, 영산대학교 박사학위 논문, , 2015
53. 주택 정책과 거시경제 변수가 주택 가격에 미치는 영향, 조수희, 목원대학교 석사학위 논문, , 2007
54. “주택경기 변동이 주택점유형태 선택에 미치는 영향”, 윤성현, 단국대학교 대학원, 단국대학교 박사학위논문, , 2011
55. 시장 친화적 토지 공개념 실현을 위한 공공의 역할 연구, 최인호, 한국토지공법학회, 토지 공법연구, 제38, pp607-621 한국 토지 공법학회, , 2007
56. 주택 금융 변수가 주택 매매 및 전세시장에 미치는 영향, 김용길, 영산대학교 박사학위 논문, , 2013
57. “주택가격과 관련 경제변수간의 상관관계에 관한 연구", 김용철, 대한국토·도시계획학회, 국토계획, 제31권, 제6호, pp67-82, , 1996
58. 부동산 정책 변화가 인천지역 아파트 거래에 미치는 영향, 박연훈, 인하대학교 석사학위논문, , 2012
59. 서울 아파트 가격이 6대 광역시 아파트 가격에 미치는 영향, 천인호, 한국지역사회학회, 지역사회연구, 14권, 4호, pp181-200 한국지역사회학회, , 2006
60. 은행대출이 주거용 부동산가격에 미치는 영향에 관한 연구, 변계새, 세한대학교 석사학위 논문, , 2014
61. “시계열분석을 통한 부산 아파트 가격 특성에 관한 연구”, 이규헌, 영산대학교 박사학위 논문, , 2011
62. 「주택시장과 경기변동에 대한 주택정책 반응에 관한 연구」, 박유석, 목원대학교 박사학위논문, , 2015
63. 부동산 정책이 수도권 아파트 시장의 가격 및 거래량에 미친 영향, 정주희, 단국대학교 박사학위 논문, , 2013
64. 주식시장. 채권시장. 부동산시장의 경기순환 관계. 경영학연구, 275, 지호준, 특별호, pp1277-1296 한국 경영학회, , 1999
65. 주택재개발 구역내 국. 공유지 관리 방안연구. 서울시정 개발연구원, 이상경, 한국 건설기술연구원 자료 p86, , 2000
66. “벡터오차수정모형(VECM)을 이용한 금리, 아파트가격, 주가의 상관관계”, 박종철, 동아대학교 박사학위논문, , 2007
67. 서울 아파트 전세 및 매매가격 결정요인의 권역별 특성 변화에 관한 연구, 이석주, 한양대학교 박사학위 논문, , 2015
68. 수도권 주상복합 아파트의 지역 및 주택규모별 가격격정 요인에 관한 분석, 송호창, 한양대학교 석사학위 논문, , 2008
69. 시장 친화적 토지 공개념의 정책에 관한연구. 토지공법연구. 제 38집, pp701-720, 엄수원, 한국 토지공법 학회, , 2007
70. 주거지역 그린환경에서 발생하는 양과 부의 외부효과의 상쇄현상에 관한 연구, 양성돈, 국토계획, 제38권, 제1호, pp137-148, , 2003
71. 현행 토지공개념 제도의 시장 친화적 제도로의 변환 가능성고찰. 토지공법연구. 제38집, pp643-676, 백승주, 한국토지 공법학회, , 2007
분석정보
연관 공개강의(KOCW)
이 자료와 함께 이용한 RISS 자료
나만을 위한 추천자료
