Housing market dynamics and economic growth in South Africa (1994 – 2019)
- Authors: Muchaonyerwa, Forward
- Date: 2023-09
- Subjects: Economic development -- South Africa , Housing -- Prices -- South Africa , Housing forecasting -- South Africa
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/28628 , vital:74477
- Description: The housing market contributes significantly to economic growth. On this background, the study examined South Africa’s housing market dynamics, particularly determinants of demand, supply, and formal housing prices. Furthermore, the study looked at the impact of housing prices on economic growth from 1994:Q1 to 2019:Q2. The study period is important as it covers the new political dispensation in South Africa where the country entered a new democracy in 1994. The first three objectives of the study were to identify the determinants of housing demand, supply, and prices. The theory of demand and supply provided the theoretical framework for these models. Estimation of the housing demand, supply and price models was done by the employing Seemingly Unrelated Regression (SUR) technique. The Three Stage Least Squares (3SLS) model was estimated for robustness. Findings from SUR and 3SLS confirmed that Housing Demand (HD) is negatively and significantly influenced by residential Building Costs per Square Meter (BCSM), Housing Supply (HS) and Financial Costs (FC); and positively influenced by House Prices (HP). In addition, HS is negatively affected by BCSM, HD, Production Costs (PC) and Urban Population (UP); and positively influenced by HP and Residential Construction Confidence (RC). Lastly, HP are negatively affected by Prime Overdraft Rate (POR) and RC; and positively influenced by BCSM, HS, HD, Coincident Business Cycle Indicator (CBC) and residential Valuation (VAL). The fourth objective was to examine the impact of house prices on economic growth. An economic model was specified with Gross Domestic Product (GDP) as its dependent variable. The new growth theory provided the theoretical framework for this model. The Johansen co-integration technique confirmed a long run-term relationship between economic growth and house prices. The Vector Error Correction Model (VECM) was estimated to analyze the long and short run relationship among the variables. Empirical results confirmed that house prices have a positive impact on economic growth. Results further confirmed that CBC and Unemployment Rate (UR) are also positively related to GDP. POR and Leading Business Cycle indicator (LEBC) are negatively related to GDP. Granger Causality test was performed to analyze the causality between house prices and economic growth. The results indicated that there is a long run unidirectional causality from house prices to economic growth. With these results, the study recommends policy formation emanating from continuous research by establishing a human settlement agency or task team. The team can establish procedures for data collection and maintain a database for all kinds of housing market data. Their mandate includes research on commissioning of new towns and/or cities to boost housing supply. The government should avail more land and relax restrictive regulations and minimize red tape to ensure that houses are supplied to meet the growing demand as well as to stabilize prices. Policies to promote confidence and stabilize building costs are needed. These variables indicated significant influence on housing dynamics. It is also recommended to incentivize households to participate on the mortgage market. This assist both households through the wealth effect which positively influence increase in economic activity in South Africa. , Thesis (DCom) -- Faculty of Management and Commerce, 2023
- Full Text:
- Date Issued: 2023-09
- Authors: Muchaonyerwa, Forward
- Date: 2023-09
- Subjects: Economic development -- South Africa , Housing -- Prices -- South Africa , Housing forecasting -- South Africa
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/28628 , vital:74477
- Description: The housing market contributes significantly to economic growth. On this background, the study examined South Africa’s housing market dynamics, particularly determinants of demand, supply, and formal housing prices. Furthermore, the study looked at the impact of housing prices on economic growth from 1994:Q1 to 2019:Q2. The study period is important as it covers the new political dispensation in South Africa where the country entered a new democracy in 1994. The first three objectives of the study were to identify the determinants of housing demand, supply, and prices. The theory of demand and supply provided the theoretical framework for these models. Estimation of the housing demand, supply and price models was done by the employing Seemingly Unrelated Regression (SUR) technique. The Three Stage Least Squares (3SLS) model was estimated for robustness. Findings from SUR and 3SLS confirmed that Housing Demand (HD) is negatively and significantly influenced by residential Building Costs per Square Meter (BCSM), Housing Supply (HS) and Financial Costs (FC); and positively influenced by House Prices (HP). In addition, HS is negatively affected by BCSM, HD, Production Costs (PC) and Urban Population (UP); and positively influenced by HP and Residential Construction Confidence (RC). Lastly, HP are negatively affected by Prime Overdraft Rate (POR) and RC; and positively influenced by BCSM, HS, HD, Coincident Business Cycle Indicator (CBC) and residential Valuation (VAL). The fourth objective was to examine the impact of house prices on economic growth. An economic model was specified with Gross Domestic Product (GDP) as its dependent variable. The new growth theory provided the theoretical framework for this model. The Johansen co-integration technique confirmed a long run-term relationship between economic growth and house prices. The Vector Error Correction Model (VECM) was estimated to analyze the long and short run relationship among the variables. Empirical results confirmed that house prices have a positive impact on economic growth. Results further confirmed that CBC and Unemployment Rate (UR) are also positively related to GDP. POR and Leading Business Cycle indicator (LEBC) are negatively related to GDP. Granger Causality test was performed to analyze the causality between house prices and economic growth. The results indicated that there is a long run unidirectional causality from house prices to economic growth. With these results, the study recommends policy formation emanating from continuous research by establishing a human settlement agency or task team. The team can establish procedures for data collection and maintain a database for all kinds of housing market data. Their mandate includes research on commissioning of new towns and/or cities to boost housing supply. The government should avail more land and relax restrictive regulations and minimize red tape to ensure that houses are supplied to meet the growing demand as well as to stabilize prices. Policies to promote confidence and stabilize building costs are needed. These variables indicated significant influence on housing dynamics. It is also recommended to incentivize households to participate on the mortgage market. This assist both households through the wealth effect which positively influence increase in economic activity in South Africa. , Thesis (DCom) -- Faculty of Management and Commerce, 2023
