tiểu luận kinh tế lượng FACTORS AFFECTING THE PRICE OF THE HOUSE

FOREIGN TRADE UNIVERSITY INTERNATIONAL ECONOMICS FACULTY ▯▯▯▯ ECONOMETRICS FINAL EXAM TOPIC: FACTORS AFFECTING THE PRICE OF THE HOUSE Lecturer: Assoc Prof Tu Thuy Anh Class: K57 JIB Name Nhân Thanh Tùng Nguyễn Hoàng Phương Oanh Vũ Minh Hằng ID 1815520237 1815520213 1815520169 Contents I Abstract Background: This paper looks at quantifiable and easily obtainable factors that affect housing price such as Age of the house, square of the house, the distance from the house to university, whether the house has pool or not and whether the house has fireplace or not , using data from our survey The goal of this analysis is to test the relationship between these five variables: Age, Sqft, Utown, Pool, Fplace using a regression model Methods: A survey was conducted with people mainly in Ha Noi and Ho Chi Minh City between September 17 and September 30/2019 The participants provided data about potential risk factors Results: Among 1100 eligible participants, 1000 (90.9%) had refraction and questionnaire data available All of the participants were surveyed with housing price From that, we analyzed the information and then the result comes out: Distance to the university is the most important factor that affect housing prices Conclusions: It can be concluded that age, acreage, location, utilities affect, or at least statistically so, the housing prices From that, the relevant people can rely on to make the right decision in the future II Introduction: 2.1 Basic Concept: 2.1.1 What is econometrics? Literally interpreted, econometrics means “economic measurement” Econometrics is the application of statistical and mathematical theories in economics for the purpose of testing hypotheses and forecasting future trends It takes economic models, tests them through statistical trials and then compares and contrasts the results against real-life examples 2.2.2 Why we have to study econometrics? Econometrics is a set of research tools also employed in the business disciplines of accounting, finance, marketing and management In today’s world, where there is an insurmountable amount of data, econometrics is a vital skill to have It can help us get a better understanding of the available data - which can help in most economic decision-making processes That’s why it is used by social scientists, specifically researchers in history, political science and sociology, etc Econometrics plays an important role in such diverse fields as forestry, and in agricultural economics, Most economists use economic data to estimate economic relationships, test economic hypotheses, and predict economic outcomes “Econometrics fills a gap between being “a student of economics” and being “a practicing economist.” “ 2.2 Why we carry out this survey and our expectation of the overall results? First and foremost, the fact that we carry out this subject help us tremendously with our future careers when we have to apply what we learned into practice For any student deciding to pursue the field of finance or financial consultant, again econometrics plays a very important role To this, econometrics can become quite handy Hence, econometrics comes into use in some way or the other in almost all fields that a student will pursue in our career The reason why we choose the topic: “Factors affecting the price of the house” is quite simple The more Vietnamese economy develops , the higher demand for house in center city is The number of households in the country is set to grow by 3% by 2013, according to the Vietnam Housing Forecast 2013 from market research company RNCOS The government has directed the Ministry of Construction to build more homes and US$173 million is being invested in 37 low cost housing projects covering a total of 750,000 square meters A high amount of investment in the Vietnam housing sector has resulted in soaring growth over the past few years especially in cities like Hanoi and Ho Chi Minh City That is the general development of developing countries and Viet Nam can not be outside of that trend So, that is the reasons motivated us to go into this field and topic as well III Literature review Because the concerns of people about housing price, a lot of researches have been conducted to find out the main factors that affect price Here is a brief summary of the different and most commonly factors used to affect the price of house III.1 Area Many studies showed that the floor area have a positive relationship to the price of the house (Limsombunchao, 2004) This is also similar to the price of land This is because buyers are willing to pay more for a larger space, especially the functional space The land with an area larger than meet the needs of families with many members and those who can afford