Table 1 The highest profitable real estate firms listed in Vietnam in 2017Company name Sales revenue million USD After-tax profit million USD Vingroup Joint Stock Company Source: Financi
Introduction
Rationale of dissertation
Since the 2008 financial crisis, Vietnam's macroeconomic landscape has shown a remarkable recovery, particularly in the real estate sector According to KPMG's “Investing in Vietnam in 2018,” Vietnam's real GDP averaged a growth rate of 7.3% from 2005 to 2008, before declining to 5.3% in 2009 due to the crisis The economy began to recover in 2012, achieving a GDP growth rate of 6% by 2014 As reported in Savills Vietnam's “Q42017 Quarterly Market Briefing,” GDP per capita reached $2,385 in 2017, reflecting a 10% increase from the previous year Looking ahead, the growth rate is anticipated to rise to approximately 6.5% from 2018 to 2019.
Figure 1: GDP, GDP Growth and inflation of Vietnam from 2009 to 2019
The consumer price index (CPI) peaked at 22.9% in 2008 due to the financial crisis, before gradually recovering to 7.4% in 2009 as a result of government interventions like tightened monetary and credit policies From 2011 to 2016, the inflation rate fluctuated significantly, ranging from 18.1% in 2011 to 2.7% in 2016, as effective inflation control measures were implemented However, increased demand for goods and services, reduced credit, and rising investments contributed to an inflation rise to 4.1% in 2017, according to the World Bank The average inflation rate is expected to remain steady at 4% from 2016 to 2020.
In 2017, Vietnam achieved a remarkable milestone in Foreign Direct Investment (FDI), attracting a record capital inflow of 35.9 billion USD, which reflects a significant 44.4% increase compared to the previous year This surge includes both increased capital and newly-registered investments from foreign investors, highlighting the growing confidence in Vietnam's economic landscape.
19 different industries and the real estate industry ranked third with $3.05 billion, after manufacturing, distribution and power generation
In 2017, the total trading value of mergers and acquisitions in Vietnam's real estate sector reached $8 billion, with investors showing a preference for property companies in Ho Chi Minh City due to their greater financial transparency The Vietnamese real estate market remains highly attractive to foreign investors, particularly through mergers and acquisitions, as joint ventures become increasingly popular among those with strong financial backing These investors often collaborate with local developers who possess land and have established connections with local governments and communities According to JLL's report on merger and acquisition activities in Vietnam for 2017, significant investments are anticipated across various real estate sectors, including office, residential, hospitality, industrial, and retail Overall, optimism persists among investors seeking transparency in land transactions within Vietnam's thriving real estate market.
Page 3 recorded the highest number of foreign equity investors, mainly private capital funds, searching to reimburse their capital in swift and efficient manner
The demand for office space in prime locations is on the rise, reflecting a significant investment interest in the Vietnamese market This trend aligns with the increasing property requests across the Asia-Pacific region, where investments surged by 12% to reach $97 billion from January to September 2017 compared to the previous year.
In 2017, Vietnam experienced significant growth in its business landscape, with approximately 127,000 new enterprises established, marking a 15.2% increase compared to 2016, as reported in JLL's "1Q18 Vietnam Property Market Brief." The newly registered capital for these companies surged by 45.4% from the previous year, resulting in an average capital of 10.5 billion VND Additionally, the number of businesses ceasing operations at the end of 2017 saw a slight decline of 0.2% compared to 2016.
Vietnam has emerged as one of the top ten fastest-growing tourist destinations globally, attracting over 13 million international visitors in 2017, a 30% increase from the previous year, according to Colliers International The country's hospitality sector is rapidly expanding, featuring more than 25,000 accommodations, including over 100 five-star hotels, 250 four-star hotels, and nearly 500 three-star hotels.
Vietnam's economic growth is poised for enhancement through increased consumption, rising foreign direct investment, improved export performance, and stronger integration into the global economy The stable political climate, low labor and operational costs, and ongoing government support position Vietnam as a dynamic and attractive destination for both foreign and domestic investors, fostering significant contributions to the nation's economic development.
The rapid economic recovery since 2012 has spurred an upward trend in the real estate industry, with demand, supply, and prices consistently rising In 2017, the real estate sector experienced a 4.07% growth, marking the highest rate since 2011 and contributing 0.21% to the total GDP growth According to the Vietnam Real Estate Association, the proportion of credit allocated to trading and construction by real estate companies decreased to approximately 15% in 2017 from 17.1% in 2016 However, consumer credit for housing-related purposes, including purchasing, renting, building, and repairing homes, grew by 3.4% in 2017, reaching 52.9%, reflecting increasing demand in the market.
The real estate market is thriving due to high liquidity from numerous suppliers and large-scale projects, supported by city governments There is a strong and consistent demand from investors, driven by rising incomes and a demographic shift from rural areas to modern urban centers This has led to a significant increase in apartment transactions and a decrease in real estate inventory As a result, the positive growth trend in the real estate sector presents promising opportunities for developers and profitable business prospects.
The Vietnam Real Estate Association reports a significant 62% increase in the number of real estate companies since 2016 Additionally, the Vietnam Investment Review highlights that in 2017, 59 major listed companies on the Vietnam Stock Exchange reported a remarkable profit of approximately 155.093 trillion VND (6.8 billion USD), reflecting a 39% increase from 2016 Notably, Vingroup's consolidated sales revenue surged to 3.97 billion USD, marking a 57% rise compared to the previous year, and representing 58% of the total sales revenue of all listed real estate firms Consequently, Vingroup's consolidated net profit reached a record 186.6 million USD, a substantial 74% increase from 2016.
Table 1 The highest profitable real estate firms listed in Vietnam in 2017
Company name Sales revenue (million USD) After-tax profit (million
Quoc Cuong Gia Lai Group 35.8 18.63
According to the financial statements of listed companies and the Vietnam Investment Review, NovaLand Group ranked second among the most profitable real estate companies, achieving sales revenue of $511.2 million and an after-tax profit of $90.58 million, marking increases of 58% and 22.5% compared to 2016, respectively Vincom Retail Joint Stock Company, a subsidiary of VinGroup, secured the third position on the Ho Chi Minh Stock Exchange with approximately $240 million in revenue and $88 million in net profit.
In 2017, among 59 companies, 13 achieved remarkable profit growth exceeding 100% Leading this group was VRC Real Estate and Investment Joint Stock Company, which saw an astonishing 33-fold increase in profit compared to 2016 Following closely, Van Phu Invest Company secured the second position with a profit surge of 22 times De Tam Company ranked third, reporting a net profit of 3.6 billion VND for the year.