- Full Text:
- Date Issued: 2023-09
Housing price volatility: exploring metropolitan property markets in South Africa
- Authors: Zwane, Reuben Mabutho
- Date: 2018
- Subjects: Housing -- Prices -- South Africa
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/21560 , vital:29554
- Description: This study analyses the housing price volatility in metropolitan areas in South Africa, particularly Port Elizabeth and East London residential housing markets. This study uses secondary statistical data, obtained from secondary sources. The study uses quarterly time series data for the period 1981:1 to 2015:3 giving 139 observations. The data will be collected from different sources. The main sources of data are real estate agencies (Trafalgar, Harcourts and Property24), the South African Department of Trade and Industry (dti) and supplemented by the South African Reserve Bank (SARB) and Statistics South Africa (Stats SA). The study shall use the ordinary least squares (OLS) method to estimate its results. Ordinarily, this is a generalised linear modelling technique that may be used to model a single response variable which has been recorded on at least an interval scale. This method requires that the underlying stochastic processes of the variables are stationary. That is, explanatory variables should exhibit constant means and variances over time. If the stochastic processes are not stationary, OLS produces unreliably significant coefficients. Results showed that household savings, household income and total growth in household buildings (TGH) are statistically significant in explaining changes in house prices. Jointly, all the explanatory variables can account for almost 52% of the changes in the dependent variable. The Durbin Watson statistic showed that there is no autocorrelation in the model. This shows that the model is good. Results from the regression show that there is a negative relationship between house prices and household savings. A one-unit increase in household savings leads to a 0.407 decrease in house prices. This relationship makes economic sense because when households save, there is less income available to buy houses. When there is less income available to buy houses, it would mean there is less demand for houses.
- Full Text:
- Date Issued: 2018
- Authors: Zwane, Reuben Mabutho
- Date: 2018
- Subjects: Housing -- Prices -- South Africa
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/21560 , vital:29554
- Description: This study analyses the housing price volatility in metropolitan areas in South Africa, particularly Port Elizabeth and East London residential housing markets. This study uses secondary statistical data, obtained from secondary sources. The study uses quarterly time series data for the period 1981:1 to 2015:3 giving 139 observations. The data will be collected from different sources. The main sources of data are real estate agencies (Trafalgar, Harcourts and Property24), the South African Department of Trade and Industry (dti) and supplemented by the South African Reserve Bank (SARB) and Statistics South Africa (Stats SA). The study shall use the ordinary least squares (OLS) method to estimate its results. Ordinarily, this is a generalised linear modelling technique that may be used to model a single response variable which has been recorded on at least an interval scale. This method requires that the underlying stochastic processes of the variables are stationary. That is, explanatory variables should exhibit constant means and variances over time. If the stochastic processes are not stationary, OLS produces unreliably significant coefficients. Results showed that household savings, household income and total growth in household buildings (TGH) are statistically significant in explaining changes in house prices. Jointly, all the explanatory variables can account for almost 52% of the changes in the dependent variable. The Durbin Watson statistic showed that there is no autocorrelation in the model. This shows that the model is good. Results from the regression show that there is a negative relationship between house prices and household savings. A one-unit increase in household savings leads to a 0.407 decrease in house prices. This relationship makes economic sense because when households save, there is less income available to buy houses. When there is less income available to buy houses, it would mean there is less demand for houses.
- Full Text:
- Date Issued: 2018
Trends and volatility in residential property prices in South Africa
- Authors: Anyikwa, Izunna Chima
- Date: 2012
- Subjects: Housing -- Prices -- South Africa
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:9012 , http://hdl.handle.net/10948/d1018221
- Description: This study sought to empirically investigate trends and volatility in residential property prices in South Africa using quarterly data over the period 1980Q1 to 2011Q4. The empirical analysis uses a range of unit root and stationarity tests as well as a number of ARCH-family of models. The results from the trend analysis suggest that the behaviour of house prices in South Africa follows a random walk process. The randomness in the behaviour of house prices could be attributed to permanent effect of shock. Investigation into the dynamic behaviour of the house prices supports the existence of conditional volatility that is time-varying and highly persistent. Moreover, volatility is found to be asymmetric in news suggesting evidence of anti-leverage effects. These findings have important portfolio implications especially, considering the fact that large-scale losses are possible if house prices exhibit the type of persistent in behaviour as captured in this study. Also, the existence of asymmetric effects in volatility suggests that more caution needs to be placed on news arrival as they may have significant impacts on the house price behaviour. Accordingly, this study suggests the need for residential property market to be treated like other asset markets with regards to risk.
