With China’s rapid economic growth in recent years and the acceleration of urbanization, the real estate price has
also shown a substantial increase, especially the housing prices have always been high in first-tier cities. This paper
systematically combs the research on the factors affecting house price and the forecasting method of house price at
home and abroad, and puts forward some suggestions on the regulation and control of real estate price in our
country. It also points out the deficiency of data statistics and research perspective in empirical research. On this
basis, it is proposed that we should strengthen the scientificalness and comprehensiveness of the empirical data, put
forward a reasonable and appropriate hierarchical classification method for influencing factors, make clear the
importance and coupling mechanism of each level, and excavate the dominant influencing factors.
This document provides an introduction and background on the determinants of residential real estate prices in Kenya. It discusses several factors that can influence residential real estate prices, including interest rates, GDP, money supply, and inflation rate. The research problem is identified as determining the relative relationship and impact of these key factors on house prices in Kenya. The objective is to investigate the determinants of residential real estate prices. The study is expected to provide valuable insights for real estate investors, homeowners, financing institutions, and the government.
This document examines the relationship between macroeconomic variables (interest rate, inflation, exchange rate, and GDP) and share prices on the Nairobi Securities Exchange from 2008 to 2014. It finds that GDP and exchange rates had a positive relationship with share prices, while interest rates had a negative relationship and inflation a significant negative relationship. Overall, the four macroeconomic variables combined had a strong positive and significant relationship with share prices, accounting for 86.97% of changes in share prices. The study concludes that maintaining macroeconomic stability in Kenya is important for optimal stock market performance.
INFLATION, INTEREST RATE AND EXCHANGE RATE IN NIGERIA: AN EXAMINATION OF THE ...AJHSSR Journal
ABSTRACT: This study examined the linkages among inflation, interest rate and exchange rate along with
money supply and GDP with the aim of showing how the interactions among variables should influence
monetary policy decisions in Nigeria using quarterly data from 2010 to 2018. The relationship among variables
was captured in a Vector Autoregressive (VAR) model. Co integration test was used to examine the long run
relationship among variables and consequently the estimates of a Vector Error Correction (VEC) model was
used to examine the short run relationship among variables. In our findingsexchange rate is indicated as the
most important monetary policy variable because it has a significant link with all variables in the model. The
findings show that price stability and economic growth could be achieve through effective exchange rate and
interest rate policies. It is recommended that the monetary authority should continue to intervene in the foreign
exchange market to stabilize exchange rate because as shown in this study, exchange rate in Nigeria has
significant links with inflation, interest rate, money supply and GDP; and increase in money supply to boost
domestic production by givinglow cost credit to firms that make use of more domestic inputs in production to
ensure that the increase in money supply does not lead to increase in import.
Testing for fisher’s hypothesis in nigeria (1970 2012)Alexander Decker
This document summarizes a study that tested Fisher's hypothesis in Nigeria between 1970-2012. The study used quarterly data on interest rates and inflation rates to examine the causal relationship between the two variables. It employed cointegration and Granger causality tests and found that:
1) There is no significant long-run relationship between interest and inflation rates, violating Fisher's hypothesis in the long-run.
2) In the short-run, there is no causal link from interest rates to inflation, but there is a causal link from inflation to interest rates, supporting Fisher's hypothesis.
3) Fisher's hypothesis that nominal interest rates consist of expected inflation plus a real interest rate component is validated in the short-run
This document summarizes a research paper that analyzes short-run endogenous factors that influence real estate prices in Tirana, Albania using a VAR model. The paper reviews literature on factors influencing real estate markets, including economic indicators like income, construction costs, interest rates, and exchange rates. It then describes the methodology, which involves unit root testing and estimating an unrestricted VAR model to analyze relationships between housing prices and endogenous variables like remittances, the EUR/ALL exchange rate, and a construction index. The empirical analysis applies this methodology to quarterly time series data from 2002-2018 on the Tirana housing market to identify which short-run factors have a statistically significant impact on prices.
This paper examine the impact of macroeconomic factors on firm level equity premium. Following
the concept of macro-based risk factor model, we consider macroeconomic variable set of equity premium
determinant. The macroeconomic variables include interest rate, money supply, industrial production, inflation
and foreign direct investment. The macroeconomic variables are not in control of the firm's management. These
are the external factors which affect the company as well as the overall market returns. The Macro-based
Multifactor Model is estimated for the whole sample. It is found that the market premium and the selected five
macroeconomic factors significantly affect the firm level equity premium of non-financial firms. Increase in
market premium, money supply, foreign direct investment and industrial production positively affect the firm
level equity premium while increase in interest rate and inflation negatively affects the firm level equity
premium. These findings are beneficial for the common shareholders, institutional investors and policy makers
to find more specific insight about the relationship between macroeconomic variables and equity premium of
non-financial sectors.
The study tried to examine the effect of environmental forces on foreign exchange market in Nigeria. The PEST- Political variables such as change in government (CIG) and democratic rule (DMR); Economical variables such as interest rate spread (IRS) and inflation in consumer prices (ICP); Social variable like population growth (PGR); and Technological variables such as fuel exports in merchandise (FEM) and technology export (TEX) were used to evaluate the impact these environmental factors have on foreign exchange market (official exchange rate). This study employed a time series data with the time frame 1973-2015. A multiple regression model was developed and analyzed using the ordinary least square method (OLS) with the help of E-views, a statistical package. The result showed that in isolation, IRS, FEM and DMR significantly influenced dealing rates in the Nigerian foreign exchange market while ICP, CIG, PGR, and TEX did not show any significant influence on foreign exchange market in Nigeria. However, the overall result showed a significant positive relationship between the environmental forces and the foreign exchange market in Nigeria with a p -value of 0.000000. We therefore concluded that environmental factors have significant influence on the Nigerian Foreign Exchange market. Hence, we recommended that relevant stake holders should pay proper attention to those environmental factors with significant impact on our Foreign Exchange Market in Nigeria.
This document summarizes a study that examines the causal relationships between stock prices and macroeconomic variables in Turkey from March 2001 to June 2010. The study uses the Granger causality test to analyze the relationships between the ISE-100 stock index and five macroeconomic variables: foreign exchange rate, gold price, money supply, industrial production index, and consumer price index. The results suggest there is unidirectional long-run causality from stock prices to the macroeconomic variables. This implies that stock prices can serve as a leading indicator for future movements in exchange rates, gold prices, money supply, industrial production, and inflation in Turkey.
This document provides an introduction and background on the determinants of residential real estate prices in Kenya. It discusses several factors that can influence residential real estate prices, including interest rates, GDP, money supply, and inflation rate. The research problem is identified as determining the relative relationship and impact of these key factors on house prices in Kenya. The objective is to investigate the determinants of residential real estate prices. The study is expected to provide valuable insights for real estate investors, homeowners, financing institutions, and the government.
This document examines the relationship between macroeconomic variables (interest rate, inflation, exchange rate, and GDP) and share prices on the Nairobi Securities Exchange from 2008 to 2014. It finds that GDP and exchange rates had a positive relationship with share prices, while interest rates had a negative relationship and inflation a significant negative relationship. Overall, the four macroeconomic variables combined had a strong positive and significant relationship with share prices, accounting for 86.97% of changes in share prices. The study concludes that maintaining macroeconomic stability in Kenya is important for optimal stock market performance.
INFLATION, INTEREST RATE AND EXCHANGE RATE IN NIGERIA: AN EXAMINATION OF THE ...AJHSSR Journal
ABSTRACT: This study examined the linkages among inflation, interest rate and exchange rate along with
money supply and GDP with the aim of showing how the interactions among variables should influence
monetary policy decisions in Nigeria using quarterly data from 2010 to 2018. The relationship among variables
was captured in a Vector Autoregressive (VAR) model. Co integration test was used to examine the long run
relationship among variables and consequently the estimates of a Vector Error Correction (VEC) model was
used to examine the short run relationship among variables. In our findingsexchange rate is indicated as the
most important monetary policy variable because it has a significant link with all variables in the model. The
findings show that price stability and economic growth could be achieve through effective exchange rate and
interest rate policies. It is recommended that the monetary authority should continue to intervene in the foreign
exchange market to stabilize exchange rate because as shown in this study, exchange rate in Nigeria has
significant links with inflation, interest rate, money supply and GDP; and increase in money supply to boost
domestic production by givinglow cost credit to firms that make use of more domestic inputs in production to
ensure that the increase in money supply does not lead to increase in import.
Testing for fisher’s hypothesis in nigeria (1970 2012)Alexander Decker
This document summarizes a study that tested Fisher's hypothesis in Nigeria between 1970-2012. The study used quarterly data on interest rates and inflation rates to examine the causal relationship between the two variables. It employed cointegration and Granger causality tests and found that:
1) There is no significant long-run relationship between interest and inflation rates, violating Fisher's hypothesis in the long-run.
2) In the short-run, there is no causal link from interest rates to inflation, but there is a causal link from inflation to interest rates, supporting Fisher's hypothesis.
3) Fisher's hypothesis that nominal interest rates consist of expected inflation plus a real interest rate component is validated in the short-run
This document summarizes a research paper that analyzes short-run endogenous factors that influence real estate prices in Tirana, Albania using a VAR model. The paper reviews literature on factors influencing real estate markets, including economic indicators like income, construction costs, interest rates, and exchange rates. It then describes the methodology, which involves unit root testing and estimating an unrestricted VAR model to analyze relationships between housing prices and endogenous variables like remittances, the EUR/ALL exchange rate, and a construction index. The empirical analysis applies this methodology to quarterly time series data from 2002-2018 on the Tirana housing market to identify which short-run factors have a statistically significant impact on prices.
