The topic of the research is "Determinants of housing prices in selected developed and developing economies". This study attempts to assess the determinants of house prices in selected developed and developing economies.
RESEARCH OVERVIEW
Introduction
Real Residential Property Price Index
The repeat sales method is considered a variant of the hedonic method to overcome property heterogeneity. This variable is involved in the inflation process and economic development is part of the effect (Fenwick, 2013).
Research Background
- RRPPI in Developed Economies
- RRPPI in Developing Economies
This is due to the slowdown in the Serbian economy and the reduction of loan facilities, which has affected the increase in Serbian house prices (Djordjevic, 2019). Serbia's rising house prices amid a softening of the domestic real estate market due to high unemployment (Mitrović, 2022).
Problem Statement
Finally, the lack of housing affordability can also slow down economic growth in developed and developing economies. Therefore, the lack of supply of affordable housing has many negative consequences for the developed and developing countries.
Research Objectives
- General Research Objective
- Specific Research Objectives
To examine the relationship between unemployment and house prices in the selected developed and developing economies. iv). To investigate the relationship between the population and the house price in the selected developed and developing countries. v).
Research Questions
To investigate the relationship between the construction cost and the house price in the selected developed and developing economies.
Significance of Study
Then, the log transformation is used in this study to convert the data set to near-normal distribution. This study incorporated correlation analysis to explore the strength of the relationship between the related determinants and house price in selected developed and developing economies. This study helps the government and policy makers to implement effective policies to regulate the determinants affecting house prices in both selected developed and developing economies so that the issues of house prices can be addressed.
This study also helps home builders to make an informed decision on the best allocation of resources and sales. Meanwhile, this study provides some advice to investors and potential home buyers in selected developed and developing economies using the findings obtained from the study as well as examining the relationship between selected factors and housing prices before investing and buy a house for management. their return and ownership of the house accordingly. Finally, this study serves as a reference for future researchers to study the related topic of housing prices.
Conclusion
LITERATURE REVIEW
- Introduction
- Underlying Theoretical Models Review
- Modern Portfolio Theory
- Migration and Urbanization Theory
- The Law of Supply and Demand (Economic Theory)
- Review of Variables
- Housing Price
- Gross Domestic Product and Housing Price
- Inflation Rate and Housing Price
- Unemployment Rate and Housing Price
- Population and Housing Price
- Construction Cost and Housing Price
- Conceptual Framework
- Relevant Conceptual Framework
- Proposed Conceptual Framework
- Hypothesis of the Study
- GDP and Housing Price
- Inflation Rate and Housing Price
- Unemployment Rate and Housing Price
- Population and Housing Price
- Construction Cost and Housing Price
- Gap of Literature Review
- Conclusion
Therefore, this study hypothesized that GDP would have a significant impact on house prices in both developed and developing economies. Therefore, this study hypothesized that inflation would have a significant impact on house prices in both developed and developing economies. In contrast, Xu and Tang (2014) claimed that the unemployment rate is positively associated with house prices in the cointegration vector.
In short, this study hypothesized that the unemployment rate would have a significant impact on house prices in both developed and developing economies. On the contrary, there are researchers who have argued the negative relationship between population and house prices. Therefore, this study hypothesized that population would have a significant impact on house prices in both developed and developing economies.
Therefore, this study hypothesized that construction costs could have a significant impact on house prices in both developed and developing economies.
METHODOLOGY
- Introduction
- Research Design
- Data Description and Collection Method
- Data Processing
- Sources of Data
- Econometric Model
- Variables and Measurement
- Real Residential Property Price Index (RRPPI) …
- Gross Domestic Products (GDP)
- Inflation Rate (INF)
- Unemployment Rate (UNP)
- Population (POP)
- Construction Cost (CCOST)
- Econometric Technique
- Diagnostic Checking
- Autocorrelation (Breusch-Pagan LM Test) …
- Stationarity (Levin-Lin-Chu Test)
- Normality (Jarque-Bera Test)
- Conclusion
In addition, the data source is Bank for International Settlements, and the scope of data collection is from 2004 to 2018. In addition, the data source is World Bank, and the scope of data collection is from 2004 to 2018. Then, the data source is World Bank, the scope of data collection but it is from 2004 to 2018.
In addition, the data source is the World Bank and the range of the data collected is from 2004 to 2018. In addition, the data source is the World Bank and the range of the data collected is from 2004 to 2018. In addition, the data source is the World Bank and the range of the data collected runs from 2004 to 2018.
The residual of the model is being examined and the p-value is obtained from the residual.
DATA ANALYSIS
Introduction
Selection of Econometric Model
- Likelihood Ratio Test
- Lagrange Multiplier Test (LM Test)
- Hausman Test
- Conclusion for Selection of Econometric Model …
Conclusion: REM is the preferred model for selected developed countries, while POLS appears to be the preferred model for selected developing countries.
Descriptive Statistic
- Descriptive Statistic for Developed Countries Model …
- Descriptive Statistic for Developing Countries Model
Correlation Analysis
- Correlation Analysis for Developed Countries Model
- Correlation Analysis for Developing Countries Model .…. 61
- Interpretation of Slope Coefficient for Developed
- Interpretation of Slope Coefficient for Developing
- R-Squared of Developed and Developing Countries
- F-Test for Developed and Developing Countries
- T-Test for Developed and Developing Countries
If GDP increases by 1% on average, the RRPPI of selected developed countries will increase ceteris paribus. If LPG increases by 1% on average, the RRPPI of selected developed countries will decrease ceteris paribus. If GDP increases by 1% on average, the RRPPI of selected developing countries will increase ceteris paribus.
