Sample Population, Sampling Technique and Data Processing

Sample Size and Sampling Techniques for Objective Research 1
Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d

land and construction tax policies; Rebecca S. Rudnick and Richard K. Gordon (1996) [123] on property taxes; Chris Edwards (2006) on real estate tax reform in the US; Hong Zhang (2008) on assessment and reform

Property tax in China…

Information and trust

To limit risks in real estate investment, individual investors often pay attention to information and trust in investors (real estate developers), the reputation of architects, contractors and information about the projects they have participated in.

perform…

Factors of customs, traditions and tastes

To measure the herd mentality variable, we can rely on the scale suggestion of Kahneman and A.Tversky (1974, 1983) Factors of habit, tradition and taste (behavior), herding is the behavior that individuals establish based on observing the behavior of others.

the actions of others, or in other words, it is the act of imitating each other.

Real estate market development

Jane H. Malme and Joan M. Youngman (2001) also conducted a study on the development of real estate in the transitional economies of Central and Eastern European countries. This study was completed in collaboration with the WB and the Lincoln Institute of Land Policy, with specific research in the following countries: Poland; Estonia; Czech Republic; Republic of

Slovakia; Russian Federation; Armenia.

Maybe you are interested!

Source: author's synthesis and analysis

3.3.5. Sample population, sampling technique and data processing

Sample size and sampling method

To clarify the research problem before giving the questionnaire, the author directly interviewed and sent emails, consulted about 15 experts who are managers in the field of real estate and banking finance in Ho Chi Minh City. These experts are knowledgeable in the field of real estate and have a good grasp of banking operations (lending operations for the real estate sector). In order to have a basis for asking questions about factors affecting credit efficiency for the development of the real estate market in Ho Chi Minh City. Thereby, there are comments and assessments on the current status of credit efficiency for the development of the real estate market in Ho Chi Minh City, specifically considering the level of impact of factors on the development of the real estate market in Ho Chi Minh City. (The questionnaire is attached in Appendix 1). After interviewing and consulting about 15 experts, the author

Conduct official survey steps to collect official data to serve the research work of the thesis. The official survey was conducted in Ho Chi Minh City.

Based on the sample size selection table published by Krejcie &. Morgan (1970), the number of samples to ensure the reliability of quantitative analysis is 375 samples (Appendix 5). In addition, to ensure the representative value of commercial banks participating in lending investment in the real estate market in Ho Chi Minh City, the author selected 125 more research samples from real estate trading floors in Ho Chi Minh City. To ensure the number of samples for analysis, the topic conducted a survey and the number of survey forms issued was 500, of which (350 forms were for experts in real estate investment and trading enterprises and individuals with the need to invest, buy and sell (consume) real estate and 150 forms were for bank staff).

The thesis uses non-probability sampling method, specifically the convenience sampling method. Based on information collected from commercial banks, real estate investment companies and real estate trading floors. The author has selected research locations. The research samples are carried out conveniently for the survey subjects who are willing to provide information. However, the interview still pays attention to the distribution of customers by region. Therefore, the research sample is a convenient sample but takes into account the distribution of the overall research population.

Survey data processing method

Data processing method is carried out in two specific steps as follows:

- Entering survey data and processing raw data: the author used IBM SPSS16 software to enter data, then processed raw data: checking the reasonableness of the data, checking for missing data...

- Descriptive statistical analysis and test analysis: from the raw processed data, all analysis steps in the quantitative research method are processed through SPSS16 statistical analysis software.

3.4. CONCLUSION OF CHAPTER 3

In chapter 3, the thesis has completed the following main contents:

Firstly, chapter 3 of the thesis proposed a research method for the entire content of the thesis and proposed a data collection method for the entire content of the thesis.

Second, propose a research process and implementation steps for the survey of factors affecting the development of the real estate market in Ho Chi Minh City to achieve the research objectives of the topic, according to the research process in (Figure 3.1) of the thesis.

Third, chapter 3 of the thesis presented the quantitative research design for the thesis, specifically presenting the purpose and objects of the survey, research hypotheses and reliability of the scale. Thereby, measuring the variables used in the research model of the scale and the final content in chapter 3 presented the sample size, sampling techniques and data processing of the quantitative research of the thesis.