This paper examine the impact of macroeconomic factors on firm level equity premium. Following
the concept of macro-based risk factor model, we consider macroeconomic variable set of equity premium
determinant. The macroeconomic variables include interest rate, money supply, industrial production, inflation
and foreign direct investment. The macroeconomic variables are not in control of the firm's management. These
are the external factors which affect the company as well as the overall market returns. The Macro-based
Multifactor Model is estimated for the whole sample. It is found that the market premium and the selected five
macroeconomic factors significantly affect the firm level equity premium of non-financial firms. Increase in
market premium, money supply, foreign direct investment and industrial production positively affect the firm
level equity premium while increase in interest rate and inflation negatively affects the firm level equity
premium. These findings are beneficial for the common shareholders, institutional investors and policy makers
to find more specific insight about the relationship between macroeconomic variables and equity premium of
non-financial sectors.
The study tried to examine the effect of environmental forces on foreign exchange market in Nigeria. The PEST- Political variables such as change in government (CIG) and democratic rule (DMR); Economical variables such as interest rate spread (IRS) and inflation in consumer prices (ICP); Social variable like population growth (PGR); and Technological variables such as fuel exports in merchandise (FEM) and technology export (TEX) were used to evaluate the impact these environmental factors have on foreign exchange market (official exchange rate). This study employed a time series data with the time frame 1973-2015. A multiple regression model was developed and analyzed using the ordinary least square method (OLS) with the help of E-views, a statistical package. The result showed that in isolation, IRS, FEM and DMR significantly influenced dealing rates in the Nigerian foreign exchange market while ICP, CIG, PGR, and TEX did not show any significant influence on foreign exchange market in Nigeria. However, the overall result showed a significant positive relationship between the environmental forces and the foreign exchange market in Nigeria with a p -value of 0.000000. We therefore concluded that environmental factors have significant influence on the Nigerian Foreign Exchange market. Hence, we recommended that relevant stake holders should pay proper attention to those environmental factors with significant impact on our Foreign Exchange Market in Nigeria.
This document summarizes a study that examines the causal relationships between stock prices and macroeconomic variables in Turkey from March 2001 to June 2010. The study uses the Granger causality test to analyze the relationships between the ISE-100 stock index and five macroeconomic variables: foreign exchange rate, gold price, money supply, industrial production index, and consumer price index. The results suggest there is unidirectional long-run causality from stock prices to the macroeconomic variables. This implies that stock prices can serve as a leading indicator for future movements in exchange rates, gold prices, money supply, industrial production, and inflation in Turkey.
This document summarizes a research journal article that examines the determinants of fiscal growth in Jordan. The study uses time series data from 1982-2010 to analyze the relationships between fiscal growth rates and several independent variables, including available liquidity in banking, private sector credit rates, stock market capitalization, and government fiscal policy. The study found positive statistical relationships between fiscal growth and the first three variables, but did not prove an impact of fiscal policy. It recommends policies to encourage bank mergers, intellectual property rights, and coordinating fiscal and monetary policies to link them with economic growth.
This document summarizes a study that analyzed the impact of several macroeconomic indicators (money supply, exchange rate, oil prices, and interest rate) on inflation in Kenya from 2003-2013. Using vector error correction modeling and Granger causality tests, the study found that:
1) All variables except exchange rate had positive and significant effects on inflation in the long run.
2) In the short run, only interest rates and money supply significantly impacted inflation.
3) Changes in inflation Granger caused changes in oil prices, and changes in interest rates Granger caused changes in inflation.
4) The study concluded inflation in Kenya is triggered by both demand and supply factors, but money supply and oil prices are
Cointegration, causality and fisher effect in nigeriaAlexander Decker
This document examines the relationship between expected inflation and nominal interest rates in Nigeria from 1970 to 2011. It finds:
1) There is a long-run partial Fisher effect in Nigeria, meaning there is a long-run positive and significant relationship between inflation and interest rates.
2) There is a unidirectional causality running from inflation to interest rates in Nigeria.
3) The results indicate the existence of a long-run equilibrium relationship between inflation and nominal interest rates in Nigeria, providing evidence for the Fisher effect.
48 variable macroeconomics on stock return 25 ags 2019Aminullah Assagaf
This study examines the effect of macroeconomic variables (inflation, interest rates, money supply, exchange rates) on stock returns of companies listed on the Indonesia Stock Exchange from November 2016 to June 2018. Using multiple linear regression analysis on monthly data, the study found that macroeconomic variables have a significant effect on stock returns. Specifically, changes in inflation rates, interest rates, money supply, and the Rupiah exchange rate influence the overall stock price index and company stock returns in Indonesia. The results indicate macroeconomic conditions impact stock market performance.
Statistical Analysis of Interrelationship between Money Supply Exchange Rates...Atif Ahmed
Several researches have been conducted to study the impact of different macro-economic variables and their influence on government expenditure. By using different statistical tools researchers have examined that how money supply and exchange rate influence the government expenditure. Few other studies also conducted work on the quarterly time series data to examine the long run equilibrium association between the macroeconomic variables.
11.the impact of macroeconomic indicators on stock prices in nigeriaAlexander Decker
This document summarizes a study that examines the impact of macroeconomic indicators on stock prices in Nigeria. The study uses secondary data on stock prices of selected firms and six macroeconomic variables between 1985 and 2009. The macroeconomic variables examined are money supply, interest rate, exchange rate, inflation rate, oil price, and gross domestic product. A pooled or panel model is used to analyze the impact of these macroeconomic variables on stock prices at the individual firm level. The empirical findings reveal that macroeconomic variables have varying significant impacts on stock prices, with inflation rate and money supply having no significant impact. The study concludes that trends in macroeconomic variables can predict stock price movements in Nigeria to a great extent.
1.[1 14]the impact of macroeconomic indicators on stock prices in nigeriaAlexander Decker
This document summarizes a study that examines the impact of macroeconomic indicators on stock prices in Nigeria. The study uses secondary data on stock prices of selected firms and six macroeconomic variables between 1985 and 2009. The macroeconomic variables examined are money supply, interest rate, exchange rate, inflation rate, oil price, and gross domestic product. A pooled or panel model is used to analyze the impact of these macroeconomic variables on stock prices at the individual firm level. The empirical findings reveal that macroeconomic variables have varying significant impacts on stock prices, with inflation rate and money supply having no significant impact. The study concludes that trends in macroeconomic variables can predict stock price movements in Nigeria to a great extent.
This document reviews the literature on the relationship between monetary policy and economic growth. It begins with an overview of the evolution of theories from classical quantity theory to modern New Keynesian and New Consensus models. While theories differ in their assumptions around price flexibility and market clearing, most support some short-run effect of monetary policy on output. Empirically, studies find mixed results, with some supporting and others finding no relationship, depending on factors like country development and institutional quality. Overall, the literature suggests monetary policy can impact growth in developed markets with independent central banks, while the relationship is weaker in developing economies.
The document discusses using the Mundell-Fleming model to predict patterns of macroeconomic stability in Indonesia. It analyzes the interaction of fiscal and monetary policy variables on GDP, investment, inflation, and exchange rates from 2000-2014. The analysis uses a vector autoregression model to predict the effects in the short, medium and long term. It finds that tax policies were more effective than government spending at controlling growth, investment and inflation, while government spending more effectively controlled exchange rates. Interest rates more effectively controlled inflation and exchange rates, while money supply was more effective at GDP and investment.
11.monetary policy, exchange rate and inflation rate in nigeriaAlexander Decker
This document summarizes research on the relationship between monetary policy, exchange rates, and inflation rates in Nigeria from 1986 to 2010. It finds that there is a cointegrating relationship between the variables using a vector error correction model. Specifically, it finds unidirectional causation from exchange rates and inflation to interest rates (the monetary policy measure), and bidirectional causation between inflation and exchange rates. No causation was found from interest rates to exchange rates or inflation. This provides evidence that changes in exchange rates and inflation cause changes in monetary policy rather than the other way around. The study recommends appropriate management and control of exchange rates and inflation.
This study examines the relationship between inflation, monetary policy, and economic growth in Pakistan from 1989-2020. It uses inflation as the dependent variable and GDP, interest rate, money supply, and exchange rate as independent variables. Auto Regressive Distributive Lag techniques are employed. The findings show an inverse relationship between inflation and GDP, meaning inflation decreases as GDP increases. There is also a negative relationship between inflation and interest rate, but positive relationships between inflation and both money supply and exchange rate. Overall, the study aims to analyze how monetary policy tools like interest rates and money supply impact inflation and economic growth in Pakistan.
This document provides an overview of PESTEL analysis, a framework for analyzing the macroenvironmental factors that may impact a business. It discusses the origins and history of PESTEL analysis, noting it was developed in the 1960s-1970s and expanded to include additional factors over time. The document then defines each factor of the PESTEL analysis: political, economic, social, technological, legal, and environmental. For each factor, it provides examples of macroeconomic trends or events that could influence businesses.
QUALITY ASSURANCE FOR ECONOMY CLASSIFICATION BASED ON DATA MINING TECHNIQUESIJDKP
Researchers in the quality assurance field used traditional techniques for increasing the organization income and take the most suitable decisions. Today they focus and search for a new intelligent techniques in order to enhance the quality of their decisions. This paper based on applying the most robust trend in computer science field which is data mining in the quality assurance field. The cases study which is discussed in this paper based on detecting and predicting the developed and developing countries based on the indicators. This paper uses three different artificial intelligent techniques namely; Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), and Fuzzy k-Nearest Neighbor (FKNN). The main target of this paper is to merge between the last intelligent techniques applied in the computer science with the quality assurance approaches. The experimental result shows that proposed approaches in this paper achieved the highest accuracy score than the other comparative studies as indicates in the experimental result section.