Table 2 Top ten listed real estate firms with fastest increasing in profit
Company name After-tax profit (billion VND)
(decrease) (%) VRC Real Estate and Investment Joint stock company
De Tam Joint Stock Company 3.67 0.2 1,771.94
Quoc Cuong Gia Lai Joint Stock
Khang Phuc House Trading Investment
Investment and Trading Of Real Estate
Cuu Long Petro Urban Development &
Source: Financial statements of listed real estate companies and Vietstock
In 2017, the Ministry of Planning and Investment reported that newly registered capital reached 388.376 billion VND, marking a significant increase of 66.5% from 2016 and representing 30% of the total newly registered capital The real estate sector had the highest average registered capital per company, at approximately 76.7 billion VND Additionally, the number of listed companies surged from 11 in 2016 to 66 in 2017.
However, there were 12 real estate companies witness a remarkable reduction in revenue in
In 2017, six companies in the real estate sector reported significant losses compared to 2016, with Viet Property Investment Joint Stock Company facing the highest net loss of 146 billion VND due to a high cost of goods sold at 633 billion VND against sales revenue of 549 billion VND Additionally, twelve real estate enterprises experienced substantial revenue declines, with Song Hong Construction Joint Stock Company reporting a staggering 93% drop in revenue Furthermore, Vietnam Mechanization Electrification & Construction JSC has seen a gradual reduction in profit since 2014.
Table 3 Top listed real estate firms with declined profit in 2017 comparing to 2016
Company name After-tax profit (billion VND)
(decrease) (%) VRC Real Estate and Investment Joint stock company
De Tam Joint Stock Company 381.88 1,014.34 (62.35)
Quoc Cuong Gia Lai Joint Stock
Khang Phuc House Trading Investment
Investment and Trading Of Real Estate
Cuu Long Petro Urban Development &
Source: Financial statements of listed real estate companies and Vietstock
Conceptual Basics
Profit, as defined by Adamu Yahaya in 2016, is the excess of revenues over related expenses within a specific timeframe Profitability refers to a company's capacity to generate profit from its business operations, reflecting the effectiveness of management in utilizing company resources to achieve returns on initial investments.
Page 9 resources available in the companies Although a company is able to produce profit, this cannot guarantee that the company can be profitable and owners as well as financial managers cannot only rely on the profit Therefore, the financial managers need to analyze the profitability in order to evaluate the ability of the company to utilize its capital investment and resources Specifically, profitability ratios can be used to evaluate an ability of a company to earn profit against its related expenses In majority of these ratios, higher ratios mean higher ability to generate profit and this indicates the how well is financial performance of the companies
Profitability ratios, such as Return on Assets (ROA) and Return on Equity (ROE), are essential for evaluating a company's financial performance ROA measures how effectively a company uses its assets to generate sales revenue and profits, indicating that increased asset utilization should lead to higher sales and net profits As a result, lower costs and improved profit margins can enhance ROA Conversely, ROE assesses a company's ability to generate profits from its equity investment, highlighting that significant profit growth can occur without additional equity, as the company leverages its existing investment for greater returns.
The profitability of listed real estate companies in Vietnam is influenced by various financial and non-financial factors, including organizational culture, corporate social responsibility, innovation, and financial management Key financial management indicators, such as accounts receivable cycle ratio, inventory ratio, accounts payable ratio, debt ratio, and current ratio, play a crucial role in assessing a company's financial health Analyzing these financial ratios can reveal significant correlations with profitability, providing valuable insights for stakeholders in the real estate sector.
Research methods of the dissertation
The study measures profitability through Return on Assets (ROA) as the dependent variable, while independent variables include receivable turnover (RT), payable turnover (PT), the ratio of short-term debt to total debt (TOTAL_DEBT), company size (SIZES), current ratio (CR), macroeconomic factors like the growth rate of Gross Domestic Product (GDP), firm growth (FGR), sales to capital employed ratio (SCE), inventory turnover (INVENTORY_TURNOVER), and interest cover (INTEREST_COVER) The relationship between these variables will be analyzed using the Ordinary Least Squares (OLS) model.
- H0: Independent variables cannot explain the profitability of Vietnamese real estate companies
Independent variables play a crucial role in explaining the profitability of Vietnamese real estate companies This study employs multiple regression analysis and an econometric model to estimate the relationships between these variables and profitability.
The regression analysis reveals the correlation, whether negative or positive, between the dependent variable and the independent variables, highlighting which independent variables exert the most influence on the dependent variable.
Organization of the dissertation
To resolve the proposed objectives of the dissertation, the dissertation would be organized as
5 chapters and the specific content of each chapter are as following:
This chapter outlines the rationale behind selecting the dissertation topic, emphasizing its importance in the context of Vietnam's economic landscape It provides an overview of the current state of the Vietnamese real estate market and the financial health of local real estate companies Additionally, it defines key concepts related to profit and profitability, sets forth the dissertation's objectives, and describes the data collection and research methodologies employed Finally, it presents the overall structure of the dissertation.
This chapter reviews existing literature on the financial performance of both international and domestic listed real estate companies, summarizing and analyzing previous research It highlights the rationale behind selecting specific financial indicators to effectively explain the profitability of these companies.
Chapter 3: Research Methods of the dissertation
This chapter defines and measures dependent and independent variables while presenting the econometric model along with relevant hypotheses It also outlines the principles and methods for analyzing data resources following measurement.
Chapter 4: Analyze the researched data and present findings
This chapter presents the research findings and provides an explanation for these results It includes descriptive statistics, analyzes the correlation between dependent and independent variables, verifies the accuracy of the research model's hypotheses, and assesses the extent to which independent variables can explain the dependent variable.
Chapter 5: Conclusion, limitations and recommendation of the study
This chapter summarizes the financial factors influencing the profitability of real estate companies, based on the study's findings It also offers several recommendations for the government to enhance the effectiveness of the real estate market in Vietnam.
Page 12 financial condition of real estate industry and for the real estate companies to improve the financial performance, the limitations of the study and orientation for next researches.
Purposes of the dissertation
The specific purposes of the dissertation are:
- To examine the correlation between profitability and related financial indicators
- To identify which financial factors have stronger impacts on profitability based on the outcome of coefficients from the tests
- To verify the suitable strategies to improve the profitability of the real estate companies in Vietnam
Methods of collecting data
This study analyzes the financial statements of 53 listed real estate companies in Vietnam, categorized by size (small, medium, and large) from 2013 to 2017 Data will be sourced from the Vietnam Stock Exchange and the companies' official websites Financial ratios and indicators will be calculated from these statements, which will serve as independent and dependent variables in a multiple regression analysis The Ordinary Least Squares method and fixed effects of cross-section will be applied to assess the relationships between these financial metrics.
