We can discuss our research methodology, the sample, analytical procedures, results of the various analyses conducted on the data (descriptive statistics calculations**, correlation matrix data and positive coefficients) as well as importance of calculating regression weights.

**Descriptive statistics provides methods and tools for collecting and exploring the real estate data collected.  Measures of central tendency include the mean and standard error of the mean while the measures of variability include the minimum and maximum variables, the standard deviation and the standard error.

 

 

 

 

We conduct a study that uses executed sales data in order to evaluate metrics of success affecting brokerage firms.  The panel data (longitudinal data) set contains observations on multiple brokerage firms where each company is observed at 2 or more points in time. Data on 3 real estate franchising firm, each company is observed in 7 years, for a total of 21 observations.

 

Observational unit: a year in a firm

- 3 Real Estate Franchises

- 7 Years (2005, 2006, 2007, 2008, 2009, 2010 & 2011)

- Balanced panel (no missing observations), so total # Observations = 7 x 3 = 21

 

Variables:

- Number of associates

- Number of offices

- Initial Outlays

- S&P/Case-Shiller Home Price Index

 

The data is analyzed in two ways. The first involved a descriptive statistics analysis and second approach to analyzing data utilized a correlation matrix of all vectors and regression weights.

 

Since the data is heavily skewed, a logarithmic transformation is performed (logarithms with base 10). Logarithmically transformed data exhibit log-normality, and thus allow for using the Pearson correlation coefficient. 

 

As an initial step in factor analysis, the vector data is log normalized before using the Pearson correlation. Therefore, the panel data contains normalized variables.

 

 

These are the selected independent variables

The number of offices and associates, initial outlays(weighted), number of executed sales transactions as well as S&P/Case-Shiller Home Price Index alter sales volume that a real estate brokerage firm produces. 

Delighted to share a hypothesis with you:

There are indicators that are predictors of later success / profitability in a real estate brokerage company.

Independent variables (regressors) presumed to affect or determine a dependable variable.

One of the selected independent variables is categorized as "initial outlays" which is directly connected to the brokerage firm.

* Initial outlays (Present Value)

   Formula: PV = FV ( 1 + r) - n

   FV = Franchise fee + Renewal fee

   r   = Continuing royalty fee based on a

           percentage of sales

   n   = Term

 

After the panel data of normalized variables is created, the selection of independent variables  is generated from a listing and selling brokerage perspective.

        

   Contact

   Edgard Asensio, MBA

  Address:  2071 Torrance Boulevard

                  Torrance, California 90501 USA

  Office:     1-310-618-0808

  Fax:        1-310-618-0505

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  Email:     edgard@asensiorealestate.com

   

 

Attention Website Visitors including Multi-National Corporations (MNC's) and Multi-National Enterprises (MNE's)

If you are interested in pursuing business alliances and strategies in connection with our real estate modeling and research , you can contact Edgard Asensio, MBA.

A Real Estate Brokerage Firm

Quantitative Analysis

prepared by Edgard Asensio, MBA

edgard@asensiorealestate.com

BRE #01253842

Research

BRE #01253842

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