As of 2022, South Africa's number of households increased to approximately 18.5 million in total, of which the majority lived in Gauteng, Kwazulu-Natal, and the Western Cape. Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized, with nearly 5.6 million households, according to estimates. Kwazulu-Natal counted 3.2 million households, whereas the Western Cape had roughly 2.1 million.

As of 2022, the number of households in South Africa increased and amounted to approximately 18.48 million, roughly 530,000 more than in the previous year. Between 2002 and 2022, the number of families in South Africa grew by around 65 percent. Looking at the number of households from a regional perspective , the Gauteng province (includes the city of Johannesburg) has the bulk of households, with almost 5.6 million residences. Although Gauteng is the smallest region in the country, it is highly urbanized and houses most of the population.

Households headed by women

The number of households headed by women averaged around 42 percent. Rural areas such as the Eastern Cape and Limpopo had a higher proportion of women in charge of their family unit. Urbanized regions, namely Gauteng and the Western Cape, were more likely to be headed by men.

Languages spoken in households

The most spoken language within and outside of South African households was isiZulu, with around 25 percent of the population utilizing it. The English language was the second most common language spoken outside of households, with a share of roughly 17 percent. However, within households, individuals preferred to speak other official languages such as isiXhosa and Afrikaans. South Africa has a diverse range of cultures, and language plays a crucial role in preserving these cultures.

South Africa Index: FTSE/JSE: Household Goods data was reported at 8.853 NA in Jun 2018. This records an increase from the previous number of 7.686 NA for May 2018. South Africa Index: FTSE/JSE: Household Goods data is updated monthly, averaging 155.470 NA from Jan 2006 (Median) to Jun 2018, with 150 observations. The data reached an all-time high of 664.661 NA in Mar 2016 and a record low of 7.686 NA in May 2018. South Africa Index: FTSE/JSE: Household Goods data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z001: Johannesburg Stock Exchange: Index.

Abstract

In 1990 the Central Statistical Service of South Africa sponsored a household expenditure survey in a sub-set of households in 12 major metro/urban areas in the country. The aim of the survey was to obtain data on income and expenditure patterns of South African households on which the Consumer Price Index (CPS) and various other economic indicators could be based. The survey was conducted by Markdata, the fieldwork arm of the Human Sciences Research Council (HSRC). All population groups were enumerated but this dataset does not contain data files for the "white" population group.

Geographic coverage

The IES 1990 only collected data on expenditure from the 12 largest urban areas in the country, leaving out buying patters in small towns and rural areas. Areas enumerated were: Cape Peninsula, Port Elizabeth- Uitenhage, East London, Kimberley, Pietermaritz burg, Durban, Pretoria, Johannesburg, Witwatersrand (excl Jhb), Klerksdorp, Vaal Triangle, Orange Free State-Gold Fields, Bloemfontein.

Analysis unit

Units of analysis in the survey includes households and individuals

Universe

The survey covered all household members in the selected areas

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Two survey instruments were provided in the IES 1990: A detailed "long" questionnaire and a "short" questionnaire without detailed classification of expenditure items. The "short" questionnaire was completed by two out of three households enumerated. The "short" and "long" questionnaires are identified separately in the variable q_type. "Long" questionnaires are indicated as questionnaire = 1 and "short' questionnaires as questionnaire = 2.

Abstract

The Gauteng City-Region Observatory (GCRO) (based at the University of Johannesburg (UJ)) in partnership with the Gauteng Provincial Government, contracted Development Research Africa (DRA) to conduct an integrated Quality of Life/Customer Satisfaction Survey in the Gauteng City-Region (GCR). The objective of the GCRO is to assist the Gauteng Government to build Gauteng as an integrated and globally competitive region, where the economic activities of different parts of the province complement each other in consolidating Gauteng as an economic hub of Africa, and an internationally recognised global city-region. The this end, the main aim of the survey, conducted from July to October 2009, was to inform the GCRO and the Provincial Government, as well as other role-players about the perceived state of the municipalities within the GCR footprint about the quality of life of their inhabitants.

Geographic coverage

The Quality of Life Survey covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Analysis unit

Households and individuals

Universe

The Gauteng City-Region Observatory Quality of Life Survey 2009 covered all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Kind of data

Qualitative and quantitative data

Sampling procedure

For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling units within the wards being selected in the second stage and respondents selected at the third stage.