Literature review
Review of related literature and researches
The primary goal of effective company management is to generate net profit, which serves as a key indicator of operational efficiency Profitability is essential for a company's survival and its potential for growth and expansion As noted by Gabriela Loagă (2010), to maximize profits, companies must balance several critical factors: efficient production processes, the satisfaction of both owners and clients, ongoing research and innovation, and strategic growth initiatives Consequently, companies need to develop a comprehensive understanding of their business environment and carefully assess the factors influencing profitability.
Profitability refers to a company's ability to generate profit from its operations, which can be assessed through various methods and financial performance indicators Costea Valentin (2012) categorizes the determinants of profitability into modern and classic indicators Modern indicators focus on value added through production, including economic and market value, as well as investment ratios like dividend payout ratio and price-earnings ratio In contrast, classic indicators encompass ratios such as gearing ratio, profitability ratios, and return on equity Among these, profitability ratios are the most commonly used to evaluate a company's profitability, as highlighted by Ľubica Lesáková (2007), who notes that these ratios reflect a company's profit-generating capability, return on investment, and overall financial health.
To assess company profitability, net profit margin, defined as net profit divided by sales revenue, serves as a key dependent variable (Vijaykumar, 2011) A regression analysis of panel data from 18 automobile companies identified significant factors influencing profitability, including company size, financial leverage, inventory days ratio, and asset tangibility Additionally, Basha and Islam (2014) utilized gross profit margin as another dependent variable for profitability evaluation However, the study highlights limitations, noting that profitability ratios like gross and net profit margins are often short-term measures influenced by various objective factors, such as seasonality, inflation, industry leadership, and differing operational and accounting practices Consequently, alternative financial indicators should be considered for a more comprehensive assessment of company profitability.
Review for related literature and researches in Europe
Valentine Cosmin Saracin (2012) identifies key financial factors influencing the profitability of Romanian real estate firms, including financial leverage, the proportion of fixed assets, gross margins, and turnover rates for receivables and payables The study highlights a strong correlation between these variables and Return on Equity, emphasizing that financial risks can adversely affect profitability by restricting available resources due to debt obligations Additionally, low gross margins can lead to increased market competition, further reducing profitability Notably, the financial performance of these companies is positively correlated with the number of rotation days for receivables and payables.
Page 15 rapidly collect debts and make payment to the suppliers punctuality, the financial health of the companies would enjoy the upward trend There is also negative correlation between the profitability and inflation
A study by Klaus Hammes and Yinghong Chen (2005) found that profitability, measured by profit before tax, is significantly correlated with factors such as debt ratio, tangibility, company size, and age In most European countries, excluding Sweden, there is a negative relationship between profitability and debt ratio, indicating that higher debt levels are associated with lower profit before tax Company size generally positively impacts profitability across various countries, except for Sweden, as larger firms tend to have greater borrowing capacity and lower fixed costs compared to smaller firms Additionally, tangible assets typically exert a negative influence on overall profit levels and earning potential in many European nations The age of a firm shows varying effects, being positive in Finland and negative in France.
Research by Klaus Hammes and Chen (2005) indicates a strong correlation between asset rotation, interest rates, firm age, and company size with the financial performance of real estate firms Specifically, larger companies tend to exhibit higher profitability, while older firms face challenges in competing for profitable investments, leading to a negative correlation with financial condition Additionally, Kim Hiang Liow and Ho Kim Hin David (2009) highlight that stock returns are significantly influenced by the market-to-book ratio, company size, return on equity, debt ratio, cost of equity, spread, fixed tangibility, and earnings retention ratio In many Asian and European countries, larger real estate companies with sustainable growth rates demonstrate a positive impact on profitability, resulting in higher return on assets Conversely, these variables show a positive yet insignificant correlation with profitability in North American real estate firms.
American enterprises show a positive correlation with profitability, though this relationship is weaker compared to European real estate companies Overall, real estate firms across all regions exhibit a negative relationship between profitability and capital structure, as higher debt ratios adversely affect financial performance, with borrowing costs being a significant factor Research by Goddard, Molyneux, and Wilson (2005) highlights a positive correlation between Gross Domestic Product and profitability among approximately 600 European commercial banks Additionally, studies by Doma Rema Marak and Sirion Chaipoopirutana (2014) indicate that company profitability is positively linked to national economic growth Furthermore, this study reveals a negative relationship between inflation rates and company profitability, supported by Houssem (2013), who identified a negative correlation between the Consumer Price Index and firm profitability using a panel model.
Review for related literature and researches in Asia
According to Farah Margaretha and Nina Supartika (2016), several indicators significantly influence the profitability of Small and Medium Enterprises (SMEs) listed on the Indonesia Stock Exchange, including firm size, sales growth rate, past profitability, labor productivity, and industry affiliation, measured as the ratio of sales minus cost of goods sold The study found that while firm size, sales growth rate, and past profitability negatively correlate with profitability, labor productivity and industry affiliation positively impact it Notably, the age of the firms does not have a statistically significant effect on profitability These findings highlight the importance of focusing on specific factors to enhance a company's profitability.
Page 17 financial performance, the manager could identify a suitable strategy to maximize the profitability by focusing on productivity of labor forces and industry affiliation which is the ratio between sales and cost of goods sold
Additionally, according to John Francis T Diaz and Martha Christianie Tjokro Hindro
(2017), the research analyzes the correlation between financial factors and the profitability of
A study of 47 Indonesian real estate companies from 2010 to 2014 employed multiple linear regression to analyze factors affecting profitability, including receivables days ratio, inventory days ratio, payables days ratio, company size, tangibility, debt ratio, current ratio, and sales growth ratio Findings revealed that the receivables days and inventory days ratios negatively impact profitability for most companies, except medium and small firms, where the correlation is positive for larger firms due to their liquidity and capacity to manage inventory costs Company size and sales growth are generally positively correlated with profitability, although this does not apply to medium-sized firms Furthermore, the current ratio positively correlates with profitability in large real estate companies, while smaller firms experience a negative correlation due to lower current assets Lastly, tangibility shows a negative correlation with profitability in large firms, contrasting with a positive correlation in medium-sized firms.
Dr Anupam Mehta's 2014 study reveals a significant negative correlation between profitability, as indicated by Return on Assets, and the length of a firm's cash conversion cycle Specifically, a longer cash conversion cycle results in reduced profitability The research also investigates the individual components of the cash conversion cycle, including Days Sales Outstanding, Days Inventory Outstanding, and Days Payables Outstanding, to understand their impact on overall profitability.
