文章基本信息

标题：Factors Affecting Spatial Autocorrelation in Residential Property Prices
本地全文：下载
作者：Daniel Lo ; Kwong Wing Chau ; Siu Kei Wong 等
期刊名称：Land
印刷版ISSN：2073-445X
出版年度：2022
卷号：11
期号：6
页码：931
DOI：10.3390/land11060931
语种：English
出版社：MDPI, Open Access Journal
摘要：Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A.