econometrics project report house rent of fb20 students in hanoi

HANOI UNIVERSITY

FACULTY OF MANAGEMENT AND TOURISM -000 -

ECONOMETRICS PROJECT REPORT HOUSE RENT OF FB20 STUDENTS IN HANOI

Tutor’s name: Luong Minh Hoang Tutorial Class: 03

Group: 03

Neuyén Thi Bich Phuong ID: 2004040090

Hanoi, April 2023

Contribution: 100% Contribution: 100% Contribution: 100% Contribution: 100%

Table of Contents

1 Testing the overall significance of all coefficients (F-test) cccssssesesessssscesesseseeees 6

I INTRODUCTION

Currently, among high school graduates preparing to enter college, the necessity to rent a home is fairly prevalent You will experience a lot of stress, issues, and troubles while looking for an ideal rental during the process Tenants will initially look about to find a place close to their workplace or college As a result, they emphasize factors like cleanliness, comfort, and a home with ample space The most significant consideration is that the rental cost suits your budget There are actually lots of pupils, all in different financial situations Students who live in a not-so-well-off family can hardly rent a satisfactory room To be able to cover this expense, their parents must toil day in and day out to make a living Additionally, renting a home in a big city, like Hanoi, is not at all inexpensive In order to draw conclusions about these features, we decide to undertake research to find out more about why and how they pick where to reside We will use appropriate models to analyze these aspects in more detail in the following sections We focus on the three factors of distance, quality, and total income that have the biggest effects on the project's outcome in order to get the most useful results from this research The most effective approach for us for assessing the outcome of the economic relationship with its support is a linear regression model Based on the outcome, we are convinced that linear regression models will give us more understanding of the economic theory under examination

As we all know, finding a place to live in Hanoi is a hardship that almost every student who comes to the city to study faces The student housing cannot keep up with the increasing enrollment quota For most students, the housing issue is a constant source of irritation and stress Finding out how each student will use and make rental decisions, as well as delivering them, is part of the accommodation behavior research The student's choice to stay in a hostel is something we are aware of Based on an investigation of the hiring practices of FB20 students (Hanoi University), the study found three factors-quality, distance and spending-that are crucial and have an influence on the behavior of those looking to rent a hostel

The cost of renting is utilized to examine its effect on students’ rental choices Students from economically disadvantaged backgrounds will accept accommodation even if the room rate is a little costly if it has good quality-for example, big, clean, and sturdy rooms-because they can pool

their money and split costs (Industry and Trade Magazine) Each student, however, has unique standards for both cost and quality As a result, the findings of our research are relevant to this issue There is no doubt that the cost of renting is determined by the amount of money students spend during each period Three error-checking tests are utilized for the test: Heteroscedasticity by implying White’s Heteroscedasticity, Autocorrelation by applying Durbin-Watson Test and Multicollinearity by using auxiliary The procedure is explained using the following function: Price of renting = B1 + B2* quality + B3*distance + B4 *total income + 1

The dwelling quality, which includes elements like security, convenience, and service, is another crucial consideration Students are more likely to rent a home with robust security If there is no theft, gambling, fighting, or bickering near where they reside, they feel more at ease and secure The cleanliness, openness, dryness, and brightness of the inn are used to describe the service quality here By creating a boarding house that satisfies these requirements, students’ daily life will be enjoyable and healthy, improving their ability to concentrate on their academics Lastly, inform the landlord of the equipment damage since some hostels do not have an owner nearby and hostels are often maintained by students For instance, there were various issues with the lighting, fans, and water pipelines The quality change that affects the decision to rent is represented by the following equation:

Quality = a1 + o 2*distance o 3*total income + u;

The largest monthly expense that affects a student's housing decision is rent To pay for expenses like rent, transportation, meals, living expenses, incidentals like medicine, recreation, etc., students must make responsible financial decisions The aforementioned costs are not too tough for students to pay if they have revenue from part-time jobs They can even go clothing shopping and go on trips with buddies Additionally, some students’ financial stability depends on their parents; these students must exercise restraint and financial wisdom They won't be able to pay for their lodging and other necessities if they don't spend sensibly, as their budget will run out quickly Therefore, total income per month is an important factor affecting student accommodation

The location distance is the last element of our model that we wish to examine According to Industry and Trade magazine: “Students always tend to find and rent hostels near the school to

facilitate study, as well as save a part of travel costs” The majority of students always want to rent a home close to their place of study because it makes moving around easier and reduces travel expenses However, we don't think this factor has a big impact on a student's decision to rent Students have several options for transportation, including riding a motorcycle or bicycle or purchasing a monthly bus ticket for about 100,000 dong, which is an affordable price for a student As a result, location still affects and has a big impact on renting student accommodation If DATA ANALYSIS