Phase 1 The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.

Phase 2 During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.

The following process of household selection was adhered to: From the starting point 20 houses were counted in a ward. However, if there were:

• 1-5 target number of interviews to be completed in a ward; 01 starting point was used; • 6-10 target number of interviews to be completed in a ward; 02 starting points were used; • 11-15 target number of interviews to be completed in the ward; 03 starting points were used; • 16-20 target number of interviews to be completed in the ward; 04 starting points were used; • 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and • 25 and above target number of interviews to be completed in a ward; 06 starting points were used

In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.

Phase 3 For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria: • Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and • 18 years of age or older • In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Gauteng City-Region Observatory and Data Research Africa (DRA) developed the quantitative evaluation tool for the survey. DRA reformatted the pre-pilot questionnaire and provided input into the layout and flow as well as question structure to ensure accurate data capturing. DRA field managers piloted the questionnaire with 30 interviews with individuals from households with different demographic characteristics . The Gauteng City-Region Observatory Quality of Life Survey 2009 questionnaire collected data on demographic details of the enumerated population (population group, gender, age, language) and on housing (dwelling type, tenure, satisfaction with dwelling, perceived quality of housing and housing allocation) as well as household services (water, sanitation, refuse, energy sources). Questions included those on migration, health (including disability), education and employment (including employment sector). Questions on community services and amenities were included, and questions on transport, leisure activities and safety and crime. Financial data was collected (including on debts, income, and social grants) and data on household assets. Finally, data on public participation and governance was also collected, and data on the perceived personal wellbeing and quality of life of respondents.

As of 2022, some 42 percent of the households in South Africa were female-headed. Provinces with larger portions of rural areas, such as Eastern Cape (49.6 percent), and Limpopo (47.1 percent), were more likely to share large numbers of female-headed households. In contrast, rather urbanized provinces, such as Gauteng (35.3 percent), which includes Johannesburg, were less likely to have families headed by women.

Abstract

The Gauteng-City Region Observatory (GCRO) commissioned Data World to conduct its Second Quality of Life Survey, with surveys being conducted in second half of 2011.

The Gauteng City-Region Observatory (GCRO) was established in 2008 as a partnership between the University of Johannesburg (UJ), the University of the Witwatersrand, Johannesburg (Wits) and the Gauteng Provincial Government (GPG), with local government in Gauteng also represented. The objective of the GCRO is to inform and assist the various spheres of the Gauteng government in building and maintaining the province as an integrated and globally competitive region.

The Second Quality of Life Survey must comprehensively represent the whole of Gauteng, which consists of 10 municipalities, which in turn covers 508 wards. Data World was contracted to undertake 15000 surveys across this sphere. Among the main aims of the Quality of Life Survey, is to inform the GCRO as well as provincial government and other relevant parties with regards to the perceived states of the municipalities within Gauteng, with focus on the quality of the lives of people who live within these municipalities.

Geographic coverage

The Gauteng City-Region Observatory Quality of Life Survey 2011 covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Analysis unit

The units of analysis in theGauteng City-Region Observatory (GCRO) Quality of Life Survey are households and individuals

Universe

The Gauteng City-Region Observatory Quality of Life Survey 2009 covered all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling units within the wards being selected in the second stage and respondents selected at the third stage.

Phase 1 The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.

Phase 2 During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.

The following process of household selection was adhered to: From the starting point 20 houses were counted in a ward. However, if there were: • 1-5 target number of interviews to be completed in a ward; 01 starting point was used; • 6-10 target number of interviews to be completed in a ward; 02 starting points were used; • 11-15 target number of interviews to be completed in the ward; 03 starting points were used; • 16-20 target number of interviews to be completed in the ward; 04 starting points were used; • 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and • 25 and above target number of interviews to be completed in a ward; 06 starting points were used In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.

