Factors influencing apartment price in hanoi valuation by the hedonic pricing model in the first quarter of 2024

BANKING ACADEMY OF VIETNAM FACULTY OF FINANCE GRADUATION THESIS FACTORS INFLUENCING APARTMENT PRICE IN HANOI: VALUATION BY THE HEDONIC PRICING MODEL IN THE FIRST QUARTER OF 2024 Stud

RELEVANCE OF THE THESIS

The year 2023 was a slowing-down time for the global economy The income of most people was affected by recession, causing a decrease in the number of transactions as well as the decision to invest, bringing the real estate market through a challenging period In Vietnam, signs of difficulties began to flare up at the end of 2022, leaving troubles covering the entire market in 2023 Especially, in only one year, the real estate market had witnessed the departure and closure of corporations due to out of capital along with the burden of bonds due laid on their shoulders More positive signals had been sent in 2024, though there would be differentiation between market segments, the pessimism and suspicion of investors had gradually decreased, proven in the exponential growth of volume of successful transactions Since the beginning of the year, new policies have been issued, along with cooling interest rates, and legal problems are resolved in a clearer schedule However, due to many reasons, the lack of supply has not been able to meet people's needs, leading to a sharp increase in housing real estate prices

Within this expansive sector, one segment stands out for its enduring significance and adaptability, now being demanded significantly: apartments As urbanization continues to surge and housing preferences change, apartments are promising to meet the demand for flexible, convenient, and community-centric living spaces That is one of the reasons apartments are least affected by the recession and still record an upward trend in price In fact, scarce supply and asymmetry in the segment has caused apartments in Hanoi, the capital of Vietnam, to show signs of strong price increases, even raising suspicions of price inflation

It needs further study to analyze the determinants of this uptrend Apartment prices, the same as every other real estate price, are driven by supply and demand Estimating price, or valuation, in this field is a necessary activity which provides input to interpret the true value of a property, saving transaction participants from overpriced and underpriced There are currently many studies related to this field, especially those that apply the HPM to quantify factors affecting real estate prices HPM is a multivariate regression model, starting from the idea that several explanatory variables sorted into groups can be used to predict the value of a dependent variable, which in this case is the value of an apartment, thereby quantifying the impact of groups

In this thesis, based on the experience of applying HPM in previous research, a multivariate regression model is built to estimate the influence of factors on the value of apartments in Hanoi Hanoi apartments are chosen as the subject due to the large size of market with representative traits, besides the suitability regarding the author's background and knowledge Data for the thesis consists in the transaction prices of sample apartments in Hanoi in the first three months of 2024, and incorporates the following variables: district population density, distance from apartment to airport, center of city and nearest hospital, floor space, number of room and balcony, apartment floor, building age, investor’s experience, and quality of the building

The purpose mentioned above could be achieved by answering three questions:

- What is the current statement of the real estate market of Vietnam, and the situation of the Hanoi apartment market in particular?

- What are the factors influencing the value of apartments in Hanoi?

- What is the appropriate model to quantify the factors influencing apartment prices in Hanoi?

RESEARCH OBJECTIVE

General research objective: Analyze the correlation between apartment selling prices in Hanoi and the influencing factors, hence giving proposals for the current situation

Specific objectives: Systematize theory and former research related to the topic; Identify factors influencing apartment price; Determine and completing the correlation model between apartment price and influencing factors, hence quantifying the level of impact from each in the case of Hanoi; Proposing recommendations to help the participants taking part in apartment transaction

The result model and conclusions achieved from this thesis would be useful for the participants taking part in apartment transactions Not only would it help buyers determine the real value of the apartment, thereby considering financial resources to make appropriate choices, but it would also provide support to the investors to understand the needs and tastes of customers and detect what are the unreasonable factors pushing the price up for other institutions to write out solutions.

RESEARCH SUBJECT AND SCOPE

The research subject is apartments from the affordable to high-end segment (including the factors influencing their prices)

The scope of the research is in Hanoi, specifically in 12 districts such as: Bac Tu Liem, Cau Giay, Gia Lam, Ha Dong, Hai Ba Trung, Hoai Duc, Hoang Mai, Long Bien, Nam Tu Liem, Tay Ho, Thach That, Thanh Xuan The research is processed during the period from January to March, the first quarter of 2024.

RESEARCH METHODOLOGY

To serve the research topic, data of 219 apartments from 17 projects covering 12 districts of Hanoi is collected from valuation certificates of VNG Value Co ltd Further information relating to distance from apartments to objectives is taken from Google Maps, and the information relating to investors is taken from their reports and other websites

Through collected data, the thesis chose quantitative as the research method to determine the influence of factors on apartment prices in Hanoi By using HPM along with OLS, the relationship between variables would be modelized, furthermore declaring the reasons behind the statistics

The research would use specialized statistical software Stata 14 to analyze the collected data Data cleaning operations and the test of correlation, multicollinearity, random error variance are also performed on this software.

STRUCTURE OF THE THESIS

The structure of the thesis includes four chapters:

- Chapter 1: Literature review and theoretical framework

- Chapter 4: Contribution of results and recommendations

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

LITERATURE REVIEW

In contemporary times, residential real estates have evolved beyond merely serving as living places; they now constitute a significant portion of households' financial assets Particularly in developed nations, real estate holdings represent the primary component of private wealth Consequently, fluctuations in property values profoundly impact households’ consumption patterns and opportunities for saving (Case, B., Clapp, J., Dubin, R., & Rodriguez, M., 2004) Given this, house prices garner considerable attention from various stakeholders such as real estate developers, financial institutions, policymakers as well as current and prospective owners (Schulz, R., & Werwatz, A.,

2004) Numerous methodologies have been employed to assess the value of real estate, particularly in the context of apartments and housing Pagourtzi et al (2003) categorized these methodologies into two main groups: traditional and advanced Traditionally, most valuation approaches rely on comparisons, direct or through regression models, to determine market worth Within this realm, traditional valuation methods include comparable method, income method, costs method, multiple regression, and stepwise regression methods Conversely, “advanced” valuation techniques seek to emulate market participants’ decision-making processes to estimate transaction points, encompassing ANN, HPM, and ARIMA approaches

Over the past three decades, HPM has gradually become popular in real estate valuation Wen, H Z., Sheng-hua, J., & Xiao-yu, G (2005) analyzed housing prices in Hangzhou, China by choosing 18 characteristics as independent variables (focusing floor area, proximity to West Lake, interior conditions, distance to the city center, quality of transportation, and security), and setting up a linear HPM The model was tested with

2473 housing samples and field survey data of 290 housing communities, therefore found out that 14 out of 18 characteristics, classified into 5 groups, had significant influence on housing price

Kim, A M (2007) explored the impact of legal aspects on apartment prices in Hanoi and Ho Chi Minh city Using ethnographic fieldwork and HPM, the study shows that land and housing markets price tenure ambiguity differently in two cities The different price structures indicate the importance of norms, as socially constructed by local political interests and culture, in the efficacy of land title regularization The main finding of this model concerns how the two markets value “legal papers” over official- owning rights; the coefficient is negative in Hanoi and positive in Ho Chi Minh city

One year later, Kunovac, D., Đozović, E., Lukinić, G., & Pufnik, A (2008) analyzed in a quantity method that the real estate price in Croatia was affected by two groups of factors: location and quality By using HPM, this research presented a real estate price index, in addition to the price of the characteristics of real estate and provides for the estimation of pure change in the price between two points in time This paper also strives to answer several interesting questions such as: has the sharp increase of real estate prices resulted in the purchase of smaller dwellings, how has the growth of real estate prices affected real demand for housing loans, and can databases containing the asking prices of real estate be used to create a reliable real estate price index?

Cebula, R J (2009) conducted research on house prices in Savannah, Georgia This study applies HPM to the housing market of Savannah with the database of 2,888 single-family homes in the period 2000 - 2005, in which 591 are in the Historic District The model reveals it is positively affected by the number of bathrooms, fireplaces, bedrooms, stories in structure, garage car spaces, square feet of finished living space, the presence of a deck, a private courtyard, a pool and/or hot tub, an exterior construction of brick or stucco, the presence of an underground sprinkler system, and whether the house was new Compared to former research, Richard proved that housing prices tended to increase regularly in May and July, which was outstanding

As said above, HPM is not the only tool to be favored by researchers Kain, J F.,

& Quigley, J M (1970), based on an extensive sample of individual dwelling units, estimated the market value of specific aspects of the bundles of residential services Quantitative estimates were obtained by regressing market price on measures of the qualitative and quantitative dimensions of the housing bundle The measures of residential quality associated with individual dwelling units were obtained by using factor analysis to aggregate some 39 indexes of the quality of narrowly defined aspects of the dwelling units, including an interesting factor: building age

Selim, S (2008) utilized research data from Turkey to identify factors influencing house prices, including location, housing type, and number of rooms Because of potential non-linearity in the HPM functions, ANN is employed in this study as an alternative method By comparing the prediction performance between the HPM and ANN, this study demonstrates that ANN can be a better alternative for prediction of the house prices in Turkey

Miles, D (2012) published research of population density, house prices and mortgage design This study develops a model of the housing market with the property that the major determinant of house price rises relative to incomes is the evolution of population density The model implies that if population density is on an upward trajectory, rises in population and in incomes increasingly generate price responses and diminishing rises in the stock of housing Rises in population density make high and rising levels of house prices likely

Bełej, M., Cellmer, R., & Głuszak, M (2020) worked on the impact of airport proximity on single-family house prices The research authors analyzed airports’ impact on the prices of single-family homes located in LUA of Gdańsk Lech Walesa Airport and the Warsaw Chopin Airport in Poland with the research time of 2013 - 2017 Therefore, two markets in the vicinity of airports were examined, one is in an LUA which is closer to the airport, and the other market is outside the LUA They used a time series analysis, a classic multiple regression model, a spatial autoregressive model, and geographically weighted regression models, thereby proving prices for homes within the LUA are lower than those outside So as long as it is outside the range of airport noise, the closer the distance to the airport, the more beneficial it is for house prices

In Vietnam, real estate prices also receive deep attention, especially in the field of using HPM on valuation Van T.T and Giang N.T (2011) applied HPM to analyze the factors influencing real estate prices in Ho Chi Minh city The study is based on survey data of 6 main roads dividing into two main impact groups and analyzing through a logit regression model Location-related variables include location, distance to center, street surface, alley width, and business purpose, while quality-related ones include land area, house, number of floors, terrace, shape The results of this research showed that the location factor has the greatest influence

The same as former research, location is the most influential group in the research of Vinh N.N (2012) This study has identified 18 factors affecting real estate value, including 5 groups: location (infrastructure, security, environment, traffic, landscape), quality (shape, size, architecture, construction), economic (potential benefits, business benefits, property rights), use status (usage functions, interior decoration), non-real estate (attached real estate attachment, policy, intangible value)

After that, there was also thoughtful research of apartment prices in District 2 of