- Full Text:
- Date Issued: 2012
- Authors: Anyikwa, Izunna Chima
- Date: 2012
- Subjects: Housing -- Prices -- South Africa
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:9012 , http://hdl.handle.net/10948/d1018221
- Description: This study sought to empirically investigate trends and volatility in residential property prices in South Africa using quarterly data over the period 1980Q1 to 2011Q4. The empirical analysis uses a range of unit root and stationarity tests as well as a number of ARCH-family of models. The results from the trend analysis suggest that the behaviour of house prices in South Africa follows a random walk process. The randomness in the behaviour of house prices could be attributed to permanent effect of shock. Investigation into the dynamic behaviour of the house prices supports the existence of conditional volatility that is time-varying and highly persistent. Moreover, volatility is found to be asymmetric in news suggesting evidence of anti-leverage effects. These findings have important portfolio implications especially, considering the fact that large-scale losses are possible if house prices exhibit the type of persistent in behaviour as captured in this study. Also, the existence of asymmetric effects in volatility suggests that more caution needs to be placed on news arrival as they may have significant impacts on the house price behaviour. Accordingly, this study suggests the need for residential property market to be treated like other asset markets with regards to risk.
- Full Text:
- Date Issued: 2012
The impact of macroeconomic and financial factors on the performance of the housing property market in South Africa
- Authors: Kwangware, Debra
- Date: 2009
- Subjects: Microeconomics , Housing -- South Africa , Housing -- Prices -- South Africa , Real property -- South Africa , Interest rates -- South Africa , Foreign exchange rates -- South Africa
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:1042 , http://hdl.handle.net/10962/d1005641 , Microeconomics , Housing -- South Africa , Housing -- Prices -- South Africa , Real property -- South Africa , Interest rates -- South Africa , Foreign exchange rates -- South Africa
- Description: This study exammes the impact of macroeconomic and financial variables on the performance of the housing property market in South Africa using monthly data for the period January 1996 to June 2008. Orthogonalised and non-orthogonalised house price returns and real estate returns are utilised as proxies for the housing property market in separate models. Three main issues were empirically analysed in relation to the linkage between selected variables and the housing property market. The first aspect examined the relationship between selected macroeconomic and financial factors and property returns. Secondly, the study examined the influence that a unit shock to each variable has on property returns over a period of time. The third aspect focused on determining the proportion of property returns variation that results from changes in the macroeconomic and financial variables. VAR modelling was thus adopted to empirically analyse these three aspects. The results reveal that house price returns are influenced by most of the macroeconomic and financial variables used in this study. Specifically, the real effective exchange rate, interest rate spread and manufacturing production positively impact on house price returns while the domestic interest rate, the dividend yield and expected inflation have a negative effect. Furthermore, manufacturing production has a lagged effect on house price returns while the real effective exchange rate and domestic interest rate have a contemporaneous effect. Real estate returns are not influenced by most of the variables except for the domestic interest rate and dividend yield which have a negative effect.
- Full Text:
- Date Issued: 2009
- Authors: Kwangware, Debra
- Date: 2009
- Subjects: Microeconomics , Housing -- South Africa , Housing -- Prices -- South Africa , Real property -- South Africa , Interest rates -- South Africa , Foreign exchange rates -- South Africa
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:1042 , http://hdl.handle.net/10962/d1005641 , Microeconomics , Housing -- South Africa , Housing -- Prices -- South Africa , Real property -- South Africa , Interest rates -- South Africa , Foreign exchange rates -- South Africa
- Description: This study exammes the impact of macroeconomic and financial variables on the performance of the housing property market in South Africa using monthly data for the period January 1996 to June 2008. Orthogonalised and non-orthogonalised house price returns and real estate returns are utilised as proxies for the housing property market in separate models. Three main issues were empirically analysed in relation to the linkage between selected variables and the housing property market. The first aspect examined the relationship between selected macroeconomic and financial factors and property returns. Secondly, the study examined the influence that a unit shock to each variable has on property returns over a period of time. The third aspect focused on determining the proportion of property returns variation that results from changes in the macroeconomic and financial variables. VAR modelling was thus adopted to empirically analyse these three aspects. The results reveal that house price returns are influenced by most of the macroeconomic and financial variables used in this study. Specifically, the real effective exchange rate, interest rate spread and manufacturing production positively impact on house price returns while the domestic interest rate, the dividend yield and expected inflation have a negative effect. Furthermore, manufacturing production has a lagged effect on house price returns while the real effective exchange rate and domestic interest rate have a contemporaneous effect. Real estate returns are not influenced by most of the variables except for the domestic interest rate and dividend yield which have a negative effect.
- Full Text:
- Date Issued: 2009
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