Page 18 outstanding) is having the most significant influence on the profitability The study brings out that the day’s payables outstanding is inversely related to profitability, this means the sooner the companies make payment to creditors, the better it will be for the overall profitability This could also indicate that the less profitable concerns take more time to make the payment Consistent with previous studies, the firms can improve profitability by reducing the number of days the required to convert the inventory into sales The Liquidity also has inverse relationship with profitability Higher the funds are tied up in current assets lesser will be the profitability The study also brings out that the Size of the concern is immaterial for enhancing the profitability Thus the study concludes that the UAE’s real estate and construction companies can significantly increase their profitability by giving focus on management of the working capital and shortening the length of the cash conversion cycle by effectively managing the working capital components especially the payables and Inventories Furthermore, in the research of Mahmood and Rozimah (2007), this paper analyzes the profitability and capital structure among real estate firms and construction companies in Malaysia from 1996 through 2003 The results indicated that real estate companies in Malaysia can have larger size and are able to gain more profit due to their capital gearing and debt equity ratio were reducing The results also revealed that the debt of construction companies is considerably high and the demand to follow the obligation of this debt is very high leading to quite low profit margin The results are similar to the outcomes on the industrial market in Hong Kong showing that capital gearing have shown the negative correlation with price earnings ratio and profit after tax margin for real estate and construction industry because the companies with significant capital gearing have to fulfill their debt obligations in which would make their profit margin and price earnings ratio decline, irrespective of their business sizes Generally, the study illustrates that the amount of debt over equity is negatively correlated with the ratio of net profit to total profit and price earnings ratios for property developers
Review for related literature and researches in Africa
In Basman Al Dalayeen's 2017 study, the impact of working capital management on the profitability of real estate firms was examined, focusing on key metrics such as the current ratio, debtor turnover ratio, and inventory turnover ratio, with profitability measured by Return on Capital Employed The findings revealed that both the debtor turnover ratio and current ratio have a significant positive correlation with profitability In contrast, the inventory turnover ratio showed a positive but weaker correlation with the profitability of real estate companies Additionally, research by Fidelis Ifeanyi Emoh and Ikhuoshio Uzuanje further explores these dynamics.
Research from 2015 shows that real estate companies generate revenue through rentals and leverage borrowing for equity investment, which creates financial leverage This leverage is beneficial as long as the companies' return rates exceed their capital costs However, a recent study highlights that the rising cost of capital for Nigerian real estate firms over the past five years has resulted in negative returns The capital-intensive nature of real estate development, coupled with Nigeria's struggling economy, means many investors rely on borrowing rather than personal income for funding High lending rates from financial institutions significantly increase capital costs, reducing income flow and leading to negative returns on investment Overall, the study concludes that the escalating cost of capital adversely affects profitability in the sector.
Jane Nduku's 2015 study reveals that capital structure affects the profitability of real estate companies, albeit with a weak statistical relationship Notably, the ratio of short-term debt to total debt significantly influences profitability, as measured by Return on Assets (ROA), defined as net profit before tax divided by total assets.
Page 20 capital structure have weak effects on profitability of the companies such as long-term debt to total debt or debt to equity ratio Therefore there are other major indicators impacting profitability of the real estate firms other than capital structure and the real estate firms should focus more on current debts over total debts because this has the most remarkable impacts comparing to other the variables.
Review for related literature and researches in Vietnam
In Vietnam, research has identified key factors influencing company profitability, particularly in the banking and real estate sectors Duy Nguyen (2017) found that asset structure, bank size, and diversification have minimal impact on profitability, while asset quality and reduced administrative expenses are critical Additionally, banks with higher invested capital often yield lower profits compared to those utilizing financial leverage, and their performance is significantly affected by macroeconomic variables like inflation There exists a positive but statistically insignificant relationship between equity levels and Return on Assets, alongside a notably negative correlation with Return on Equity Similarly, Hoai and Thanwadee (2015) noted that the profitability of real estate firms is influenced by financial indicators such as GDP per capita, savings ratio, and construction costs, with a strong positive correlation between GDP per capita and profitability Conversely, negative correlations were found between profitability and factors like the Consumer Price Index, deposit interest rates, and debt-to-equity ratios Furthermore, Thi Kim Nguyen (2013) revealed that from 2003 to 2009, real estate securities underperformed compared to market shares during stable economic conditions.
Page 21 shares can deliver a high rate of return, it is just the compensation for the significant underlying high risk and eventually leaded to the reducing in risk-adjusted return than the return of bonds In addition, Vietnam property securities outperformed the real estate securities of many developed markets such as United States, United Kingdom or Australia from 2003 to 2009 and the beginning period global financial crisis but underperformed three benchmark markets in the period near the global crisis in 2009 This paper also considers the efficient frontiers, the best possible return for investment portfolios of real estate companies in Vietnam from 2003 to 2009 The analysis reveal that real estate securities always underperformed bonds and the real estate companies securities do not offer the high profit for investment portfolio The reason for this result is the unstable return of emerging market as Vietnam
This dissertation will utilize Return on Assets (ROA) as the key metric for assessing the profitability of listed real estate companies in Vietnam ROA is preferred due to its stable correlation with Return on Equity and Return on Investment over time, while remaining relatively unchanged itself Unlike Return on Equity, ROA encompasses all types of assets and shareholder equity, providing a comprehensive evaluation of resource utilization within companies Additionally, ROA aids in tracking asset usage, enhancing company performance, increasing sales revenue, improving productivity, and minimizing operating expenses and costs of goods sold The study will also examine independent variables such as receivable turnover, payable turnover, company size, current ratio, firm growth rate, sales to capital employed, inventory turnover, interest coverage ratio, and asset tangibility.
Methodology of research
Data collection methods
Between 2013 and 2017, 56 real estate companies were listed in Vietnam, with data analyzed from 53 firms based on data availability and varying company sizes The research utilized yearly financial statements published on company websites, reports from newspapers, and official resources from the Ho Chi Minh and Hanoi Stock Exchanges Financial ratios and indicators were calculated using the figures obtained from these financial statements.
Dependent variable and independent variables
The profitability of listed real estate companies in Vietnam is assessed through Return on Assets (ROA), which indicates how effectively total assets are employed to generate profit ROA is calculated using a specific formula that highlights the relationship between total assets and profitability.
The receivable turnover ratio assesses how effectively companies collect outstanding debts from customers A high ratio indicates a robust debt collection strategy and suggests that the company has reliable customers This ratio is calculated by dividing net credit sales by average accounts receivable.