Ho Chi Minh city, written by Nhai H.T (2015) The study performed quantitative regression analysis using HPM based on survey data collected from 130 apartment samples from 20 apartment buildings that were opened for sale and had successful transactions in District 2 being affected by 12 independent variables Research results show that there are only 7 variables that affect the price including: Distance to the center, floor location, number of toilets, apartment location on the floor, surrounding environment, utility services, and investor reputation

Phong N.T (2015) conducted similar research in a larger area, including District

2 and District 7 of Ho Chi Minh City Applying HPM, the research was conducted on a data set of 680 survey samples of apartments in two price segments, from 15 to 25 million VND/m2 (341 samples), and over 25 million VND/m2 (339 samples) The results determined that there are 9 factors affecting apartment prices including floor area, security, social infrastructure, utility services, distance to the center, landscape environment, payment method, location floors, investor reputation In which the investor reputation variable has the strongest influence

In the case of Hanoi, the research of Dao, B & Lan Linh, T H (2016) also constructed a HPM to estimate the value of apartments Data of 176 apartments from 18 projects covering four districts is collected In the model of Total price, structural, characteristic, and locational variables can explain up to 96.80% of the dependent variable In particular, these variables are floor number, size of apartment, number of bathrooms, light direction, hand-over time, social housing project, distance to city center, distance to nearest market, distance to nearest high school… The second model of Price per square meter can statistically explain up to 94.66% of the variance in unit price of an apartment in Hanoi

THEORETICAL FRAMEWORK

When land refers to the earth’s surface down to the center and up to the airspace, real estate is defined as the land and any permanent structures, such as homes, or improvements attached to the land, whether natural or man-made Real estate is a form of real property, and any additions or changes to the land that affect the property’s value are called an improvement

One of the most orthodox ways of classifying property also originates from ancient Roman law, which is recognized in the Civil Codes of many countries around the world Property is divided into movable property and immovable property (real estate) Land as defined must be understood as a separate piece of land, which attached to it is the right to use the land According to Article 107, Civil Code 2015 of Vietnam (Immovable property and moveable property), real estate includes land; Houses and structures attached to land; Other property attached to land, houses and structures; Other property as provided by law According to the provisions of Vietnamese law, real estates are determined on the map, as well as on the ground, which means that land as real estate must include the land with the space on the ground and the block depth below ground Housing and construction work attached to land are considered derivative real estate from the original Other assets attached to houses and construction works are also considered real estate.

Real estate can be classified into 5 main types:

Residential real estate: Any property used for residential purposes (single-family or multifamily homes, condominiums, cooperatives, duplexes, townhouses…)

Commercial real estate: Any property used exclusively for business purposes (apartment complexes, gas stations, grocery stores, hospitals, hotels, offices, parking facilities, restaurants, shopping centers, stores, and theaters…)

Industrial real estate: Any property used for manufacturing, production, distribution, storage, and research and development

Land: Includes undeveloped property, vacant land, and agricultural lands (farms, orchards, ranches, and timberland)

Special purpose real estate: Property used by the public (cemeteries, government buildings, libraries, parks, places of worship, and schools)

Real estate has seven specific characteristics related to its economic impact or physical nature Regarding economic characteristics, the main features are scarcity, investment permanence, improvements, location

Scarcity: Land, being a finite resource, is subject to limitations in its supply

Consequently, there often exists a disjunction between the demand for land and its actual availability, imbuing real estate with considerable value and desirability leading to an escalation in prices

Improvements: Constructing enhancements on a parcel of land can influence not only its own value and utility but also the properties of neighboring parcels and the wider community Enhancements encompass any alterations made to the land or structures on it Moreover, they can exert a substantial influence on the surrounding environment, including boosting property values and enticing new commercial endeavors

Location: The geographical placement of a property can exert a profound impact on its value and allure, caused by the preference of people over certain geographic areas Additionally, location plays a pivotal role in the prosperity of commercial and industrial properties, with enterprises often seeking sites that offer customer convenience or strategic advantages, such as proximity to suppliers or competitors

The permanence of investment: Real estate is inherently a long-haul investment, and its permanence as an investment vehicle stems from its stability and enduring nature Furthermore, real estate investments often yield dependable cash flow via rental or lease payments, rendering it an appealing choice for investors seeking a dependable and consistent income stream

Regarding physical nature, the main features are uniqueness, immobility, and indestructibility

Immobility: The immutability attribute pertains to the inherent inability to relocate real estate This lack of mobility may diminish the liquidity of real estate investments relative to other asset classes, as swiftly selling or divesting a property can prove challenging Nevertheless, the immutability of real estate can also confer advantages, fostering a perception of stability and enduring presence that appeals to numerous investors and tenants

Indestructibility: A defining feature of real estate is its indestructibility While buildings and enhancements may necessitate upkeep, the underlying land endures indefinitely This enduring nature renders real estate a resilient asset, capable of weathering fluctuations in economic and market conditions

Uniqueness: It is impossible for any two pieces of land to be identical since each parcel of real estate possesses its own distinctiveness This individuality presents challenges in accurately assessing property values and comparing different properties

Since the 6th century BC, Romans have employed the notion of “apartment” under the term “condominium”, where “con” signifies “common property” and

“dominium” denotes ownership Historically, Romans are also recognized as trailblazers in constructing apartment buildings worldwide An apartment refers to a residential unit situated within a residential building or occasionally within a house as a separate dwelling, featuring its own entrance, bathroom, and kitchen facilities These units are typically single-story Although the building itself may be classified as commercial property based on the number of units it contains, the individual apartments constitute residential real estate

In Vietnam, based on the provisions of Article 3, 2014 Housing Law, it can be understood that apartment building means any multi-story building which has multiple apartments, public stairs, hallways, private areas, common areas and common infrastructural works for organizations, households, or individuals, including apartment buildings for residential use and mixed-use buildings for both business and residential purposes.

Apartments could be sorted differently depending on countries, definition, and characteristics Wholly, there are three types of apartments that are most popular

Rentals: Rental apartments encompass units situated in multifamily residences, high-rise structures, or various other forms of multi-unit buildings Tenants commonly enter into lease agreements, frequently spanning one to two years

Condos: Condos share numerous similarities with rental apartments, often featuring comparable amenities like well-kept outdoor areas and shared walls Condos are units within buildings owned by individuals rather than being leased, and their owners are responsible for maintaining their units, typically through monthly carrying charges and special assessments, along with property taxes

Co-ops: Co-ops are units within buildings; however, these units are collectively owned by a housing cooperative, which is a corporate association of the building’s residents Instead of purchasing a specific unit, individuals buy shares in this resident-owned housing corporation, granting them the right to reside at the property, with their shares representing their apartment Co-op owners pay monthly maintenance fees, which can be partially tax deductible as they encompass the building’s mortgage payments and property taxes without paying individual property one

In Vietnam, according to Circular 31/2016/TT-BXD, apartment buildings are classified into 3 classes: class A, class B, and class C based on 04 groups of criteria including: Planning-architecture; Technical equipment system; Services, social infrastructure, and Quality, management, and operations

1.2.2.2 Features of apartment and apartment market

Regarding the characteristics of apartments, there are bright sides compared to other forms of real estate First is affordability, or flexibility In the case of renting an apartment, it is less of a commitment than owning a house, allowing people to move to another apartment complex or another part of the city or the country within a year or two Second is the access to multiple amenities with the communities offering on-site fitness centers, swimming pools, sports courts, parks… Another advantage of having an apartment is the already moved-in units, which bonuses thorough cleaning, or often brand-new Moreover, condo fees and maintenance fees for co-ops, though can and often rise each year, are generally fixed costs Maintaining would not be a hassle since all maintenance is conducted by the landlord

SUMMARY

In this chapter, the author has summarized the results of domestic and foreign experimental studies related to the topic In addition, the theoretical basis section presented theoretical foundations related to real estate, apartments and the characteristics and classification of these objects Besides, specific valuation methods have been presented based on the Vietnam Valuation Standard and former research Finally, the factors that affect the price of a real estate, specifically an apartment, have been clearly outlined along their impacts.

DATA AND METHODOLOGY

RESEARCH PROCESS

In the planning stage of the thesis, the author has summarized the important steps:

Collect data from VNG Value and other means

The data used in this thesis is secondary data Data about prices, apartments related structural characteristics and information is collected through the appraisal certificate of VNG Valuation Co., Ltd The data is compiled from about 55 sets of valuation certificates from January to March 2024 (i.e the first quarter of 2024)

After having the detailed address of the apartment, the author collected information on location data Population density rate is based on Hanoi Statistical Yearbook 2022, which was summarized by the Maison Office Geographic distance data are extracted from Google Maps By entering the apartment address, the distance from the apartment to the nearest hospital is automatically suggested by Google Maps within a radius of 5 to 10km The distance to the airport is determined to be the nearest route from the apartment building to Noi Bai International Airport, Phu Minh Commune, Soc Son District, Hanoi The distance to the city center is determined to be the closest route from the apartment building to Hoan Kiem Lake, Hoan Kiem District, Hanoi

Data related to apartment investors, such as the founding year is collected in homepages or websites of each business Using the project names, the information of project completion date, and quality of the project was declared on the investor's official homepage, and with additional help from Cafeland.vn

According to Green (1991), quoted in Tho N.D (2014), the sample size to be collected is calculated empirically:

…where n is the sample size, x is the number of independent variables of the model

Therefore, under allowable conditions, the sample size for the thesis was determined to be at least 138 samples to ensure representativeness After collecting data, the sample size includes 235 apartments spread across 12 districts in Hanoi

In terms of data processing, all response data is cleaned and encrypted on Excel, then processed with the support of Stata 14 software

When encoding the data collected from the valuation certificate, it is inevitable to enter incorrectly or error information coming from the website Therefore, checking and cleaning data is necessary to detect inaccurate information and invalid samples which cannot be used for regression At the same time, this step includes arranging data and making statistics, briefly describing the collected data

First, after entering the necessary data into Excel (including address, price, floor space, number of rooms and balconies, apartment floor, distance to nearest hospital, center of city, airport, project completion date, building quality, investor founding year), samples with missing information will be eliminated to avoid errors in running the model Inaccurate information may come from missing full access to valuation certificates, or the Google Maps software being unable to determine the actual location of the apartment Since the data collected is more than the minimum required, it is understandable to discard the sample instead of using information with low accuracy

Then, the author proceeds to create calculation functions based on the collected data With the building age variable, the data will be calculated by subtracting the year of construction completion from 2024 Similarly, the investor experience variable will subtract the year 2024 from the year of establishment of the investment enterprise The

“if” function will help encode and sort qualitative variables such as district location by population density, apartment floor location, and building quality Specifically, the district location variable ranked by decreasing population density will be numbered according to table 2.1 (Simplified to 12 districts in the study) The apartment floor variable is divided into low floor, middle floor, high floor, numbered from 1 to 3 The building quality variable will be divided into third class, second class, and first class, numbered from 1 to 3

