Identifying Multiple Scales of Spatial Heterogeneity in Housing Prices Based on Eigenvector Spatial Filtering Approaches
Identifying Multiple Scales of Spatial Heterogeneity in Housing Prices Based on Eigenvector Spatial Filtering Approaches
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DOI：
10.3390/ijgi11050283
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Zhan Peng;R. Inoue
中科院分区：
文献类型：
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作者：
Zhan Peng;R. Inoue
研究城市真实的房地产市场的兴趣，特别是研究房价与相关住房特征之间的关系的兴趣正在迅速增长。然而，这种日益增加的关注是由这些关系的多尺度空间异质性的有限考虑的障碍。本研究使用东京都市圈72，466套公寓的租金价格数据，在多个空间尺度上研究真实的房地产市场的空间异质性。在空间变系数（SVC）建模的框架内，我们利用了随机效应特征向量空间滤波的SVC（RE-ESF-SVC）模型，以前没有在真实的地产研究中使用的方法，并将其与传统的ESF-SVC模型，它没有随机效应。我们的研究结果表明：（1）除了一个住宅特性对东京都内的房价产生持续影响外，其他特性与房价的关系在地域和全球范围内都存在差异。（2）RE-ESF-SVC利用了随机效应，具有在保持高性能的同时灵活地进行估计的独特优势。
Interest in studying the urban real estate market, especially in investigating the relationship between house prices and related housing characteristics, is rapidly growing. However, this increasing attention is handicapped by a limited consideration of the multi-scale spatial heterogeneity in these relationships. This study uses the rental price data of 72,466 apartments in the Tokyo metropolitan area to examine spatial heterogeneity in the real estate market at multiple spatial scales. Within the framework of spatially varying coefficient (SVC) modeling, we utilized a random effect eigenvector spatial filtering-based SVC (RE-ESF-SVC) model, an approach not previously employed in real estate studies, and compared it with the traditional ESF-SVC model, which has no random effects. Our results show that: (1) except for one housing characteristic that impacts prices consistently throughout the Tokyo metropolitan area, relationships between other characteristics and prices vary from local to global spatial scales; (2) because of the utilization of random effects, RE-ESF-SVC has the unique advantage of making estimations flexibly while maintaining a high performance.