The payable turnover ratio assesses a company's capacity to meet its liabilities over a specific period, reflecting the frequency with which it pays its creditors throughout the fiscal year A high payable turnover ratio signifies prompt payments to vendors for credit purchases This ratio is calculated to provide insights into a company's financial health and efficiency in managing its obligations.
In the study "Factors Affecting the Profitability of Indonesian Real Estate Publicly-listed Companies" by Dr John Francis and Martha Hindro, company size is assessed through total assets, as larger firms can leverage economies of scale to enhance profitability The size is quantified using the logarithm of total assets, which can be found on the companies' balance sheets.
Short term debt over total debt
The short-term debt to total debt ratio assesses the proportion of short-term liabilities within a company's overall debt structure A high ratio suggests that companies must prioritize maintaining liquid assets to meet immediate debt obligations, potentially limiting their ability to invest in new opportunities and diminishing short-term profitability This ratio is crucial for understanding a company's financial health and liquidity management.
The current ratio is a key indicator of a company's liquidity, reflecting its ability to meet both short-term and long-term obligations This financial metric provides insights into the company's overall financial health and operational efficiency.
Page 24 ability of the companies to pay back their liabilities with the high liquidity assets such as cash, short term account receivable, short term investment and inventory Additionally, the current ratio shows to what extend the companies has operated effectively to turn their production into liquidity assets If companies having troubles in collecting their receivables or reducing their high inventory turnover, they may encounter the liquidity problems as the companies cannot meet the short term debt obligations in a timely manner
The growth rate of a firm, particularly in the context of Small Medium Enterprises (SMEs) listed on the Indonesia Stock Exchange, is primarily measured by the annual sales revenue growth rate This sales growth rate serves as a crucial indicator of profitability for real estate companies Firms that experience a high sales growth rate typically demonstrate strong financial performance, reflecting their enhanced capability to generate profits.
Sales to Capital Employed Ratio
The Sales to Capital Employed ratio measures how effectively a company utilizes its capital to generate sales revenue, with capital employed representing the total equity invested in the business A higher ratio indicates more efficient capital use and greater potential for profit generation However, an excessively high ratio may signal insufficient financial resources to support sales activities This ratio is calculated by dividing sales revenue by capital employed.
The inventory turnover ratio measures a company's ability to quickly sell and replace its inventory within a financial period A low ratio indicates weak sales and excess inventory, which can negatively impact profitability Conversely, a high ratio suggests successful sales campaigns or significant discounts that have boosted sales revenue The calculation of the inventory turnover ratio is essential for assessing a company's operational efficiency.
The interest coverage ratio measures a company's ability to meet its financial obligations from outstanding liabilities using its earnings, reflecting its financial stability A higher ratio indicates a stronger capacity to cover financial expenses related to debt, while a ratio below 1 suggests potential financial distress and decreased profitability The formula for calculating the interest coverage ratio is essential for assessing a company's financial health.
Gross Domestic Product (GDP) is defined by Dr S Ghosh (2007) as the market value of all final goods and services produced within a country over a specific period It serves as a critical measure of economic growth, reflecting the percentage change in the quantity of goods and services produced The growth rate of GDP significantly influences firm profitability, making it essential to analyze GDP trends over consecutive years For comprehensive data, the General Statistics Office of Vietnam provides official GDP growth figures.
Data analyzes process
3.1 Overview of panel data used for regression model
Regression models typically utilize three main types of data: panel data, cross-section data, and time series data Cross-section data involves collecting and analyzing information from a specific location at a particular time, such as the inflation rate in Vietnam in 2017 In contrast, time series data enables researchers to examine variables over a designated period, like the fuel prices of Asian countries from 2013 to 2017 or the economic development of Vietnam since its accession to the World Trade Organization.
From 2008 to 2017, panel data, which combines time series and cross-sectional data, enables the analysis of multiple variables across various conditions and time periods According to Marno Verbeek (2004), this type of data allows researchers to account for non-statistical factors, such as cultural influences and varying company regulations, which may change over time but not across sectors Additionally, panel data enhances the degree of freedom and significantly reduces multicollinearity among variables It offers a comprehensive perspective on economic issues that cannot be addressed by cross-sectional or time series data alone, revealing differences among cross-sectional variables effectively.
This research utilizes fixed cross-section panel data to gather information from various listed real estate companies over different time periods, which will be analyzed using a regression model.
3.2 The formula of panel data
According to Grunfeld (1958), the panel data was used with the following formula:
Y ab = β 1 + β 2 α ab + β 3 α 3ab + …+β ab α ab + U ab with a is the a th cross-sectional
Page 27 b is for the b th time period
Y is the dependent variable α is the independent variables βis the regression coefficient of independent variables
U is the error term of the model
3.3 Techniques for estimating panel data:
Different types of estimation method for regression model of panel data are Ordinary Least Squares (pooled model), fixed effects model and random effects model
The pooled model assumes that all variables are constant over time, resulting in minimal differences between individuals Additionally, a low Durbin-Watson coefficient suggests significant correlations among the variables The formula utilized for this regression model is as follows:
The fixed effects model allows for correlation between individual heterogeneity and the error term, focusing on time-invariant variables to analyze fluctuations in their values over time This model effectively eliminates the specific effects of these constant features, enabling the estimation of the net impact of independent variables on the dependent variable Importantly, changes in the dependent variable are attributed solely to independent variables, as the unobserved variable or error term remains unchanged throughout the period Unlike the Ordinary Least Squares method, the fixed effects model recognizes that each variable exerts different influences on the dependent variable.
The random effects model shares similarities with the fixed effects model, but it requires that there is no correlation between the independent variables, observed variables, and the unobserved variables or error term In this model, the individual-specific effects are considered random and are uncorrelated with the explanatory variables over time.
The fixed random effects model is commonly employed to assess factors influencing profitability Research by Hindro and Diaz (2017) utilized three multiple regression models—Ordinary Least Squares, fixed effects, and random effects—and found that the fixed effects model is the most appropriate for analyzing profitability determinants Similarly, Sami and Mohamed (2010) confirmed the effectiveness of the fixed effects model in identifying independent variables correlated with the profitability of commercial banks Masood and Ashraf (2012) also achieved comparable results by applying the fixed effects model to explore the relationship between macroeconomic variables and bank profitability Consequently, this research adopts the fixed effects model to analyze the relationship between dependent and independent variables, assuming consistent intercepts over time and location, with unchanged slope coefficients and a time-varying error term, effectively removing spatial and temporal dimensions from the pooled data model.