Phase 3 For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria: • Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and • 18 years of age or older • In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument (questionnaire) which was used was provided by the GCRO. The instrument was similar to the questionnaire used for the initial Quality of Life survey, with new questions being added only where questions from the previous survey were removed. This was done with the intention of keeping the duration of the survey the same as the initial one. The survey instrument was a 20 page questionnaire, broken up into 12 sections. The bulk of the possible answers were pre-defined, such that most of the survey could be answered using a combination of tick-boxes or by writing down a number answer from a predefined set. To this end there are not many open - ended questions in the survey.

The survey instrument was reformatted by Data World to ensure optimal flow, as well as to cater for the technology platform which was used to conduct the surveys.

Data appraisal

The survey company, Data Research Africa, utilised a range of quality control measures during fieldwork for the survey. In the field, fieldworkers checked completed questionnaire schedules immediately after interviews to ensure that all questions were answered and relevant skips were followed. The checked questionnaires were then handed to field or office managers who, whilst in field, performed a second quality check on each questionnaire. They focused on skip patterns, as well as on ensuring that answers corresponded with previous responses and followed a logical process.

South Africa Market Cap: FTSE/JSE: Household Goods data was reported at 3,335.000 ZAR mn in Jun 2018. This records an increase from the previous number of 2,904.356 ZAR mn for May 2018. South Africa Market Cap: FTSE/JSE: Household Goods data is updated monthly, averaging 38,485.865 ZAR mn from Jan 2006 (Median) to Jun 2018, with 150 observations. The data reached an all-time high of 246,760.784 ZAR mn in Mar 2016 and a record low of 2,904.356 ZAR mn in May 2018. South Africa Market Cap: FTSE/JSE: Household Goods data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z004: Johannesburg Stock Exchange: Market Capitalization: by Index.

Abstract

This dataset is from the Gauteng City-Region Observatory which is a partnership between the University of Johannesburg, the University of the Witwatersrand, the Gauteng Provincial Government with several Gauteng municipalities. The GCRO has conducted previous Quality of Life Surveys in 2009 (Round I), 2011 (Round II), 2013-2014 (Round III) and 2015-2016 (Round IV). The 2017-2018 data is from Round V. Round 6 of the survey was conducted in 2020-2021.

Geographic coverage

The survey covers the Gauteng province in South Africa.

Analysis unit

Households and individuals

Universe

The survey covers all adult residence in Gauteng province, South Africa.

Kind of data

Sample survey data

Sampling procedure

For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling units within the wards being selected in the second stage and respondents selected at the third stage.

Phase 1 The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.

Phase 2 During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.

The following process of household selection was adhered to: From the starting point 20 houses were counted in a ward. However, if there were: • 1-5 target number of interviews to be completed in a ward; 01 starting point was used; • 6-10 target number of interviews to be completed in a ward; 02 starting points were used; • 11-15 target number of interviews to be completed in the ward; 03 starting points were used; • 16-20 target number of interviews to be completed in the ward; 04 starting points were used; • 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and • 25 and above target number of interviews to be completed in a ward; 06 starting points were used

In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.

Phase 3 For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria: • Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and • 18 years of age or older • In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed

If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument (questionnaire) which was used was provided by the GCRO. The instrument was similar to the questionnaire used for the initial Quality of Life survey, with new questions being added only where questions from the previous survey were removed. This was done with the intention of keeping the duration of the survey the same as the initial one. The survey instrument was a 20 page questionnaire, broken up into 12 sections. The bulk of the possible answers were pre-defined, such that most of the survey could be answered using a combination of tick-boxes or by writing down a number from a predefined set. There are therefore not many open-ended questions in the survey.

In 2022, South African households had an average disposable income of over 50,500 South African rand (approximately 2,738 U.S. dollars). This was slightly higher than the previous year where the average disposable income was 50,343 South African rand (around 2,725 U.S. dollars). Within the observed period, the disposable income of households in the country was highest in 2018 at 51,236 South African rand (about 2,773 U.S. dollars), while it was lowest in 2004.

The examine the impact of COVID 19 on household indebtedness

South Africa Dividend Yield: FTSE/JSE: Household Goods data was reported at 0.000 % pa in Jun 2018. This records a decrease from the previous number of 197.680 % pa for May 2018. South Africa Dividend Yield: FTSE/JSE: Household Goods data is updated monthly, averaging 1.090 % pa from Jan 2006 (Median) to Jun 2018, with 150 observations. The data reached an all-time high of 197.680 % pa in May 2018 and a record low of 0.000 % pa in Jun 2018. South Africa Dividend Yield: FTSE/JSE: Household Goods data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z005: Johannesburg Stock Exchange: Dividend Yield.

