Abstract
A good location goes beyond the direct benefits from its neighbourhood. Unlike most previous statistical and machine learning based housing appraisal research, which limit their investigations to neighbourhoods within 1 km radius of the house, we expand the investigation beyond the local neighbourhood and to the whole metropolitan area, by introducing the connection to significant influential economic nodes, which we term Regional Economic Clusters. By consolidating with other influencing factors, we build a housing appraisal model, named HNED, including housing features, neighbourhood factors, regional economic clusters and demographic characteristics. Specifically, we introduce regional economic clusters within the metropolitan range into the housing appraisal model, such as the connection to CBD, workplace, or the convenience and quality of big shopping malls and university clusters. When used with the gradient boosting algorithm XGBoost to perform housing price appraisal, HNED reached 0.88 in \(R^2\). In addition, we found that the feature vector from Regional Economic Clusters alone reached 0.63 in \(R^2\), significantly higher than all traditional features.
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Acknowledgement
This work has been partly funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 824019 and the DAAD-PPP Australia project “Big Data Security”.
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Kou, J., Du, J., Fu, X., Zhang, G.Z., Wang, H., Zhang, Y. (2021). The Effect of Regional Economic Clusters on Housing Price. In: Qiao, M., Vossen, G., Wang, S., Li, L. (eds) Databases Theory and Applications. ADC 2021. Lecture Notes in Computer Science(), vol 12610. Springer, Cham. https://doi.org/10.1007/978-3-030-69377-0_15
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