If the INF increases by 1% on average, the RRPPI of selected developing countries will increase ceteris paribus. If LPG increases by 1% on average, the RRPPI of selected developing countries will increase ceteris paribus. If POP increases by 1% on average, the RRPPI of selected developing countries will decrease ceteris paribus.
If the CCOST increased by 1% on average, the RRPPI of selected developing countries would decrease ceteris paribus.
Diagnostic Checking
- Autocorrelation (Breusch-Pagan LM Test)
- Stationarity (Levin-Lin-Chu Test)
- Normality (Jarque-Bera Test)
Based on Table 4.9, for the developed country model, INF, UNP, POP and CCOST are significant at the 1% level of significance because their p-values are lower than 0.01. Only GDP, however, is insignificant at the 5% level of significance, as its p-value is greater than 0.05. In this study, the Levin-Lin-Chu test, which is a panel unit root test, is performed to determine the stationarity of the model.
The reliability of research results will be weakened if the data set is not normally distributed.
Discussions of Major Findings
- Gross Domestic Product
- Inflation Rate
- Unemployment Rate
- Population
- Construction Cost
On the other hand, it finds that GDP affects RRPPI in the selected developing economies positively and significantly at 1% significance level. The regression result discovers that inflation rate affects RRPPI in the selected developed economies negatively and significantly at 1%. The regression result also shows that the inflation rate does not significantly affect the RRPPI in the selected developing economies of 5%.
The regression result finds that the unemployment rate has a negative and significant effect on the RRPPI in the selected developed economies at the 1% significance level. A possible explanation is that an increase in home ownership will lower housing prices and limit labor mobility due to an increase in the unemployment rate (Blanchflower & Oswald, 2013). Furthermore, it is shocking to see that the regression result shows that unemployment rate has a positive and significant effect on RRPPI in the selected developing economies at the 5% level of significance.
Similar positive results are also shown in the studies by Degen and Fischer (2017); Nistor and Reianu (2018).
Conclusion
CONCLUSION
Introduction
Implications of the Study
- Government
- House Developers
- Investors
The findings in Chapter 4 show a positive relationship between population and housing prices in selected developed countries, so their growth is a fundamental factor in housing demand, which increases rents and housing purchase prices. A negative relationship is shown between the unemployment rate and housing prices in selected developed economies, as rising unemployment reduces housing affordability, causing housing prices to fall. The findings in Chapter 4 reveal that the relationship between the unemployment rate and housing prices is positive in selected developing economies because it causes less turnover and restricts labor mobility, which increases production costs and housing prices.
The government can also try to reduce occupational immobility in the labor force to reduce the unemployed population which can control the house price as people can afford the house with more income. As investors can help develop and maintain the success of the housing sector, this study can serve as a reference for those who would like to invest in the housing market of both selected developed and developing economies, as it shows the sensitivity of house prices to its determinants. Moreover, the GDP and house prices have a positive relationship in the selected developing countries, as it can affect the house prices which positively reflect housing wealth.
Therefore, the investors are advised to sell the house at a higher price when the economy grows in the selected developing countries.
Limitations of the Study
Investors can adopt a "buy low, sell high" strategy, as the results in Chapter 4 show a negative relationship between the inflation rate and house price in selected developed economies due to the monetary illusion that encourages mispricing of the housing product. Therefore, it is recommended that investors buy a house with a lower price when inflation is high to reduce costs or reduce the amount of the home loan. Investors should keep a close eye on and closely monitor GDP trends and inflation rates to make more diversified housing investments in terms of location, asset class and asset risk profile over different time periods as they witness different results in developed countries. and developing economies to reduce overall market risk.
Recommendations for Future Researchers
Conclusion
For selected developed economies, the empirical results showed that the population affects house prices positively and significantly. Furthermore, the results showed that inflation, unemployment and construction costs affect house prices negatively and significantly. This study also provides some guidelines for the authority or the policy makers to manage the housing prices considering the factors that determine the housing price regardless of the selected developed and developing economies.
The determinants of house prices and construction : an empirical study of the Swiss housing economy. Looking for alternatives in the city of the slopes : housing as a process to reduce socio-spatial segregation in Lima, Peru. Assessing the needs of first-time buyers in purchasing affordable housing in Malaysia.
U.S. Assistance to Peru under the Alliance for Progress Submitted by the Peru Division of the U.S. Department of State. Rethinking the relationship between housing prices and inflation: new evidence from 29 large cities in China.
Likelihood Ratio Test (Developed)
Likelihood Ratio Test (Developing)
Lagrange Multiplier Test (Developed)
Lagrange Multiplier Test (Developing)
Hausman Test (Developed)
Hausman Test (Developing)
Descriptive Statistic (Developed)
Descriptive Statistic (Developing)
Correlation Analysis (Developed)
Correlation Analysis (Developing)
The Fixed Effect Model (Developed)
The Fixed Effect Model (Developing)
Breusch-Pagan LM (Developed)
Breusch-Pagan LM (Developing)
Levin-Lin-Chu Test (Developed)
Levin-Lin-Chu Test (Developing)
