tiểu luận kinh tế lượng factors affecting house price

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMY REPORT ECONOMETRICS HOUSING PRICES AND FACTORS AFFECTING HOUSING PRICES IN CALIFORNIA, USA Instructor: MSc Chu Thi Mai Phuong Student: Group AH – KTEE309.2 Nguyen Lan Anh 1612250005 Le Mai Huong 1612250015 Le Thu Huong 1616250014 Hanoi, December 2017 EVALUATION Name Nguyen Lan Anh Le Mai Huong Le Thu Huong Job Evaluation - Estimate the linlin model, loglog model - Test the lin-lin model, log-log model - Find source data - Write conclusion - Enthusiastic, responsible for the work assigned, completed the work well - Have the sense of helping the team members work - Describe the data, variables, correlation - Estimate the log-lin model - Test the log-lin model - Write introduction, abstract - Be responsible for the work, motivate, support members work - Division of work for the reasonable members - Complete the deadline - Final check and Mark 10/10 10/10 9/10 edit the report CATEGORY EVALUATION .2 INTRODUCTION ABSTRACT ANALYSIS SECTION 1: DESCRIBE THE VARIABLES, DATA AND CORRELATION I Describe the variables II Describe the data III Describe the correlation between variables SECTION 2: ESTIMATED MODEL AND STATISTICAL INFERENCES .9 I Linear – linear model II Log – linear model 15 III Log – log model 21 CONCLUSION 28 REFERENCES .30 INTRODUCTION Econometrics is the study of the social sciences in which the tools of economic theory, mathematics and statistical speculation are applied to analyze economic problems Econometrics uses the mathematical statistics methods to find out the essence of statistics, make conclusions about the collected statistics that can make predictions about economic phenomenon Since its inception, econometrics has provided economists with a sharp instrument for measuring economic relations As economics students, we recognize the need to study and learn about Econometrics in logical and problem analysis To better understand how to put the Econometrics into reality and to apply the Econometrics effectively and correctly, our team would like to develop the ECONOMETRICS REPORT under the guidance of MSc Chu Thi Mai Phuong In this report, we used the econometric analysis tool GRETL to analyze the topic "Housing Prices and Factors Affecting Housing Prices in California, USA " We sincerely thank our instructor - MSc Chu Thi Mai Phuong for helping us to implement this report During the course of the report, despite all the efforts, we certainly can not avoid the errors, we look forward to your comments so that our team can improve this report ABSTRACT Recently, according to the report of the National Association of Realtors in the United States, Vietnam is one of the 10 countries in the world with the highest investing in real estate in the USA (VnExpress) California is one of the places where there are many overseas Vietnamese living and also the state has a vibrant real estate market that Vietnamese and people from other countries would like to invest in So what has affected housing prices in this area? As economics students interested in real estate, we decided to research on the topic "Housing Prices and Factors Affecting Housing Prices in California, USA” In the process of searching for documents, we read a lot of foreign writings about the factors affecting housing prices in many regions and countries around the world such as "Macroeconomic Determinants of the Housing Market "- LSE," House Price Dynamics in the United States "- IMF, After synthesizing and discussing, we decided to select a few factors that affect the price of a house to conduct research on the subject Due to the limited time, we can only pick up a few prominent factors, hope for your understanding Thank you! ANALYSIS SECTION 1: DESCRIBE THE VARIABLES, DATA AND CORRELATION I Describe the variables Function we have in this report will include these following variables: Dependent variable: salepric - Sale price and characteristics of house in communities of California: Dove Canyon and Coto de Caza (thousands of dollars) Independent variables: sqft – Living area in square feet garage – Number of car spaces city – City: for Coto de Caza and for Dove Canyon II Describe the data Data collections We collect data of Sale price and characteristics of house in communities of California: Dove Canyon and Coto de Caza from