Table 2.1 District in Hanoi ranking on ascending population density

The most important part of data cleaning is to ensure the correct data format After all data is encoded in numeric format, it is necessary to ensure “Number” format for all data The accuracy of this will be proven when uploaded to Stata 14 software, correctly formatted data will be black, and formatting errors will be red Variables such as population density by district, apartment floor location, and apartment quality are coded qualitative variables, in the form of discrete variables The remaining data are measured and calculated as continuous random variables

In data analysis and statistics, correlation analysis is a way to measure the relationship between two or more variables, from which it will be decided whether it is necessary to analyze the correlation between these variables, and in-depth analysis can be conducted Checking for a strong linear correlation between the dependent variable and the independent variables is necessary because the condition for regression is to first be correlated

The Pearson correlation coefficient indicates the degree of correlation between variables in the model According to Nham H.N (2008), the correlation coefficient (r) takes a value between -1 and 1 A linear correlation will take a positive r value The closer the r index is to 1, the stronger the relationship, yet it is optimal when less than 0.8 because multicollinearity phenomenon would partly be ensured not to appear

The author uses OLS regression method to build a regression model to determine the coefficients affecting apartment prices in Hanoi According to Nham H.N (2008), the regression model could be written as:

In which β0 is the free coefficient, while βj (j = 1,2…k) is the individual regression coefficient Some assumptions are applied:

- Xi values are predetermined and not random quantities

- The expectation or arithmetic mean of the errors is zero

- The errors have equal variance

- The errors are not correlated

- The errors are independent of the explanatory variable Xi

- Random quantity has a normal distribution

In the multiple regression model, it is assumed that there is no collinearity among the explanatory variables of the model, that the explanatory variables are not correlated with each other Models with multicollinearity can make the variance and covariance of OLS estimates larger, with wide confidence intervals, and become very sensitive to small changes in the data, thereby causing the regression coefficient to be opposite to the expected effect

This phenomenon is detected when the R squared coefficient is greater than the t ratio, or the correlation coefficient between pairs of explanatory variables is high, or the variance exaggeration molecule VIF is used If VIF exceeds 10, the variable is considered highly multicollinear and must be corrected by removing the variable For HPM in particular, multicollinearity testing is the most important step because most independent variables in the same group will be related to each other

Using a model with autocorrelation will make the OLS estimates, while still linear and unbiased, no longer effective However, autocorrelation usually occurs only when using time series data, which means the correlation of the signal having a lagged copy of itself

The data set used in the study was randomly collected over a short period of time from January to March 2024 through valuation certificates For the difficulty to access the specific time in the deed as well as comparing prices over a three-month period remains accurate, the author has eliminated the time element from the research data and does not consider this a form of time series data Therefore, testing for autocorrelation in this study is not necessary

RECOMMENDED RESEARCH MODEL AND ASSUMPTIONS

Ln(P) = β0 + β1L_PD + β2L_CEN + β3L_AIR + β4L_HOS + β5S_FS + β6S_NoR + β7S_NoB + β8S_AF + β9C_BA + β10C_IE + β11C_Q + ε

PD: population density of each district This is a qualitative variable, ranked in ascending order from 1 to 12 based on population density (Table 2.1)

CEN: distance to city center, a quantitative variable with unit measurement of km

AIR: distance to airport (specifically Noi Bai airport), a quantitative variable with unit measurement of km

HOS: distance to nearest hospital, a quantitative variable with unit measurement of km

FS: floor space of the apartment, a quantitative variable with unit measurement of m 2

NoR: number of rooms of the apartment, a quantitative variable with unit measurement of unit

NoB: number of balconies or logia, a quantitative variable with unit measurement of the unit

AF: apartment floor location, a qualitative variable, ranked in ascending order from 1 to 3 based on floor height of low floor, middle floor, and high floor Characteristic group:

BA: Building age, a quantitative variable with unit measurement of year

IE: investor experience, a quantitative variable with unit measurement of year

Q: Quality of the apartment, a qualitative variable, ranked in ascending order from

1 to 3 based on floor height of third class, second class, and first class

General research model is shortened as below:

P: Price of the apartment in research period βi,j,k,m: Regression coefficients β0: Intercept coefficient

C: Group of characteristics factors ε: Error of the model

2.2.2 Description of components in the research model

Based on previous domestic and foreign research, within the scope of the research topic and the data collectable, the author identifies factors the impact on apartment prices in Hanoi includes 11 factors In addition, legal factors have been considered and eliminated on the basis that all apartment samples within the research scope have certificates of land-use rights and ownership of house and other property on land The detail would be made according to the following table:

Table 2.2 Variables of the recommended research model

Total price P Total price stated by VNG Valuation Co ltd

No Category Variable Name Predicted effect

L_PD + Population density of the district

L_CEN - Distance to city center km (Quantitative)

3 Airport L_AIR - Distance to airport km

4 Hospital L_HOS - Distance to nearest hospital km (Quantitative)

5 Structural Floor space S_FS + The size of the apartment m 2 (Quantitative)

S_NoR + The number of rooms in the apartment

S_NoB + The number of balconies in the apartment

S_AF + The floor at which the apartment situates

C_BA - The number of years from the apartment was handed out until now

C_IE + The number of active years of project owner

11 Quality C_Q + The quality of apartment

Hypothesis H1: Population density (PD) has a positive (+) influence on apartment prices

This is closely related to supply and demand Cities with greater population density are likely to result in higher apartment prices, while cities with a smaller population typically have lower ones, this goes the same to districts As the population increases, demand for housing real estate concentrates in that area Supply sources in housing real estate cannot match the pace of increasing demand, leading to increased prices

Hypothesis H2: Distance to center of city (CEN) has a negative (-) influence on apartment prices

The distance from the city center can have a significant impact on apartment prices Generally, apartments closer to the city center tend to be more expensive, while those located further away are usually more affordable Apartments located near the city center often provide easier access to public transportation, employment opportunities, shopping, and entertainment options Besides, in most cities, land in the city center is scarce and expensive, which increases the cost of constructing and maintaining properties

Hypothesis H3: Distance to airport (AIR) has a negative (-) influence on apartment prices

Most people consider living next to an airport undesirable due to the noise from airplanes, and to a lesser extent, the traffic generated by the airport So, all things being equal, housing prices around airports are usually less However, if a reasonable distance is maintained (outside LUA), an apartment further from the airport will be evaluated lower than a closer one This will be convenient for travel needs, especially the need to travel by plane on business trips, especially when needing a quick travel route

Hypothesis H4: Distance to nearest hospital (HOS) has a negative (-) influence on apartment prices

In this case, the study did not include commune and ward health stations, but only calculated the distance to major hospitals In fact, living too close to a hospital is sometimes a lower priority because of noise and congestion However, according to most research, apartments that are too far from the hospital are considered less valuable than apartments that are closer, because everyone needs to ensure health care and take advantage of medical benefits

Hypothesis H5: Floor space (FS) has a positive (+) influence on apartment prices

An apartment usually has two types of floor area and clear area In the scope of the research topic, the author uses floor area According to the results of previous studies, area usually affects price according to the law of normal distribution, increasing area to a certain level will cause price per unit area to decrease Generally, the author expects the area variable to have an impact in the same direction as price

Hypothesis H6: Number of rooms (NoR) has a positive (+) influence on apartment prices

In the realm of real estate, apartments with more rooms command higher prices since these units offer increased square footage, providing residents with enhanced comfort and functionality The versatility of multiple rooms allows for diverse uses, catering to various lifestyle needs

Hypothesis H7: Number of balconies/logia (NoB) has a positive (+) influence on apartment prices

In many cases, apartments with a balcony tend to be more expensive than those without Balconies are considered desirable features that can add value to a property, as they provide outdoor space and often enhance the overall living experience, especially in districts having signature views as Tay Ho Building a balcony costs money, which is also the reason for increasing apartment prices

Hypothesis H8: Number of floors which apartment locates (AF) has a positive (+) influence on apartment prices

This is considered a common preference and may also be the psychology of people liking to live on high floors, leading to investors selling apartments on higher floors that are more expensive The higher the apartment floor, the cooler and fresher the wind will be than the lower floor, making the apartment airy and eliminating bad odors On the contrary, the lower floors will be hot and there will not be good wind circulation High- rise apartments are expensive because of their wide and sweeping views of the city

Hypothesis H9: Building age (BA) has a negative (-) influence on apartment prices

A property's age can significantly affect its market value New buildings are mostly more expensive since they are equipped with modern amenities and updated infrastructure On the contrary, most old buildings, even with regular maintenance, are easily outdated in design and preference

Hypothesis H10: Year of investor experience (IE) has a positive (+) influence on apartment prices

Experienced investors wield a significant influence on apartment prices, leveraging their years of expertise in the real estate market to make informed decisions Their seasoned understanding of market trends, valuation methods, and risk management strategies enables them to identify properties with strong growth potential and invest accordingly The reputation and track record of experienced investors also inspire confidence among market participants, driving increased demand and upward pressure on apartment prices

Hypothesis H11: Quality of the apartment (Q) has a positive (+) influence on apartment price

First class apartments will have full amenities, be more luxurious, and gradually decrease with the remaining levels The more convenient, the more costs that investors had to pour into the project, which is reasonable if the apartment prices would rise The lower quality apartments do not have services and such amenities, so the price would decrease.

SUMMARY

In chapter 3, the author introduces the research process, data collection methods, and quantitative research content Besides, the author further presents a detailed research model, explains the variables, and describes the independent variables Hypotheses related to the direction of impact are also predicted.