This document provides an overview of macroeconomics. It discusses what macroeconomics studies, including long-term economic growth, business cycles, unemployment, inflation, international trade, and macroeconomic policy. It describes what macroeconomists do, such as forecasting, analysis, and research. It also explains that macroeconomists sometimes disagree in their positive and normative analyses due to differing classical and Keynesian approaches.
INVESTIGATION OF EFFECT OF SALES OF ARAB REAL ESTATE COMPANIES ON INCOME OF T...AkashSharma618775
This document investigates the impact of sales by Arab real estate companies on Turkey's income from 2015-2020. It analyzes changes in inflation-adjusted housing prices, residential real estate price indices, and nominal residential real estate prices in Turkey over this period. The study uses time series analysis and unit root tests to examine the relationship between these real estate variables and Turkey's GDP. The results show a relationship between changes in inflation-adjusted housing prices and Turkey's GDP, as well as between nominal residential real estate prices and GDP. However, the study also finds no relationship between residential real estate price indices and GDP in Turkey. Graphs of the variables show mostly increasing trends from 2015-2020, with a downturn in 2018 associated with
This document summarizes a working paper on monetary policy effectiveness in Pakistan. The paper estimates various VAR models and compares results to DSGE models. Key findings are:
1) VAR models using a conventional identification scheme find insignificant effects of monetary policy shocks on output and inflation in Pakistan.
2) A DSGE model that incorporates financial market frictions still shows significant monetary policy effects, contradicting the VAR results.
3) Simulating data from the DSGE model and estimating a VAR reveals that the recursive identification scheme may misidentify monetary policy shocks and underestimate their true effects on output and inflation.
Boston's economy has grown steadily over the past decades. Key points from the document include:
- Boston's gross city product has increased at an average annual rate of 2.7% since 1970.
- The city's largest industries are health care, education, and finance. Health care employment grew by over 35,000 jobs between 2001-2013.
- The city has seen strong job growth, adding over 100,000 jobs between 2003-2013, outpacing national growth. By 2013, Boston had nearly 699,000 total jobs.
- Boston workers are highly productive, generating $157,152 in GDP per worker in 2013, 33% higher than the national average. The city's core
This document analyzes the impact of fiscal and monetary policy on economic growth in Vietnam from 2004 to 2013 using a Vector Error Correction Model (VECM). The results show there is cointegration between macroeconomic policies and economic growth. Variance decomposition and impulse response functions from the VECM model indicate fiscal and monetary policies have a limited impact on economic growth, with monetary policy having a slightly greater effect than fiscal policy. The document recommends improving the effectiveness of implementing these policies in Vietnam.
An Analysis on the Influence of Mortgage Rates on Housing Prices - Final DraftCaleb Goettl
This paper analyzes the relationship between mortgage rates and housing prices in the United States using monthly data from January 1973 to August 2015. The author first reviews previous literature which finds that higher mortgage rates negatively impact housing demand and prices. An error correction model is estimated to examine the long-run and short-run effects. Empirical results show virtually no short-run relationship but significant long-run coefficients, though the impact is small. Testing confirms that housing prices, mortgage rates, and gasoline prices (included as another independent variable) are all non-stationary and integrated of order one, requiring the use of cointegration techniques like error correction modeling.
This paper investigates the macroeconomic drivers of house
prices in Malaysia using VECM, over a fifteen year period.
The key macroeconomic factors investigated were real
GDP, bank lending rate, Consumer Sentiment, Business
Condition, Money Supply, number of loans approved, Stock
market (KLSE) and Inflation. The macroeconomic factors
found to be significantly related to the Malaysian housing
prices were inflation, Stock Market (KLSE), Money Supply
(M3) and number of residential loans approved. The results
hint at the potential of a housing price bubble as GDP
wasn’t identified as a driver of house prices.
Predicting_housing_prices_using_advanced.pdfAyesha Lata
This document discusses various regression techniques that can be used to predict housing prices based on different housing characteristics and features. It first provides background on housing price prediction and factors that influence prices. Then it describes several regression algorithms (hedonic pricing model, artificial neural networks, lasso regression, XGBoost) that can be used to predict prices. The document uses the Ames Housing dataset to test a lasso regression model and analyze impact of features like size, bedrooms, location on prices. The goal is to determine the most accurate advanced methodology for housing price prediction.
This document summarizes a research journal article that examines the determinants of fiscal growth in Jordan. The study uses time series data from 1982-2010 to analyze the relationships between fiscal growth rates and several independent variables, including available liquidity in banking, private sector credit rates, stock market capitalization, and government fiscal policy. The study found positive statistical relationships between fiscal growth and the first three variables, but did not prove an impact of fiscal policy. It recommends policies to encourage bank mergers, intellectual property rights, and coordinating fiscal and monetary policies to link them with economic growth.
This document summarizes a study that analyzed the impact of several macroeconomic indicators (money supply, exchange rate, oil prices, and interest rate) on inflation in Kenya from 2003-2013. Using vector error correction modeling and Granger causality tests, the study found that:
1) All variables except exchange rate had positive and significant effects on inflation in the long run.
2) In the short run, only interest rates and money supply significantly impacted inflation.
3) Changes in inflation Granger caused changes in oil prices, and changes in interest rates Granger caused changes in inflation.
4) The study concluded inflation in Kenya is triggered by both demand and supply factors, but money supply and oil prices are
Cointegration, causality and fisher effect in nigeriaAlexander Decker
This document examines the relationship between expected inflation and nominal interest rates in Nigeria from 1970 to 2011. It finds:
1) There is a long-run partial Fisher effect in Nigeria, meaning there is a long-run positive and significant relationship between inflation and interest rates.
2) There is a unidirectional causality running from inflation to interest rates in Nigeria.
3) The results indicate the existence of a long-run equilibrium relationship between inflation and nominal interest rates in Nigeria, providing evidence for the Fisher effect.
48 variable macroeconomics on stock return 25 ags 2019Aminullah Assagaf
This study examines the effect of macroeconomic variables (inflation, interest rates, money supply, exchange rates) on stock returns of companies listed on the Indonesia Stock Exchange from November 2016 to June 2018. Using multiple linear regression analysis on monthly data, the study found that macroeconomic variables have a significant effect on stock returns. Specifically, changes in inflation rates, interest rates, money supply, and the Rupiah exchange rate influence the overall stock price index and company stock returns in Indonesia. The results indicate macroeconomic conditions impact stock market performance.
Statistical Analysis of Interrelationship between Money Supply Exchange Rates...Atif Ahmed
Several researches have been conducted to study the impact of different macro-economic variables and their influence on government expenditure. By using different statistical tools researchers have examined that how money supply and exchange rate influence the government expenditure. Few other studies also conducted work on the quarterly time series data to examine the long run equilibrium association between the macroeconomic variables.
11.the impact of macroeconomic indicators on stock prices in nigeriaAlexander Decker
This document summarizes a study that examines the impact of macroeconomic indicators on stock prices in Nigeria. The study uses secondary data on stock prices of selected firms and six macroeconomic variables between 1985 and 2009. The macroeconomic variables examined are money supply, interest rate, exchange rate, inflation rate, oil price, and gross domestic product. A pooled or panel model is used to analyze the impact of these macroeconomic variables on stock prices at the individual firm level. The empirical findings reveal that macroeconomic variables have varying significant impacts on stock prices, with inflation rate and money supply having no significant impact. The study concludes that trends in macroeconomic variables can predict stock price movements in Nigeria to a great extent.
1.[1 14]the impact of macroeconomic indicators on stock prices in nigeriaAlexander Decker
This document summarizes a study that examines the impact of macroeconomic indicators on stock prices in Nigeria. The study uses secondary data on stock prices of selected firms and six macroeconomic variables between 1985 and 2009. The macroeconomic variables examined are money supply, interest rate, exchange rate, inflation rate, oil price, and gross domestic product. A pooled or panel model is used to analyze the impact of these macroeconomic variables on stock prices at the individual firm level. The empirical findings reveal that macroeconomic variables have varying significant impacts on stock prices, with inflation rate and money supply having no significant impact. The study concludes that trends in macroeconomic variables can predict stock price movements in Nigeria to a great extent.
This document reviews the literature on the relationship between monetary policy and economic growth. It begins with an overview of the evolution of theories from classical quantity theory to modern New Keynesian and New Consensus models. While theories differ in their assumptions around price flexibility and market clearing, most support some short-run effect of monetary policy on output. Empirically, studies find mixed results, with some supporting and others finding no relationship, depending on factors like country development and institutional quality. Overall, the literature suggests monetary policy can impact growth in developed markets with independent central banks, while the relationship is weaker in developing economies.
The document discusses using the Mundell-Fleming model to predict patterns of macroeconomic stability in Indonesia. It analyzes the interaction of fiscal and monetary policy variables on GDP, investment, inflation, and exchange rates from 2000-2014. The analysis uses a vector autoregression model to predict the effects in the short, medium and long term. It finds that tax policies were more effective than government spending at controlling growth, investment and inflation, while government spending more effectively controlled exchange rates. Interest rates more effectively controlled inflation and exchange rates, while money supply was more effective at GDP and investment.
11.monetary policy, exchange rate and inflation rate in nigeriaAlexander Decker
This document summarizes research on the relationship between monetary policy, exchange rates, and inflation rates in Nigeria from 1986 to 2010. It finds that there is a cointegrating relationship between the variables using a vector error correction model. Specifically, it finds unidirectional causation from exchange rates and inflation to interest rates (the monetary policy measure), and bidirectional causation between inflation and exchange rates. No causation was found from interest rates to exchange rates or inflation. This provides evidence that changes in exchange rates and inflation cause changes in monetary policy rather than the other way around. The study recommends appropriate management and control of exchange rates and inflation.
