A residential location search model based on the reasons for moving out

ABSTRACT

Modeling spatial search processes such as residential location search are challenging, particularly, due to the need to deal with a large dataset and wide array of factors. This introduces a multi-dimensionality challenge to location search modeling. With the motivation to accommodate multi-dimensionality, this paper develops a machine learning–based Gaussian mixture model (GMM) for location search. This study accommodates the effects of several factors including accessibility, land use, dwelling, transportation infrastructure, and neighborhood attributes on location search decisions. The spatial unit of analysis is dwelling-level. This study conceptualizes that households’ search for location based on their reason to move. The pool of alternatives for each household is generated based on probability estimates of GMM. The location choice model considering the reason-based GMM outperforms the model without considering relocation reasons in GMM and random sampling-based model in-terms of predictive performance. The search model has been implemented in an integrated urban model.

KEYWORDS:

• location search model
• reasons for moving out
• choice set
• machine learning
• Gaussian mixture
• location choice model

Acknowledgments

The authors would like to thank BC Assessment for providing the dwelling-leveldata for analysis. The authors would also like to acknowledge Trevor Nikodym for proof-reading the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Natural Sciences and Engineering Research Council of Canada [Discovery Grant]