Efficacy of the national policy in the provision of low-cost housing




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Efficacy of the National Housing Policy in the provision of low-cost housing in the Metropolitan Municipalities of South Africa


by Moloto Johannes Sekhobela [M.Sc (Public Finance Management), M.Com (Economics)]


Abstract


The primary aim of the national housing policy is to create sustainable communities and to improve the quality of life for the people of South Africa through the provision of housing. This paper will examine the efficacy of the national housing policy in the provision of low-cost housing in the metropolitan municipalities of South Africa, by using the Shift-Share Analysis of informal settlements and the housing backlog, based on information obtained from Rex-Data of Global Insight (2021). An increase in informal settlements and the housing backlog signify policy ineffectiveness while a decrease in informal settlements and the housing backlog indicate policy effectiveness.


The Shift-Share Analysis is a technique used to understand regional contributions to the development of a national economy, in terms of the regional economy, political economy, urban studies, geography and marketing.


South Africa has eight metropolitan municipalities spread across five of the nine provinces: City of Cape Town (Western Cape Province); eThekwini (Kwa-Zulu Natal Province); Mangaung (Free State Province); Nelson Mandela Bay and Buffalo City (Eastern Cape Province); and Ekurhuleni , City of Johannesburg , and City of Tshwane (Gauteng Province).


The national housing policy was found, on average, to be effective. However, the individual metros indicate otherwise. In six of the eight metros i.e. City of Cape Town (CPT), eThekwini (ETH), Ekurhuleni (EKU), Nelson Mandela Bay (NMA), City of Tshwane (TSH) and Buffalo City (BUF) the policy was effective while in the remaining two, City of Johannesburg (JHB) and Mangaung (MAN), the policy was ineffective.


Key words - National housing policy, shift-share analysis, metropolitan municipalities, informal settlements and housing backlog


Introduction


The objective of this study is to examine the efficacy of the national housing policy in delivering low-cost housing in the Metropolitan Municipalities of South Africa, that is, City of Cape Town (CPT), eThekwini (ETH), Mangaung (MAN), Nelson Mandela Bay (NMA), Buffalo City (BUF), Ekurhuleni (EKU), City of Johannesburg (JHB), and City of Tshwane (TSH). ( RSA, 2019).


Increased urbanisation, population growth and migration to big cities led to high rates of unemployment, poverty, homelessness and an increase in informal settlements, (Govender & Reddy, 2019). Levenson (2019) estimated the proportion of urban to rural population in South Africa to be 69% / 31% in 2020, 70% / 30% in 2030, 72% / 28% in 2040 and 80% / 20% in 2050.


The African National Congress (ANC) government introduced a White Paper on Housing as a response to the housing challenges faced by the majority of black South Africans. The provision of low-cost housing is intended to afford poor communities access to the property market, wealth creation, address market failures and reduce asset poverty. A fixed-amount capital subsidy of R1500 is provided to households earning R3500 per month and below, to build a (15x20) m2 house, as a way of offering a sustainable human settlement, healthy environment and sustainable livelihoods (Amisi, Marais & Cloete, 2018; Ojo-Aromokudu & Loggia, 2017)


Housing is not only a basic, fundamental human right but it adds to people’s sense of belonging, ownership, identity, citizenship and self-sufficiency. Adequate housing includes the need for privacy and personal space, security and protection from harsh environmental elements, social development and integration. The number of people living in informal settlements and the size of the housing backlog reflects the challenges in housing provision for the poor (Marutlulle, 2021).


Overview of housing in South Africa


Housing Mandate


The Department of Human Settlements (DHS) derives its mandate from the Constitution of the Republic of South Africa, 1996 (Act 108 of 1996). Section 26 of the Constitution deals with housing and states that everyone must enjoy the right to adequate housing and the state must ensure the progressive realisation of this right and that no one must be evicted or have their homes demolished without an order of court (RSA, 1996).


Policy, legislative framework and strategic objectives


The policy and legislative framework of the DHS (RSA, 2020) includes, but is not limited to the: White Paper on Housing (1994), Comprehensive Plan for the Development of Sustainable Human Settlements (2004), Housing Consumer Protection Measures Act (1998), Rental Housing Act (1999), Home Loan and Mortgage Disclosure Act (2000), Housing Code (2000), Social Housing Policy (2005), Social Housing Act (2008), Property Practitioners Act (2019), Housing Development Agency Act (2008), Community Schemes Ombudsman Services Act (2011) and Spatial Planning and Land Use Management Act (2013).