This study examines the relationship between inflation, monetary policy, and economic growth in Pakistan from 1989-2020. It uses inflation as the dependent variable and GDP, interest rate, money supply, and exchange rate as independent variables. Auto Regressive Distributive Lag techniques are employed. The findings show an inverse relationship between inflation and GDP, meaning inflation decreases as GDP increases. There is also a negative relationship between inflation and interest rate, but positive relationships between inflation and both money supply and exchange rate. Overall, the study aims to analyze how monetary policy tools like interest rates and money supply impact inflation and economic growth in Pakistan.
This document provides an overview of PESTEL analysis, a framework for analyzing the macroenvironmental factors that may impact a business. It discusses the origins and history of PESTEL analysis, noting it was developed in the 1960s-1970s and expanded to include additional factors over time. The document then defines each factor of the PESTEL analysis: political, economic, social, technological, legal, and environmental. For each factor, it provides examples of macroeconomic trends or events that could influence businesses.
QUALITY ASSURANCE FOR ECONOMY CLASSIFICATION BASED ON DATA MINING TECHNIQUESIJDKP
Researchers in the quality assurance field used traditional techniques for increasing the organization income and take the most suitable decisions. Today they focus and search for a new intelligent techniques in order to enhance the quality of their decisions. This paper based on applying the most robust trend in computer science field which is data mining in the quality assurance field. The cases study which is discussed in this paper based on detecting and predicting the developed and developing countries based on the indicators. This paper uses three different artificial intelligent techniques namely; Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), and Fuzzy k-Nearest Neighbor (FKNN). The main target of this paper is to merge between the last intelligent techniques applied in the computer science with the quality assurance approaches. The experimental result shows that proposed approaches in this paper achieved the highest accuracy score than the other comparative studies as indicates in the experimental result section.
This document provides an overview of macroeconomics. It discusses what macroeconomics studies, including long-term economic growth, business cycles, unemployment, inflation, international trade, and macroeconomic policy. It describes what macroeconomists do, such as forecasting, analysis, and research. It also explains that macroeconomists sometimes disagree in their positive and normative analyses due to differing classical and Keynesian approaches.
INVESTIGATION OF EFFECT OF SALES OF ARAB REAL ESTATE COMPANIES ON INCOME OF T...AkashSharma618775
This document investigates the impact of sales by Arab real estate companies on Turkey's income from 2015-2020. It analyzes changes in inflation-adjusted housing prices, residential real estate price indices, and nominal residential real estate prices in Turkey over this period. The study uses time series analysis and unit root tests to examine the relationship between these real estate variables and Turkey's GDP. The results show a relationship between changes in inflation-adjusted housing prices and Turkey's GDP, as well as between nominal residential real estate prices and GDP. However, the study also finds no relationship between residential real estate price indices and GDP in Turkey. Graphs of the variables show mostly increasing trends from 2015-2020, with a downturn in 2018 associated with
This document summarizes a working paper on monetary policy effectiveness in Pakistan. The paper estimates various VAR models and compares results to DSGE models. Key findings are:
1) VAR models using a conventional identification scheme find insignificant effects of monetary policy shocks on output and inflation in Pakistan.
2) A DSGE model that incorporates financial market frictions still shows significant monetary policy effects, contradicting the VAR results.
3) Simulating data from the DSGE model and estimating a VAR reveals that the recursive identification scheme may misidentify monetary policy shocks and underestimate their true effects on output and inflation.
Boston's economy has grown steadily over the past decades. Key points from the document include:
- Boston's gross city product has increased at an average annual rate of 2.7% since 1970.
- The city's largest industries are health care, education, and finance. Health care employment grew by over 35,000 jobs between 2001-2013.
- The city has seen strong job growth, adding over 100,000 jobs between 2003-2013, outpacing national growth. By 2013, Boston had nearly 699,000 total jobs.
- Boston workers are highly productive, generating $157,152 in GDP per worker in 2013, 33% higher than the national average. The city's core
This document analyzes the impact of fiscal and monetary policy on economic growth in Vietnam from 2004 to 2013 using a Vector Error Correction Model (VECM). The results show there is cointegration between macroeconomic policies and economic growth. Variance decomposition and impulse response functions from the VECM model indicate fiscal and monetary policies have a limited impact on economic growth, with monetary policy having a slightly greater effect than fiscal policy. The document recommends improving the effectiveness of implementing these policies in Vietnam.
An Analysis on the Influence of Mortgage Rates on Housing Prices - Final DraftCaleb Goettl
This paper analyzes the relationship between mortgage rates and housing prices in the United States using monthly data from January 1973 to August 2015. The author first reviews previous literature which finds that higher mortgage rates negatively impact housing demand and prices. An error correction model is estimated to examine the long-run and short-run effects. Empirical results show virtually no short-run relationship but significant long-run coefficients, though the impact is small. Testing confirms that housing prices, mortgage rates, and gasoline prices (included as another independent variable) are all non-stationary and integrated of order one, requiring the use of cointegration techniques like error correction modeling.
This paper investigates the macroeconomic drivers of house
prices in Malaysia using VECM, over a fifteen year period.
The key macroeconomic factors investigated were real
GDP, bank lending rate, Consumer Sentiment, Business
Condition, Money Supply, number of loans approved, Stock
market (KLSE) and Inflation. The macroeconomic factors
found to be significantly related to the Malaysian housing
prices were inflation, Stock Market (KLSE), Money Supply
(M3) and number of residential loans approved. The results
hint at the potential of a housing price bubble as GDP
wasn’t identified as a driver of house prices.
Predicting_housing_prices_using_advanced.pdfAyesha Lata
This document discusses various regression techniques that can be used to predict housing prices based on different housing characteristics and features. It first provides background on housing price prediction and factors that influence prices. Then it describes several regression algorithms (hedonic pricing model, artificial neural networks, lasso regression, XGBoost) that can be used to predict prices. The document uses the Ames Housing dataset to test a lasso regression model and analyze impact of features like size, bedrooms, location on prices. The goal is to determine the most accurate advanced methodology for housing price prediction.
Factors influencing the rise of house price in klang valleyeSAT Journals
Abstract
There is an increase of house price radically in Klang Valley that affect to Malaysian house buyer. House price is the value to be paid for the dealing of buying a residential property. House price rises continuously respecting few factors and had impacting house buyer in decision to buy their house. This study becomes necessary since there is less research that gives information in the factors influencing the rise of house price. The factors are found out through detailed literature reviews and information from pilot study. Pilot study is conducted through interviewing representative from National House Buyer Association, pioneer in solving house related problem, to provide legal suggestion and etc. The data is collected via questionnaire survey form distributed to respondents in sample area. The sample area is Klang Valley region, 10 municipal districts including Kuala Lumpur, the Capital City. In result and analysis stages, the factors had to be refined by analyzing the data using statistical tests. Every single factors are calculated its average index respect to few level of influence under respondents’ opinion. The index will then treated as influencing level of the factors. Based on the study, fluctuation in housing market, increasing in construction cost, population growth and increasing demand are factors which give major influence to rise of house price. The study also identified housing criteria to be considered during setup of house selling price and also preference among house buyer nowadays. This study also identified cost contributors in construction being foresees as control measure concerned in respect to respondents point of view.
Keywords: House price, affordable, and construction cost
1) The document discusses factors that influence the rise of house prices in the Klang Valley region of Malaysia.
2) It identifies key factors like fluctuations in the housing market, increasing construction costs, population growth, and increasing demand as major influences on increasing house prices.
3) The study found that a lack of responsive housing supply to growing demand, high material and land costs, and a shortage of skilled labor in the construction sector contributed to increased construction costs and limited affordable housing options, exacerbating the rise in house prices.
This document summarizes a study that investigated factors influencing real estate property prices in Meru Municipality, Kenya. The study used a descriptive research design and questionnaires to collect data from 390 real estate owners. The findings showed that incomes contributed 70% of variations in prices, while demand contributed 20%. Location and realtors were found to be insignificant in determining prices. A regression model explained about 70% of variations in prices. The study recommends further investigation into why location and realtors did not significantly impact prices in Meru municipality.
This document presents a study analyzing the relationship between home prices in Ottawa and several key economic indicators from 1990 to 2012. It examines home prices as the dependent variable and how they may be influenced by independent variables like the consumer price index, mortgage rates, overnight lending rates, and hourly income rates. Statistical tools like descriptive statistics, hypothesis testing, regression analysis, and chi-squared tests are used to analyze the relationship between these variables and identify the factors that have most significantly impacted home price changes over the period under review. Limitations of the study and opportunities for future research are also discussed.
Price dynamics in public and private housing markets in SingaporeLee Poh Seng
In down-payment constrained housing consumption models, increases in house prices could trigger household mobility decisions in housing markets. This study empirically tests house price dynamics associated with the mobility of households in the public resale and private housing markets in Singapore.
The determinants of terrace housing rates in malaysiaLdyn Jfr
- The document discusses a study on the determinants of terrace housing rates in Malaysia. It aims to examine the relationship between housing rates and factors like foreign direct investment, inflation rate, and population growth rate.
- The study uses secondary data from 2006 to 2013 and multiple regression analysis to analyze the impact of the independent variables on terrace housing rates. It finds that the data meets the assumptions of no multicollinearity and serially independent errors based on statistical tests.
