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Population aging, unemployment and house prices in South Africa

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Abstract

This paper examines the joint dynamics between house prices, population aging and unemployment in South Africa. It uses provincial-level data set to compare the demographic effects of house prices across different housing segments over the period from 1995 to 2015. When heterogeneity, endogeneity and spatial effects are controlled for, the analysis finds that on average in the past 22 years, population aging has contributed to the decline of the South African house prices by 6.28 and 7.52 basis point in the large and medium housing segments, respectively, while the small segment has remained unaffected. Likewise, unemployment appears to have played a significant role in slowing down the growth rate of house prices across segments but to a lesser extent. While the response of real house prices to demographic shift is consistent with the life cycle hypothesis, the insensitivity of small house prices to aging might reveal the mitigating effect of the retirees’ relocation from larger segment houses to smaller ones. The relocation effect might induce higher demand of small segment houses which drives up their prices and offsets the detrimental effect of aging. These findings suggest that increasing the incentive to prolong the retirement age or engage elderly population in other income-generating activities to meet their increasing financial needs given the meagre social security system is likely to sustain the growth prospective of housing value in South Africa.

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Notes

  1. In Africa, South Africa has the highest proportions of older population, with 13.3% of the total population aged 50 years or older, and nearly 7% aged 60 years or older (Kinsella and Ferreira 1997; SAGE South Africa, WHO 2011). This occurs as a result of a sharp decline in fertility rate associated with the increase in the life expectancy (from 52.7 years in 2002 to about 59 years in 2015) (WHO 2015).

  2. In micro-level studies, price-to-income ratio has also been used as an alternative proxy for housing affordability (Kim and Cho 2010), which is, however, not included in our analysis due to data availability.

  3. The estimated coefficients are obtained using Stata code provided by Drukker et al. (2013).

  4. Part of this heterogeneity has been inherited from the historical residential segregation introduced in 1966 by the Apartheid administration through the Group Areas Act 36 which forced people to live in separate residential areas based on their race. According to Kotze (1999), this residential segregation bore significant consequences on post-democracy regional property prices.

  5. Though in the small-segment housing the log-likelihood function appears to be greater in non-IV spatial model, the adjusted R2 remains greater in the spatial IV specification, consistently with the remaining segments.

  6. The normal distribution assumption fails to hold for the middle-medium housing segment; possible suggesting a misspecification issue. However, the objective of comparing the responses of real estate prices to demographic changes across housing segments is conditioned upon the use of identical empirical strategy which appears to be that of the majority of the housing submarkets.

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Funding

Funding was provided by National Research Foundation (Grant No. TTK150707123686).

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Authors and Affiliations

  1. PEERC, School of Economics, University of Johannesburg, Auckland Park, Johannesburg, South Africa

    Beatrice D. Simo-Kengne

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  1. Beatrice D. Simo-Kengne
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Correspondence to Beatrice D. Simo-Kengne.

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Appendix

See Fig. 2.

Fig. 2
figure 2

Real house prices and demographic trends in South Africa, 1995–2015. Note Fig 1 depicts the historical evolution of real house prices across different housing segments (primary axis, that is left axis) and the demographic trends (secondary trends, that is right axis)

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Simo-Kengne, B.D. Population aging, unemployment and house prices in South Africa. J Hous and the Built Environ 34, 153–174 (2019). https://doi.org/10.1007/s10901-018-9624-3

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