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Predicting Housing Price Using Spatial Panel Data Models in Korea
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Type
Academic journal
Author
Chun, Hae Jung (Sungkyul University)
Journal
Residential Environment Institute Of Korea RESIDENTIAL ENVIRONMENT : JOURNAL OF THE RESIDENTIAL ENVIRONMENT INSTITUTE OF KOREA Vol.15 No.4 (Wn.38) KCI Accredited Journals
Published
2017.12
Pages
41 - 53 (13page)

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Predicting Housing Price Using Spatial Panel Data Models in Korea
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Abstract· Keywords

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본 연구의 목적은 패널자료를 이용해 공간계량경제모형인 공간자기회귀모형(Spatial Autoregressive Model:SAR), 공간오차모형(Spatial Errors Model:SEM), 일반공간모형(Spatial Lagged Term with Spatially Correlated Error Structure Model:SAC)과 공간더빈모형(spatial dubin model:SDM)을 적용해 어떤 모형의 주택가격 예측이 우수한지를 알아보는 것이다. 본 연구의 공간적 범위는 한국 15개 광역시·도로, 시간적 범위는 2011년 8월부터 2017년 3월까지로 설정하였다.
분석결과, SAR, SEM과 SAC모형에 비해 SDM모형의 R2값이 0.309, Log Likelihood값이 2468.605 가장 높게 나타났고 평균자승오차근(Root Mean Square Errors:RMSE)은 0.0065로 가장 낮게 나타나 SDM의 주택가격 예측력의 정확성이 가장 우수한 것으로 나타났다. SDM 분석결과, 아파트전세가격지수 변동률과 소비자물가지수 변동률이 1% 증가하면 아파트매매가격지수 변동률은 각 0.402%와 0.421% 증가하였다. 주택소비심리지수 변동률과 주택거래량이 1% 증가하는 경우 각 0.017%와 0.003% 증가한 반면에 주택담보대출금과 미분양주택량이 1% 증가하면 아파트매매가격지수 변동률은 각각 0.002% 감소하는 것으로 나타났다.
#주택가격 #공간패널모형 #공간더빈모형 #평균자승오차근 #예측 #Housing Price #Spatial Panel Data Model #Spatial Durbin Model #RMSE #Prediction

Contents

Abstract
Ⅰ. Introduction
Ⅱ. Literature Review
Ⅲ. Model
Ⅲ. Empirical Results
Ⅳ. Conclusion
References

References (22)

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[단행본] - Anselin, L. / 1988 / Spatial Econometrics:Methods and Models / Kluwer Academic Publishers google schola [학술지(정기간행물)] - Basu, S. / 1998 / Analysis of spatial autocorrelation in house prices / The Journal of Real Estate Finance and Economics 17 (1) : 61 ~ 85 google schola [학술지(정기간행물)] - Can, A. / 1990 / The Measurement of Neighborhood Dynamics in Urban House Prices / Economic Geography 66 (3) : 254 ~ 272 google schola [학술지(정기간행물)] - Chica Olmo, J. / 1995 / Spatial estimation of housing prices and locational rents / Urban Studies 32 (8) : 1331 ~ 1344 google schola [학술지(정기간행물)] - 전해정 / 2016 / 공간계량분석기법과 GIS를 이용한 주택가격모형 비교에 관한 연구 / 부동산학보 (64) : 46 ~ 56 dbpia

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