Housing challenges

  • Misuse of low-cost houses

Low-cost houses are intended to improve the living conditions of the poor by providing a safe and stable place of abode that may enhance their prosperity, health and education. However, some homeowners misuse these low-cost houses by selling them without following proper procedures, renting them out unlawfully, turning them into business dwellings without permission and making additions and/or alterations that violate the municipal by-laws and regulations (Charlton, 2018).

  • State capacity to provide low-cost housing

The municipalities are unable to deliver housing to low-income households due to the lack of human and financial capacity, contradictory national policies, mandate creep and poor inter-governmental coordination (Gumede, 2021).


Poor delivery of low-cost houses stems from lack of accountability, absence of performance and consequence management and lack of consultation with beneficiary communities. The above failures lead to corruption in granting of housing subsidies, selection of building contractors and allocation of houses to the rightful beneficiaries resulting in duplication, wastage, unfinished infrastructure projects such as schools, roads, bridges and dams (Marutlulle, 2021; Gumede, 2021).


Gumede (2021) posits firstly, that reciprocal cooperation between the state, communities, business, organised labour and non-governmental organisations determines the capacity of the state to deliver basic services including housing. Secondly, a zero-tolerance policy against corruption, high levels of policy coordination, planning, implementation, and a functional monitoring and evaluation system.

  • Availability of land

Acquisition of land and housing delivery is hampered by the slow, complex identification and allocation of developed land, the government’s reluctance to deal decisively with private land ownership and land speculation. The lack of urban development strategies and the failure of the national government to regulate land markets exacerbates the problem (Marutlulle, 2021).


Housing policies post-1994 increased spatial inequalities by building houses for the poor on the outskirts of cities, increasing their transport costs and the cost of accessing public and social services such as education, health and employment (Knipe, 2019).


As a result, the housing backlog grows bigger than delivery in every major city, because of the growing gap between housing provision and the demand, fiscal constraints, and over-reliance on private consultants (Levenson, 2019).


Literature review


Pre-neoliberalism


Before neoliberalism, good housing consisted of government regulated land and housing policies, subsidised construction, strong tenant protections and high-quality housing standards (Listerborn, Molina & Richard, 2020).


Governments provided land, housing and finance directly to beneficiaries for servicing of sites and upgrading of informal settlements. The World Bank, International Monetary Fund (IMF) and the United Nations Habitat influenced housing policies in developing countries in the 1970s because they were struggling with the provision of housing for low-income groups. (Taruvinga & Mooya, 2018). However, these housing programmes became unsuccessful due to land acquisition problems, lack of financial sustainability, poor cost recovery and replicability problems for the infrastructure (Taruvinga & Mooya, 2018).


In order to overcome the housing challenges, low-income households resorted to self-organised buildings because of their lack of income or access to mortgage finance and credit (Grubbaer, 2019).


Neoliberal housing policies


Governments moved from direct provision of housing to policies that are market-oriented because of cost over-runs, failed subsidy allocation and designs that compromised adequate housing for the poor. The state became an enabler rather than a provider by creating a legal and institutional framework conducive for private- sector home building for low-income households (Taruvinga & Mooya, 2018).


According to Beswick, Imilan & Olivera (2019), neoliberalism entails the interaction of the state, private sector, and financial institutions in providing a market-based housing solution to the poor. The state provides a regulatory and institutional framework that creates conditions that are conducive for the private sector to participate. During the 1990s and 2000s, Latin American countries moved away from direct state provision of public housing towards market provision where the emphasis is on home-ownership and the housing finance system.


Taruvinga & Mooya (2018) argue that neoliberal housing policy favours minimum government intervention in the housing market, promotes home-ownership, private property rights and binding financial commitments. Governments provide instruments that address constraints in property rights development, access to mortgage finance, subsidy rationalisation and infrastructure for residential land development, land regulations and organisation of the building industry.


Neoliberalism stimulates economic growth by promoting homeownership, subsiding developers and agencies, driving use-value (basic need) and exchange value (asset). The state acts as a private company, which uses economic growth as its social policy to solve social and housing crises (Di Feliciantonio & Aalbers, 2017; Listerborn et al., 2020).


Neoliberalism: challenges faced by developing countries

  • Access to mortgage finance and credit by the poor

The World Bank, IMF and the UN-Habitat use neoliberalism to target struggling developing countries with huge public debts, decreasing public finance and increasing welfare burden.