CHAPTER 4

RESEARCH RESULTS AND DISCUSSION

4.1. FORMATION AND DEVELOPMENT PROCESS OF HO CHI MINH CITY REAL ESTATE MARKET

4.1.1. Geographical location and economic and social potential of Ho Chi Minh City

Ho Chi Minh City has natural, economic and social conditions that are favorable for socio-economic development in general and real estate market development in particular:

4.1.1.1. Geographical location

Ho Chi Minh City borders Binh Duong province to the North, Tay Ninh province to the Northwest, Dong Nai province to the East and Northeast, Ba Ria - Vung Tau province to the Southeast, Long An and Tien Giang provinces to the West and Southwest.

Ho Chi Minh City is nearly 1,730 km from Hanoi by road, located at the international crossroads between maritime routes from North to South, from East to West, and is the center of Southeast Asia. The city center is 50 km from the East coast as the crow flies. This is a traffic hub connecting provinces in the region and an international gateway. With the largest port and airport system in the country, Saigon port has a capacity of 10 million tons/year. Tan Son Nhat International Airport with dozens of routes is only 7 km from the city center.

Ho Chi Minh City accounts for 0.6% of the country's area and 6.6% of its population, located in the Southern Key Economic Zone, is the economic center of the country, with a high economic growth rate. Ho Chi Minh City is the most dynamic economic activity, leading the country in economic growth rate. Economic development with a high growth rate has created a large GDP contribution to the country. The city's GDP accounts for 1/3 of the country's GDP.

4.1.1.2. Socio-economic potential

The 9th Congress of the Ho Chi Minh City Party Committee for the 2011-2015 term identified 6 breakthrough programs for the City's economy to grow in depth and Decision No. 2631/QD-TTg dated December 31, 2013 of the Prime Minister approving the master plan for socio-economic development of Ho Chi Minh City to 2020, with a vision to 2025 and agreed: The economic structure will shift towards services - industry - agriculture.

Regarding trade and services, the city is the largest import-export center in the country. The city's import-export turnover increasingly accounts for a large proportion of the total import-export turnover of the whole country. The commercial infrastructure has been strengthened with about 400 retail markets, 81 supermarkets, 18 shopping centers, and 3 wholesale markets. The service sector has grown beyond the plan, increasingly meeting the needs of production - business and serving the people's lives.

Ho Chi Minh City is the largest financial and banking center in Vietnam, the city leads the country in the number of banks and financial and credit relations. The revenue of the city's banking system accounts for about 1/3 of the total revenue of the whole country. Many modern credit services are applied, the payment network through ATM cards is expanded.

Ho Chi Minh City is increasingly asserting its role as a center of high-quality education and training. Regarding education, human resource training has developed in an increasing direction, the number of trainings is often higher year after year; training types are also diverse, and facilities are invested in.

The number of universities and colleges in the area has increased rapidly along with economic development. Ho Chi Minh City has a large number of scientific and technical staff (tens of thousands of people), who have been trained in a variety of ways from different sources, and are qualified to absorb advanced science and technology in the world. With the achievements, along with the strength of scientific and technical human resources, the City

The city has become a center for training, science and technology transfer in the region and the whole country.

4.1.2. The formation and development process of the real estate market in Ho Chi Minh City

The Land Law was issued in 1987, creating important conditions and legal foundation for the development of the real estate market. By 1993, the Land Law was amended, the real estate market in our country was aroused, formed and gradually developed.

According to this law, land is also the property of the entire people, managed by the State, but the State allocates land for long-term stable use to organizations, households and individuals. Households and individuals assigned land by the State have the right to convert, transfer, lease, inherit, mortgage, sublease, contribute capital to joint ventures, guarantee... with the value of land use rights. This is the first time that the concept of land and land use rights in our country has been recognized by law and considered as a "property". Accordingly, the real estate market is increasingly bustling, the demand for housing and land is increasing day by day.