This document analyzes housing affordability for Generation Y in Jakarta based on price to income ratios. It finds that over 95% of Indonesian Gen Y cannot afford to own a house due to high property prices rising faster than salaries. Housing prices in Jakarta increased 17% from 2017-2019, far more than the typical 10% salary increase. Gen Y preferences for housing focus on specifications, future considerations, and dweller characteristics, but high prices and taxes negatively impact affordability. Solutions proposed to improve affordability include increasing housing supply, adjusting planning systems, and expanding affordable housing developments to outlying areas.
Analysis of factors affecting urban per capita housing area in ChinaIJAEMSJORNAL
Housing problems have become one of the hottest topics, influencing people's livelihood and national economy. This paper intends to re-analyze the per capita housing area, which characterizes the residents' happiness index, in order to measure the basic living condition. Taking into account of the large expansion of the floating population in the process of urbanization, we choose “urban resident population” to amend the “registration population”, which is the denominator of the index. We selected the data of residential investment, urban residents' consumption level and residential completion area from 1978 to 2015 to analyze the influence of independent variables on the per capita housing area, we found the volatility of housing price, which reduces the average level of urban per capita housing empirically.
- The document analyzes factors that affect the amount of real estate loans in the Philippines, including OFW remittances, interest rates, employment rates, housing prices, property values, and number of buildings constructed.
- Regression analysis found that OFW remittances and interest rates negatively impact loan amounts, while housing prices, property values, and new construction positively impact loan amounts. Employment rates did not have a significant impact.
- A stepwise regression removing employment rates improved the model, with all remaining factors found to significantly impact loan amounts. The analysis shows these 5 factors explain around 96% of changes in real estate loan amounts.
This document examines the effect of accessibility by road on rental values of residential property in Benin City, Nigeria. It analyzes the road network patterns and accessibility levels of roads in three neighborhoods - Ogboka, Ugbowo, and GRA - using questionnaires, graph theory, and statistical analysis. The results showed that accessibility had a significant influence on variation in rental values, with more accessible roads having higher property values. The study recommends improving Benin City's road networks to enhance property values and quality of life.
Influence of Government Regulations on the relationship between Borrower's C...MUTURIPETERGITHAE
The large capital outlay needed to build a house is mainly derived from mortgage financing yet its uptake in Kenya remains low. Mortgage lenders are concerned with borrower characteristics which are found to be key predictors of performance in real estate. However, studies on this relationship remain scanty. It is against this background that this study rests. Additionally, the moderating role of the government regulations on this relationship has also explored in this study.
This document summarizes a study on customer perspectives of housing loans in Chennai, India. It discusses the importance and growth of home loans in India. The study aims to examine types of loans, reasons for taking loans, and the impact of EMI and interest rates on customers. It analyzes how factors like age, income, education, and profession are associated with reasons for loans and bank/loan choices. The document outlines the research methodology, including questionnaires and data analysis methods. Key findings indicate associations between various demographic factors and reasons for taking loans.
CAPITAL MARKET DEVELOPMENT AND INFLATION IN NIGERIAAJHSSR Journal
ABSTRACT :This study examined the impact of inflation and capital market development in Nigeria. The
ultimate objective of the study is centered on an empirical investigation of inflation and its impact on the growth
of the Nigerian capital market, and also the trend of inflation and capital market development in Nigeria. In
order to achieve these objectives, the study used tables and graphs to examine the trend of inflation and capital
market development in Nigeria. Augmented Dickey Fuller unit root test was used to check the behavior of data,
and the ARDL bound test was used to check if variables are cointegrated. Post estimation test which includes
the serial correlation, heteroskedasticity and the histogram normality test was also conducted. Data were
collected from secondary sources, such as central bank of Nigeria statistical bulletin and the world development
indicator. The unit root test revealed that the financial sector, financial intermediaries and interest rate were
stationary at levels but exchange rate, inflation, government spending and trade openness became stationary
after the first difference. Empirical findings confirmed that there is a statistically significant long- and short-run
negative effect of inflation on capital market development. On the contrary, economic growth has a statistically
significant long- and short-run positive impact on capital market performance. In addition, results confirmed
that there is positive support of the previous financial sector policies on capital market performance in the
current period.
Machine Learning Based House Price Prediction Using Modified Extreme Boosting IIJSRJournal
In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate policy initiatives and estimate housing costs. In this project, models for forecasting changes in home prices are developed using machine learning methods.
A causal relationship between stock indices and exchange rates empirical evid...Alexander Decker
This document summarizes a research paper that examines the causal relationship between stock prices and exchange rates in India from 2001 to 2011. The results indicate there is a bidirectional causal relationship, with negative causality from most stock indices to the exchange rate, and positive causality from technology indices to the exchange rate. The exchange rate also has negative causality to all stock market indices. In addition, the paper reviews several other studies that have examined the relationship between stock prices and exchange rates in other countries, finding mixed results.
11.a causal relationship between stock indices and exchange rates empirical e...Alexander Decker
This document examines the causal relationship between stock prices and exchange rates in India from 2001 to 2011. It finds a bidirectional causal relationship, with negative causality from most stock indices to the exchange rate, and positive causality from technology indices to the exchange rate. The exchange rate also has negative causality to all stock market indices. The document reviews several previous studies on the relationship between stock prices and exchange rates in various countries and time periods.
Similar to Real Estate Level Forecasting - Review (20)
Although performance appraisal is concerned with the evaluation of workers job performance, it at the same time serves to highlight the specific objectives of an organization. As the employee is being evaluated the organization is also evaluating itself by comparing objectives and standards of performance, reviews the whole appraisal framework and design as well as organizational values and culture. Performance appraisal is a veritable tool for organizations to evaluate and increase the quality of education and training of their workforce with a view to developing lifelong learning patterns and strategies to sustain productivity throughout longer working periods. Motivation as it relates to employee productivity is often behind the drive for performance and self-actualization and provides opportunities for higher productivity. Productivity is an important measure of goal achievement because getting more done with less resources increases organizational profitability. Using the exploratory research design and 109 participants the result of the study indicates a strong positive correlation between performance appraisal and employee productivity. It suggests that the issue of performance appraisal in charitable organizations should be addressed. In view of the result of the study, the paper recommends that performance appraisal should carefully review employee’s strengths and weaknesses against requirements for possible future higher responsibilities.
The integration between innovation and business is a key factor in competitiveness between organizations. That is, innovation applied to a business makes no sense if not considered as an integral tool for the processes of the organization. Companies should therefore adopt a policy where innovation plays a strategic role in the design of business models to become lean, effective and competitive entities (Moraleda, 2004). The objective of this paper is to show the importance of innovation within companies, identifying the concept, the various models that different entities might adopt in order to develop better processes of innovation, as well as indicators that represent innovation at global and national levels in order to develop strategies that lead to an increase in competitiveness. For this work the method used was a bibliographical review of relevant articles from a range of authors was conducted.
The practitioners and academicians in the business arena are highly concern about the enhancement of employee performance in this competitive age for achievement of business goals. Considering the issue, this study aimed to measure the influence of Human Resource Management (HRM) practices on the performance of employees. The data of this study have been collected from 392 on-the-job operational level employees using survey method who are working at different garment factories in Bangladesh. The collected data are analyzed through structural equation modeling to partial least square method. The study empirically proves that employee training and development, promotion opportunity, and job security has significant influence on the employees’ performance. Theoretically, this study proves that training and development, job security and promotion opportunity together influence on the performance of employees in the developing economy. The practitioners and policy makers of the organizations are expected to make necessary adjustments in their existing HRM practices based on the findings of this study in the context of Bangladesh for enhancing the employees’ performance level so that their whole-hearted efforts can be gained for the achievement of business goals.
Child labor is one of the issues receiving much attention from researchers and scholars around the world. Child labor still occurs in most countries around the world. Viet Nam is also one of the countries with relatively high child labor and increasing trend. This article is based on critical discourse analysis and data from the General Statistics Office of Vietnam to analyze some fundamental issues of child labor in Vietnam, thereby giving policy suggestions to the Vietnam government in minimizing the current child labor situation.
The rapid trend of changes and social issues in managing the global workforce has forced organizations to look for innovative ways of enhancing the job satisfaction of employees. Among these innovative approaches is the provision of Flexible Working Arrangements (FWAs). The purpose of this exploratory research was to identify the effects of FWAs, i.e., flextime schedule, compressed workweek, and telecommuting on job satisfaction from the perspective of the Ethiopian national employees of the United Nations Economic Commission for Africa (ECA) in Addis Ababa. To achieve this objective both descriptive and inferential statistics were conducted. The total population of the study was 250; out of which, 71% of responses were collected. A primary data collection method was implemented using a structured questionnaire. The analysis showed that there is significant positive effect of flextime schedule (R = .39, R2 = .264, p = .001) and compressed workweek (R = .39, R2 = .159, p = .039). This means that increase in the use of flextime schedules and compressed workweek enhances job satisfaction for employees of the ECA in Addis Ababa. The independent variables reported R = .39 and R2 = .15 which means that 15% of corresponding variations in employee job satisfaction can be explained by flexible working arrangements. Nevertheless, this study found out that there are no significant relationship of telecommuting (R = .39, R2 = .065, p = .398) on job satisfaction. Therefore, since the provision of FWAs is at the nascent stage, further studies on the effect of telecommuting on job satisfaction from Ethiopian employees context are highly recommended.
