빅데이터(POS)를 활용한 백화점 방문수요 결정요인에 관한 연구

신성윤,박정아,Shin, Seong Youn,Park, Jung A 한국토지주택공사 토지주택연구원 2022 LHI journal of land, housing, and urban affairs Vol.13 No.4

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

서울시 셰어하우스 1인당 임대료 결정요인에 관한 연구

신성윤(Shin, Seong Youn),윤영식(Yoon, Young Sik) 한국주거환경학회 2018 주거환경(한국주거환경학회논문집) Vol.16 No.1

This study attempts to investigate the determinants of rent prices for share house in Seoul through empirical analyses with using Hedonic Price Model. This study data were collected from hompages of share house companies. The spatial scope of this study was limited in Seoul and the temporal scope was limited from September 2016 to March 2017. The collected data included monthly rent prices per one person, regional characteristics, building characteristics, and share house management characteristics. According to the result of this study, in the regional characteristics, there is a difference by regional locations in the rent prices of share house. The regional variables, which are Gangnam-Gu, Mapo-Gu, Yeongdeungpo-Gu, and Yongsan-Gu, increase the rent prices. As the distance of subway station and market become shorter, the rent prices become higher. Also, as the transfer of subway station becomes possible, the rent prices are raised. These results are reflected in the desire of convenience for purchasing foods and using public transportation. In case of commercial buildings, it is associated with higher rent prices than other types of buildings, whereas the multi-household house variable is associated with lower rent prices. Also, as the number of bathroom in share house increases, the rent prices are decreased. As the area of room in share house become wider, the rent prices are increased. From the result of the variable which is room type of share house, we could know that the rent prices are decreased as many people live with sharing a room as possible. The implication of this study is that it analyzes share house which is one of alternatives to solve problems such as poor housing of young single households, and suggests the trend and efficient management of share house. Here we expect that our research about share house, which has not studies actively yet, would contribute to the improvement for support and policies of share house.

주택 매매가격 및 전환임대료 결정요인에 관한 비교분석

신성윤(Shin, Seong Youn),이현준(Lee, Hyun Jun),이한민(Lee, Han Min),엄수원(Eom, Su Won) 한국주거환경학회 2021 주거환경(한국주거환경학회논문집) Vol.19 No.3

This study analyzes the price structure of the domestic housing market in Korea, which has been accumulating. It is assumed that the sales price represent the property value. The monthly rent, Chonsei and guaranteed monthly rent are converted into conversion rent. This study uses the Hedonic Price Model to empirically analyze two dependent variables to determine how the characteristics of a house affect the sales price and conversion rent. It analyzes the determinants of housing sales price and conversion rent comparatively. The results show that the regional characteristics of Seoul have the highest positive effect on sales prices and conversion rent, followed by those of Jeju. Regarding housing characteristics, the sales prices and conversion rent vary depending on the type of house. Apartments have both property value and value in use since they have higher selling prices and conversion rent than other types of housing. Single-family housing with commercial functions has a positive effect on the sales price but a negative effect on the conversion rent, which is the value in use. In the facility and household sectors, exclusive use of urban gas, district heating, toilet facilities, and hot water supply baths have positive effects on both sales prices and conversion rent. This study also finds that sales price decreased as the year flows and then increased 38 years later because of favorable factors of reconstruction. Moreover, conversion rent decreased as the housing area increased but increased from 79m² because of changes in demand for residential area. This study establishes the relationship between the housing sales market and the rental market, and we hope to contributes to the research on the price and structure of the housing market and national housing supply politics in Korea.

셰어하우스 운영수익 결정요인에 관한 연구

신성윤(Shin Seong Youn),이새나리(Lee Sae Na Ri),윤영식(Yoon Young Sik) 한국부동산학회 2018 不動産學報 Vol.73 No.-

최근 청년 1인 가구가 지속적으로 증가함에 따라 생기는 주거 빈곤과 공급부족 같은 문제를 해소하기 위한 방안으로 셰어하우스가 떠오르고 있다. 본 연구는 이러한 점에 주목하여 셰어하우스의 운영수익 결정요인에 대하여 헤도닉 가격모형을 이용하여 실증적으로 분석을 하였다. 본 연구의 데이터는 셰어하우스의 운영업체 홈페이지의 자료를 활용하여 수집하였다. 해당 자료들은 셰어하우스의 임대료, 지역적 특성, 건물 특성, 셰어하우스 운영 특성 등의 정보를 포함하고 있다. 본 연구의 결과로 셰어하우스의 운영수익과 양(+)의 관계로 유의한 변수들은 ‘마포구’, ‘강남구’, ‘영등포구’, ‘지하철역 환승여부’, ‘방 수’, ‘상업용 빌딩’, ‘최대거주인원’, ‘발코니/베란다 여부’, ‘외국인거주 여부’, ‘방 유형’으로 나타났으며, 운영수익을 증가시키는 요인들로 분석되었다. 반면 운영수익과 음(-)의 관계로 유의한 변수들로는 ‘마트 최단거리’, ‘화장실 수’, ‘단독주택’, ‘빌라(다세대+다가구)’으로 나타났다. 본 연구는 청년 주거문제의 대안인 셰어하우스의 운영수익 결정요인을 분석하여 공급자 측면에서 셰어하우스의 트렌드와 효율적인 운영방안을 제시하고자 한다.

계량시계열과 인공신경망모형을 이용한 아파트매매가격지수 예측 - 서울과 6대 광역도시를 중심으로 -

윤만식 ( Yoon Man-sik ),신성윤 ( Shin Seong-youn ) 한국지역사회발전학회(구 한국지역사회개발학회) 2020 地域社會開發硏究 Vol.45 No.2

Due to the household's asset structure concentrated on real estate, changes in real estate prices have a great influence on the national economy. Accurate forecasting of house prices not only reduces uncertainty in house prices, but can also help in making relevant policy decisions. The purpose of this study is to estimate housing prices using a time-series model and artificial neural network model for the actual transaction price index for apartment sales in Seoul and six metropolitan cities from January 2006 to November 2019, and forecast with high predictive power. It is in building a model. In this study, by adding bidirectional LSTM and GRU models in addition to the existing weighing time series model and Vanilla LSTM model, we compared the predictive power of the proposed model and presented an artificial neural network model with high predictive power. As a result of empirical analysis, it was confirmed that the artificial neural network model with high predictive power differs depending on the region. After constructing a highly predictive artificial neural network model for each region, it is possible to grasp the effects of macroeconomic variables suitable for the region on the housing price index through free manipulation such as adding and deleting variables to achieve desired policy effects.