Vietnam began to open its economy to attract foreign investment resources since 1992. In 1995, Vietnam normalized relations with the US and foreign investment capital began to flow into Vietnam, further increasing the demand for real estate. These were the reasons for the increase in real estate prices in Vietnam in general and Ho Chi Minh City in particular, starting a period of price fever, the price fever lasted from 1994 - 1996, initially in the two major cities of Hanoi and Ho Chi Minh City and spreading to all provinces and cities in the country, real estate prices increased at an unprecedented rate in the history of Vietnam.

The Asian financial and monetary crisis in 1997-1998, originating from the real estate crisis in Thailand, caused housing and land prices in Vietnam to continuously fall, leading to a freeze. However, by 1999, the market began to warm up and became bustling again in late 2000, and by 2001, along with the recovery and strong growth of the domestic economy.

After the new Land Law 2003 was passed with new regulations on controlling land supply and demand and the real estate market in 2003. By the end of 2007, due to the Bank's low-interest lending policy, people's concerns about inflation, along with the shift of some investors from the stock market to the real estate market, once again increased the demand for real estate in Vietnam in general and especially in Ho Chi Minh City, causing local prices of real estate projects and high-end apartments to increase. However, by March 2008, under the impact of the Government's inflation control policies such as limiting the real estate lending rate, high lending interest rates, etc., the real estate market quickly "cooled down".

In June 2008, the real estate market in Ho Chi Minh City decreased sharply in price and number of transactions, especially the price of luxury apartments decreased by 40% - 50%, while the price of land for individual houses and villas decreased by about 35%. Following the decline of the real estate market from mid-2008, by early 2009 the real estate market in Ho Chi Minh City was still in a frozen state, the prices of most real estate products decreased sharply, by mid-2009 the market showed signs of recovery, mainly with low-cost houses (house prices increased by about 12 - 30%), high-end apartments increased slightly (about 2 - 3%).

At the end of 2009, due to the sharp increase in gold prices and the information that the Government would continue to support interest rates until March 31, 2010, the real estate market in Ho Chi Minh City still increased slightly every month. However, in 2010, the State Bank raised the basic interest rate from 7% to 8%/year and then to 9%/year. The information that the Government would not continue to support interest rates caused the real estate market to stagnate, with only successful transactions mainly in projects with average and low prices.

4.1.3. Development status of Ho Chi Minh City real estate market from 2012 to 2016

4.1.3.1. General situation of the real estate market in Ho Chi Minh City

Current status of transactions in the real estate market

Table 4.1: Number of trading floors and personnel working in the real estate sector

Unit: thousand people

Indicators

Quantity
Human resources	Human resources in real estate	36,000
	Real estate broker	17,000
	Real estate valuation staff	9,500
	Floor management and operation staff real estate transaction	9,500
	Nationwide	669
Real estate floor number	In which: Hanoi	300
Real estate floor number	Ho Chi Minh City	369

Source: Report of the Ministry of Construction on the real estate sector in 2016

The 2006 Law on Real Estate Business regulates the establishment and operation of real estate trading floors, aiming to contribute to market transparency, create a level playing field for real estate investors and bring many benefits to people when accessing real estate products, especially housing. This is an important channel for providing market information to investors, consumers and state management agencies, and at the same time, the operation of trading floors has brought a new face to the Vietnamese real estate market.

The number of trading floors has grown rapidly but is inversely proportional to the volume of transactions. What these floors contribute to the current real estate market is only in terms of quantity. According to the Department of Housing and Real Estate Market Management, the total number of real estate transactions through the floors nationwide reached 3,679 transactions, of which the Northern region (mainly Hanoi) had 327 transactions; the Southern region had 3,352 transactions. It is estimated that the rate of real estate transactions through the trading floor system accounts for only 15% of the total market transactions. Thus, the current Vietnamese real estate market is considered to be less transparent with a very high rate of underground transactions. This is also the main reason why our country's real estate market is slow to develop, manipulated by speculation, "price manipulation" causing real estate "fever" affecting the national economy as a whole.

4.1.3.2. Effective use of real estate in Ho Chi Minh City

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