Excerpt

## Table of Contents

List of Tables

1 Introduction

2 Statistical Analysis

2.1 Variable Determination

2.1.1 Dependent Variable

2.1.2 Independent Variables

2.1.3 Independent Dummy Variables

2.2 Descriptive Statistics

2.2.1 Mean

2.2.2 Median

2.2.3 Standard Deviation

2.2.4 Minimum and Maximum

2.3 Correlation Analysis

2.4 Regression Analyses

2.4.1 Regression Analysis

2.4.2 Regression Analysis II

3 Conclusion

4 References

5.1 Raw Data

5.2 Descriptive Analysis

5.3 Correlation Analysis

5.4 Regression Analysis

5.5 Regression Analysis II

## List of Tables

Table 1: Means and their meanings in terms of the sample

Table 2: Medians and their meanings in terms of the sample

Table 3: Standard deviations and their meanings in terms of the sample

Table 4: Minima and maxima and their meaninigs in terms of the sample

Table 5.1: Raw Data Part I

Table 5.2: Raw Data Part II

Table 6: Descriptive Analysis

Table 7: Correlation Analysis

Table 8: Regression Analysis I

Table 9: Regression Analysis II

## 1 Introduction

In Wichita Falls, Texas, a new real estate company was established. In order to become acquainted with the local residential market, the company requires a statistical analysis of the determinants that are likely to influence the price per square foot of single family homes in this area. For this purpose a consultant was commissioned. In order to investigate the information required by the real estate company, the relevant data was gathered from realtor.com, a real estate agent.

In order to conduct the analysis, at first the necessary data has to be collected. For this purpose a sample of fifty houses will be collected. The consultant decides to investigate nine variables that are likely to be factors in determining the asked price per square foot. Based on this data collection, a descriptive analysis will be conducted where the variables will be analyzed for their means, medians, standard deviations, as well as for their minimum and maximum values. The next step consists of conducting a correlation analysis where the relations between the variables will be investigated. Afterwards, a regression analysis will be conducted in order to find out whether the independent variables can explain the dependent variable. Finally, the findings will be summarized in a conclusion.

## 2 Statistical Analysis

### 2.1 Variable Determination

In order to figure out factors that might determine the asked price per square foot of a single family home in Wichita Falls, Texas, a detailed set of variables has to be established that can be measured and expressed in numbers. This set consists of a dependant variable and several independent variables that will be described in the following paragraphs.

#### 2.1.1 Dependent Variable

A dependent variable is the observed result that is explained by one or several factors (i.e. independent variables). The dependant variable is the price per square foot of a single family home.

#### 2.1.2 Independent Variables

The independent variables are possible determinants of the dependent variable (i.e. the price per square foot). These variables should not be influenced by each other. The following variables were chosen as potential determinants for the dependant variable:

- Age: This factor was chosen because people might associate different levels of dilapidation with respect to the age of the house. Furthermore, newer houses are probably built using more modern architectural knowledge in terms of energy efficiency (e.g., heat insulation) and stability (e.g., hurricane secure). Those attributes could influence the house price per square foot.

- Number of Bedrooms: The quantity of bedrooms available in a single family home can be a major reason for a decision purchase a house. Especially, young families that plan to have many children might be more willing to pay higher prices for higher numbers of bedrooms in order to have enough space for their family.

- Number of Bathrooms: The quantity of bathrooms that a house possesses seems to be an important factor for a buying decision. Especially for families with several children, a higher number of baths might increase the quality of life in terms of convenience. Therefore, the number of bathrooms might affect the house price per square foot.

- Living Room Size: The living room is the place where the inhabitants, in many cases, spend the most of their time in order to communicate with each other and to round out the day. Furthermore, this room provides space to welcome guests and therefore, its size might be associated with wealth. Thus, the living room size might represent an influencing factor for the dependent variable.

- Kitchen Size: For several reasons, the kitchen size might influence the dependent variable. On the one hand, modern kitchens require more space than traditional kitchens. Furthermore, kitchens do not only provide a place to prepare food, but also have functions related to the living room. Often meetings with friends take place there. Therefore its size might represent a status symbol and thus influence the price per square foot.

#### 2.1.3 Independent Dummy Variables

Dummy variables will be used to determine relationships between qualitative independent variables and the dependent variable. These variables can take a value of either one or zero, depending on whether a certain condition is available or not. The following variables were used as independent dummy variables:

- Pool Availability: A swimming pool can increase the value of a house because of the additional leisure activities it provides. Furthermore, it might represent a status symbol. Therefore, its availability might be related to the asked price per square foot. A value of one is assigned if a pool is available and zero otherwise.

- Corner Lot: A corner lot can provide advantages and disadvantages and therefore might influence the asked price per square foot. An advantage is that the house has two front sides to showcase, and therefore can make the house more prominent. Disadvantages can be, e.g., the substantially more sidewalk to clear of snow in winter or more noise caused by more traffic at crossroads. If the house’s lot is a corner lot, this variable has the value of one; zero otherwise.

- Family Room Availability: A family room is an all-purpose room that can have similar functions as the living room does. However, its existence implies more value to the house because it provides more space for the family to perform more activities. Therefore, its availability might influence the price per square foot. This variable has the value of one if a family room exists and zero otherwise.

- Quantity of Stories: More than one story makes a house look more worthwhile but also can provide more obligations in terms of maintenance. Therefore, the quantity of stories can be an influencing factor of the dependent variable. If the house has more than one story, the variable has the value of one. If the house consists of only one story then this variable has the value of zero.

**[...]**

- Quote paper
- M.A., MBA Lukas Scisly (Author), 2009, Analytical Report , Munich, GRIN Verlag, https://www.grin.com/document/159427

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