2018, Vol.96, No.

The Korea Spatial Planning Review 2023 KCI Impact Factor : 1.2

pISSN : 1229-8638
https://journal.kci.go.kr/krihs

This study reviewed theories about the determinants of conversion rate of Jeonse price to Monthly rent and empirically analyzed the determinants of conversion rate of Jeonse price to Monthly rent through the SVAR model. The variables were selected by theoretically and the SVAR model was constructed with 5 variables such as mortgage loan interest rate, Housing Consumer Sentiment index, mortgage loan balance, Jeonse price to Housing price ratio, including conversion rate of Jeonse price to Monthly rent. Impulse and response function and variance decomposition were analyzed to find the determinants. As a result, in the short term, the conversion rate of Jeonse price to Monthly rent is strongly affected by the interest rate. And in the long term, it is influenced not only by interest rate but by also Housing Consumer Sentiment index, mortgage loan balance. It shows that the conversion rate of Jeonse price to Monthly rent fluctuates according to interest rate, but it can be affected by expectation of housing prices and activation of housing finance.

The purpose of this study is to analyze the influence of social capital and urban characteristics on competitiveness of local governments. The data from all local governments in Korea is used for the analysis. Independent variables are social capital characteristic (such as participation, network, norms, knowledge activity and diversity) and urban characteristics (such as the ratio of aged population, the ratio of manufacturing industry, land use, cultural service and roadway length). The results of this study are summarized as follows; In consideration of the social capital, the effect of knowledge activity and diversity variables on competitiveness of local governments is very large. Social capital variables (knowledge activities, diversity, norms and private participation) and urban characteristic variables (the olderly, manufacture, residential area, commercial area, roadway length) are significant when considering both of them at the same time. The importance of social capital in improving competitiveness of local governments has been recognized and it will help to plan ways to promote urban growth and development.

The purpose of this study is to forecast changes in trade areas of the Seoul Metropolitan Area as a result of the introduction of the Great Train Express (GTX) which connects Seoul and its neighboring cities. Existing studies have not shown consistent analysis findings as to whether the introduction of high-speed transportation system such as the GTX would aggravate the population concentration on megacities such as Seoul. Some argue that the population concentration on big cities will be unavoidable, while others maintain that such concentration will not take place and travel time between cities will only reduce. This study extracted the travel time and the size of existing trade areas as main factors affecting the formation of trade areas and identified a probability model that determines trade areas from such factors. While this study adopted general study methodology in this regard, it is differentiated from existing studies in that (1) it used the discrete probability model to reflect actual effects of travel time, which represents accessibility, on the selection of a trade area; and (2) it conducted feedback analysis on the complex network through which results of an analysis serve as input values for another analysis. The feedback analysis using the probability model proposed by this study allowed us to predict that the introduction of the GTX would aggravate the population concentration on Seoul.