A modified spatial house price to income ratio and housing affordability drivers study: using the post-LASSO approach

Qifeng Wang, Bofan Lin and Consilz Tan

International Journal of Housing Markets and Analysis, 2024, vol. 17, issue 6, 1443-1460

Abstract: Purpose - The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis. Design/methodology/approach - The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with thek-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting. Findings - Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence. Research limitations/implications - The paper provides important implications for policymakers, real estate professionals and researchers. For example, policymakers will be able to design policies that target the most influential factors of housing affordability in their region. Originality/value - This study introduces a new urban spatial house price-to-income ratio, and it examines how macroeconomic indicators, government regulation, real estate market supply and urban infrastructure level have a significant impact on housing affordability. The problem of having too many variables in the decision-making process is minimized through the post-LASSO methodology, which varies the parameters of the model to allow for the ranking of the importance of the variables. As a result, this approach allows policymakers and stakeholders in the real estate market more flexibility in determining policy interventions. In addition, through thek-fold cross-validation methodology, the study ensures a high degree of accuracy and credibility when using drivers to predict housing affordability.

Keywords: Housing affordability; Machine learning; Post-LASSO variable selection; K-fold cross-check; Price-to-income ratio; ArcGIS (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijhmap:ijhma-12-2023-0169

DOI: 10.1108/IJHMA-12-2023-0169

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