2018, Vol.28, No.1

Korea Real Estate Review 2023 KCI Impact Factor : 0.56

pISSN : 2092-5395 / eISSN : 2733-8339
https://journal.kci.go.kr/krer

This study was conducted to determine the location determinants that influence the sales of the "Storyway" convenience stores built at the country’s railway stations. The preceding studies were about the convenience stores located in the residence-business areas or along the roadsides. This study, on the other hand, focused on the characteristics of the stations, based on a theory that is different from the existing theories. The targets of this study were the 301 "Storyway" convenience stores doing business in the 198 railway stations in the country, and the dummy parameter and hedonic-price model function were used for multiple regression analysis. For the study results, it was found that the number of people using the railway and the size of the store have a positive effect on the sales whereas the other brand competitors have a negative effect thereon. Second, the subway stations holding 89% of the total passengers in the country have unexpectedly no positive influence on the sales. Third, depending on the transfer, it was found that no transfer station had smaller sales than the transfer stations. Finally, as for the location of the stores in the station, the stores located on the platforms or passageways have a smaller turnover rate than the stores in the welcoming spaces and squares. This research result shows that when starting a convenience store business, the number of people using the railway, the size of the store, the transfer possibility, and the location of the store inside the station have to be considered under the circumstance of recession on the part of the convenience stores due to excessive competition.

The genetic algorithm (GA) and branch and bound (B&B) methods are the useful methods of searching the optimal project combination (combinatorial optimization) to maximize the project effect considering the budget constraint and the balance of regional development with regard to the Urban Regeneration New Deal policy, the core real estate policy of the Moon Jae-in government. The Ministry of Land, Infrastructure, and Transport (MOLIT) will choose 13 central-city-area-type projects, 2 economic-base-type projects, and 10 public-company-proposal-type projects among the numerous projects from 16 local governments while each government can apply only 4 projects, respectively, for the 2017 Urban Regeneration New Deal project. If MOLIT selects only those projects with a project effect maximization purpose, there will be unselected regions, which will harm the balance of regional development. For this reason, an optimization model is proposed herein, and a combinatorial optimization method using the GA and B&B methods should be sought to satisfy the various constraints with the object function. Going forward, it is expected that both these methods will present rational decision-making criteria if the central government allocates a special-purpose-limited budget to many local governments.

The purpose of this study was to analyze the housing tenure choice decisions of older households, with focus on the activity constraints of the householders. For analysis, the probit model based on the data of the householders over 55 years of age from the 2015 KLIPS data was implemented. The research findings indicate that the ownership choice probability decreases when the head of the household has activity or social activity constraints. If there is an activity restriction, it is considered that the possibility of self-residence will be low due to the increase in medical expenses, the possibility of participation in economic activities will be low, the possibility of early retirement will be high, and the income will decrease. Therefore, if the head of the household has a personal or social activity constraint, such case is more likely to be categorized as falling under the poor residential group. This result suggests that the activity constraint can be utilized as a useful index for the housing welfare policy considering physical vulnerability.