to pay for a better standard of living For example, Limsombunchao (2004) studied in the housing market in New Zealand found that adding more area to increase the value of a land is about 0.08% Bajari and Kahn (2000) reported that large land area related to the price of land 3.2 Location Location factors to be considered in many studies Factors related to the location identified in relation to the entire metropolitan area Location factors easiest and most common implementation is to measure position distance from the house to the centre which significantly impacted on land pricing which had been proven by researchers (such as Follain and Jimenez (1985); Bajari and Kahn (2000); Limsombunchao (2004)) Buyers tend to trade-off between the cost of housing or land to build house to the cost of travel Positive impact of public transport services on land prices have been examined empirically Therefore, when it comes to calculating a home’s value, location can be more important than even the size and condition of the house 3.3 Security The safety of the area in which the land as located or crime rate also plays an important role in determining land value If the area is one that is crime riddled then the value will be lower (Gregory Akerman, 2009) Babawale and Adewunmi (2011) indicated that the outside factors such as security, parking- lot, the distance from apartments to church also impacts the price of real estate It is important to the explanation of variations in land prices are variables derived from urban theory, such as distance to the CBD, and from the amenity literature, such as a community's crime rate, arts, and recreational opportunities (Haurin and Brasington, 1996) Austin Troy and J Morgan Grove (2008) using Hedonic analysis of property data in Baltimore, they attempted to determine whether crime rate mediates how parks are valued by the housing market The results showed that parked proximity is positively valued by the land market where the combined robbery and rape rates for a neighbourhood are below a certain threshold rate but negatively valued where above that threshold 3.4 Social infrastructure The price of land also depends on how far social infrastructure from the land Infrastructure is the large scale public services or systems, services and facilities of a country or region that are necessary for economic activity, including power and water supplies, public transportation, telecommunications, roads and school.Closing to shopping area or shopping centre showed the impact on the value of surrounding residential properties Leong et al (2002) noted that there is a shopping centre within km radius making the price of land will increase by around 0.11% in Penang, Malaysia Besides that, external benefits, including beautiful scenery, quiet atmosphere and the presence of urban green space has been studied experimentally by Sander and Polasky (2009) used data in the city of Ramsey, United States Results also showed that people appreciated residential areas with green space and access to the recreation area with trees The quality of environment also influences prices of apartments in Brazil The apartments located near sewage treatment factory has low value, while near the public service establishment has positive impact to the apartment’s price (Furtado 2009) All in all, real estate has no value if it has no utility, if it is not scarce and if it is not effectively demanded In conclusion, social infrastructure has vital position when it comes to housing price Although the factors above are the most precise factors that affect housing price worldwide but there is no research focusing on Viet Nam, especially the housing price in the center city such as Ha Noi and Ho Chi Minh City So, that really motivated us to conduct this research and find out the result IV DESCRIPTION OF DATA 4.1 How did we collect the data? At first, we had to answer questions: Which data to collect? (What is the main factors that affect the housing price), How to collect data? (Online and offline), Whose data will we collect from? (People from different ages), When to collect data? (In Ha Noi and Ho Chi Minh City of 13 days from 17/9/2019) Next, we listed main factors that affect housing price(Age of the house, square of the house, the distance from the house to university, whether the house has pool or not and whether the house has fireplace or not ) in general By listing these factors, we can prepare the detail questions for the form It took almost days to fully developed the question list and make sure that all the questions were carefully chosen, also with the capable answers We created a simple form by Google Forms and printed it out, made it as convenient as possible for people who filled that form Most of the questions were multiple-choice or