We estimate the relationship between Price of rent and the quality, distance and spending of students majoring in Finance-Banking (K20), Hanoi University Available data is the process of sending random surveys to 50 students of the University The analysis included 50 observations as “Cross-Sequence Data” A series of tests to measure the stability of the data and model were effectively performed We used Eviews software for data analysis and processing this data

1 Price of Renting

The biggest concern of students when renting is the cost of their rental room Each student has a different requirement for their rental condition, it depends on the distance to the school, students’ income, their family conditions and so on We could not predict how much the room rental rate is suitable for each student We are analyzing data based on students from the Faculty of Finance and Banking in Hanoi university; therefore, we have found the average room rental price in Hanoi ranges from 2 million VND to 4 million VND

2 Quality of renting

Currently, the area of residential land is increasingly shrinking due to the rapid population growth and the speed of urbanization, especially in big cities like Hanoi Therefore, not only inns, but also people's houses are in a situation of too small an area According to Dung Nhi,2021, the average living area for each student ranges from 18m2 to 25m2 Therefore, in order to make their life relatively comfortable, students usually want to have a space to live which is large enough for their daily activities The larger space of the room could increase the number of people living together,

reducing the cost of renting Therefore, this is also a criterion that students are quite interested in when renting a hostel

3 Distance

The factor that students are most interested in when choosing to rent an accommodation is the geographical location Students often have no means of transportation when they live far from their parents, therefore the distance from the dormitory room to the school is also a big concern for students majoring in K20, Finance and Banking students, Hanoi University The dormitory room should be close to the school, which is convenient for students to move and easy for them to participate in extracurricular activities

4 Spending

Cost of renting is one of the most important monthly expenses for students Currently, most of the students are still dependent on their parents, some students get extra income through part-time jobs, they need to manage their money wisely otherwise they would not be able to pay for their rent on time

IV RESULT

We assumed that all the variables in our project were normally distributed After that, we assembled the data and organized it in Microsoft Excel before importing it into EViews and running the model to see whether or not the variables we selected have a linear relationship To begin, we use the F-test to state the null hypothesis and alternative hypothesis, such as Hy: By = B = By =0 (means all variables have no effect on Y) and H,: at least one variable has effect on Y or at least one variable is not zero, to define the overall significance of independent variables in our model

Second, in order to be the best-fit model, we used adjusted R square, R square (coefficient of determination), and C.V (coefficient of variance) on four fundamental functional forms: Lin-Lin, Lin-Log, Log-Log, and Log-Lin As a result, we can create a more preferable equation based on the highest R square "Goodness of Fit”.

Third, we used the T-test to determine if each slope coefficient has an effect (differs from zero) or not; in other words, we tested the significance of each individual coefficient that influences our dependent variable rent price

Following that, we utilized the Wald test to eliminate the variable that was not suitable We also utilize the Chow test to determine whether the model is stable

Finally, we want to make sure that there are no flaws in the model As a result, we conducted the following three error checking tests: The Breusch-Godfrey (BG) test of higher order autocorrelation is used to determine heteroskedasticity and autocorrelation Auxiliary regressions, Variance inflation factor, and intercorrelation matrix are used to test and multicollinearity

1 Testing the overall significance of all coefficients (F-test) We have stated hypothesis:

2 Testing for individual coefficient significance

To determine if quality, distance, and total income have an impact on rental prices or not, we will first run three distinct t-tests

T-test of individual partial coefficients We stated hypothesis:

Ho: Bj = 0 i= 2/ 1=3/ i=4)

Based on EViews, we have the table below:

Compare t-statistic vs t-critical |t-statistic| > t, |t-statistic| <t, | [t-statistic| > £„

To sum up, ổ; and f, is statistically significant and different from zero, while a 1s statistically insignificant In other words, while location has no impact on rent prices, total income and quality do

3 Testing drops variables We start with the model:

Price of renting = Bi + B2+quality + B3-distance + By *total income + u, We assume that £3 has been dropped, the hypothesis would be Hạ: Ø62=0

Hạ: 6# 0 We have new model:

Price of renting = Bi + B2*quality + Ba *total income + u, We use F-test and the result of F-statistic is:

