Earticle

본 연구는 대규모 공공개발사업 주변 지역의 투기적 거래와 지가상승으로 발생하는 토 지시장 교란 행위를 모니터링하고 토지거래허가구역 지정제도의 적절하고 합리적인 운영 을 위해 토지 이상거래의 시공간적 특성을 분석하였다. 세종시의 실거래 자료를 활용해 공 간분석기법으로 이상거래의 특성을 분석하였다. 공시지가 대비 거래가격의 비율로 이상거 래를 판단하였고, 거래가격비율을 행정리 단위로 6개월 단위 이동 방식으로 나눈 제3사분 위를 사용하였다. 분석결과, 법인이 개인에게 매도하는 지분거래인 임야에서 매우 높은 가 격으로 거래가 이루어졌으며, LISA 분석으로 비정상적인 토지거래가 인접한 지역과 함께 발생하는 것을 확인하였다. 또한, 이상거래의 발생은 시간이 지날수록 점차 확산 되었다. 공시지가 대비 거래가격비율을 이용한 시공간적 특성은 대규모 공공개발의 초기단계에서 모니터링 수단으로 이용될 수 있으며, 모니터링을 통해 초기 단계의 이상거래에 대해 조사 체계 구축으로 행정적 규제 및 조치로 시장 교란 행위를 방지하고 공공개발의 효율성을 높 일 수 있음을 확인하였다.

This study monitored land market disruptions caused by speculative transactions and land price increases in areas surrounding large-scale public development projects. It analyzed the spatiotemporal characteristics of abnormal land transactions to ensure the proper and rational operation of the land transaction permission system. Using actual transaction data from Sejong City, spatial analysis techniques were employed to examine the characteristics of abnormal transactions. The ratio of the transaction price to the officially announced land price was used as an indicator to identify abnormal transactions, and the third quartile of this ratio was applied using a six-month moving window method at the administrative 'ri' unit level. The analysis revealed that forest land, transacted as co-owner shares from corporations to individuals, was traded at significantly high prices. Additionally, the LISA analysis confirmed that abnormal land transactions occurred alongside neighboring regions. Furthermore, the occurrence of abnormal transactions gradually expanded over time. The spatiotemporal characteristics of transaction price ratios relative to officially announced land prices can serve as a monitoring tool in the early stages of large-scale public development. Through such monitoring, an investigative framework can be established in the early phase to implement administrative regulations and measures, thereby preventing market disruptions and enhancing the efficiency of public development.

The objective of this study is to analyze spill-over effects of infrastructure investments on productivities of manufacturing sectors using a spatial weight matrix. This paper finds that the road shock has a positive effect on the productivity both in the invested region and its neighboring regions. The effects on the productivities of the latter regions are estimated as about half of those on the former region. The marginal effects of the road shock on the manufacturing productivity tend to gradually decrease down over the time.

This study is trying to find out the spatial econometrics model in order to reflect the true reality in the housing price analysis. Due to the influence of spatial autocorrelation with respects to the housing price in the housing market, the estimation of the OLS is not preferable. Then the spatial econometrics model reflecting spatial dependence and spatial heterogeneity is the best alternative in terms of housing price estimation, probably. "Everything is related to everything else, but near things are more related than distant things". More realistic spatial weight matrix and spatial model scheme, better performance in the housing price prediction. Therefore, this study can show that the model improvement by spatial model scheme can be achieved by the comparing the spatial econometrics models such as SAR(Spatial Autoregressive Models), SEM(Spatial Errors Models), SAC(General Spatial Models), and GWR(Geographically Weighted Regression). Depends on the perspective of methodology, the SAC and the GWR models are preferable in terms of global(nomothetic approach) or local(idiographic approach) point of view, respectively.