short answers that would make people comfortable to fill it because it didn’t waste lots of time All members of the group were responsible for sharing the form to as many people as possible that would be more and more people filling the form By promoting on many Facebook groups and asking people from different ages in Ha Noi and Ho Chi Minh City, we finally got a quite good result: 1000 filled forms which means 1000 observations, more than 60% forms are filled by adults in different universities and university students in the age group of 18 to 22 made up the rest of the number 4.2 Explaining variables Unit of Name Measu Meaning re Explanation and Predictions Dollars is a highlyDependent variable (Y) Price An index to evaluate Dollars price of a house convertible currency, which is commonly used in a lot of transactions The greater amount of Sqft The total square of square the house takes the house (Square up, the more creating footage of house) positive effect on its Feet price Number of years for Age which the house has Years been used (Age of house) Independent has been used, the less its price would be If Utown = means variables (X) An index to evaluate Utown located close to at least located close to a one university university or not If not, Utown = if the house has at least pool or not Pool that the house is if the house is An index to evaluate If Pool = means that the house has at least pool An index to evaluate If not, Pool = If Fplace = means that if the house has at the house has at least least one fireplace Fireplace or not If not, Fplace = Fplace 4.3 The longer the house Using GRETL to analyze the variables 4.3.1 Summary of all Variables 10 V ECONOMETRICS MODEL V.1 Population regression function (PRF) PRF: Price = β0 + β1× Sqft+β2×Age+β3×Utown+ β4×Pool + β5×Fplace + ui 5.2 Sample regression function (SRF) PRF: Price = β0 + β1× Sqft+β2×Age+β3×Utown+ β4×Pool + β5×Fplace 5.3.Expectation about the variables Name Meaning Sign An index to Dependent variable (Y) Price which is commonly price of a used in a lot of transactions The total variables (X) The greater amount of square of Sqft the house (Square square the house takes + house) Number of up, the more creating positive effect on its footage of Age Predictions Dollars is a highlyconvertible currency, evaluate house Independent Explanation and price - The longer the house years for has been used, the less which the its price would be house has been used (Age of house) 12 An index to evaluate if the house is Utown located that the house is close to a located close to at least university one university or not An index to If not, Utown = evaluate if Pool If Pool = means that the house the house has at least has at least pool pool or If not, Pool = not An index to evaluate if the house has at least one fireplace or Fplace If Utown = means not 13 If Fplace = means that the house has at least pool If not, Fplace = VI RESULT ESTIMATION 6.1.Run the Model with GRETL: 6.2.Interpretation of the GRETL model Name Number of observation Symbols Meaning Using There are 1000 observations observations= 11000 F-statistic F(5,994) = This is the F-statistic is the 1313.837 Mean Square Model divided by the Mean Square Residual, yielding F= 1313.837 The numbers in parentheses are the Model and Residual degrees of freedom 14 Co-efficient β1 = 83.1832 Holding that other factors (Coef.) remain constant, when the square of house increases by feet, its Price increases by 83.1832 Dollars β2 = -192.991 Holding that other factors remain constant, when the age of the house increases by year, its Price decreases by 192.991 Dollars β3 = 60196.2 Holding that other factors remain constant, when there is at least university that the house is located close to, its Price increases by 60196.2 Dollars β4 = 4352.57 Holding that other factors remain constant, when the house has at least pool, its Price increases by 4352.57 Dollars β5 = 1398.81 Holding that other factors remain constant, when the house has at least Fireplace, its Price increases by 1398.81 Dollars Constant β0 = 6911.88 When other independent (_cons) variables equal 0, the expected value of Price is 6911.88 15 R2= 0,8686 R-squared Indicates that the model is able to explain 86.86% changes in the Price of the house Adjusted R- = 0,8679 A modified version of R- squared squared that has been adjusted for the number of predictors in the model 6.3.Rebustness Check 6.3.1.Multicollinearity Test 16 *Conclusion: The value of VIF of these variables are less than 10 As a result, it indicates that the model does not have multicollinearity 6.3.2.Normality Test JB (Jarque - Bera Test) Hypothesis 17 *Conclusion: We have p-value equals to 0.934936, which is comparatively high; thus we have enough evidence not to reject H0, the model has a normal distribution 