Neoliberalism promotes the building of houses for middle and high-income groups as a way of capital accumulation to the exclusion of the poor through high house prices and the inability of the low-income households to access mortgage finance and credit (Sengupta, 2019).

  • Socio-spatial practices

Neoliberal social housing models result in unequal socio-spatial practices in the provision of social housing. The poor are displaced to the periphery of cities where land is cheap resulting in gentrification, social exclusion and uneven socio-spatial segregation, leading to a compromise in human sustainable development, community empowerment and environmental quality. The vulnerable groups’ cost of accessing public and social amenities such as public transport, health, education and employment increases as result (Dattwyler, Rivas & Link, 2019; Stiphany & Ward, 2019).


In instances where low-income communities have a perception that their constitutional rights to adequate housing are denied and their sense of belonging is diminished, they may resort to informal settlements and slums as a solution to their housing problems (Lata, 2020).

  • Public housing: a social or economic concept?

Housing policies should have a social rather than an economic character. The capitalist approach to maximise the exchange value of housing at the expense of use-value undermines governments’ objective of providing affordable or social housing to low-income groups. Markets reduce housing to an economic rather than a social one, because of the profit motive (Shimbo, 2019; Santoro, 2019).

  • Economic stability

Taruvinga & Mooya (2018) contend that market stability and by extension economic stability is a prerequisite for long-term, low-cost housing finance by the private sector. The inability of developing countries to effectively and efficiently implement neoliberal housing policy in the low-income segment may be due to, inter alia, macroeconomic instability, fluctuating inflation, foreign exchange risk, and short-term investment horizons.


Methodology


The traditional and static shift-share analysis method and the Rex-Database of Global Insight (2021) are used in this study to conduct an empirical investigation into the efficacy of South Africa’s National housing policy in the provision of low-cost housing in the metropolitan municipalities of South Africa.


The shift-share analysis method seeks to determine the change in a system by comparing the change in an area of interest with a relevant reference region. Change is sub-divided into three components, that is, National Growth or Share, Industry Mix or Structural Effect, and Competitive Share or Regional Shift (Lingzhi, 2021; Tissot-Daguette & Grether, 2021).


According to Melchor-Ferrer (2020) and Li and Fang (2019), shift-share is a method that was originally designed to analyse changes in regional employment mapped against national or provincial growth but was later applied to various other fields of the economy. This method can also be used to quantify the development of an industry in a region and comparatively analyse it against the regional average.


The shift-share analysis method has been criticised by Firgo and Fritz (2016) for its failure to determine a region’s performance independent of its sectoral structure. Thus, the dynamic regression shift-share analysis is recommended as a remedy.


Empirical results


Equation (1) defines Total Change in Regional informal settlements (R) as the sum of National Share Effect (N), Industry Mix Effect (M) and Regional Competitive Effect (S).


R = N + M + S (1)


Total National Informal Settlements change is defined as:


IS (v) = IS (f) –IS (i) (2)


Where IS (v) is the change in absolute terms, IS (f) is the total in the final period and IS (i) is the total in the initial period.


% change in National share: IS (%) = [(IS (f) – IS (i)/ IS (i))*100] (3)


The total change in Regional (metropolitan) informal settlements (Rm) is the difference between informal settlements in the final and initial periods


Rm = Mf – Mi (4)


Where R is the total change, Mf is the informal settlements in the final period while Mi is informal settlements in the initial period.


The percentage of total regional change growth is represented by:


Rm (%) = [(Mf - Mi)/Mi)*100] (5)


The national share effect (N) is determined by:


N = Mi1[ IS %] + Mi2[IS %] + . . . . + Min[IS %] (6)


Where N is the total national effect of informal settlements, M is informal settlements in metropolitans 1, 2 . . ., up to n, in the initial period i and IS (%) is the national average informal settlements growth rate.


Thus, the industry mix effect (M), is defined as:


M = MR1 (Mi1) + MR2 (Mi2) + . . . . + MRn (Min) (7)


Where M is the industry mix, MR is the marginal rate of growth in metropolitans 1, 2, 3…up to n and Mi is the metropolitans’ informal settlement in the initial year.


Informal Settlements


Table 5.1.1 below, summarises the changes in national informal settlements for the period from 2009 to 2018.