RESEARCH RESULT

THE STATEMENT OF HOUSING REAL ESTATE MARKET

3.1.1 Housing real estate market in Vietnam

According to Chart 3.1, published by GSO, GDP of Vietnam in 2023 is estimated to increase by 5.1%, lower than the 8% increase in 2022 due to global economic weakness Total registered foreign investment capital reached 37 billion USD, an increase of 32% y-o-y Implemented capital of foreign investment projects is estimated at about 23 billion USD, a rise of 4% compared to 2022, the highest level in the past 5 years The National Assembly set a GDP growth target of 6.0 - 6.5% for 2024, determining priorities for economic growth and balance between factors of the economy Although there are bright spots compared to the gloomy world economy, the difficulties and challenges are inevitable and undeniable towards the Vietnam economy

Chart 3.1 GDP growth of Vietnam (%)

Besides large industries such as services and tourism, the real estate market syncs closely, and is also seen as one of the pillars of the Vietnam economy, helping to concentrate resources and create fixed assets for the country The development of the real estate market creates growth momentum for related industries, meeting the needs for accommodation, urban development, and tourism According to the GSO, the average contribution of the construction and real estate industry to the total GDP of Vietnam in recent years has been about 10%, of which, the real estate industry directly accounts for about 3.5%, contributing an average of about 0.5% to GDP growth However, this contribution decreased rapidly in the second half of 2022 In the second quarter of 2023, it only reached a result of 3.24%

Putting in the gloomy situation of economy, the main challenges facing Vietnam real estate market are numerous: A recession in the world economy; Export and investment markets are shrinking; International tourism recovers slowly compared to other countries; Interest rates are decreasing but still in high rate; International financial and monetary market risks increase, negatively impacting Vietnam; Disbursement of the recovery and public investment program has not yet had a breakthrough; Risks in the stock market, corporate bonds and real estate need time to be processed; Policy implementation and coordination have problems… In Vietnam, after Covid-19 period, the monetary market tightened in 2022, pushing the real estate market into a decline cycle (Tu H.N & Lan P.Q, 2023) Financial market policies are unstable, creating difficult cash flow and reducing consumer purchasing power Weak liquidity and difficult cash flow clog the market In fact, most real estate businesses record sharp declines in profits from 2022 to June 2023 Other sectors related to real estate such as construction and materials are also affected when the housing market is quiet

Despite negative fluctuations in real estate segments, apartments are still emerging through the housing segment despite the circumstances, with selling prices increasing day by day Besides the features of apartments that make their liquidity flexible for investors, for most people, the increase in housing demand is associated with population growth This is clearly shown in the data published by VARS in the third quarter of 2023 In addition to the outstanding supply, the transaction volume representing demand for apartments also overwhelms the others, making this segment the most vibrant

Chart 3.2 Supply and transaction volume of housing real estate market in 2023

To explain this statistic, analyzing the process of urbanization development and Vietnam's population is necessary Throughout 30 years of development, leaving behind an agriculture economy, Vietnam’s growth significantly exhibits the speed of urbanization It is estimated that by 2025, the number of cities will reach around 1000, doubling that of 1990 Because of the urbanization process, large migration from rural to urban areas leads to an urgent need for accommodation By 2025, the total population is estimated to be 99.33 million people, equivalent to a compound annual growth rate in the period 2000 - 2025 of 1% in general and 3% in urban areas However, this abundant demand also has its downside, which is pushing the already struggling supply into a state of scarcity

Placed in this high population but recessive economic context, the average monthly income of workers, listed in Chart 3.3 by OMRE, though gradually increased over the years, is not significant By 2023, this index increased by 6.9% compared to last year However, the price of goods continues to rocket, while the average salary of people only increases by about 1 million VND over the past three years Would this growth rate match the growth of other products, especially housing real estate? While the income of workers still does not exceed 8 million VND, the question is whether people's financial situation can meet the current price increase of apartments Especially, the monetary market is tightened, financial market policies are unstable, creating difficult cash flow and reducing consumer purchasing power

Chart 3.3 Average income of workers by month from 2019 to 2023 (million

Source: GSO, WB, ADB, OMRE CMI summarized

Regarding the market’s absorption capacity, the number of transactions is difficult to increase due to scarce supply, not suitable for the financial capacity of buyers, interest rates are decreasing but investors and businesses are not satisfied Enterprises still do not have access to credit capital because they do not meet loan conditions Some supply sources that are of special interest to investors and are expected to create a push for the market have not passed the legal requirements (VARS) These are every challenge that the housing real estate market is facing at the current situation, which leads to the continuous increase in average apartment selling price in recent years, especially accelerating in the period 2020 - 2022, and surpassing the average increase in people’s income In the urban areas of Hanoi and Ho Chi Minh City, supply is low, while housing demand is large from the annual influx of new immigrants At the beginning of 2018, the selling prices of apartments in Hanoi and Ho Chi Minh City were 27 and 31 million VND/m2, respectively In the Quarter 1/2024 Report, Batdongsan.com.vn experts also if apartments in Hanoi have an average price of 46 million VND/m2, while the price of apartments in Ho Chi Minh City is 48 million VND/m2 After 6 years, the average price increase rate of apartments in Hanoi is up to 70%, surpassing Ho Chi Minh City where apartment prices increased by 55%

Summing up 2023, according to MoC, real estate supply in 2023 continues to be limited in all segments, of which, commercial housing completed 52 projects with nearly 16,000 units, less than half of last year For the apartment type, in the first 3 quarters of

2023, there were only 47 new licensed residential real estate projects This has caused primary apartment prices to continuously increase, due to limited new sources for sale According to CBRE Vietnam’s report, in Hanoi, the supply of newly opened apartments decreased by 32% compared to 2022, at 10,300 units Meanwhile, Ho Chi Minh city recorded a sharp decrease in new apartment supply by 54%, with 8,700 units In the two largest cities in the country, although there are different fluctuations, the supply of new apartments has decreased below the threshold of 10,000 units for the first time in 5 years, enough to show a long-term scarcity over the country (Chart 3.4)

Chart 3.4 New supply of housing real estate market in Hanoi and Ho Chi Minh city

However, in Chart 3.2, the transaction volume in 2023 only accounts for half of the supply This is not reasonable with the scarcity situation, and the cause, as mentioned, may be related to people's financial conditions unmatching house prices Supply shortage is not the wobbliest problem but the supply by segment Savills Vietnam’s latest report shows that the villa/townhouse segment in Ho Chi Minh city performed the weakest since 2019 with a continuous decrease in transaction volume and absorption rate On the other hand, the affordable housing segment is in short supply even though demand in the market is huge HoREA’s recent report shows that the affordable apartment segment has completely disappeared from the Ho Chi Minh market for the past 3 years Even the mid- range segment only accounts for around 30% This is clearly reflected in Chart 3.5, when the supply of affordable apartments and mid-range apartments is smaller than the transaction volume, meaning the supply targeting those two segments is not enough to serve customers But although the first range and luxurious segments are picky about buyers, there is excess supply Compared with the buyer's needs and financial ability, the real estate supply is moving against the rules of the market economy, making the market not oriented towards buyers

Chart 3.5 Supply and transaction volume by apartment segment

Not only does the distribution of supply not serve the needs of buyers, but the supply congestion is also mainly related to legal problems In early 2023, real estate associations and agencies announced statistics: 70% of real estate market problems are related to legal issues The amendment of three major laws including: Housing Law, Real Estate Business Law, and Land Law is considered one of the important factors affecting the real estate market Delays in project approvals and a shortage of new supply have affected real estate market sentiment, but demand remains strongly driven by urbanization The difficulties mounted as in the past two years, thousands of projects have been implemented and investments across the country have had to stop to consider the situation This incident has reduced supply and aggravated the temporary freezing situation of the market (VARS)

Also, according to data from BHS R&D, the primary supply of legal housing is divided into scattered areas in all three regions: North - Central - South and unevenly distributed, with 69% of products concentrated in the South, The North and Central regions account for 24% and 7% of the total legal primary supply of the country, respectively Most of the projects were sold before 2023, currently continuing to maintain sales, very few new projects are opening for sale

Legal difficulties, stepped-up project inspections especially with fire prevention, and tightened markets have caused investors to hesitate and delay In September 2023, a mini apartment fire in Hanoi killed 56 people and injured 37 people, raising an alarm about the issue of licensing management and construction of mini apartments By the end of November 2023, the National Assembly officially passed the revised Housing Law, including new regulations on this type Accordingly, mini apartments are granted certificates from 1 /1/2025 but construction requirements are also stricter when meeting standards on land, construction projects, and fire prevention

There are many reasons leading to legal problems, in which, in addition to complex and difficult-to-enforce procedures and regulations, it is mainly due to the limited capacity of real estate investors and their inability to meet the requirements This is closely related to the financial status of real estate businesses Real estate project investors face cash flow difficulties when capital from bond issuance is tightened, borrowing capital from banks is not as simple as before

In addition to capital mobilized from buyers, real estate development enterprises in the previous 3 years expanded capital sources from the corporate bond market, mainly through private placement, and this has inflated a problem bubble Although this capital flow accounts for 6-7% of GDP, it is equivalent to the credit that real estate development businesses can borrow from the commercial banking system In 2023, real estate development businesses cannot mobilize capital in the corporate bond market when this market is paralyzed, and real estate consumers turn away According to HNX, the last quarter of 2023 is the maturity date of 65.5 trillion VND of corporate bonds, nearly 80% of which belong to the real estate group The pressure to repay bonds for the real estate industry is not small when there are nearly 113,486 billion VND of bonds due in 2024, specifically in Chart 3.6

Chart 3.6 Maturity value of real estate corporate bonds (unit: billion VND)

Source: 2024 housing real estate industry outlook report, Vietcombank

DATA ANALYSIS

According to the research process planned in Chapter 2 Methodology, the sample size for the topic was determined to be at least 138 samples to ensure representativeness

The total sample size extracted from the valuation certificate includes 235 apartments spread across 12 districts in Hanoi

After checking and cleaning the data, 16 samples were eliminated due to lacking information about the apartment structure, not being within the scope of the study, or not being assessed as quality As a result, 219 samples had complete information, met the requirements, and were used for analysis

Table 3.2 Statistics on the number of research samples divided by district

District Number of apartments Ratio (%) Average price/m 2 (VND)

In terms of research scope, the district with the largest number of survey samples is Ha Dong with 44 survey samples, the average unit price of an apartment is about 40.132 million VND/m 2 In this district, the apartment with the highest transaction price belongs to the Hi-brand Van Phu Residential Area project owned by investor Hi-brand Vietnam, its predecessor in Korea, with a price of 55.952 million VND/m 2

Besides, Hai Ba Trung and Thach That have the least number of samples with 4 survey samples, but there is a contrast between these two districts Hai Ba Trung is the city center area with the second highest population density among the 12 districts, leading to the fact that apartment prices in this area are extremely high Four surveyed apartments belong to the Hinode City Plaza apartment project of Vietracimex with an average price of 74.468 million VND/m 2 On the other hand, Thach That has the lowest population density, and the distance to the city center is also the greatest, about 36.6 km from the surveyed apartment The average apartment price only reaches 20.030 million VND/m 2

In terms of apartment quality, out of a total of 219 samples, 62 apartments are considered luxury apartments (First class) with an average price per apartment of 57.264 million VND/m 2 In this category, the highest price is an apartment belonging to the Taseco Complex project of investor Taseco with 80 million/m 2 There are 83 apartments considered middle class (Second class) with an average price of 47.863 million VND/m 2 Among them, the apartment with the highest price is the 6th Element apartment project of Bac Ha Group Joint Stock Company with a price of 81.927 million VND/m 2 The remaining 74 apartments are social housing or low-cost apartments (Third class), of which the cheapest is the Vicostone Thach That apartment with a price of 18.993 million VND/m 2

Table 3.3 Statistics on the number of samples divided by building quality

Quality Number of apartments Ratio (%) Average price/m 2 (VND)

The author's research model has 11 independent variables The description of the quantitative variables is in the following table:

Table 3.4 Description of quantitative variables in the model

Name Variable Obs Average Standard deviation Min Max

Distance to center of city

Based on the statistical results table describing the observed variables, it shows that: apartment price (P) has an average value of 4.240 billion VND, however this variable has a very high standard deviation of 2.250 billion VND Because the research sample includes both high-end luxury apartments and social housing This causes the prices of these two types of apartments to have a huge difference The cheapest apartment costs 1.100 billion VND, and the most expensive apartment costs 20 billion VND

The average floor area of each apartment is 91.2 m2 The smallest apartment area is 32 m2 and the largest is 500 m2 The average distance from the apartment complex to Noi Bai airport is 31.09 km The closest distance is 18.4 km (Bac Tu Liem district), and the farthest is 46.5 km (Thach That district) The average distance from the apartment complex to the nearest hospital is 1.9 km The closest distance is 0.1 km, and the farthest is 10.5 km The average distance from the apartment complex to the city center, set as Hoan Kiem Lake, is 11.77 km The closest distance is 6.8 km (Hai Ba Trung district), and the farthest distance is 36.6 km (Thach That district)

The table describing the correlation between variables in the model below shows that all independent variables are correlated with the dependent variable

Table 3.5 Correlation matrix between variables in the model ln_P AIR HOS CEN PD FS NoR ln_P 1.0000

NoB AF BA IE Q NoB 1.0000

Source: Extracted from Stata 14 software

In particular, the Location factor group includes the variables distance to Noi Bai airport, the nearest hospital and the city center having a negative correlation with apartment prices (L_AIR = -0.3960; L_HOS = -0.4946; L_CEN = -0.5231), and the variable population density has a positive correlation (L_PD = 0.5045) This means that the farther the apartment is from the airport, hospital, and city center, as well as the higher the population density of that district, the higher the apartment price there This is consistent with reality because buyers often pay a lot of attention to apartments near hospitals to take advantage of health care facilities Apartments near the airport are also convenient for business travel, and the closer they are to the city center, the easier it is to access events, monuments, and attractions Therefore, apartment prices in these apartments will usually be higher

Most of the remaining variables have the same positive impact on apartment prices Among them, the variables with the most significant impact are floor area (S_FS

The correlation coefficient between independent variables in the model has a relatively low value, the highest is the correlation between floor area and number of rooms (= 0.7106) Compared with the comparison standard of Hoang Ngoc Nham (2008) as 0.8, it can be said that the model does not have multicollinearity

However, adapting to the author’s expectations and the research of Kain & Quigley, 1970, the independent variable Building Age (C_BA) should have a negative impact on apartment prices That is, the newer the apartment is built and the younger it is, the more expensive it is, and apartments that were built a long time ago with degraded infrastructure should be priced low The remaining results of correlation values between the remaining variables in the model are consistent with previous studies and the author's expectations in this study

Command: reg ln_P AIR HOS CEN PD FS NoR NoB AF BA IE Q

Ln_P Coef Std Err t P>|t| [95% Conf Interval]

Number of obs = 219 F(11, 207) = 100.67 Prob > F = 0.0000 R-squared = 0.8425 Adj R-squared = 0.8341 Root MSE = 0.18667

Source: Extracted from Stata 14 software

The regression results show that, with a significance level of 5%, most variables are statistically significant (p.value < 0.05), meaning there is enough evidence to show that the independent variables have an impact on the apartment price

The results show that the model has a coefficient R 2 = 0.8425 = 84.25%, meaning the independent variables in the model explain 84.25% of the variation in apartment prices in Hanoi city

To detect multicollinearity in the model, the author uses the VIF coefficient The results obtained are as follows:

Source: Extracted from Stata 14 software

The multicollinearity test results show that the variable has an average VIF variance exaggeration index of 2.19 The largest variable is the distance to the city center (L_CEN = 3.92), which remains under the allowable number At the same time, the VIF index of the independent variables is less than 10 Therefore, according to Hoang Ngoc Nham (2008), it can be concluded that the model does not have a multicollinearity phenomenon

To test the phenomenon of heteroskedasticity, the author uses White's test with the hypothesis:

H0: There is no heteroscedasticity phenomenon

H1: There is a phenomenon of heteroskedasticity

White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(77) = 158.28 Prob > chi2 = 0.0000 Cameron & Trivedi's decomposition of IM-test

Source: Extracted from Stata 14 software

With a significance level of 5%, White's test shows that the coefficient Prob 0.0000 < 5%, so we accept the hypothesis H1, that is, the model has heteroscedasticity

According to the results of the heteroscedasticity test above, the model is no longer an effective estimate and the tests on the regression coefficients are no longer reliable Therefore, the author uses the robust estimation method of the error covariance matrix proposed by White (1980) to recalculate the test values by using the robust option when running the regression command

Command: reg ln_P AIR HOS CEN PD FS NoR NoB AF BA IE Q, robust

Linear regression Number of obs = 219

Ln_P Coef Std Err t P>|t| [95% Conf Interval]

Source: Extracted from Stata 14 software

The regression results after correcting the phenomenon of heteroskedasticity, with a significance level of 5%, can be seen that most variables in the model are statistically significant (p < 0.05) This means that there is enough basis to show that the independent variables used have an impact on apartment prices in Hanoi However, to ensure the model has the highest standardization, the author removes variables with p.value greater than 5%, including distance to the airport, distance to the nearest hospital, number of balconies, and investor experience

The results of running the regression model determine the coefficient R 2 = 0.8425, which means the model can explain 84.25% of the overall relationship between factors affecting apartment prices in the area Hanoi City Or in other words, 84.25% of the fluctuations in apartment prices in Hanoi can be explained by the fluctuations of three groups of independent variables, the rest (15.75%) is explained by other factors Other factors include model and random error

The Sig value is very small (Sig = 0.00), so the regression model is suitable for the data set and can be used.

RESEARCH RESULTS

Table 3.10 shows the result of the estimated models and the expected effect of 11 independent variables on apartment prices Several variables are eliminated by the author

Total price P Total price stated VNG Valuation Co ltd

Category Variable Name Predicted effect

7 Number of balconies S_NoB + Eliminated

Experimental research results show 7 factors including: Population density; Distance to center of city; Floor space; Number of rooms; Apartment floor; Building age; Quality of the building is the factor that affects apartment prices in Hanoi The detailed research model is written specifically with the reduction of four digits after the decimal point

Ln(P) = 21.01814+ 0.0340*L_PD – 0.0259*L_CEN + 0.0041*S_FS + 0.1402*S_NoR + 0.0567*S_AF – 0.0105*C_BA + 0.1278*C_Q + ε

From the results of the model, the impact of factors on apartment prices is explained as follows:

The regression coefficient of the population density variable by apartment district (L_PD) has a value of 0.0340 and has a (+) sign This means that under the condition that other factors remain unchanged, at the 5% significance level, when choosing an apartment in a crowded populated district, the apartment price will increase by 3.4% As stated in the hypothesis, high population density will lead to increased demand, thereby causing a shortage of supply and pushing up prices

The regression coefficient of the variable distance from the apartment to the city center (L_CEN) has a value of 0.0259 and has a (-) sign This means that under the condition that other factors remain unchanged, with a significance level of 5%, the farther an apartment is from the city center by 1 km, the price of the apartment in that apartment will decrease by 2.59% In addition, in the study in Hanoi, the districts with the highest population were Thanh Xuan, Hai Ba Trung, Cau Giay, Hoang Mai and were also the districts closest to the city center In the model, this is shown by a relatively high correlation coefficient of 0.5493

The regression coefficient of the floor area variable (S_FS) has a value of 0.0041 and has a (+) sign That means that under the condition that other factors remain unchanged, with a significance level of 5, when the floor area of the apartment increases by 1m2, the apartment price will increase by 0.41%

The regression coefficient of the room variable in an apartment (S_NoR) has a value of 0.1402 and has a (+) sign This means that under the condition that other factors remain unchanged, with a significance level of 5%, the more rooms an apartment has, the apartment price in that apartment will increase by 14.02% of its value Similar to the first two variables, the floor area variable and the number of rooms are also closely correlated (shown in the correlation coefficient of 0.7106) The larger the area, the more room it will create, and vice versa, apartments under 60 m2 usually only have 1 bathroom and 2 bedrooms

The regression coefficient of the apartment floor location variable (S_AF) has a value of 0.0567 and has a (+) sign That means that under the condition that other factors remain unchanged, with a significance level of 5%, the higher the apartment is in the apartment building, the price of the apartment in that apartment building will increase by 5.67% of its value

The regression coefficient of the apartment age variable (C_BA) has a value of 0.0105 and has a (-) sign That means that under the condition that other factors remain unchanged, with a significance level of 5%, the longer an apartment is built and the older it is, the price of the apartment in that apartment will decrease by 1.05% of its value This is a relatively modest number, consistent with the current situation of apartments in Hanoi When the affordable segment shrinks and the newly built mid- and high-end segments have too high prices, people will rush to spend money on old apartments, and the impact of apartment longevity on apartment prices will also be reduced

The regression coefficient of the apartment quality variable (C_Q) has a value of 0.1278 and has a (+) sign This means that under the condition that other factors remain unchanged, with a significance level of 5%, the higher the quality of an apartment, the completer and more luxurious the amenities, the price of the apartment in that apartment will increase to 12.78% value This result is completely consistent with reality as well as theory Because the objects being pushed up and sought after are mainly second-class and first-class apartments, which are invested in a higher amount of money It would be reasonable for prices to increase in apartments with more elaborate internal amenities

In addition, for four variables that are eliminated by the author to ensure the accuracy of the remaining variables, but there are also reasonable explanations as follows:

The distance from the apartment to the airport (L_AIR) is often not emphasized, because in general, most people do not need to travel by plane as much as by bus or motorbike taxi For Hanoi, except for Bac Tu Liem district, the distance from other districts to Noi Bai Airport will be greater than 20 km, making this variable almost no longer considered in the valuation process

The distance from the apartment building to the nearest hospital (L_HOS) is considered by the author to be relatively important because everyone needs to use medical services But it must be mentioned that First class and even some Second-class apartments have now built medical facilities within the area Not to mention, many households have private doctors who come to examine them at home, so the need for medical services is also met in many ways In the survey data, except for Thach That district, Vicostone apartment building is 10 km from the nearest hospital, all other apartment buildings have a large hospital within a 5 km radius

The number of balconies/loggias is also reduced In general, a balcony is a form of decoration and takes advantage of the apartment's view, so it is only appreciated when the apartment faces a beautiful view like West Lake, or an open space In the research, the author did not collect information about the apartment's view, nor did it survey whether customers required balconies/logos or not Therefore, it can be said that this variable does not show its full value in the current model and can be eliminated

Finally, there is the investor's experience The author expects that the longer the investor has been operating, the more elaborate and prestigious the project will be and will be trusted by buyers, thereby increasing apartment prices However, this assessment is not objective enough without considering other aspects such as revenue, recognition, financial performance of the business In the research data, there are large enterprises such as the Group Sunshine with the Sunshine City project that was only established in

2016 The giant Vinhomes was also only established in 2008

Omitted variables could be better handled with additional data or advanced remediation However, removing them does not greatly affect the research, and will help the model run more smoothly.