18 *Conclusion: We have p-value = 0.86 > 0.05 With a 5% significance level, the model has a normal distribution 6.3.3.Heteroscedasticity Test (White’s Test) Hypothesis 19 *Conclusion: We have p-value = 0,108>0.05 With a 5% significance level, heteroscedasticity is not presented 6.3.4.Breusch – Pagan test 20 *Conclusion: We have p-value = 0,424>0.05 With a 5% significance level, heteroscedasticity is not presented 6.4.Hypothesis testing of the coefficient number and variations Population regression function (PRF) PRF: Price = β0 + β1× Sqft+β2×Age+β3×Utown+ β4×Pool + β5×Fplace + ui Sample regression function (SRF) 21 SRF: Price = β0 + β1× Sqft+β2×Age+β3×Utown+ β4×Pool + β5×Fplace 6.4.1 Hypothesis testing of coefficient numbers *METHOD: Calculating t0 Name Hypothesis testing Result Reject H0 Reject H0 Reject H0 Reject H0 Not reject H0 22 * Conclusion: With the level of significant is 5%, of coefficient numbers including , , and are statistically significant, the coefficient number is not statistically significant As a result, there is enough evidence to reject the null hypothesis and conclude that at least one independent variable in the subset (sqft, age, utown, pool) does have explanatory or predictive power on Price, so we don’t reduce the model by dropping out this subset 6.4.2 Hypothesis testing of R2 (How well the model predict the dependent variable?) Hypothesis Using Fisher R2 = 0.8686 , so we have F = 1313.837 * With the level of significant is 5%, we have = 2.21 F > As a result, there is enough evidence to reject the null hypothesis and conclude that The model is statistically fit with the level of significant is 5% *Conclusion: With the level of significant is 5%, the model is statistically fit with the level of significant is 5% 23 VII Result analysis and policy implication From data analysis in preceding sections, we have gained an overall view of the data set given in terms of statistical proof of the relationship between housing prices and each of the factors proposed As mentioned at the beginning of this report, we aim to learn how age, acreage, location, utilities associated with housing price In other word, we are concerned about what is the willingness of buyers to pay for these components Following the analysis of data, regression model run and hypothesis testing, it can be concluded that age, acreage, location, utilities affect, or at least statistically so, the housing prices Therefore, tenants, investors or constructors should take all of these ingredients into account when making deals 24 VIII Conclusion This report is completed by the contribution of all members of our group and also from all the knowledge that we’ve gained from Econometrics By running GRETL model and giving the hypothesis testing, we have full comments on the impacts of each variable and their significance on the dependent variable, thereby giving us the real factors affect housing price in Viet Nam Again, due to the limitation of knowledge and resources, our report may contain misinterpretations We hope that Mrs Tu Thuy Anh and readers can give us constructive comments on report so that we would improve ourselves and better in the future IX REFERENCES Follain, J.R., Jimenez, E., 1985 Estimating the demand for housing characteristics: a survey and critique Regional science and urban economics 15, 77-107 Haurin, D.R., Brasington, D., 1996 School quality and real house prices: Inter- and intrametropolitan effects Journal of Housing Economics 5, 351-368 Leong, C.T., University of South, A., International Graduate School of, M., 2002 Residential property preferences in Penang, Malysia [sic] : a hedonic price approach In 25 Limsombunchao, V., 2004 House price prediction: hedonic price model vs artificial neural network Sander, H.A., Polasky, S., 2009 The value of views and open space: Estimates from a hedonic pricing model for Ramsey County, Minnesota, USA Land Use Policy 26, 837-845 Troy, A., Grove, J.M., 2008 Property values, parks, and crime: A hedonic analysis in Baltimore, MD Landscape and urban planning 87, 233-245 Appendix: Due to the length of our appendix, we attached it in the folder Please click on that to see our specific figure 26 ... obtainable factors that affect housing price such as Age of the house, square of the house, the distance from the house to university, whether the house has pool or not and whether the house has... housing price( Age of the house, square of the house, the distance from the house to university, whether the house has pool or not and whether the house has fireplace or not ) in general By listing these... Price which is commonly price of a used in a lot of transactions The total variables (X) The greater amount of square of Sqft the house (Square square the house takes + house) Number of up, the