Journal of Transport and Land Use

A prototype machine learning residential land-use classifier using housing market dynamics

Shivani Raghav

University of Toronto Transportation Research Institute

Stepan Oskin

Prodigy

Eric J. Miller

University of Toronto

DOI: https://doi.org/10.5198/jtlu.2022.1905

Keywords: Housing market, Land use, Machine learning

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Abstract

There is ample evidence of the role of land use and transportation interactions in determining urban spatial structure. The increased digitization of human activity produces a wealth of new data that can support longitudinal studies of changes in land-value distributions and integrated urban microsimulation models. To produce a comprehensive dataset, information from various sources needs to be merged at the land-parcel level to enhance datasets with additional attributes, while maintaining the ease of data storage and retrieval and respecting spatial and temporal relationships. This paper proposes a prototype of a workflow to augment a historical dataset of real estate transactions with data from multiple urban sources and to use machine learning to classify land use of each record based on housing market dynamics. The study finds that engineered parcel-level attributes, capturing housing market dynamics, have stronger predictive power than aggregated socio-economic variables, for classifying property land use.

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Author Biography

Eric J. Miller, University of Toronto

Eric J. Miller, Ph.D.
Professor, Department of Civil & Mineral Engineering
Director, University of Toronto Transportation Research Institute (UTTRI)
Research Director, Data Management Group
Research Director, Travel Modelling Group
University of Toronto

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