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Regression Analysis: Real Estate Sector

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Regression Analysis: Real Estate Sector

Regression Analysis: Real Estate Sector

Avishek Dasgupta 13P136

Rahul Agarwal 13P158

Raman Mahajan 13P160

8/2/2014

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TABLE OF CONTENTS

  1. Objective of the study
  2. Description of data
  3. Empirical analysis
  4. Conclusion

OBJECTIVE OF THE STUDY

To determine the regression equation to forecast real estate index (CNX Realty) with respect to various associated independent variables.


DESCRIPTION OF DATA

Frequency of the data: Monthly

Time span of the data: 37 months (Aug-2011 to Aug-2014)

No. of observations: 37

Dependent Variable:

 

CNX Realty

Independent Variables:

CNX Metal Index Values

Ultra Tech Cement Prices

Dollar Prices

Interest Rates (Repo rate)

IIP Index Values

Crude Oil Prices

Details of the Dependent Variable

CNX 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 Variables

CNX Metal

The 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 Prices

The dollar prices would affect the amount of investment in the realty sector and hence could be an important independent variable for analysis.

 Interest Rates

The 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 Production

This 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 analysis

The 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_CEMENT

Dependent Variable: CNX_REALTY

Method: Least Squares

Date: 08/02/14   Time: 14:09

Sample: 2011M08 2014M08

Included observations: 37

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

535.8208

145.3440

3.686571

0.0009

CNX_METAL

0.055683

0.015674

3.552637

0.0013

CRUDE_OIL

-0.005052

0.012089

-0.417867

0.6790

DOLLAR_PRICE

-5.104570

2.134630

-2.391314

0.0233

IIP

0.339317

0.485912

0.698310

0.4904

INTEREST_RATES

-28.24337

15.61245

-1.809028

0.0805

ULTRA_TECH_CEMENT

0.038290

0.024198

1.582349

0.1241

R-squared

0.798654

    Mean dependent var

217.5122

Adjusted R-squared

0.758385

    S.D. dependent var

38.83500

S.E. of regression

19.08910

    Akaike info criterion

8.904770

Sum squared resid

10931.82

    Schwarz criterion

9.209539

Log likelihood

-157.7383

    F-statistic

19.83286

Durbin-Watson stat

0.945321

    Prob(F-statistic)

0.000000


We 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.

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