Ramanathan - Gretl III Describe the correlation between variables  Correlation Matrix for Linear – linear Model: Correlation coefficients, using the observations - 224 5% critical value (two-tailed) = 0.1311 for n = 224 - salepric 1.0000 sqft 0.9193 1.0000 garage 0.6536 0.5818 1.0000 city 0.5033 0.4275 0.2421 1.0000 salepric sqft garage city - Salepric is directly proportional to sqft The set standard between these two variable is quite high - Salepric is directly proportional to garage The set standard between these two variable is medium - Salepric is directly proportional to city The set standard between these two variable is medium  Correlation Matrix for Log – linear Model: Correlation coefficients, using the observations - 224 5% critical value (two-tailed) = 0.1311 for n = 224 l_salepric 1.0000 sqft 0.8857 1.0000 garage 0.6135 0.5818 1.0000 city 0.6486 0.4275 0.2421 1.0000 l_salepric sqft garage city As we can see - - - Salepric is directly proportional to sqft The set standard between these two variable is quite high Salepric is directly proportional to garage The set standard between these two variable is medium Salepric is directly proportional to city The set standard between these two variable is medium  Correlation Matrix for Log – log Model: Correlation coefficients, using the observations - 224 5% critical value (two-tailed) = 0.1311 for n = 224 - l_salepric 1.0000 l_sqft 0.9001 1.0000 l_garage 0.5988 0.5665 city 0.6486 0.4750 l_salepric l_sqft 1.0000 - - - 0.2365 l_garage 1.0000 city Salepric is directly proportional to sqft The set standard between these two variable is quite high Salepric is directly proportional to garage The set standard between these two variable is medium Salepric is directly proportional to city The set standard between these two variable is medium SECTION 2: ESTIMATED MODEL AND STATISTICAL INFERENCES I Linear – linear model Estimation Model 1: OLS, using observations 1-224 Dependent variable: salepric Coefficient −704.854 0.220060 129.286 101.275 Const Sqft Garage City Mean dependent var Sum squared resid R-squared F(3, 220) Log-likelihood Schwarz criterion Std Error 53.3132 0.00891506 20.3863 19.0453 642.9294 3641423 0.881604 546.0556 −1403.821 2829.289 t-ratio −13.22 24.68 6.342 5.318 S.D dependent var S.E of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn p-value 10.0 may indicate a collinearity problem sqft garage city 1.742 1.512 1.224 VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient 18 between variable j and the other independent variables Belsley-Kuh-Welsch collinearity diagnostics: - variance proportions lambda cond const sqft garage 3.591 1.000 0.002 0.004 0.001 0.354 3.185 0.008 0.003 0.004 0.044 9.020 0.169 0.787 0.013 0.011 18.192 0.821 0.206 0.981 lambda = eigenvalues of X'X, largest to smallest city 0.022 0.858 0.120 0.001 cond = condition index note: variance proportions columns sum to 1.0 We see: VIF (sqft) = 1.742 < 10 VIF (garage) = 1.512 < 10 VIF (city) = 1.224 < 10 → The model does not contain perfect multicollinearity 2.2.3 Testing Heteroskedasticity  Given that the hypothesis is: { : :  White’s test: White's test for heteroskedasticity OLS, using observations 1-224 Dependent variable: uhat^2 coefficient std error t-ratio p-value -const 0.0199661 0.0562010 0.3553 0.7227 sqft −1.76611e-05 1.40238e-05 −1.259 0.2093 garage 0.0326536 0.0276360 1.182 0.2387 city −0.0776614 0.0555116 −1.399 0.1633 sq_sqft −2.70427e-012 8.80744e-010 −0.003070 0.9976 X2_X3 6.84208e-06 3.88359e-06 1.762 0.0795 * X2_X4 7.65262e-06 9.38796e-06 0.8152 0.4159 sq_garage −0.0127315 0.00500544 −2.544 0.0117 ** X3_X4 0.0170934 0.0166541 1.026 0.3059 Unadjusted R-squared = 0.216999 19 Test statistic: TR^2 = 48.607688, with p-value = P(Chi-square(8) > 48.6077) = 7.55903e-008  We see: p-value = P(Chi-square(8) > 48.6077) = 7.55903e-008 < α = 0.05 → Reject H0 → The model has heteroskedasticity problem  Method: Using Robust to fix the problem: Model 4: OLS, using observations 1-224 Dependent variable: l_salepric Heteroskedasticity-robust standard errors, variant HC1 Coefficient Std Error 5.01704 0.0615381 0.00020749 1.69491e-05 0.117941 0.0247488 0.267482 0.0189490 Const Sqft Garage City Mean dependent var Sum squared resid R-squared F(3, 220) Log-likelihood Schwarz criterion 6.365959 4.038026 0.888862 336.2140 131.9375 −242.2283 t-ratio 81.53 12.24 p-value