LIMITATIONS AND SOLUTION FOR THE MODEL

Through quantitative analysis, the research results show that apartment prices in Hanoi Affected by 7 factors It should be noted that these factors can be adjusted and supplemented to suit each market, each country, and each period Research has not yet estimated many other factors that have been proven to have a major impact on apartment prices, such as environmental factors, economic factors, and social factors

Of scope, the research was conducted under limited time conditions in the first three months of 2024, only within 12 districts in Hanoi, not covering the entire study area Therefore, the selected variables may change when building models in other areas Especially with real estate, specifically apartments, prices can change with great intensity over time The number of samples is still relatively small (219 samples), so it does not fully represent the whole situation of Hanoi Current input data sources for analysis in the Hanoi area are difficult to collect fully and accurately due to the skyrocketing price

The findings of this thesis do not cover detailly factors impacting the price of each sort of apartment, but only research overall factors Future research needs to concentrate on the factors impacting selling price of each segment of apartments to see how these sets of factors and their levels of importance differ from one customer segment to another toward a broader research findings’ generalization

Of methodology, Google maps is only an acceptable and approximate method to evaluate accessibility of an apartment If the road is improved or has problems, the measurements in this study will no longer be accurate

From the above comments, a few options are proposed to improve the model First, environmental factors should be included to better mimic market reality, especially in the context of a highly polluted environment in big cities such as Hanoi It is recommended to include variables such as level of air/water/noise pollution in that area, proportion of green space in the projects, average amount of waste dumped, distance to nearest garbage dump or nearest green bodies… This thesis is not able to include these factors due to limited time and data resources

Secondly, macroeconomics factors should also be taken into consideration Apparently, the changes in the macroeconomics environment are closely correlated with the change in the real estate market The suggested variables include interest rate, inflation rate, GDP growth rate, total investment in the real estate market…

Finally, a larger and more complete sample should be addressed to formulate an equation not only for Hanoi apartment market but also Vietnam apartment market, and then a model for housing price in Vietnam in general.

CONCLUSION OF THE MODEL

Currently, apartment prices in Hanoi are being inflated Compared to the average price of 50 million/m 2 surveyed by CafeLand, the research average price is approximately 46 million/m 2 , a figure that is relatively close to information in the press The fact that apartment prices were pushed up in a short period of time, forcing the Ministry of Construction to intervene, signaled that the current situation was not going well, and considering all aspects of the problem and finding the cause of price inflation was extremely necessary

Based on scientific theory, results of previous research, and data set from the deed of VNG Valuation Company Limited, the author has built a HPM to analyze the impact of factors influencing apartment prices in Hanoi Specific models are as follows:

Ln(P) = β0 + β1L_PD + β2L_CEN + β3 S_FS+ β4 S_NoR+ β5S_AF + β6C_BA + β7C_Q + ε

Through quantitative analysis based on a data set of 219 observation samples collected through valuation certificates of VNG Value covering 12 districts, the research results show that apartment prices in Hanoi are affected by 7 factors, such as: population density by district, distance from the apartment to the city center, apartment floor area, number of rooms in the apartment, apartment floor location, age of the building apartments, and building quality The strongest impact is the number of rooms in an apartment and the quality of the building

Based on the model results, the NoR factor is pushing up prices too much, affecting 14.02% of an apartment's value, while this factor is closely correlated with FS floor area, a variable that only impacts 0.41% on house prices The difference between the impact levels of these two variables is too large, showing that although the number of rooms depends on the area, prices are often pushed up for apartments with many rooms That is, there are apartments with smaller areas, but if divided into more rooms, they can still push the price higher than some large apartments with few rooms The author's assessment of this factor is reasonable, because the number of rooms required depends on customer needs For example, if the number of members is large and the need to use many rooms for many functions, buyers will appreciate apartments with many rooms Currently, households tend not to buy houses but apartments, so apartments are prioritized with many rooms to have privacy for each member

The second most influential variable is the apartment quality factor, which determines 12.78% of the apartment value This result is completely consistent with reality as well as theory This is also an alarming situation, because higher quality apartments lead to sharp increases in prices, while supply is focusing on the mid-range and high-end segments This naturally causes buyers to have to bear a 12.78% price increase if they cannot find an affordable apartment

High-floor apartments are more expensive and partly depend on consumer psychology For many families, moving to a floor that is too high is considered inconvenient, as well as unsafe in the event of a fire or incident (even though there are adequate fire-fighting measures) After recent sad accidents, many households also have the mentality of choosing low-rise apartments to promptly respond to bad cases However, based on the model results, high-floor apartments, spacious and beautiful views are still a trend to increase value

The remaining factors such as population density by district, distance from the apartment to the city center, and apartment floor location generally have a reasonable impact compared to the author's expectations and the actual situation Although currently, the Western and Eastern areas are being focused on pouring capital into high-end projects, promising to transform the city center, results cannot be achieved in a day or two Districts that are both densely populated and close to the city center as analyzed will still have expensive prices.

SUMMARY

In conclusion, this chapter illuminates the intricate dynamics of Vietnam's housing real estate market amid economic recession High demand, particularly in the apartment segment, coupled with limited supply, has driven prices upward, notably in Hanoi Through the Hedonic pricing model, the thesis gained valuable insights into pricing determinants, albeit with acknowledged limitations such as data availability and model assumptions.

CONTRIBUTION OF RESEARCH AND RECOMMENDATIONS

RESEARCH CONTRIBUTIONS

The findings of this study have important implications for the current situation of escalating apartment prices in Hanoi Based on the quantified results using the HPM, the analysis of the results can be used to analyze the factors influencing apartment prices in Hanoi, thereby figuring out which factors were not reasonable and able to be fixed In addition, the theoretical model of factors affecting apartment prices in Hanoi will make a small contribution and supplement the theoretical system of assessing the influence of common apartment price factors residing in a particular market It is expected that this thesis might deliver apartment buyers and sellers a reliable tool to measure their properties The constructed model can act as references to build a macro-Hedonic model for the real estate market in Vietnam, providing households, investors, and policy – makers an empirical tool to ensure market transparency and equity

The thesis literature review has systematized the so far theoretical approaches and issues of assets (in this case, apartments) pricing and related impacting factors The set of identified factors, that is the output of theoretical reviews could be a basis for various research in the field of real estate valuation which is a very important teaching and research area interested and investigated by university researchers In terms of practical contribution, the thesis has found out and discussed the level of importance of the identified factors that impact the selling price of apartments in Hanoi, Vietnam, from that base, suggesting recommendations for researchers and business practitioners as well as for the ordinary customers making decisions to sell or purchase apartments regardless of their purposes.

RECOMMENDATION

4.2.1 For investors, developers, and builders of the project

For investors and owners of residential real estate projects in general, and apartment buildings in particular, the results drawn can help reshape the factors that affect the price of apartments sold to make reasonable adjustments

First, project owners should reallocate supply volume by segment The situation of supply gap by segment has been clarified in the study: supply does not aim to satisfy demand, causing the high-end segment to stagnate and the popular segment to be scarce Quality, affecting 12.78% value of an apartment, proved its importance, which should be focused by investors of the projects Normally, since apartment quality is an important factor determining price, investors tend to pour money into projects with high quality, modern amenities, and attractive designs However, investors need to pay more attention to low segments such as affordable apartments, which are lacking new supply in the market today Investigation and survey of buyers' needs, and income is necessary to develop projects in a reasonable segment Besides middle to upper-middle-class buyers who are willing to invest in higher-priced, quality apartments, project investors should pay more attention to lower financial ability customers

Second, investors should consider the project's planning and construction location This can be a positive solution to help maintain revenue without having to follow the trend of building high-end apartments While proximity to the city center is important, also consider areas with burgeoning demand, improving infrastructure, and access to amenities such as schools, hospitals, and shopping centers The population in Thanh Xuan, Hai Ba Trung, Cau Giay and Hoang Mai districts, which are already located close to the city center, is overwhelming However, based on the analyzed statistics, the East area promises to transform the city center soon, as well as Western areas also have a lively pace of life Investors should conduct thorough market research to identify emerging neighborhoods with potential for future growth, thereby distributing their projects in these locations This not only helps to distribute and increase the quality of life in areas with a lot of urban vacant land, but also to gain long-term benefits Moreover, before proceeding, investors need to carefully understand how long the surrounding environment will remain, how fast, or slow the improvement progresses, the system of surrounding utilities will be formed in the future when apartments are handed over to customers, thereby determining a reasonable selling price

Third, adapting to demographic changes is one of the recommendations that investors should keep in mind In research, it has been shown that the number of rooms in an apartment affects 14.02% of the price, while the area only affects 0.41%, and the number of floors affects 5.67% This partly shows that people appreciate a multi- functional apartment with many rooms more than a large apartment This is also suitable for the lifestyle of families and their preference for independence and high-rise living Investors can pay attention to this result to design a reasonable apartment layout and location Additionally, amenities such as energy-saving devices, green space or smart home technology can be added By offering something unique, you can attract discerning buyers and command a premium price

Finally, investors should pay attention to their reputation and status In the model, investors with long-term experience are unlikely to sell projects at higher prices than emerging investors This depends on new and unique business ideas, financial capacity, as well as the investor's attention to customers For example, with older apartment buildings, the older they are, the lower the price is by up to 1.05% However, in a situation where buyers are flocking to these long-standing buildings to reduce costs, investors can focus on restoring and upgrading to improve prices and attract customers This is also a way to increase the reputation of the investors

For prospective apartment buyers in Hanoi, here are some recommendations to consider in the current market scenario

First, buyers need to research market trends, including price fluctuations and supply and demand dynamics This will help avoid buying at times of skyrocketing prices like today Analyzing price trends can pay attention to average prices, price growth compared to previous years, quarters, and price predictions soon In addition, you can survey by area, choose an area suitable for the price, for example areas far from the city center, or apartments with prices lower than the general average such as VHSC, VHOP In this way, customers can prepare better financially, in accordance with their budget

Second, buyers should identify their priorities by clearly outlining preferences for location, size, and quality to match the budget Those factors will greatly affect the price of the apartment, as analyzed in the model, which can lead to exceeding the budget, so balance is needed The non-negotiable aspects should be prioritized The number of rooms factor is considered to have the strongest impact, so buyers need to clearly determine whether the need for various rooms is necessary and find an apartment with fewer rooms to save costs if needed In addition, construction quality and building amenities also need to be considered, especially with the current supply, the mid-range and first-class segments are popular, buyers need to pay attention to budget and preference when choosing a suitable segment In terms of location and accessibility, choose a location that suits lifestyle and has convenient access to work or school Buyers can consider choosing locations far from the city center or with a sparse population to reduce costs