Where i stands for the i th cross-section unit t stands for the t th time period
Xi stands for the dependent variable β stands for the coefficient of the independent variables
U stands for the error term
3.4 Testing for the significance of statistics:
Statistical inference allows us to make general statements about a population based on gathered data The methodology employed for this inference involves significance testing to determine whether to accept or reject hypotheses regarding the sample population.
3.4.1 Testing for the significance of variables:
This study utilizes the null hypothesis and panel data to assess the constancy of α and β over time and space, employing a simple t-test for analysis By examining each coefficient of the independent variables (β), the research aims to establish a null hypothesis that evaluates the ability of these independent variables to explain variations in the dependent variable The formulated null hypotheses guide this evaluation process.
The hypothesis H0: βi = 0 indicates that there is no significant relationship between the dependent and independent variables, suggesting that the independent variables do not account for any variations in the dependent variables.
H1: βi ≠ 0 is significant meaning that there is a relationship or correlation between dependent variable and independent variables Therefore the independent variables are capable of explaining any changes of the dependent variables
In hypothesis testing, the null hypotheses are evaluated by comparing the p-values of independent variables against a significance level (α) set at 0.05 for a 95% confidence level If the p-values are lower than the significance level, the null hypotheses are rejected; conversely, if the p-values exceed the significance level, the null hypotheses are accepted.
3.4.2 Testing for joint significance of variables:
By applying the F-test, the null hypotheses with multiple parameters would be evaluated The null hypotheses are as follows:
After comparison, if the p-values of independent variables are lower than significant level α, these null hypotheses will be rejected Otherwise, these null hypotheses will be accepted
When employing regression models with panel data, multicollinearity among variables frequently arises While some correlation between independent variables is normal, excessive correlation leads to multicollinearity, which can create significant issues in the regression analysis.
If the independent variables are correlated with each other, the relationship between each variable would be as A 1 X 1 + A 2 X 2 + A 3 X 3 =0 or A 1 X 1 + A 2 X 2 + A 3 X 3 +Z=0
Perfect multicollinearity among variables prevents the construction of a reliable regression model, leading to skewed results where hypotheses appear easier to accept and variances become disproportionately significant relative to estimated values To assess the correlation between variables, it is crucial to first analyze the correlation coefficients of independent variables; a coefficient exceeding 0.8 indicates the presence of multicollinearity The regression model formula for each independent variable is as follows:
H 0 : β 2 =0, meaning that there is no correlating relationship between X 1 and X 2
H1: β2≠0, meaning that there is correlating relationship between X1 and X2
After comparison, if the p-value of significant independent variables is higher than the significant level α =0.05, then the null hypothesis will be accepted Otherwise, the null hypothesis will be rejected
Heteroskedasticity refers to the variance of error terms (Ui) in panel data, characterized by fluctuations in variance over short periods Specifically, it occurs when the variation or standard deviation of the error term changes over time, rather than remaining constant across observations Several factors can contribute to the presence of heteroskedasticity.
- There is an outlier of panel data, meaning that there is an significant distance between the lowest value and highest value
- The shortage of redundant variables
- The data is collected from cross sectional rather than longitudinal
This research assumes the absence of heteroskedasticity due to the lack of contributing factors The data is sourced from a cross-sectional study of 53 real estate companies listed on the stock market, rather than a longitudinal analysis over five years According to Stock and Watson (2008), heteroskedasticity typically arises in panel data studies, and while attempts to address this issue have been made, completely eliminating heteroskedasticity is nearly impossible, as testing for it may introduce bias into the results Consequently, this limitation is acknowledged, and the dissertation will not examine this phenomenon.
Autocorrelation relates to the correlation of time series data with its own value in the past and future This phenomenon, sometimes can be referred as “lagged correlation” or “serial
Analyzing the researched data and presenting the findings
Testing for significant of statistics
1.1 Testing for individual significance of statistics
This dissertation employs Eviews to execute a regression model with Fixed Cross Section, analyzing Return on Assets as the dependent variable against various independent variables Each variable's significance is tested individually, and the estimation is conducted using the Panel Least Squares method with fixed cross-sections The resulting P-Values for each independent variable are presented in the accompanying table, with corresponding null hypotheses established for the estimation process.
The regression model on independent variables is as follow:
Variable Coefficient Std Error t-Statistic Prob
CR -3.66E-06 3.57E-05 -0.102479 0.9185 GDP -0.305569 0.915139 -0.333904 0.7388 FGR -0.000106 3.70E-05 -2.874887 0.0045 SCE 0.078348 0.027493 2.849722 0.0048 INVENTORY_TURN
At a 95% Confidence Interval, four variables—receivable turnover, company size, firm growth rate, and sales to capital employed—exhibit P-Values below the significance threshold of α = 0.05 (0.0089, 0.0004, 0.0045, and 0.0048, respectively) Notably, the P-Value for company size (X3) approaches zero, indicating its strong significance in the regression model Conversely, the P-Value for the growth rate of Gross Domestic Product (X6) is 0.7388, exceeding α = 0.1, suggesting that macroeconomic conditions do influence profitability changes, but this impact is not statistically significant Consequently, future analyses will focus on the significant variables—company size, firm growth rate, sales to capital employed, and receivable turnover—regarding their effects on company profitability, as measured by Return on Assets Among these, company size has the most substantial effect on profitability, followed by firm growth rate, sales to capital employed, and receivable turnover.
The independent variables in the regression model are significant in explaining the changes in the dependent variable, leading to the rejection of the null hypothesis H0 To enhance the model's accuracy, it is essential to eliminate the influence of other insignificant independent variables, including payable turnover, short-term debt over total debt, current ratio, GDP growth rate, inventory turnover, and interest.
Page 36 cover), the regression model is continued to run and each insignificant independent variables will be rejected based on the Probability value of these variables
To enhance the accuracy of the regression model, it is essential to systematically eliminate insignificant independent variables based on their Probability values The first variable to be removed is X2, or payable turnover, which has a P-value of 0.2289 Following the removal of X2, the regression model is re-estimated to reflect these changes.
Variable Coefficient Std Error t-Statistic Prob
CR -5.03E-06 3.58E-05 -0.140655 0.8883 GDP -0.293692 0.916114 -0.320584 0.7489 FGR -0.000107 3.70E-05 -2.895442 0.0042 SCE 0.080835 0.027447 2.945186 0.0036 INVENTORY_TURN
The analysis indicates that removing X2 PT, or Payable Turnover, has no effect on the new regression model, demonstrating its lack of influence on the overall results.