(Ruk —RaVuR-KR) I-Rip 2

nur kur

F — statistic = = 0.001587 < 2.806845 = Fy, kun—Khn, ttụR—Kun

where kup = 4, kp = 3, Thun — 50

We do not reject Hp

Therefore, distance is not quite relevant in our model, we can decide to drop distance from this model In other words, there is enough statistical evidence to conclude that the variable distance has not a significant effect on price of renting so we should drop distance variable

Thus, we have estimated equation:

Price df renting-187794.4 * quality + 0.481302 * total income

4 Testing for functional form

Testing all functional forms, including lin-lin, lin-log, log-lin, and log-log model, is necessary to determine which is optimal To select the suitable functional form from those models, we will use R-squared and C.V (coefficient of variation) The best model can be determined by looking at the one with the highest R-squared and lowest C.V We will get the following information

Dependent Variable: LOG(PRICE_OF_RENTING) Method: Least Squares

Date: 04/18/23 Time: 00:00

J -o-|-a-] Forecast|Stats|Resids]

Sample: 150 Included observations: 50

Variable Coefficient Std Error t-Statistic Prob c 1.562665 1.530743 1.020854 0.3127 LOG(QU, -0.140157 0.075014 -1.868417 0.0681 LOG(DISTANCE) 0.014384 0.060431 0.238031 0.8129 LOG(TOTAL_INCOME) 0.862876 0.101342 8.514474 0.0000 R-squared 0.676116 Mean dependent var 1454179 Adjusted R-squared 0.654993 SD dependentvar 0.380943 S.E of regression 0.223756 Akaike info criterion -0.079906 Sum squared resid 2.303064 Schwarz criterion 0.073056 Log likelihood 5.997638 Hannan-Quinn criter -0.021657 F-statistic 32.00871 Durbin-Watson stat 1.398141 Prob(F-statistic) 0.000000

Therefore, we have estimated equation:

log (Pricé-bf renting> 1.562665 - 0.140157*log (quality) + 0.014384*log (distance) + 0.862876* log (total income)

5 Chow test

We assume that the model is divided into RSS, (from 1 to 25 observations) and RSS, (from 26 to 50 observations) After running Eviews we have

(RSSp — RSS, — RSSyVk (RSSI+RSS;)Ám—2k)

E-statistic = 0.66347 < 2.594263 = Fo rire

Where: n=50, k=4

Therefore, we do not reject Ho

In conclusion, there is no structural change in our sample 6 Error — checking test

6.1 Multicollinearity

There are numerous methods for detecting multicollinearity, including using EViews output to determine whether the R-square in the new model is greater than the R-square in the original model, supplemental regressions, variance inflation factor (VIF), and intercorrelation matrix To detect multicollinearity error, we apply the F-test in Auxiliary regression

First, we examine if our model has any functional relationships between quality and the other independent variables We have a formula:

Quality =«,+ «, «distance + ©, * total income+u The hypothesis would be:

Ho: Multicollinearity does not exist H,: Multicollinearity exists

We use F-test and the result of F-statistic is:

2

Roe x) x3, sua Ấy Ak-l) q= Rei wos, se XR XŒ-#)

F-statistic = = 1.9656 < 2.8068 = Fy f-1 nk Where k=4, n=50

Therefore, we do not reject Hy We may retain the quality variable in our model

Next, we examine if our model has any functional relationships between distance and the other independent variables We have another formula:

Distance = fy + My * Quality + u * Totalincome+u

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The hypothesis would be: Ho: Multicollinearity does not exist H,: Multicollinearity exists

We use F-test and the result of F-statistic is:

2

Roe x) x3, cu Ak-l) d- Re paca, wo Xk XŒ-#)

F-statistic = = 0.5186 < 2.8068 = Fa f-1 nk Where k=4, n=50

Therefore, we do not reject Hy We may retain the distance variable in our model

Finally, we examine if our model has any functional relationships between total income and the other independent variables We have another formula:

Total income =y, + y2 * Quality + y3 * Distance +u The hypothesis would be:

Ho: Multicollinearity does not exist H,: Multicollinearity exists

We use F-test and the result of F-statistic is:

2

đã x2 x3, cu Ak-l) d- Re yaa, wo Xk XŒ-#)

F-statistic = = 1.2949 < 2.8068 = Fa f-1 nk Where k=4, n=50

Therefore, we do not reject Hy We may retain the total income variable in our model

In addition, we also use variance inflation factors (VIF) to determine whether or not multicollinearity exists, comparing the centered VIF with 10

We can conclude that this model has multicollinearity if the centered VIF is more than 10

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