Third, buyers should consider seeking guidance from real estate professionals if encountering any problems during research A professional can provide valuable insights, assist in finding the right property and negotiate on their behalf to get the best deal Experts also assist in recommending appraisal and legal services that need to be secured by the buyers Through their analysis, the buyers’ rights are guaranteed, and they can further look at the property they intend to buy, for example future development plans and infrastructure projects in the area

And above all, all buyers should be patient and flexible Buying an apartment is a significant and long-term decision, which should take time to explore options Buyers should be prepared to be flexible and willing to compromise on certain factors to find the best possible fit for needs and budget, especially in the nowadays market with less choices among segments By following these recommendations, prospective apartment buyers can make informed decisions and navigate the Hanoi property market effectively to find a property that meets their needs and aspirations

Providing recommendations to government authorities and regulators can inform policy decisions, promote sustainable development practices, and address challenges such as affordability, housing supply, and urban planning For the government authorities and regulators, their improvement in legal issues is most necessary due to recent situations

The first is to streamline and speed up the legal review process Regarding the regulatory framework related to real estate, the government of Vietnam has been doing successfully However, the permitting procedures should be paid more care since it is now much slower after the appearance of new projects, which caused scarcity in supply Simplifying bureaucratic hurdles can encourage investment and expedite the delivery of housing projects Practically the legal by monitoring and enforcement is necessary Enhance monitoring and enforcement mechanisms along with penalties for violations and improve coordination among regulatory agencies to enforce compliance with regulations

Second, the government should introduce policies to encourage and organize affordable apartment construction projects Currently, the imbalance in supply distribution has caused prices to rise and many people are struggling The government can promote the construction of social housing and welfare apartments at reasonable prices, helping people with low incomes to settle down

Third, the government needs to pay attention to improving infrastructure in a sustainable and environmentally friendly way Although not quantified in the study, environmental factors have been shown to be important in previous studies, which can be positively improved thanks to the cooperation of local authorities In Vietnam, the improvement of the surrounding landscape and infrastructure is still inconsistent Infrastructure development and sustainable development can come together by prioritizing mixed-use development and green infrastructure, taken under along with infrastructure investment in critical projects Promote environmentally sustainable development practices by adopting green building standards, incentivizing energy- efficient construction methods, preserving natural resources, and incorporating renewable energy and green spaces into projects

Real estate agents and brokers, as the “drivers” of the market, should stay updated on market knowledge such as the trends, property values, legal changes, and regulatory changes to provide informed advice to clients Embrace technology tools and platforms to streamline your workflow, market properties effectively, and provide a seamless experience for clients Ethical practices are extremely important in recent situations, when prices are being inflated as well as upholding high ethical standards in your dealings with clients and colleagues, prioritizing honesty, integrity, and professionalism always

Financial institutions and lenders: Conduct risk assessments to assess borrower creditworthiness and the viability of projects, considering factors such as market conditions, feasibility of the project and the financial stability of the borrower Providing financing options tailored to the needs of developers and buyers Implement strict appraisal procedures to verify the accuracy of borrower information, evaluate collateral value and minimize potential risks related to lending in the real estate sector Provide educational resources and guidance to borrowers about real estate financing options, loan terms, and the implications of borrowing, empowering them to make informed decisions.

SUMMARY

The last chapter delves into the contributions of the research findings and provides targeted recommendations for key stakeholders such as investors of the project, customers, and government authorities and regulators Additionally, recommendations are extended to other institutions, emphasizing collaboration and knowledge-sharing to address the multifaceted challenges facing Vietnam's housing sector These insights and recommendations aim to catalyze positive change and promote resilience within the real estate landscape.

The analysis of the recent Vietnamese real estate market, with a specific focus on the apartment sector in Hanoi in the first quarter of 2024, reveals a challenging landscape characterized by a downturn influenced by various factors The market has experienced a decline attributed to a combination of policy tightening, bonds due burden, and changes in legal frameworks These factors have led to a shortage of supply, particularly in the affordable housing segment, exacerbating the disconnect between housing prices and the economic conditions of the populace

Through the application of the HPM, this thesis has identified seven key factors that significantly influence apartment prices in Hanoi These factors include population density, distance to the city center, floor space, number of rooms, apartment floor, building age, and building quality Among these, the number of rooms and building quality emerge as the most influential determinants Conversely, building age, while still a factor, exerts a relatively smaller impact on pricing decisions

The findings underscore the complexity of the real estate market dynamics Investors and stakeholders must navigate these complexities with a nuanced understanding of market trends, regulatory environments, and consumer preferences to make informed decisions and mitigate risks Moving forward, addressing the challenges facing the market requires a holistic approach that encompasses policy reforms, regulatory measures, and strategic interventions aimed at promoting sustainable growth, enhancing affordability, and improving access to quality housing for all segments of society

In conclusion, while the current period presents significant challenges for the Hanoi apartment market, it also offers opportunities for innovation, adaptation, and collaboration to shape a more robust and equitable real estate landscape in the years ahead.

Dao, B & Lan Linh, T H (2016) Determinants of Hanoi Apartment Price Using

Hedonic Approach Determinants of Hanoi Apartment Price Using Hedonic Approach,

General Statistics Office Hanoi Statistical Yearbook 2022

Nhai H.T (2015) Phân tích các yếu tố tác động lên giá căn hộ chung cư, nghiên cứu điển hình trên địa bàn Quận 2 Thành phố Hồ Chí Minh Master thesis University of Economics Ho Chi Minh City

Nham, H.N (2008) Giáo trình kinh tế lượng University of Economics Ho Chi

Phong N.T (2015) Xây dựng chỉ số giá gốc căn hộ chung cư nghiên cứu điển hình tại Quận 2 và Quận 7 thành phố Hồ Chí Minh Master thesis University of

Economics Ho Chi Minh City

Tho N.D (2014) Phương pháp nghiên cứu khoa học trong kinh doanh Ho Chi Minh city: Finance publishing house

Tinh, B D (2023) LƯỢNG HÓA CÁC NHÂN TỐ ẢNH HƯỞNG ĐẾN GIÁ ĐẤT Ở ĐÔ THỊ MỚI TẠI KHU VỰC BÌNH TRỊ THIÊN Hue University Journal of Science:

Tu H.N & Lan P.Q (2023) Chính sách tiền tệ tác động đến thị trường bất động sản tại các quốc gia trên thế giới và một số khuyến nghị đối với Việt Nam Banking magazine

Van T.T and Giang N.T (2011) Ứng dụng mô hình Hedonic về các yếu tố ảnh hưởng tới giá bất động sản tại thành phố Hồ Chí Minh Development Economics

Vinh N.N (2012) Nhận diện các yếu tố tác động lên giá trị bất động sản Real estate magazine, Vol 24, August 2012

Vinh N.N and Hoa N.Q (2014) Giáo trình Thẩm định giá trị Bất động sản Ho Chi Minh city: Labour and Social Publisher Company Limited

Bełej, M., Cellmer, R., & Głuszak, M (2020) The impact of airport proximity on single-family house prices Evidence from Poland Sustainability, 12(19), 7928

Case, B., Clapp, J., Dubin, R., & Rodriguez, M (2004) Modeling spatial and temporal house price patterns: A comparison of four models The Journal of Real Estate

Cebula, R J (2009) The hedonic pricing model applied to the housing market of the city of Savannah and its Savannah historic landmark district Review of Regional

Dusse, N & Jones, C (1998) A Hedonic price model of office rent Journal of Property Valuations and Investment, Vol 16, No 3, pp 297-312

Freeman, A Myrick, III (1979) The benefits of environmental improvement:

Theory and practice Johns Hopkins University Press

Haripriya G (2002) Hedonic price method - A concept note

Kain, J F., & Quigley, J M (1970) Measuring the value of housing quality

Journal of the American statistical association, 65(330), 532-548

Kim, A M (2007) North versus South: the impact of social norms in the market pricing of private property rights in Vietnam World Development, 35(12), 2079-2095

Kunovac, D., Đozović, E., Lukinić, G., & Pufnik, A (2008) Use of the hedonic method to calculate an index of real estate prices in Croatia Croatian National Bank,

Lancaster, K J (1966) A new approach to consumer theory Journal of political economy, 74(2), 132-157

Miles, D (2012) Population density, house prices and mortgage design Scottish journal of political economy, 59(5), 444-466

Musili, K J (2010) Real estate valuation based on hedonic price model

Musili, K J (2010) Real estate valuation based on the Hedonic price model

Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., & French, N (2003) Real estate appraisal: a review of valuation methods Journal of property investment & finance, 21(4), 383-401

Ridker, R G., & Henning, J A (1967) The determinants of residential property values with special reference to air pollution The review of Economics and Statistics,

Schulz, R., & Werwatz, A (2004) A state space model for Berlin house prices:

Estimation and economic interpretation The Journal of Real Estate Finance and

Selim, S (2008) Determinants of house prices in Turkey: A hedonic regression model Doğuş ĩniversitesi Dergisi, 9(1), 65-76

Syaaga (1994) Real Estate Valuation Handbook with Special reference to Kenya Nairobi University Printing Press

Wen, H Z., Sheng-hua, J., & Xiao-yu, G (2005) Hedonic price analysis of urban housing: An empirical research on Hangzhou, China Journal of Zhejiang University-

Batdongsan.com (2024) Toàn Cảnh Thị Trường Bất Động Sản Quý 1/2024

Available at: Báo Cáo Thị Trường Bất Động Sản Quý 1/2024 [Cập Nhật]

CafeLand Available at: https://cafeland.vn/

Circular 31/2016/TT-BXD Ministry of construction of Vietnam Available at: Circular 31/2016/TT-BXD classification recognition apartment buildings

Civil Code (2015) National Assembly of the Socialist Republic of Vietnam Available at: CIVIL CODE

Cushman & Wakefield (2023) Nhịp đập thị trường Hà Nội Q4/2023 Available at: BÁO CÁO QUÝ – THỊ TRƯỜNG HÀ NỘI | Việt Nam | Cushman & Wakefield

General Statistics Office (2023) Báo cáo tình hình kinh tế - xã hội quý IV và năm

Google Maps Available at: https://www.google.com/maps

Housing law (2014) National Assembly of the Socialist Republic of Vietnam Available at: 65/2014/QH13 in Vietnam, Law No 65/2014/QH13 on housing in Vietnam

MaisonOffice Dân số Hà Nội là bao nhiêu? [Cập nhật chính xác 2024] Available at: https://maisonoffice.vn/tin-tuc/dan-so-ha-noi/

One Housing (2024) Phân khúc Chung cư và Thổ cư dẫn dắt sự hồi phục thị trường

Bất động sản Hà Nội 2024 Available at: Dự báo thị trường BĐS năm 2024: Phân khúc

Chung cư và Thổ cư sẽ dẫn dắt sự hồi phục thị trường Bất động sản Hà Nội 2024

Ordinance on Prices (2002) National Assembly of the Socialist Republic of Vietnam Available at: Pháp lệnh Giá 2002 40/2002/PL-UBTVQH10