This study evaluates the impacts of urban road investment and operation in China, especially the spillover effect attributable to the investment of urban road projects. Using the synthetic control method and difference-in-differences technique and taking the opening of Jiaozhou Bay Bridge and its Subsea Tunnel in China on 30 June 2011 as a natural experiment, this paper investigates the causal effect between urban road investment and its economic impacts. Results show that the project has a positive externality in terms of its contribution to the output and employment: taken the industrial relative output as outcome variable, no matter whether the covariates are controlled or not, the parameters of the interactive terms are positive; taken the industrial relative employment rate as outcome variable, the gap between the treated unit and its counterpart indicates a direct program effect for the treated city as well as a spillover effect across the cities within the sample province. Furthermore, the permutation test ascertains that the probability of achieving a spillover effect as large as the treated city is around 5.88 per cent. Overall, the investment and operation of urban road transportation infrastructure has a noticeable spillover effect. Our results are robust across a series of placebo tests.
Poor public management defined by corruption and lack of prudence in public life continues to hold Nigeria hostage and makes good governance difficult. Since the 1980s government has been using many methods including the processes of privatization and commercialization as means of re-engineering the public sector for total quality management, and to increase the share of the public sector’s contribution to the gross domestic product. The experiment never achieved the desired level of success partly due to lack of political will on the part of government to wedge a total war against corruption, and also partly because the public sector is a large scale administration that has many entry and revolving doors which government finds difficult to close. These limitations provide the incentives for widespread public corruption that is recognized as one of the greatest challenges of government in carrying out its mandate. 110 respondents participated in this study conducted through the exploratory research design. The participants provided useful data that were triangulated with data from secondary sources for the purpose of the study. To achieve the objective of the investigation, data were analyzed through statistical techniques and the result showed significant positive correlation between good governance and good management. It was recommended that appointments in the public sector should feature a combination of people from private and public sectors of the economy to enhance competence with the aim of reducing public sector corruption. Further study should examine the reasons behind rising budget deficits as a way of reducing cost of governance in Nigeria.
This document analyzes shareholding in companies in Mali based on data from the 2015 census of industrial enterprises by Mali's Ministry of Trade and Industry. The key points are:
- Since Mali's economic opening in the 1980s and privatization programs in the 1990s, the private sector has grown while many state-owned enterprises have disappeared.
- Most companies in Mali are privately owned, young (less than 15 years old), and small individual/family businesses rather than corporations.
- Between 2010-2014, the majority of shareholders were in the agri-food sector, and the gender distribution of shareholders was nearly equal.
- The document discusses different concepts of shareholding structure and
This paper’s objective is to present the importance of the strategic planning in business management. Speaking of strategic planning is always speaking in general terms and how to fix paths of behavior will necessarily affect deeply and significantly in the future evolution of the company or organization that adopts it. Today we think of the organization as part of an environment and in terms of options or choices based on what you have, of its surroundings and the opportunities or pathways that can lead to achieving the objective, (Garrido, 2009). For this work the method used was a bibliographical review of relevant articles from a range of authors was conducted. The conclusions were that the be properly analyzed and adapted to the precise conditions and characteristics of the small business or, more generally, to any type of business for which the planning is intended. Strategic planning brings multiple benefits (which exceed its disadvantages) if applied in the right way, however, there are inherent risks, which can be overcome with proper monitoring and control.
The study examined the relationship between non-financial incentives and workers’ motivation in Akwa Ibom State Civil Service exploring five key variables of continuing professional development, performance feedback, employee employment, employee participation in decision-making and task autonomy. Survey research design was adopted involving the use of questionnaire to gather data from 392 respondents drawn from a population of 20465 civil servants in state using Taro Yamene Sample Size Determination Table. The sample was drawn across all ministries and departments through stratified and convenience sampling techniques. Data collected were analysed using descriptive and inferential statistics. Hypotheses were tested at 0.05 level of significance. The five dimensions of non-financial incentives were positively correlated with workers’ motivation from the results of the analysis. Continuing Professional Development (CPD) had the highest correlation value (r = 0.33, P<0.01). Also, the five null hypotheses were rejected implying that the variables of study influence workers’ motivation in Akwa Ibom State Civil Service, Nigeria with beta coefficients and t-values of CPD (0.29;4.313); PF (0.117; 3.500); EE (0.2.141); PDM (0.182; 2.935), and TA (0.231;2.817). It was concluded that since workers’ motivation is a vital tool to organizational effectiveness and growth, employers should explore more of non-financial incentives in formulating and implementing employee benefits related policies.
This literature review is organized in five sections. Firstly, we begin with general ideas and continue with the origin of the fraudulent. Secondly, we discuss the struggle of the phenomena, insisting on the available mechanisms. Finally, we’ll discuss the link between audit and fraud.
Accounting function aims at providing accurate and sufficient accounting information to facilitate proper financial reporting and management performance. Accounting information is usually in the form of periodic or annual financial statements which are products of costing, financial and management accounting prepared for the benefit of a number of external interest groups. Accounting has its roots in the stewardship approach and as a management performance tool to guide the agent and the principal over the exact status of the going concern. Accounting function also involves financial statement analysis, interpreting the accounts by computing and evaluating ratios which relate pairs of financial information or items with one another. This analysis of ratios can be cross-sectional comparing the results of one company with another or trend. In doing so close attention is usually paid to profitability ratio to help keep pace with effective management performance. The exploratory research design was adopted for the study and result showed positive correlation between accounting function and management performance. The study was not exhaustive, therefore, further study should examine the relationship between audit failure and business failure as a matter of finding a solution to the problem. It was recommended that management should always carefully study audit reports to enhance decision making and management performance.
This study examines the effect of the trademark on consumer behavior of consumers of air conditioners in Sudan, in order to know the dimensions of the trademark that affect consumer behavior in Sudan, and provide information to companies on the dimensions of the trademark that affect the purchasing decision of the customer and contribute to customer satisfaction. The study adopted descriptive analytical method using a sample of 230 individuals who consume air conditioners in Sudan. The results showed that there is a positive significant relationship between the trademark of air conditioning and consumer behavior as well as a positive significant relationship between the trademark name of air conditioning and consumer behavior and finally there is a positive significant relationship between the trademark logo and consumer behavior.
In recent years, retired workers eligible for social security receive their emoluments from the appropriate regulatory agency and this provides more realistic evidence on the better living standard of the aged (retirees) under the scheme. Empirically, this paper examines the impact of social security on economic growth in Ghana using time series secondary (monthly) data ranging from 2000 – 2018. The author answers in two questions: 1) how significant are pensioners benefit payments dependent on economic growth and also, 2) how business environmental policy is contributing to economic performance as far as pensioners well-being are concerned. Using STATA analytical software, the findings show a positive significant relationship between social security and economic growth. The study concludes by outlining appropriate policy measures to help strengthen the current social security scheme in Ghana.
This research begins by showing the different meanings attributed to the term cluster by different currents and authors, which suggests definitions that are found around its spatial framework. Next, the factors that intervene in the competitiveness of a region and its growth are shown, for the development of these, Porter’s model of competitiveness which was taken as reference, and the contexts: geographical and economic. Therefore, the methodology was used based on a qualitative design, with descriptive and correlational scope since it will analyze differences of each cluster, with respect to the factors of dimensions, establishments, growth, economic impact and policies. To do this, the information-gathering tool was two semi-structured interviews with cluster leaders in both countries, because the approach is based on data collection methods that are not completely standardized or predetermined. And finally, the results of the comparison of the Mexican Bajío automotive cluster with the German cluster located in Baden-Württemberg are presented.
This research aims at identifying the impact of excellence in drawing up the following four marketing mix strategies (Product, Pricing, Promotion and Distribution) of the small and medium enterprises in Jordan, in terms of their marketing performance in its dimensions (Sales Growth, Profit Growth, Customer Attraction and Customer Retention).In order to reach the results of this study, A total of (187) valid questionnaire surveys were collected from companies belong to the SME Association in Jordan. The Statistical Package for the Social Sciences (SPSS) approach was used to analyze the collected data. The empirical results indicated there is a significant relationship between the building of marketing strategies of the marketing mix elements in the Jordanian SME and their marketing performance, by (sales growth, profit growth, customer attraction, and customer retention) dimensions. Consequently, decision makers in small and medium organizations need to choose strategies based on their target market to the positive impact on the mind of the consumer, which in turn could improve modern scientific methods in SME to divide their markets into sub-market sectors.
The study investigates the impact of team building on organisational productivity. The objective of this study is to evaluate the impact of team building among the members of the selected case study and to assess the effect of training and retraining of team members on organisational productivity. The study also x-rayed the absence of team building in a workplace which led to low levels of turnover and productivity. the total population of the study was 750 while researcher employed Yaro Yamane sampling technique to select sample size of 261 because of the large population and hypothesis were tested using Pearson correlation. The finding revealed that if members of the team can work in synergy without considering the differences in the likes of level of educational background and others, the expected productivity will be very high. It was also observed that capabilities of team leader in carrying out the assigned task determined its output especially if the team leader understands the technical knowhow of job and he is friendly with co-team members with a lot of motivation, that this would definitely enhance employees’ efficiencies and productivities. The study recommends that team members should trust, support and respect one another individual differences in order to accomplish group common goals and tasks.
Compared with general commercial reverse logistics operators, the recovery and treatment of expired drugs and medical waste is a complex and highly technically difficult project. The qualifications required by the relevant service providers are also more stringent. For medical institutions, the selection of reverse logistics operators is always a critical issue. On the perspective of sustainability, this paper aims to investigate and explore the critical factors of selecting a medical reverse logistics service provider. Through the process of the Delphi method, the experts’ assessments were collected, and 24 factors affecting the selection of medical reverse logistics service provider were screened and summarized. Then, Decision-Making Trial and Evaluation Laboratory (DEMATEL) was employed to calculate the total influence values and net influence values between factors that could be used to draw the visual causal map. Referring the causal map, “Green process operation level” and “Recycling process greening degree” are significantly higher than other factors in terms of total influence value and net influence value. Therefore, they can be regarded as crucial factors. This finding implies that medical reverse logistics providers must have the ability to improve the greening of facilities, as well as equipment, integrating existing processes to make it greener and environmentally friendly.