Abstract

The survey was undertaken in 2005 and conducted face-to-face interviews with a sample of 1 000 adults from Cape Town, Durban, Pretoria and Johannesburg, where ninety percent of the country's Jewish population reside.

Geographic coverage

The survey covered selected Jewish households in Cape Town, Durban, Pretoria and Johannesburg

Analysis unit

Households and individuals

Universe

The target population of the survey consists of Jewish South Africans.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire was used for the study

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

Household socio-economic status of the children at the schools.

As of 2022, 16.3 percent of the households in South Africa were involved in agricultural activities. Provinces with larger portions of rural areas, such as Limpopo (35.2 percent) and Mpumalanga (33.4 percent), were more likely to share large numbers of households involved in agricultural production. In contrast, relatively urbanized provinces, such as Gauteng (5.9 percent), which includes Johannesburg, and Western Cape (3.3 percent), which includes Cape Town, were less likely to be involved in such activities. Moreover, the share of households involved in agricultural activities dropped in all provinces except the Northern Cape, North West, and Western Cape compared to 2021.

In 2024, Pietermaritzburg in South Africa ranked first in the crime index among African cities, scoring 82.5 index points. The six most dangerous areas on the continent were South African cities. Furthermore, Pretoria and Johannesburg followed, with a score of 81.9 and 80.8 points, respectively. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). Contact crimes are common in South Africa Contact crimes in South Africa include violent crimes such as murder, attempted murder, and sexual offenses, as well as common assault and robbery. In fiscal year 2022/2023, the suburb of Johannesburg Central in the Gauteng province of South Africa had the highest number of contact crime incidents. Common assault was the main contributing type of offense to the overall number of contact crimes. Household robberies peak in certain months In South Africa, June, July, and December experienced the highest number of household robberies in 2023. June and July are the months that provide the most hours of darkness, thus allowing criminals more time to break in and enter homes without being detected easily. In December, most South Africans decide to go away on holiday, leaving their homes at risk for a potential break-in. On the other hand, only around 42 percent of households affected by robbery reported it to the police in the fiscal year 2022/2023.

Kidnapping cases in South Africa have reached alarming levels, with a total of 17,061 incidents reported in 2023/2024. This represents an 11 percent increase from the previous year, highlighting a growing concern for public safety across the nation. Gauteng province, home to major cities like Johannesburg and Pretoria, recorded the highest number of kidnappings at 8,683 cases, followed by KwaZulu-Natal with 3,329 cases.
Ransom and extortion drive kidnapping surge The rise in kidnappings appears to be driven by organized crime, with ransom-related abductions being the most common motive. In a select sample from the second quarter, 561 kidnappings were linked to ransom demands, while 93 cases were associated with extortion. This trend suggests a quarterly increase in kidnapping incidents, pointing to a persistent and evolving threat to public safety. The Moroka area in Gauteng province reported the highest number of kidnapping offenses, with nearly 240 cases, followed by Orange Farms with over 210 cases. The South African Police Services (SAPS) have reported that most cases were carried out during aggravated robberies such as hijackings and armed robberies at homes, businesses and public areas.
Regional context and broader implications South Africa's kidnapping rate of 9.57 per 100,000 inhabitants in 2023 was the highest among countries in Africa, surpassing Benin, which held the second-highest rate. This underscores the severity of the issue within the broader African context. The kidnapping crisis in South Africa occurs against a backdrop of wider regional instability, with countries in the Sahel like Mali, Niger, and Burkina Faso experiencing significant conflict exposure, affecting between eight and 14 percent of their populations. In this semi-arid region of west and north-central Africa, kidnappings are used as a strategic warfare tool and perpetrated for financial gain, which proved to be a lucrative method to help fund some of al-Qaeda affiliates located on the continent.

Cape Town was the most expensive metro to buy a home in South Africa in 2024. The average sales price of residential property was 1.9 million South African rands in that year, which was roughly double the price paid in Port Elizabeth.