Savills Vietnam (2023) Báo cáo thị trường Bất động sản Việt Nam Q4/2023

Available at: Báo cáo thị trường Bất động sản Việt Nam Q4/2023

VARS (2023) Báo cáo Thị trường BĐS Việt Nam Q3/2023 Available at: Báo cáo Thị trường BĐS Việt Nam Q3/2023

Vietcombank Securities (2023) Báo cáo triển vọng ngành bất động sản nhà ở 2024 Available at: báo cáo triển vọng ngành bất động sản nhà ở 2024

Vietnam Valuation Standards Ministry of Finance of Vietnam Available at: HỆ THỐNG CÁC TIÊU CHUẨN THẨM ĐỊNH GIÁ VIỆT NAM

POPULATION AND POPULATION DENSITY OF HANOI DISTRICTS

Mật độ dân số (người/km2) Số phường, xã

2 Quận Bắc Từ Liêm 45,24 354.364 7.833 13 phường

6 Quận Hai Bà Trưng 10,26 304.101 29.639 18 phường

10 Quận Nam Từ Liêm 32,17 282.444 8.780 10 phường

13 Thị xã Sơn Tây 117,2 151.090 1.289 9 phường, 6 xã

14 Huyện Ba Vì 421,8 305.933 725 1 thị trấn, 30 xã

15 Huyện Chương Mỹ 237,48 347.564 1.464 2 thị trấn, 30 xã

16 Huyện Đan Phượng 77,83 185.653 2.385 1 thị trấn, 15 xã

17 Huyện Đông Anh 185,68 437.308 2.355 1 thị trấn, 23 xã

18 Huyện Gia Lâm 116,64 292.943 2.512 2 thị trấn, 20 xã

19 Huyện Hoài Đức 84,92 257.633 3.034 1 thị trấn, 19 xã

20 Huyện Mê Linh 141,29 241.633 1.710 2 thị trấn, 16 xã

21 Huyện Mỹ Đức 226,31 203.778 900 1 thị trấn, 21 xã

22 Huyện Phú Xuyên 173,56 229.847 1.324 2 thị trấn, 25 xã

23 Huyện Phúc Thọ 118,5 194.754 1.643 1 thị trấn, 20 xã

24 Huyện Quốc Oai 151,22 203.079 1.343 1 thị trấn, 20 xã

25 Huyện Sóc Sơn 305,51 357.652 1.171 1 thị trấn, 25 xã

26 Huyện Thạch Thất 187,53 223.844 1.194 1 thị trấn, 22 xã

27 Huyện Thanh Oai 124,47 227.541 1.828 1 thị trấn, 20 xã

28 Huyện Thanh Trì 63,49 288.839 4.549 1 thị trấn, 15 xã

29 Huyện Thường Tín 130,13 262.222 2.015 1 thị trấn, 28 xã

30 Huyện Ứng Hòa 188,24 212.224 1.127 1 thị trấn, 28 xã

Average price Min Project Investor Max Project Investor

Dự án Khu nhà ở Xuân Đỉnh

Công ty CP Thi công cơ giới xây lắp 80.000.000 Taseco Complex

Công ty CP Dịch vụ Hàng không Thăng Long (Taseco)

Khu đô thị mới Dương Nội

Khu nhà ở Hibrand Văn Phú

Công ty TNHH HiBrand Việt Nam (Hàn Quốc)

Chung cư Hinode City Plaza

Tổng Công ty CP Thương mại Xây dựng Vietracimex 78.947.368

Chung cư Hinode City Plaza

Tổng Công ty CP Thương mại Xây dựng Vietracimex

CHUNG CƯ THĂNG LONG VICTORY PHÚC

Công ty CP Đầu tư

KD & PT Hạ tầng khu Công nghiệp 40.057.225

CHUNG CƯ THĂNG LONG VICTORY PHÚC

Công ty CP Đầu tư KD

& PT Hạ tầng khu Công nghiệp Phúc Hà

Khu đô thị mới Pháp Vân - Tứ Hiệp

Tổng Công ty Đầu tư Phát triển Nhà và Đô thị 69.230.769

Vinhomes Times City Park Hill

Công ty Cổ Phần Phát Triển Đô Thị Nam Hà Nội (Vingroup)

Khu đô thị Việt Hưng

Tổng Công ty Đầu tư Phát triển Nhà và Đô thị HUD 37.662.338

Chung cư Eco City Việt Hưng

Công ty TNHH Thiên Hương

Công ty CP Cơ khí Xây dựng Đại Mỗ (COMA 6) 76.576.577

Công ty cổ phần Đầu tư Mai Linh

Công ty CP Đầu Tư Lạc Hồng 81.927.711

Công ty Cổ phần tập đoàn Bắc Hà

Công ty Cổ phần VICOSTONE 21.079.258

Công ty Cổ phần VICOSTONE

Công ty Cổ Phần Đầu

Tư Và Phát triển Địa Ốc Thành Phố Hoàng

Công ty CP Đầu tư và Phát triển đô thị Long Giang

STATISTICS ON THE NUMBER OF SAMPLES DIVIDED BY APARTMENT QUALITY

Quality Obs Ratio (%) Average price Min Project Investor Max Project Investor

Chung cư Imperia Smart City MIK Group 80.000.000

Công ty CP Dịch vụ Hàng không Thăng Long (Taseco)

Second Class 83 38% 47.863.101 32.000.000 Dolphin Plaza Công ty CP TID 81.927.711

Công ty Cổ phần tập đoàn Bắc Hà

Công ty Cổ phần VICOSTONE 53.703.704 Dự án N07

Công ty cổ phần Thanh Bình Hà Nội

DESCRIPTION OF QUANTITATIVE VARIABLES IN THE MODEL

Name Variable Obs Average Standard deviation

Distance to center of city

CORRELATION MATRIX BETWEEN VARIABLES IN THE MODEL

DATA COLLECTED FROM VALUATION CERTIFICATES OF VNG VALUE CO LTD

No Apartment P District FS NR NB AF

Căn hộ chung cư tầng trung (15 Imperia Smart City), lô đất F4-CH05, khu đô thị mới Tây Mỗ - Đại mỗ - Vinhomes Park, phường Tây Mỗ, quận Nam Từ Liêm, Hà Nội

Căn hộ chung cư tầng 18 (15 Imperia Smart City), khu đô thị mới Tây Mỗ - Đại mỗ - Vinhomes

Park, phường Tây Mỗ, quận Nam Từ Liêm, Hà Nội

Căn hộ chung cư tầng cao (15 Imperia Smart City), khu đô thị mới Tây Mỗ - Đại mỗ - Vinhomes

Park, phường Tây Mỗ, quận Nam Từ Liêm, Hà Nội

Căn hộ chung cư tầng trung, nhờ ở chung cư cao tầng N3, dự án cải tạo xây dựng lại khu tập thể

Nguyễn Công Trứ, phường Phố Huế, quận Hai Bà Trưng, Hà Nội 3.100.000.000

Căn hộ chung cư 2917 nhà R4, dự án Royal City số 72A Nguyễn Trãi, Thương Đình, Thanh

6 Căn hộ chung cư A2605, tòa A chung cư Lạc Hồng, phường Phú Thượng, quận Tây Hồ, Hà Nội Tay Ho 80,5

7 Căn hộ chung cư Lạc HồngWestlake, phường Phú Thượng, quận Tây Hồ, Hà Nội 2.950.000.000 Tay Ho 76,6

8 Căn hộ chung cư Lạc HồngWestlake, phường Phú Thượng, quận Tây Hồ, Hà Nội 3.030.000.000 Tay Ho 76,6

9 Căn hộ chung cư B401 dự án Lạc HồngWestlake, phường Phú Thượng, quận Tây Hồ, Hà Nội 3.200.000.000 Tay Ho 80

Căn hộ 2904, tòa CT4, chung cư Quốc tế Booyoung tại khu đô thị mới Mỗ Lao, Mộ Lao, Hà Đông, Hà Nội 4.000.000.000 Ha Dong 88,6

Căn hộ tầng 29, tòa CT4, chung cư Quốc tế Booyoung tại khu đô thị mới Mỗ Lao, Mộ Lao, Hà Đông, Hà Nội 4.600.000.000 Ha Dong 107,4

Căn hộ tầng trung, tòa CT7, chung cư Quốc tế Booyoung tại khu đô thị mới Mỗ Lao, Mộ Lao,

Hà Đông, Hà Nội 4.500.000.000 Ha Dong 95

Căn hộ tầng 10, tòa CT4, chung cư Quốc tế Booyoung tại khu đô thị mới Mỗ Lao, Mộ Lao, Hà Đông, Hà Nội 4.600.000.000 Ha Dong 107

Căn hộ chung cư 3415A, tòa S1.02 khu đô thị mới Tây Mỗ, Đại Mỗ, Vinhomes smart city Tây

Căn hộ chung cư tầng 34, tòa S1.02-Z34M 2 khu đô thị mới Tây Mỗ, Đại Mỗ, Vinhomes smart city Tây Mỗ, Nam Từ Liêm 2.900.000.000

Căn hộ chung cư tầng 11, tòa S1.01 khu đô thị mới Tây Mỗ, Đại Mỗ, Vinhomes smart city Tây

Căn hộ chung cư tầng 34, tòa S1.06 khu đô thị mới Tây Mỗ, Đại Mỗ, Vinhomes smart city Tây

Căn hộ chung cư số A2709, tòa A, chung cư cao tầng HH2-1 (The Matrix One), Dự án công viên giải trí, trường học và tổ hợp nhà ở, thương mại, dịch vụ Golden Palace A, phường Mễ Trì, quận

Nam Từ Liêm, Hà Nội

Căn hộ chung cư tầng thấp, chung cư cao tầng HH2-1 (The Matrix One), Dự án công viên giải trí, trường học và tổ hợp nhà ở, thương mại, dịch vụ Golden Palace A, phường Mễ Trì, quận

Nam Từ Liêm, Hà Nội 8.500.000.000

Căn hộ chung cư tầng trung, chung cư cao tầng HH2-1 (The Matrix One), Dự án công viên giải trí, trường học và tổ hợp nhà ở, thương mại, dịch vụ Golden Palace A, phường Mễ Trì, quận

Nam Từ Liêm, Hà Nội 7.800.000.000

Căn hộ chung cư tầng 20, chung cư cao tầng HH2-1 (The Matrix One), Dự án công viên giải trí, trường học và tổ hợp nhà ở, thương mại, dịch vụ Golden Palace A, phường Mễ Trì, quận Nam

Căn hộ chung cư số 2709, tòa nhà A4, dự án ngôi sao An Bình 2 (An Bình city), khu đô thị thành phố giao lưu, phường Cổ Nhuế 1, quận Bắc Từ Liêm, Hà Nội 4.029.000.000