The regression model, X2, shows no new significant variables, with the Probability values of existing variables remaining largely unchanged Consequently, the next insignificant variable, X4 (Short-term debt over total debt), with a Probability value of 0.8419, will be excluded from the estimation The updated regression model will reflect these adjustments.
Variable Coefficient Std Error t-Statistic Prob
The removal of X4, or short-term debt, from the total debt in the regression model shows no significant impact, as the remaining variables' probability values remain largely unchanged Consequently, the analysis will proceed by eliminating the next insignificant independent variable, X5, which represents the current ratio.
Probability value is 0.9185 The re-estimation of the regression model is as follows:
Variable Coefficient Std Error t-Statistic Prob
RT -0.000861 0.000330 -2.608434 0.0098 SIZES 0.045516 0.012513 3.637568 0.0003 GDP -0.289890 0.909191 -0.318843 0.7502 FGR -0.000107 3.68E-05 -2.909521 0.0040 SCE 0.080099 0.026771 2.991995 0.0031 INVENTORY_TURN
The removal of variable X5, or the Current ratio, does not impact the new regression model, as it reveals no new significant variables and the probability values of existing variables remain largely unchanged The analysis proceeds with the elimination of the next insignificant independent variable, X6, which represents the Growth rate of Gross Domestic Products, exhibiting a probability value of 0.7388 The regression model is then re-estimated accordingly.
Variable Coefficient Std Error t-Statistic Prob
The removal of the variable X6, representing the Growth rate of Gross Domestic Products, does not impact the new regression model, indicating that no significant new variables emerge and the Probability values of existing variables remain largely unchanged The analysis proceeds with the elimination of the next insignificant independent variable, X9, which pertains to Inventory turnover, showing a Probability value of 0.6355 The updated regression model is then re-estimated accordingly.
Variable Coefficient Std Error t-Statistic Prob
RT -0.000864 0.000327 -2.640977 0.0089 SIZES 0.043216 0.010792 4.004587 0.0001 FGR -0.000109 3.65E-05 -2.975196 0.0033 SCE 0.079382 0.026651 2.978544 0.0032 INTEREST_COVE
The removal of X9, or Inventory Turnover, has no impact on the new regression model, indicating that its elimination does not introduce any significant variables.
The probability values of the existing variables remain relatively stable The analysis proceeds by eliminating the next insignificant independent variable, X10, or the Interest Coverage Ratio, which has a probability value of 0.3275 Consequently, the regression model is re-estimated.
Variable Coefficient Std Error t-Statistic Prob
The analysis indicates that the elimination of the Interest Coverage Ratio (X10) does not significantly impact the new regression model The statistical results show various coefficients and p-values, with notable values for SIZES (0.042145, p < 0.0001) and SCE (0.080389, p < 0.0029), while RT and FGR present less influence Overall, the findings suggest that the Interest Coverage Ratio's removal is inconsequential to the model's effectiveness.
Interest Coverage Ratio, the regression model does not have any new significant variables and the Probability value of the existed variables nearly remain unchanged
Eliminating insignificant independent variables from the regression model does not affect the overall estimation, indicating that no other insignificant variables influence the model The refined regression model focuses on significant independent variables, including company size, firm growth rate, sales to capital employed ratio, and receivable turnover (X3).
The variables X7, X9, and X1 are statistically significant at the α = 5% level, indicating that the growth rate of Gross Domestic Product and macroeconomic conditions influence changes in profitability However, the impact on the dependent variables is not statistically significant at the α = 0.1 level.
This study investigates the relationship between various independent variables—such as receivable turnover, payable turnover, company size, debt ratio, current ratio, GDP growth rate, inflation rate, firm growth rate, tangibility, operating profit margin, and short-term debt to total debt—and their collective impact on the profitability of real estate companies in Vietnam, as measured by Return on Assets The null hypotheses for this joint significance testing are established to assess these relationships.
Testing for autocorrelation
The econometric of regression model is as follow:
For testing the autocorrelation of the model, the Durbin Watson index will be analyzed based on the following regression model:
Variable Coefficient Std Error t-Statistic Prob
CR -3.66E-06 3.57E-05 -0.102479 0.9185 GDP -0.305569 0.915139 -0.333904 0.7388 FGR -0.000106 3.70E-05 -2.874887 0.0045 SCE 0.078348 0.027493 2.849722 0.0048 INVENTORY_TURN
The Durbin-Watson index is measured at 1.876470, which falls between 1 and 2, indicating an initial assessment that there is no autocorrelation present in this model This preliminary conclusion will be further evaluated through subsequent testing.
With a total of 265 observations and four independent variables, this analysis is conducted at a significance level of 5% Given the substantial number of observations, the testing methodology follows Bhargave (1983) The Durbin-Watson statistic, derived from Bhargave's approach, is presented as follows:
Figure 2: The Durbin-Watson value based on Bhargave (1983)
The Durbin Watson index is 1.876470, based on the initial estimation the DL = 1.935 and DU
= 1.947 Therefore 4-D U >D>D U and it can be concluded that there is no autocorrelation in the regression model and the outcome of the testing is consistent with the initial examination.
Testing for multicollinearity
Multicollinearity occurs when independent variables exhibit statistical correlations, impacting their probability values and coefficients When two independent variables are highly correlated, their probability values become misleading, leading to inaccurate significance testing for each variable The causes of multicollinearity can vary.
The data is insufficient for the regression model For several circumstances, multicollinearity can be solved by expanding the sample sizes and obtaining more data for the regression model
Dummy variables may be employed in an inaccurate way For instance, the study might not reject one category or include dummy variables for all categories
In regression analysis, an independent variable can be derived from the combination of two or more other independent variables For example, total liabilities serve as a composite variable that encompasses both short-term and long-term liabilities.
The independent variables in this study are categorized similarly and share the same monetary unit of measure, including non-current liabilities, short-term debt, net profit, long-term liabilities, and credit sales revenue Utilizing Eviews, the analysis confirmed four significant independent variables that affect the profitability of listed real estate companies in the Vietnam stock market: firm growth rate, receivable turnover, sales to capital employed, and company size The correlation among these variables will be assessed to identify multicollinearity, with a correlation value exceeding 0.8 indicating potential multicollinearity issues Several solutions can be implemented to address this concern.
- Remove highly correlated independent variables
- Obtain more data or expand the sample sizes
The regression model used for testing for multicollinearity is as follows:
Variable Coefficient Std Error t-Statistic Prob
The testing for phenomenon is as follows:
Figure 3: The result for testing of multicollinearity
The testing results indicate that multicollinearity among the variables is insignificant, as all correlations are below 0.8, suggesting that the variables are independent of one another The initial tests for significant variables and joint significance yielded appropriate results Furthermore, significant independent variables show that company size has negative correlations with receivable turnover, firm growth rate, and sales to capital employed.
