Regression Analysis: Real Estate Sector
Regression Analysis: Real Estate Sector
Regression Analysis: Real Estate SectorAvishek Dasgupta 13P136Rahul Agarwal 13P158Raman Mahajan 13P1608/2/2014[pic 1][pic 2][pic 3]TABLE OF CONTENTSObjective of the studyDescription of dataEmpirical analysisConclusionOBJECTIVE OF THE STUDYTo determine the regression equation to forecast real estate index (CNX Realty) with respect to various associated independent variables.DESCRIPTION OF DATAFrequency of the data: MonthlyTime span of the data: 37 months (Aug-2011 to Aug-2014)No. of observations: 37Dependent Variable: CNX RealtyIndependent Variables:CNX Metal Index ValuesUltra Tech Cement PricesDollar PricesInterest Rates (Repo rate)IIP Index ValuesCrude Oil PricesDetails of the Dependent VariableCNX Realty: CNX Realty Index is designed to reflect the behavior and performance of Real Estate companies. The Index comprises of 10 companies listed on National Stock Exchange of India (NSE).CNX Realty Index is computed using free float market capitalization method, wherein the level of the index reflects the total free float market value of all the stocks in the index relative to particular base market capitalization value.As the realty sector could be dependent on multiple factors like cement, steel, interest rates in the economy, crude oil prices, Index of Industrial Production (IIP) and dollar value we have considered such variables as independent variable.Details of the Independent VariablesCNX MetalThe CNX Metal Index is designed to reflect the behavior and performance of the Metals sector (including mining). The CNX Metal Index comprises of 15 stocks that are listed on the National Stock Exchange (NSE).CNX Metal Index is computed using free float market capitalization method, wherein the level of the index reflects the total free float market value of all the stocks in the index relative to particular base market capitalization value.
This variable has been considered as steel is a primary resource in the construction industry and this index captures various steel sector companies.Ultra Tech Cement This is one of the major cement companies in India and its price changes would affect the construction industry. Hence this should serve as an important independent variable for analysis.Dollar PricesThe dollar prices would affect the amount of investment in the realty sector and hence could be an important independent variable for analysis. Interest RatesThe interest rates primarily the Repo rates will indicate the general lending rate in India and will govern the major financing source of the realty sector. Thus this may be an important independent variable.Index of Industrial ProductionThis index captures the growth of various sectors of the economy thereby indicating the wellness of the economy. As the economy would play a major role in realty sector’s growth hence we consider IIP as a independent variable.Crude Oil Crude Oil prices generally affect the economy and would in turn indirectly affect the growth of realty sector hence Crude oil has been selected as one of the independent variables. EMPIRICAL ANALYSIS Running the regression analysisThe regression analysis is run in the EVIEWS software using the following command LS CNX_REALTY C CNX_METAL CRUDE_OIL DOLLAR_PRICE IIP INTEREST_RATES ULTRA_TECH_CEMENTDependent Variable: CNX_REALTYMethod: Least SquaresDate: 08/02/14 Time: 14:09Sample: 2011M08 2014M08Included observations: 37VariableCoefficientStd. Errort-StatisticProb. C535.8208145.34403.6865710.0009CNX_METAL0.0556830.0156743.5526370.0013CRUDE_OIL-0.0050520.012089-0.4178670.6790DOLLAR_PRICE-5.1045702.134630-2.3913140.0233IIP0.3393170.4859120.6983100.4904INTEREST_RATES-28.2433715.61245-1.8090280.0805ULTRA_TECH_CEMENT0.0382900.0241981.5823490.1241R-squared0.798654 Mean dependent var217.5122Adjusted R-squared0.758385 S.D. dependent var38.83500S.E. of regression19.08910 Akaike info criterion8.904770Sum squared resid10931.82 Schwarz criterion9.209539Log likelihood-157.7383 F-statistic19.83286Durbin-Watson stat0.945321 Prob(F-statistic)0.000000We observe that the DW Stat has a value of 0.94 (less than 2.0) which indicates possibility of AUTOCORRELATION. Hence we will apply the Lagrange Multiplier test to check whether there is autocorrelation or not.