인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study examined how actual zoning composition around stations and statutory mixed-use permissiveness were conditionally associated with multifamily housing prices in Seoul and Tokyo from 2020 to 2024. It focused on the structure of within-city associations rather than cross-city differences in price levels or institutional effect sizes. Seoul transactions were linked to zoning using geocoded coordinates, whereas Tokyo transactions were assigned nearest-station centroids as proxy coordinates because individual transaction coordinates were unavailable. The baseline analysis used city-specific OLS models with year fixed effects and HC3 and station-clustered robust standard errors. Because Moran’s I values for the OLS residuals were 0.661 in Seoul and 0.630 in Tokyo, spatial error and spatial lag models were estimated using identical 5,000-observation samples, and robustness was assessed across 20 repeated subsamples. XGBoost-SHAP was used to explore predictive importance only, not causal effects. Statutory permissiveness and observed zoning composition showed distinct associations with prices that varied by standard-error method and spatial-model specification. These dimensions therefore convey different information, and their associations with price cannot be characterized by a single regression coefficient.
본문·목차
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