Table 5.1.1 Changes in National Informal Settlements, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

IS (%) is equal to -10% according to Table 5.1.1. above. This means that the national average growth in informal settlements decreased by 10%. Four provinces had growths, EC (46%), KZN (16%), NC (10%) and LIM (9%) while five provinces declined, FS (-31%), GP (-22%), MP (-21%), NW (-19%) and WC (-8%).


The changes in the metros’ informal settlements for the period from 2009 to 2018 are summarised in Table 5.1.2 below.


The total informal settlements R (%) decline is -28%. This means that informal settlements in the metros are declining at approximately two and half times more than the national average of -10%.


Informal settlements are declining in all metros in the following order, with the highest being NMA (-69%), and the lowest CPT (-9%). The decline in eight Metros is high with double digits and CPT is the only one with a single digit. Of note is the decline in the EC metros, NMA and BUF that are above 60%, followed by ETH in KZN that is above 50%.


Table 5.1.2 Changes in Regional Informal Settlements, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

The national effect (N) indicates the extent to which metros’ informal settlements would have grown if each grew at the same rate as the national average. Table 5.1.3 below, is the summary of the national effect in the metros between 2009 and 2018.


From Table 5.1.3 below, Informal settlements in the metros would have decreased by this number if they declined at the national average rate of -10%.The decline in the metros is approximately two and half times more than the national effect.


This is because eight metros, except CPT, are declining at faster rates than the national average. CPT is the only metro that is declining slower, -9%, than the national average, -10%.

The last column of Table 5.1.3 below, captures the difference in the totals of national effect and regional effect, R-N, -196 551, which is a decline. Growth is found only in CPT. The rest are declining with the highest being ETH (-69 272) and the lowest being MAN (-6 694).


Table 5.1.3 National Share Effect (N), Informal Settlements, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

Industry mix effect (M) shows how change in the metros’ structures factor into the overall growth of the national informal settlements. The industry mix effect (M) for the metros for the period from 2009 to 2018 is summarised in Table 5.1.4 below.


The marginal rate of growth (MR%) is the difference between the provincial growth and the national average growth. It appears in the third column of Table 5.1.4


The total industry mix effect (M) is, according to Table 5.1.4, equal to 9398, which is an increase, because it is positive. The metros’ structure makes a positive contribution to national, however, that is not sufficient to reduce the national effect decline of -109 648. The industry mix grows positively in four metros, ETH (38 529), BUF (28 690), NMA (22 648) and CPT (3 625). Four metros experienced a decline, JHB (-29 718), EKU (-26 498), TSH (-20 761) and MAN (-7 116).


Table 5.1.4 Industry Mix Effect (M), Informal Settlements, 1996-2018
Source: Rex Data, 2021 (Author’s own calculations)

Regional competitive share effect (S) is the degree to which the metropolitans’ performance is better or worse than the national.


R = N + M + S (14)


Therefore S = R-N-M


= -306 199-(-109 648)-(9 398)


= -306 199+109 648-9 398


= -205 949


Table 5.1.5 Regional Competitive Share Effect (S=R-N-M), 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

The negative total regional competitive share effect suggests that the metros’ informal settlements declined by 205 949. However, not all metros are declining. JHB (15 026) and MAN (422) show growth. The highest decline is found in ETH (-107 801) and the lowest is in CPT (- 1 298).


Housing Backlog


Table 5.2.1 below is a summary of the changes in national housing backlog, for the period from 2009-2018.


Table 5.2.1 Changes in National Housing Backlog, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

Total change in housing backlog, the third column of Table 5.2.1 is defined as:


B (v) = B (f) – B (i) (8)


Where B (v) is the change in absolute terms, B (f) is the total in the final period and B (i) is the total in the initial period. The last column, B (%) is defined by:


B (%) = [(B (f) – B (i)/ B (i))*100] (9)


B (%) is equal to 9% according to Table 5.2.1 above. A positive total means that the national average growth in housing backlog is increasing. Six provinces recorded growth with the highest in NC (32%) and the lowest in EC (4%). Kwa-Zulu Natal (0%) showed neither growth nor decline. FS (-7%) and LIM (-3%) are the only two provinces that recorded decline.


The changes in metros’ housing backlog for the period from 2009 to 2018 are summarised in Table 5.2.2 below.


Table 5.2.2 Changes in Regional Housing Backlog, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

The total change in metros’ housing backlog (R) is the difference between the housing backlog in the final and initial periods.


