Measuring subjective housing affordability using a data-driven discrete information approach: A case study of Selangor, Malaysia

ABSTRACT

A widely adopted measure of housing affordability is that households should spend no more than 30% of their household income on housing. However, this normative threshold is an arbitrary Great Depression-era guideline and may not be relevant today. This paper proposes a subjective indicator of housing affordability by introducing a method commonly used in the medical sciences. It utilizes discrete information to estimate a subjective affordability ratio that discriminates between subjective house-poor and non-house-poor households. We apply the proposed method to household-level data collected in Selangor, Malaysia, and show that the optimal cut-off point is 23.5%. This estimated value suggests a higher prevalence of house-poor households than is implied by the regularly assumed 30% threshold. In addition, we perform a sensitivity analysis and find the bias in the estimated cut-off point is close to zero.

KEYWORDS:

• Subjective housing affordability
• Youden index
• Optimal cutpoint
• Malaysia

JEL CLASSIFICATION:

• C43
• G51
• I31

Disclosure statement

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

Ethical approval

Ethics approval for this study was obtained from the Monash University Human Ethics Committee (Project ID: 31276).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/13504851.2023.2208833.

Notes

¹ Selangor is one of Malaysia’s highly developed states with the largest urban population of 6.7 million. This translates into an urbanization rate of 95.8%. Selangor’s median house price as of Q2 2022 is RM430,000, the third most expensive state in the country (NAPIC 2022).

² ‘House-poor’ households refer to households that perceive their housing costs to be a financial burden and hence, unaffordable.

Additional information

Funding

The work was supported by the Monash University Faculty of Business and Economics Australia-Malaysia 2021-2022 Research Collaboration Development Scheme, Grant code STG-000088. Forbes also acknowledges financial support by the National Science Foundation (NSF) Grant SES-1921523. Želinský also acknowledges financial support by the Slovak Scientific Grant Agency (VEGA) Grant 1/0034/23. The responsibility for all conclusions drawn from the data lies entirely with the authors.