Abstract
Spatial dependence is often seen as a problem in econometrics rather than in economics. This study seeks to find an economic explanation for spatially correlated real estate prices. We posit spatial dependence as a process to discover price information from neighboring property transactions. Weaker spatial dependence is expected when price information in the immediate vicinity of a subject property is abundant. In the context of apartment buildings, in addition to the more commonly known horizontal dependence, there is also spatial dependence in the vertical dimension within the same building. Based on more than 18,000 transactions of highly homogeneous apartment units in Hong Kong, we found that the trading volume of a building depresses horizontal spatial dependence, but raises vertical spatial dependence. This not only confirmed the role of trading volume in the real estate price discovery process, but also questioned the validity of constant spatial autocorrelation assumption adopted in many studies.
Highlights
Spatial dependence or autocorrelation has been analyzed in the real estate field for more than three decades, but the focus has so far been on correcting for bias or improving efficiency in the estimation process rather than on finding the underlying cause of spatial dependence
Equation (4) was the hedonic model with two spatial autoregressive processes: one for the horizontal dimension and the other for the vertical dimension. We found that both spatial effects were significant at the 1 % level: the horizontal spatial dependence (WHPt-k) was 0.3021 and the vertical spatial dependence (WVPt-k) was 0.0242
This study made three contributions. It provided an economic explanation for spatial dependence in real estate prices based on the information search framework
Summary
Spatial dependence or autocorrelation has been analyzed in the real estate field for more than three decades, but the focus has so far been on correcting for bias or improving efficiency in the estimation process rather than on finding the underlying cause of spatial dependence. There are, typically, two conjectures for the cause, namely: (1) omitted variables and (2) information spillovers or searches.1 The former is a problem of the researcher—spatial dependence is detected due to specification error or data limitation. In a high-rise setting, units within the same building are typically more comparable with each other than units outside of the building, but the amount of price information (e.g. in terms of the number of prior sales) within the same building is generally small compared to the volume of transactions in other buildings This is the tradeoff that market participants face when deciding which information—vertical or horizontal—to rely more on. Our study aims to explain spatial dependence in real estate prices by way of a information search conjecture.
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