R = Bf – Bi (10)


Where R is the total change, Bf is the housing backlog in the final period while Bi is housing backlog in the initial period.


The last column in Table 5.2.2 is the percentage growth in housing backlog represented by:


R (%) = [(Bf - Bi)/Bi)*100] (11)


Total average growth percentage, 12%, is the average percentage growth of housing backlog in the metros. This means that the housing backlog in the metros is growing faster than the national average of 9%. Housing backlog is growing in five metros, with the highest in CPT (31%) and the lowest in BUF (1%). There is a decline in three metros, NMA (-37%), ETH (-13%) and MAN (-4%).


The national share effect (N) indicates the extent to which the metros’ backlog would have grown if each metro grew at the same rate as the national average.


Table 5.2.3 is the summary of the national share effect in housing backlog in the metros between 2009 and 2018.


Table 5.2.3 National Share Effect (N) Housing Backlog, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

The national effect (N) is determined by:


N = Bi1[ B(%)] + Bi2[B(%)] + . . . . + Bin[B(%)] (12)


Where N is the total national effect of informal settlements, B is the backlog in metropolitans 1, 2 . . ., up to n, in the initial period i and B (%) is the national average growth rate in the housing backlog.


From Table 5.2.3 above, the total national effect is 110 613. The housing backlog in the metros would have increased by this number if it grew at the national average growth rate of 9%.The actual average growth rate of 12% in the metros resulted in the actual average growth of 143 299 which is greater than the actual average national growth of 110 613.


The last column of Table 5.2.3 above captures the difference in national effect and the regional effect, R-N. The difference is positive, 32 686, indicative of growth. Four metros show growth, the highest is JHB (51 785) and the lowest is TSH (4 996). The remaining four metros recorded a negative difference indicative of a decline in the housing backlog. The highest decline is ETH (-43 794) and the lowest is MAN (-5 180).


5.2.4 Industry Mix Effect (M) shows how the metropolitans’ structures factor into the overall growth of the national housing backlog. The industry mix effect (M) for the metros for the period from 2009 to 2018 is summarised in Table 5.2.4 below.


Table 5.2.4 Industry mix effect (M), housing backlog, 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

The industry mix effect (M) is the product of the metros’ housing backlog in the initial year and the marginal rate of growth. The marginal rate of growth (MR %) is the difference between the provincial growth and the national average growth, the third column of Table 6.2.4. Thus, the industry mix effect (M), is defined as:


M = MR1 (Bi1) + MR2 (Bi2 ) + . . . . + MRn (Bin) (13)


Where M is the industry mix, MR is the marginal rate of growth in metropolitans 1, 2, 3…up to n and Mi is the metropolitans’ housing backlog in the initial year. The total industry mix (M), is according to Table 5.2.4, equal to 60 546, which is a growth. Four metros show growth with the highest being CPT (42 840) and the lowest being TSH (13 055). Decline is recorded in the remaining four, the highest is ETH (-18 220) and the lowest is NMA (-2 144).


Regional competitive effect (S) is the degree to which the metros’ performance is better or worse than the national growth. Equation (14) defines Total Change in Regional housing backlog (R) as the sum of National Share Effect (N), Industry Mix Effect (M) and Regional Share Effect (S). Table 5.2.5 below is a summary of the Regional Competitive Effect.

R = N + M + S (14)


Therefore, S = R-N-M


= 143 299-(110 613)-(60 546)


= -27 860


Table 5.2.5 Regional competitive share Effect (S=R-N-M), 2009-2018
Source: Rex Data, 2021 (Author’s own calculations)

The total regional competitive share effect suggests that the metros’ housing backlog declined by -27 860. The following two metros show growth, JHB (33 281) and MAN (1 284). Six metros indicated a decline, ETH (-25 574), NMA (-17 501), EKU (-8 795), TSH (-8 509), BUF (-2 013) and CPT (-48).


Comparison of Regional Competitive Effects (RCEs): Informal Settlements & Housing backlog


Table 5.3.1 below is the comparison of regional competitive effects for informal settlements and housing backlog.


Table 5.3.1 Regional competitive effects: Informal settlements & housing backlog
Source: Rex Data, 2021 (Author’s own calculations)

The totals of regional competitive effects for both informal settlements and housing backlog in Table 5.3.1 above, are declining, -205 949 and -27 860, respectively. Informal settlements are, on average, declining faster that the housing backlog.


A decline in informal settlements and housing backlog is indicative that the housing policy is, on average, effective. However, the picture in individual metropolitan municipalities is different.


