Predicting Urban Growth with Machine Learning

47 Pages Posted: 17 Feb 2021

See all articles by Simon Buechler

Simon Buechler

MIT Center for Real Estate; University of Bern

Dongxiao Niu

Tsinghua University; MIT Center for Real Estate

Anne Kinsella Thompson

Massachusetts Institute of Technology (MIT) - Center for Real Estate

Date Written: February 12, 2021

Abstract

Using machine learning (ML) models, we predict the population growth for the next two, five, and ten years for American and Chinese urban areas. To this end, we construct a rich city-level data set encompassing information on transportation, output, amenities, and human capital. The ML models choose the main urban growth predictors through cross-validation. We find that human capital, real estate investment, amenities, and geographical lo-cation are strong future urban growth predictors for both countries. In the US, intercity transit is a stronger urban growth driver than intracity transit, whereas the opposite holds for China. Our models predict that coastal urban areas in the South Atlantic and the West South Central Division in the US, and southeastern coastal cities in China, will grow the most.

Keywords: City growth

JEL Classification: R1, R12, R15, R4

Suggested Citation

Buechler, Simon and Niu, Dongxiao and Kinsella Thompson, Anne, Predicting Urban Growth with Machine Learning (February 12, 2021). MIT Center for Real Estate Research Paper No. 21/06, Available at SSRN: https://ssrn.com/abstract=3784787 or http://dx.doi.org/10.2139/ssrn.3784787

Simon Buechler (Contact Author)

MIT Center for Real Estate ( email )

United States

University of Bern ( email )

Gesellschaftsstrasse 49
Bern, BERN 3001
Switzerland
3001 (Fax)

Dongxiao Niu

Tsinghua University

Beijing, 100084
China

MIT Center for Real Estate ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

Anne Kinsella Thompson

Massachusetts Institute of Technology (MIT) - Center for Real Estate ( email )

United States

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