The major objective of any firm is to maximize the shareholders wealth. This is evidence through dividend yield and payout ratio and this encapsulate into the dividend policy of a company. The research purpose aimed at examining the influence that dividend policy has on the volatility of share prices among the listed insurance corporations in Kenya. Research design, approach and method: Data was collected from listed insurance corporations over a 10-year period with a total of 49 data points. The Pearson correlation and ordinary regression analysis were employed. The results reveal the existence of a positive link among the study variables. The correlations were found to be substantial at ninety-five percent confidence level. It is worth noting that the model summary shows forty-three-point one percent of changes in the volatility of stock price are explicated by dividend yield and payout ratio. ANOVA statistics which examines whether the analytical model as set out in the study explains variations in the dependent variable concluded that the model is analytically substantial. The outcome revealed a statistically significant positive link between stock price variations and the ratio of dividend payout. Research also established a statistically substantial negative interrelation between volatility of stock prices and dividend return. Results therefore recommend that companies should have dividend policies which are mapped to shareholders wealth maximization objective. The study suggests further studies be undertaken to determine whether there exists an analytically substantial difference between the dividend policies of various sectors in the economy.
This study is about the impact of selected macroeconomic variables on economic growth of Bangladesh. Economic growth of Bangladesh is measured in terms of annual nominal GDP growth rate. Least squared regression model has been employed considering exchange rate, export, import and inflation rate as independent variables and gross domestic product as the dependent variable in this study. The results reveal that export and import have significant positive impact on GDP growth rate. The other variables (exchange rate and inflation) are not significant, indicating that there exists no significant relationship among the variables. The findings will help the policy makers to make policies concerning the country’s economic growth to remain robust in the near future.
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Tired of chasing down expiring contracts and drowning in paperwork? Mastering contract management can significantly enhance your business efficiency and productivity. This guide unveils expert secrets to streamline your contract management process. Learn how to save time, minimize risk, and achieve effortless contract management.
The report *State of D2C in India: A Logistics Update* talks about the evolving dynamics of the d2C landscape with a particular focus on how brands navigate the complexities of logistics. Third Party Logistics enablers emerge indispensable partners in facilitating the growth journey of D2C brands, offering cost-effective solutions tailored to their specific needs. As D2C brands continue to expand, they encounter heightened operational complexities with logistics standing out as a significant challenge. Logistics not only represents a substantial cost component for the brands but also directly influences the customer experience. Establishing efficient logistics operations while keeping costs low is therefore a crucial objective for brands. The report highlights how 3PLs are meeting the rising demands of D2C brands, supporting their expansion both online and offline, and paving the way for sustainable, scalable growth in this fast-paced market.
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Real Estate Level Forecasting - Review
1. Business, Management and Economics Research
ISSN(e): 2412-1770, ISSN(p): 2413-855X
Vol. 5, Issue. 4, pp: 57-61, 2019
URL: https://arpgweb.com/journal/journal/8
DOI: https://doi.org/10.32861/bmer.54.57.61
Academic Research Publishing
Group
*Corresponding Author
57
Original Research Open Access
Real Estate Level Forecasting - Review
Hongfei Guo
Institute of Physical Internet, Jinan University, Zhuhai, 519070, China
Hao Jiang*
School of electrical information, Jinan University Zhuhai, 519070, China
Weijian Zhang
International Business School, Jinan University Zhuhai, 519070, China
Fuqian Cui
I Business School, Shandong Normal University Jinan, 250358, China
Ru Zhang
Finance Department of International Business School, Jinan University Zhuhai, 519070, China
Zhihui He
Zhuhai Hengqin Building & Construction Quality Inspection Centre Co.,Ltd Zhuhai, 519031, China
Yitao Lun
School of electrical information, Jinan University Zhuhai, 519070, China
Qiufan Chen
School of electrical information, Jinan University Zhuhai, 519070, China
Qiufan Chen
Institute of Physical Internet, Jinan University University Zhuhai, 519070, China
Abstract
With China’s rapid economic growth in recent years and the acceleration of urbanization, the real estate price has
also shown a substantial increase, especially the housing prices have always been high in first-tier cities. This paper
systematically combs the research on the factors affecting house price and the forecasting method of house price at
home and abroad, and puts forward some suggestions on the regulation and control of real estate price in our
country. It also points out the deficiency of data statistics and research perspective in empirical research. On this
basis, it is proposed that we should strengthen the scientificalness and comprehensiveness of the empirical data, put
forward a reasonable and appropriate hierarchical classification method for influencing factors, make clear the
importance and coupling mechanism of each level, and excavate the dominant influencing factors.
Keywords: Real state price; Forecasting method; Influencing factors.
CC BY: Creative Commons Attribution License 4.0
1. Introduction
At present, the real estate industry has become an important pillar industry affecting the development of the
national economy. The correct decision of real estate enterprises is not only related to their own healthy
development, but also affects the macroeconomic fluctuations at the national level. The historical lessons of the
western real estate bubble are profound. It is of practical significance to help the managers of the real estate
enterprises to maintain a rational attitude, to make scientific and reasonable investment decisions, and to avoid a
repeat of history to a certain extent. There are many factors that influence the decision of real estate enterprises. This
paper starts with the key factors of the survival and development of real estate enterprises-real estate prices, analyzes
the influencing factors of real estate prices, and forecasts the future trend of real estate prices. Hope can provide
certain help for the production development and rational decision of the real estate enterprise.
2. Literature Review
2.1. Foreign Literature Review
The foreign real estate market has developed rapidly with the process of urbanization abroad. Up till now, the
real estate market is more mature, the real estate price fluctuation is small and the relevant policies and regulations
are complete. The related theories of real estate mainly focus on the influencing factors of housing price, such as the
2. Business, Management and Economics Research
58
development of housing price model, the influence factors of real estate policy and so on. The main contents of the
study are as follows.
2.1.1. Factors Affecting Housing Prices
The research on the influencing factors of housing price mainly focuses on the correlation between population
structure and quantity, income level and real estate housing price, the correlation between real estate rent and
housing price and the correlation between real estate location and housing price. Bartik has conducted a study about
the impact of population and income level on real estate prices, which shows that the growth of population and
employment will lead to the growth of real estate prices, but the growth of real estate prices in different regions has
significant regional differences (Henry and Herzog, 1991). Abarham put forward a model of housing price change in
lag process, including construction cost, resident income, interest rate and employment rate, etc. The simulation
results show that interest rate is negatively correlated with real estate price, house construction cost, resident income
and employment rate are positively correlated with real estate price (Abraham and Hendershott, 1994). Stuart studied
the real estate price change patterns about two big cities in California. According to his research, the main reason for
the real estate price change in California this year is the large-scale population migration. Dennis et al. (2002)
Geoff’s research showed that in the long run, the price change in the real estate market is still a classic model of the
interaction between price and demand. He thought the real estate market price change is consistent with other
commodity market price change principle Geoff (2004).
As far as the influence of real estate rent on real estate price is concerned, people generally think that real estate
rent and real estate price are related to a certain extent. Dipasquale put forward Stock-Flow model, and used this
model to study the law of real estate market price change. After studying, the scholar found that real estate price and
local new There is a dynamic relationship between the number of real estate projects, housing stock, real estate rent,
local population and other factors (Dennis et al., 2002). Lee studied the relationship between the choice to buy or
rent a house and the residential telephone bill. In his study, the scholar found that in American families, different
races have different needs for buying or renting a house. Different demands for buying and renting a house have a
greater impact on the price changes in the real estate market.
As far as the location affects the real estate prices, Tang found that real estate prices are positively correlated
with the degree of convenience of transportation. In his study, the scholar found that the more convenient the means
of transportation, the lower the transportation cost, the shorter the transportation time, the higher the real estate
prices in the corresponding areas. . Henderson studied the impact of transportation costs, air quality, geographical
location on real estate prices, the scholar's study shows that the lower the transportation costs, the better the air
quality, the better the geographical location, the lower the real estate prices. Stegman's research shows that regional
environmental specificity also has a relatively important impact on real estate prices. Through field surveys of real
estate prices in cities, the scholar finds that the price of housing in the periphery of the city is not necessarily lower
than that in the vicinity of the downtown area. The larger the open space in the periphery of the city, the higher the
corresponding real estate price (Henderson and Wang, 2007).
2.1.2. Real Estate Market Bubble
Tirtiroglu studied the housing market price who found that the price of the residential market may deviate from
the real price of the house, causing a bubble in the real estate market. Bertrand found the existence of speculation in
the real estate market in 1999 who found that even in small and medium-sized cities, speculative attitudes also have
a profound impact on the prices of the real estate market. The greater the proportion of speculators in buyers occupy,
the greater the probability of real estate prices increases. Hing Lingchan studied the characteristics of price changes
in the real estate market in Hongkong in 2001 who proved that there was a serious bubble in Hong Kong's real estate
market before 1998, and the Asian economic crisis in 1998 triggered the bubble of Hong Kong's real estate market,
which led to the rapid decline of real estate prices. Brueggeman introduced inflation into the real estate asset pricing
model in 1984, verifying the correlation between inflation and real estate prices (Staley).
2.1.3. Government Policy Implication
Samuel believed that urban planning and development have a direct impact on real estate prices in the region.
The expansion of the city will lead to a rapid increase in housing prices and land values in the city, and will lead the
price of production to rise sharply (Staley). Landies has empirically analyzed the correlation between urban
construction planning and real estate prices who showed that the urban construction planning through the field
investigation which has a direct impact on real estate prices, and the local government policies also have a greater
impact on real estate prices (Staley). Mc Millan studied the propensity of planning agencies to plan for urban
development who found that planning agencies plan for urban development needs to consider the needs of the
housing market (Mcmillen and Mcdonald, 1991).
