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.
|Number of pages||16|
|Publication status||Published (in print/issue) - 17 Jun 2022|
Bibliographical noteFunding Information:
Funding: This project was funded by Small Project Funding, Research Grants Council of the Hong Kong Special Administrative Region [grant number 201209176181. The APC was funded by Ulster University, the U.K.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Spatial autocorrelation
- spatial hedonic modelling
- residential real estate price
- Hong Kong
- spatial autocorrelation