주택은 입지적 특성뿐만 아니라 인근주민의 특성 등 사회적, 물리적 주변의 환경과 상호작용관계를 갖게 됨으로써 공간적 자기상관(spatial autocorrelation)이 발생하게 된다. 이러한 공간자기상관의 영향으로 전통적인 OLS방법에 의한 가격추정은 왜곡된 결과를 낳을 가능성이 높다. 공간적 자기상관의 문제해결을 위하여 Anselin(1988)은 종속변수와 오차항에 공간가중행렬을 적용한 일반공간모형(SAC: General Spatial Models)과 공간자기회귀모형(SAR: Spatial Autoregression), 공간오차모형(SEM: spatial error model)을 제시하였다. 이러한 공간계량모형에 있어 공간적 자기상관을 파악하기 위해서는 공간적 효과를 나타내는 공간가중행렬의 정의하는 것이 매우 중요하다. 하지만 기존연구에서 공간가중행렬(spatial weight matrix)의 선택이 분석 결과에 어떤 영향을 미치는지에 대해서는 논의 없이 통계 package에서 제공하는 기준에 따라 여과 없이 공간가중행렬이 사용되고 있다. 따라서 본 연구는 공간자료의 특성을 고려한 공간계량모형이 어떤 공간가중행렬에 보다 우수한 추정치를 제공하는지를 살펴보았다. 이를 위해 공간가중행렬을 거리 기준에 따라 (특정 임계값을 기준한 가중행렬), W(1/거리), Ws(W를 횡단표준화한 가중행렬), WsT(동일아파트를 고려한 가중행렬)로 구성하였다. 분석을 위해 2006년부터 2009년까지 국토해양부에 공개된 5,850개의 부산지역 아파트 실거래가 자료를 이용하였다. 분석결과 공간계량분석에 있어 공간가중행렬은 WsT>Ws >W >WsD 순으로 모형의 적합도가 높게 나타났다.

This study is trying to find out the spatial weight matrix using actual distance between houses in order to reflect the true reality in the housing price analysis. Due to the influence of spatial autocorrelation with respects to the housing price in the housing market, the estimation of the OLS(Ordinary Least Squares) is not preferable. Then the spatial econometrics model reflecting spatial dependence and spatial heterogeneity is the first choice, probably. Specifically, the elaborate spatial weight matrix is constructed to predict the spatial relevance. It is the Euclidean distance matrix which is composed of real distance between two points with zeros on the diagonal; namely, the dimension of the weight matrix is equal to N by N. This is the very simple and logical weight matrix reflecting the spatial influence with each other; notwithstanding, there is few relevant articles considering the real distance at all, whatsoever. Rather there are a lot of articles which are used contiguity weight matrix. Even though some articles are used the distance weight matrix, there is no big difference in nature, since they usually used the distance bandwidth matrix; for example, if the distance is less than designated criterion, the assigned value of the elements of the weight matrix is set to 1, otherwise is 0. Therefore, this study clearly shows that the model improvement can be achieved by the elaboration of composing the more realistic distance weight matrix. Once again and forever, “Everything is related to everything else, but near things are more related than distant things”(Tobler, 1970). More realistic spatial weight matrix, better performance in the spatial modeling.

This paper is trying to find out the spatial weight matrix using actual 3-dimension distance(i.e., Xc=X coordinate, Yc=Y coordinate, Zc=Z coordinate, where Z is equal to the height of the transaction house; for example, the height of second floor house can be calculated approximately as 2.8meter × 2nd floor = 5.6m) between houses in order to reflect the true reality in the housing price analysis. Due to the influence of spatial autocorrelation with respects to the housing price in the housing market, the estimation of the OLS(ordinary least squares) is not preferable. Then the spatial econometrics models reflecting spatial dependence and spatial heterogeneity are the first choice, probably. Specifically, the elaborate spatial weight matrix is constructed to predict the spatial relevance. It is the Euclidean distance[수식] matrix which is composed of real distance between two houses with zeros on the diagonal; namely, the elements of the weight matrix(Wij=1/dijα, where α=1,2,3…) are equal to N by N. This is the very simple and logical weight matrix reflecting the spatial influence with each other; though, there is few relevant articles considering the real 3-dimension distance at all, in our short literature reviews. Therefore, this study clearly shows that the model improvement can be achieved by the elaboration of composing the more realistic distance weight matrix.