There is a decline in informal settlements and housing backlog in the CPT, ETH, EKU, NMA, TSH and BUF metros. The housing policy is therefore effective in these metros. In JHB and MAN informal settlements and housing backlog are increasing. An increase in both is indicative of housing policy being ineffective in these metros.


Recommendations

  • Policy makers should focus their attention, without neglecting the other metropolitans, on JHB and Mangaung, and mechanisms that will accelerate the reduction in the housing backlog.

  • Expedite the accreditation of municipalities on all three levels to enable them to manage the resources and processes related to the upgrading and resettlement of informal settlements in full.

  • The Department of Human Settlements must prioritise in-situ upgrading, resettlement and a phased funding mechanism for informal settlements in JHB and MAN.


Shortcomings of the study and scope for further research


Due to the unavailability of reliable and accurate data for housing delivery in municipalities, data relating to informal settlements and housing backlog was used to examine the efficacy of the National Housing Policy in the Metropolitan Municipalities of South Africa.


Unfortunately, the Shift-Share Analysis (SSA) used in this regard is a static method and does not capture the dynamism and interaction between the metros in the periods under review. In addition, SSA does not show causality and it is, therefore, difficult to pinpoint the causes of trends and deviations.


Further research into policy options and policy improvements aimed at providing municipalities autonomy to implement housing delivery effectively and efficiently, based on the merits of each municipality, is needed. The outcomes could assist policy-makers and stakeholders in planning and implementing differentiated policies that would benefit the poor.


Conclusion


The primary aim of the National Housing Policy is to create sustainable communities and to improve the quality of life of the people of South Africa, through the provision of low-cost housing. The study examined the effectiveness of the National Housing Policy using informal settlements and housing backlog data for the period 2009-2018.


Increased urbanisation, population growth and migration to the cities has led to an increase in informal settlements and a housing backlog. Post 1994, the African National Congress (ANC) introduced a White Paper on housing in order to address housing for the poor. They provided a subsidy of R1500 for households earning R3500 per month and less to build a (15x20) m2 house.


Housing challenges in South African include misuse of low-cost housing and lack of capacity by the municipalities to deliver low-cost houses effectively and efficiently. Compounding this problem is a lack of accountability by all levels of government, failure to consult stakeholders and corruption.


Neoliberalism is a market-oriented policy involving the state and the private sector that is intended to provide housing for the poor. However, this policy has unintended consequences that actually result in the exclusion of the poor in favour of middle and high-income groups. Low-income households have limited or even no access to mortgage finance or credit. Developing countries find it difficult to implement neoliberalism to the advantage of the poor because of their ailing economies.


The Shift-Share Analysis method, which subdivides regional employment into three components, that is, National Share Effect, Industrial Mix Effect and Regional Competitive Effect, was used to examine the changes in informal settlements and the housing backlog in South Africa.


The national housing policy was, on average, found to be effective because the totals of the Regional Competitive Effects for Informal Settlements and Housing Backlog are negative, -205949 and -27860 respectively.


Individually, six of the eight metros, that is, CPT, ETH, EKU, NMA, TSH and BUF have negative Regional Competitive Effects for Informal Settlements and Housing Backlog, an indication that the national housing policy is effective in these metros.


The national housing policy was, however, found to be ineffective in JHB and MAN because their Regional Competitive Effects for Informal Settlements and Housing Backlog are positive.

Recommendations to expedite low-cost housing through policy and mechanisms designed to reduce informal settlements and the housing backlog, are proposed.


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This article has been published by the Inclusive Society Institute

The Inclusive Society Institute (ISI) is an autonomous and independent institution that functions independently from any other entity. It is founded for the purpose of supporting and further deepening multi-party democracy. The ISI’s work is motivated by its desire to achieve non-racialism, non-sexism, social justice and cohesion, economic development and equality in South Africa, through a value system that embodies the social and national democratic principles associated with a developmental state. It recognises that a well-functioning democracy requires well-functioning political formations that are suitably equipped and capacitated. It further acknowledges that South Africa is inextricably linked to the ever transforming and interdependent global world, which necessitates international and multilateral cooperation. As such, the ISI also seeks to achieve its ideals at a global level through cooperation with like-minded parties and organs of civil society who share its basic values. In South Africa, ISI’s ideological positioning is aligned with that of the current ruling party and others in broader society with similar ideals.


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