2.2. Chinese Related Research Overview
Most of the current research in China is based on the development of the real estate market in China to study the
influence on real estate prices brought by China’s land price, consumer attitude and macroeconomic policy.
Yao Xianguo studied the relationship between land price and real estate price whose research showed that there
is a correlation between real estate prices and land prices. This scholar's research shows that although there is a
strong correlation between real estate price and land price, there is no linear relationship between them. The main
3. Business, Management and Economics Research
59
influencing factor of real estate price is the residents' demand for real estate. In other words, the greater real estate
the residents demand, the more obviously the trend of real estate rises, and for land prices, the rise in land prices is
only the cause of the rise in real estate prices but not the absolute factor 2 (Yao and Huang, 2001).
Shang Mei studied the relationship between real estate prices and investment in the construction industry,
unemployment rate, average income level, output of industrial products, interest rate on bank loans, etc. By
calculating the correlation between these index sequences and real estate series, Shang Mei points out that these
indexes are strongly related to the variables of real estate prices. Shang (2004).
Chen Duochang studied the relationship between real estate taxes and real estate prices. He showed that the
main factor affecting real estate prices is the expectation of changes in real estate prices. And the greater the
expectations of real estate prices is, the stronger the willingness of residents to buy real estate will be, and the more
obvious the upward trend of real estate market prices are. The important factor of the booming real estate market of
our country is the strong expectation of the residents' real estate purchase, as well as the influence of the economic
environment, the interest rate of the bank, the demographic dividend and so on, which might cause the real estate
price of our country to rise all the time. Chen and Tan (2004).
Yao Daquan studied the relationship between government land reserve and real estate prices who pointed out
that land policy and land reserve have a great influence on real estate prices, on one hand, local land supply
mechanism influences the total amount of real estate development and development distribution, and the more
sufficiently land supplies, the more total amount the real estate development has, so that the seller’s market of real
estate can be transformed into a real estate market. On the other hand, the scarcer the local land resources are, the
higher the land price is and the higher the real estate market price is. Yao (2003).
Zhang Daliang studied the relationship between the real estate prices and the needs of the cohabitation who
thought that the residents' price concept, consumption ideas, investment ideas and so on affect the residents' demand
for real estate. The change of real estate price will be directly affected. The stronger the demand for housing, the
higher the real estate price will be. Sex becomes bigger, and vice versa (Zhang and Zhou, 2004). The level of
residents' demand directly affects the change of real estate prices. The more the residents need housing, the more
likely it is to raise real estate prices, and vice versa.
Wang Xia studied the impact of traffic environment on real estate projects, which is based on the residential
areas along the Beijing Metro, especially studied the change of housing price in the residential area around Beijing
Metro Line 13. The scholar found that the change of the price of the residential area around Line 13 is obviously
related to the location of the region. The worse the location of the district is, the farther away from the city center is,
the greater the price will rise and the better the location of the district lies. On the contrary, the closer to the city
center is, the smaller the prices will rise Wang et al. (2004).
In response to the research field of “predicting real estate prices”, many scholars have selected different
prediction models to conduct related research and exploration.Dong Qian and other scholars selected six different
measurement models to fit and predict the price of second-hand houses and new houses in 16 cities in China. The
operating base is China's largest search engine index - Baidu.The research shows that the data obtained by the
network search engine is the ideal base for predicting the house price index. It can also be applied to the analysis of
the behavior trend and law of economic subjects, and has a time-effectiveness (Dong et al., 2014).Wang Cong used
the “least squares multiple regression analysis method” and the “LOGISTIC regression analysis method” to analyze
and predict real estate prices, and the results show that the latter has higher prediction accuracy (Li et al., 2008b).
Ding Feng selected three predictive models for studying housing prices in Shanghai: 'Time Series Prediction Model',
'Gray Prediction Model', and 'BP Neural Network Model'. The results show that house prices will continue to rise in
the next year (Staley). Pan Zhi'an and other scholars believe that the "BP neural network" with "error back-transfer
algorithm" can analyze and predict real estate prices, and the results also verify that this algorithm can predict real
estate prices well (Pan and Shen, 2014).In the study of “real estate price forecasting”, Li Wanqing and other
researchers paid attention to the accuracy and convergence speed of different types of “neural networks” to predict
real estate price indices. Both the research results and the prediction results show that the wavelet neural network has
the best prediction effect compared with other neural networks (Li et al., 2008a).In order to solve the problem of
long learning time and low precision of traditional BP neural network with factor redundant information, Zhang Wei
combines rough set theory with BP neural network to construct a new real estate price forecasting model.The
principle is that the theory of rough set is used to reduce the redundant information in the real estate price, and then
BP neural network is used in the prediction. The results show that the speed and accuracy of the model are greatly
improved (Zhang, 2011). Li Daying et al. combined the rough set theor and wavelet nerve in "fuzzy mathematics" to
construct a model for real estate price prediction (Li et al., 2009). Yang Liya and Shao Chunfu studied the influence
of urban rail transit on the surrounding real estate prices, and proposed a combined forecasting model. The principle
is to use the fitting function of BP neural network.Firstly, the real estate price data is roughly fitted, and then the
Markov chain is used for more accurate prediction. The final result shows that the model has high precision. (Peng
and He, 2012; Qian et al., 2009; Ren and Du, 2012; Yang and Shao, 2008).
As for the research on the prediction of real estate prices by the gray prediction model GM (1, 1) model, many
scholars have carried out certain research and achieved certain results (Peng and He, 2012; Qian et al., 2009; Ren
and Du, 2012).
Zhang Shanyu and Xu Hui conducted an in-depth study on the grey prediction model. They believed that there
are some unreasonableities between the improved form and the discrete model of the traditional grey prediction
model. The combination of the two forms a new gray model - DGM (1, 1) The model and verification confirmed that
the new model is reliable and effective (Zhang and Xu, 2013).
4. Business, Management and Economics Research
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3. Insufficient Research at Present
3.1. Empirical Research Data That Are Not Comprehensive and Not Uniform
The amount of data sources used for property analysis in different departments, different regions, and different
fields is limited. Moreover, its statistical caliber and standards are inconsistent, which leads to the fact that the
empirical analysis data for real estate is often not uniform. This leads to the fact that the empirical analysis data for
real estate is often not uniform. Moreover, there are many factors affecting real estate prices. Many influencing
factors are often mutually constrained, so the causal relationship between the influencing factors is not clear. For
example, it is widely believed in the literature that a drop in interest rates has led to a large influx of funds into the
real estate market, which has raised housing prices. However, from a policy-making perspective, high housing prices
may also be the reason why the government adopts a higher interest rate monetary policy.
Lack of research that combines supply-side and demand-side factors and factors that affect both supply and
demand. Current research is often limited to the limited information that can be collected at hand. The usual practice
is to use several relevant factors as variables, and use house prices as dependent variables to conduct multiple
regression analysis. These variables are either variables on the supply side or variables on the demand side. It is not
uncommon to consider the combination of the two. Secondly, the influence weights of various influencing factors
within each level and the coupling mechanism between different levels of influencing factors have not yet been
clarified, so as to unearth the leading influencing factors. As a result, the usual regression analysis method has not
been used to demonstrate the sufficiency of the selected research variables, and the blindness of the human cloud has
led to the selection of many weak variables and the omission of many important variables. In addition, the
relationship between the various influencing factors is not fully explored. The mutual conversion mechanism
between the supply and demand factors is not considered, resulting in the lack of joint variables between the factors
in the regression equation.
4. Conclusions
The research literature on the factors affecting real estate prices is reviewed, which discusses the three aspects of
demand side and supply side, and factors affecting both supply and demand, and proposes two suggestions:
Strengthen the scientific nature of data statistics in empirical research.The data of housing price research should
be more standard and comprehensive. Research should combine multiple factors such as demand side and supply
side. Propose a reasonable and moderate hierarchical classification method for a large number of influencing factors.
Clear out the influence weights of various influencing factors within each level. And the coupling mechanism
between different levels of influencing factors, so as to explore the dominant influencing factors.
This paper reviews the current literature on real estate price forecasting models and analyzes the current
mainstream mathematical models used to predict real estate prices. And make the following three
suggestions;
The current mainstream neural network models, gray-Markov prediction models, and random time series models
all have their own advantages and inherent deficiencies;
The shortcoming is mainly because its algorithm can not introduce various constraints on the impact of supply
and demand factors on housing prices, and only reflects the changes in housing prices from one aspect.
To this end, it is necessary to further clarify the influencing factors and mechanisms of the dominant house
prices, use mathematical language to describe the impact mechanism and introduce the model, and make corrections
to obtain a more objective and accurate model.
With the development of big data technology, the house price prediction method based on network keyword
search technology has good timeliness, and can study the characteristics of home purchase behavior through
consumer network behavior, which will be widely used.
Acknowledgement
The research and publication was supported by funds below:
The Fundamental Research Funds for the Central Universities, NO. 21618412;
Inner Mongolia Autonomous Region Science and Technology Innovation Guide Award Fund Project, NO. 103
413193.
Scientific Research Project of Henan Colleges and Universities in 2019, based on the research on military
science and technology innovation mechanism of colleges and universities from the perspective of civil-
military integration, NO. 19A630037.
Fund of Research on Enterprise Management Innovation Mode System, NO. 44860070;
Fund of Research on the Enterprise Management Mode and Countermeasures Based on the Production, Study
and Research, NO. 44860071.
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