Conclusion and recommendation
Conclusion
Between 2013 and 2017, Vietnam's real estate industry experienced rapid growth, fueled by the country's impressive economic recovery from the international financial crisis With the highest economic growth rate in Asia since 2012, Vietnam showcased strong macroeconomic indicators, including a high Gross Domestic Product growth rate and low inflation This economic upturn led to increased demand, supply, and prices in the real estate sector, as consumer borrowing continued to rise The market benefited from significant liquidity, supported by city governments, and a notable increase in flat purchases, alongside a decrease in real estate inventory As incomes rose and populations shifted towards urban areas, investors showed consistent interest in the real estate market, creating abundant opportunities for companies to develop and profit.
This dissertation offers valuable insights for investors and real estate firms by identifying key internal and external indicators that affect the profitability of listed real estate companies on the Vietnam Stock Market By analyzing financial indicators from 2013 to 2017, the study employs a regression model with panel data and fixed cross-section to examine the relationship between profitability and financial metrics of these companies during the specified period.
In 2017, profitability, measured by Return on Assets, served as the dependent variable in the regression model, while key financial indicators included Receivable turnover, Payable turnover, the ratio of short-term debt to total debt, company size, and current financial metrics.
Page 48 ratio, Firm Growth, Sales to capital employed ratio, inventory turnover, interest cover and macroeconomic factor measured by Growth rate of Gross Domestic Products The correlation between the independent variables and dependent variables has been tested on Eviews by testing for individual significance of statistics, joint significant testing, testing for autocorrelation and testing for multicollinearity based on the panel data of 53 listed real estate companies in Hanoi Stock Exchange and Ho Chi Minh Stock Exchange from 2013 to 2017 with the help of Eviews After performing the testing, generally the dissertation has fulfilled all the mentioned objectives in the previous chapter Specifically, the basic purposes in the dissertation are:
- To examine the correlation between profitability and related financial indicators
- To identify which financial factors have stronger impacts on profitability based on the outcome of coefficients from the tests
- To verify the suitable strategies to improve the profitability of the companies
The study identifies four significant financial indicators—company size, firm growth rate, receivable turnover, and sales to capital employed—at a significance level of α = 0.05 Notably, company size exhibits negative correlations with receivable turnover, firm growth rate, and sales to capital employed, highlighting the complex relationships among these financial metrics.
The analysis reveals that the size of the firm is the most influential financial indicator on profitability, with a probability value of 0.0004 Following closely is the firm growth rate, which ranks second with a probability value of 0.0045 Sales to capital employed comes in third, demonstrating significant effects on profitability with a probability value of 0.0048 Lastly, receivable turnover is identified as the fourth key indicator affecting profitability, with a probability value of 0.0089.
Page 49 purpose, several solutions will be proposed in the next part, Recommendations of this chapter
In summary, the dissertation successfully achieved its objectives by identifying four key determinants that significantly influence the profitability of listed real estate companies in Vietnam, measured by Return on Assets These determinants include company size, sales to capital employed, firm growth rate, and receivable turnover Among these, company size exhibits the strongest positive correlation with profitability, followed by sales to capital employed and receivable turnover, while the firm growth rate negatively impacts profitability Additionally, the chapter concludes with several recommendations based on these findings.
Recommendations
According to the initial findings of the dissertation as well as the results of the testing, several recommendations are proposed for the managers of the companies:
The first finding of the dissertation reveals a significant positive correlation between company size and profitability in the real estate sector Large real estate companies typically possess substantial inventory, significant accounts receivable, and considerable initial investments Consequently, it is essential for listed real estate firms to effectively leverage their financial resources for initial investments in construction projects, including houses, apartments, villas, and commercial buildings, as well as for acquiring land and managing human resources and office rentals.
Companies with a diverse product range and substantial inventory can attract a broad customer base, catering to various needs and enhancing sales revenue By expanding their offerings, businesses can quickly grow in size and reach Additionally, real estate companies should consider relaxing their credit selling policies, implementing effective marketing strategies, and improving customer service to boost credit sales and overall success.
Page 50 sales revenue and the sizes of the companies The companies also need to implement policies for installment payment and suitable discount for increasing Account Receivable
The dissertation's second finding reveals a significant positive correlation between sales to capital employed and company profitability, indicating that real estate firms should enhance their business efficiency by effectively utilizing total owner’s equity and long-term debt To achieve profitable investments from shareholders' initial capital, these companies must conduct accurate cash flow forecasts Furthermore, gaining in-depth insights into customer needs and developing tailored products can help avoid unnecessary product development and prevent wasted capital on ineffective sales and marketing campaigns.
The dissertation reveals a significant negative correlation between firm growth rate and profitability in real estate companies Consequently, these firms should prioritize steady and sustainable growth rather than aggressive expansion, as a measured approach is essential for enhancing long-term profitability rather than focusing solely on short-term gains.
The final finding of the dissertation reveals a significant negative correlation between receivable turnover and company profitability To enhance trade receivables, boost sales revenue, and ultimately improve profitability, real estate firms should consider relaxing their credit policies.
The government and the State Bank of Vietnam must adopt a stricter fiscal policy to lower inflation rates and ensure stable Gross Domestic Product (GDP) growth Additionally, it is essential to monitor and minimize government spending and public costs to enhance economic stability.
The dissertation analyzes financial data from 53 out of 56 listed real estate companies on the Vietnam Stock Market This selection is due to the lack of sufficient data for several companies, which only reported information for 2017 and 2018 Additionally, some companies do not publicly disclose their financial data, making it challenging to obtain the necessary information Consequently, only publicly available data from official company websites and the Vietnam Stock Market are utilized in the research.
The analysis focuses on a short timeframe of five years, from 2013 to 2018, within the rapidly developing country of Vietnam Consequently, the findings of this dissertation are likely applicable only to this specific country and period, reflecting the unique economic and industry conditions present during that time.
The dissertation reveals that the profitability of real estate companies is primarily influenced by their size, suggesting that results may vary for companies of different scales Furthermore, the study lacks a clear distinction between joint state companies, private firms, and foreign entities, indicating that addressing this gap could enhance the findings.
The current version of Eviews software does not support heteroskedasticity testing, leading to its assumption as non-existent in this dissertation However, future research utilizing panel data should enable the testing of heteroskedasticity.
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