Housing Price Structure in Mee City - Research Paper Example

List of Tables

Table 1: Correlation Coefficients of the Variables6

Table 2: t-Test: Two-Sample Assuming Equal Variances7

Table 3: regression Model Summary7

Table 4: ANOVA Table8

Table 5: Regression Coefficients8

Introduction

The personal choice of neighborhood is an important factor that plays a deterministic role in choice of the housing specifically in its pricing (Aluko 2011). The neighborhood consists of aspects such as, accessibility, amenities and the appearance (Koopman 2012). The size of the houses also helps in making a choice for the customers because, the size of the family is an important factor while selecting for the houses to purchase. In this paper, the research is centered on the question of making choices for the houses in the Mee City. In case of the single-family housing in the city, there has been a growing trend that people are choosing older neighborhood which have been established long time ago. This ensures that people will be able to live in better housing at an inexpensive rate. On the other hand, there is a considerable section of people who are willing to pay higher price in order to live in better neighborhood. In this case, living in newer and expensive neighborhood acts as the status symbol for the owners. Therefore, the researcher has taken the survey to understand the structure of pricing of the houses in the Mee City.

Research Aim and Objectives

The aim of this research is to understand the pricing structure of the houses in the Mee City and the factors responsible for the choice. The research entails the following objectives.

• To evaluate the factors affecting the purchasing decision of the neighborhood in the Mee City.
• To understand the pricing structure of the houses located in the Mee City.

Research Questions

The research questions have been framed according to the research aim and objectives and they are described below.

• What is the pricing structure of the houses in the Mee City?
• What are determining factors affecting the choice of the people in the Mee City?

Hypotheses

In order to address each of the research questions, the hypotheses have been prepared which is provided below.

Hypothesis 1: Relation between neighborhood and Mee City

H0: µ1=µ2=0

H1: µ1≠ µ2≠ 0.

Hypothesis 2:

H0: The size of the house does not affect the pricing of the houses in the Mee City.

H1: The size of the house affects the pricing of the houses in the Mee City.

Hypothesis 3:

H0: The number of bedrooms and bathrooms in the house does not affect the pricing of the houses in Mee City.

H1: The number of bedrooms and bathrooms in the house affects the pricing of the houses in Mee City.

Hypothesis 4:

H0: The construction of bricks does not affect the pricing of the houses in the Mee City.

H1: The construction of bricks affects the pricing of the houses in the Mee City.

Hypothesis 5:

H0: The offers made on a particular house do not influence the neighborhood of the houses in the Mee City.

H1: The offers made on a particular house have influenced the neighborhood of the houses in the Mee City

Data and Research Method

The researcher has collected data from 128 households in the Mee City so that the owners can enlighten on their choice parameters. The following are the variables that have been considered as the possible choice variables, which are described below.

Home id (Home): This represents the number of houses from where the data has been collected.

Neighborhood (Nbhd): This variable explains the type of neighborhood where the houses are situated explaining the preference of the owners with respect to the choice of traditional and modern neighborhood. The value 1 represents the most traditional neighborhood and 2 shows relatively older neighborhood. On the other hand, 3 shows the newer and more prestigious neighborhood.

Offers: It shows the number of offers that a particular house has in the Mee city before it has been purchased.

Square Foot (Sq Ft): It is the square footage showing the size of the houses.

Brick: This variable reflects whether the houses are primarily built from bricks or not.

Bedrooms: The number of bedrooms.

Bathrooms: The number of bathrooms.

Price (in £): The selling price of the houses.

Sampling and Sample Size

It has been widely acknowledged by the researchers around the world that working with vast amount of data can lead to issues of miscalculations while managing the data (Neelankavil 2015). Sampling is used by majority of the researchers, which is the process of selecting few individuals from the population who act as the representatives of the characteristics of the entire population (Gentles, et al. 2015). This is a systematic record of the events and attributes that helps in identifying the solutions addressing the research problem (Smith 2010). In this paper, the data on housing in the Mee City have been collected from 128 houses. The researcher has used questionnaires so that the preference patterns of the owners and the parameters affecting their purchasing intentions can be revealed.

Analysis

The researcher has used correlation, regression and t-test to understand the relationship between the variables.

Table 1: Correlation Coefficients of the Variables

(Source: Extracted from Excel File)

The above tables represent the correlation results, which reflect the factors that affect the selling price of the houses in the Mee city. The neighborhood of the houses has depicted the strongest correlation with selling price (0.732). The other positively related variables affecting the selling price are the size of the houses reflected by the square footage of the houses (0.553) and the number of bedrooms (0.526) and bathrooms (0.523). On the other hand, the selling price of the house in the Mee City is negatively correlated to the number of offers (-0.314) and the construction by bricks (-.0453). The positive correlation shows that as the independent variables increase, there is a rise in the dependent variables (Peat, Barton and Elliott 2009). However, the negative correlation shows that as the independent variable increase there is a decrease in the dependent variable (R. P. Burns, and R. Burns 2008; Boslaugh 2012). Therefore, it can be inferred that the newer prestigious localities, the size of the houses increase, and more number of bedrooms and bathrooms results in a hike in the selling price of the houses. Hence, there is direct increase in the selling prices of the houses. On the other hand, if there is less number of offers for a house and brick is not the primary construction material, there is also a direct increase in the selling prices of the houses.

Test of hypotheses

In order to check the first hypothesis to evaluate the relationship between the selling price of the houses and the neighborhood t-test has been undertaken. The null hypothesis states that the mean of the two distribution are equal which means that the difference in the neighborhood does not create any change in the selling price of the houses. The researcher has undertaken a t-test to validate the hypothesis and the results are explained below.

Table 2: t-Test: Two-Sample Assuming Equal Variances

(Source: Extracted from Excel File)

The t critical two-tail value defines the acceptance range of the null hypothesis and in this case, the range is +1.97 and -1.97. However, the t Stat of the concerned distributions is 54.92, which is beyond the acceptable range. Therefore, the null hypothesis cannot be accepted indicating that the change in the neighborhood will result in a change in the prices of the houses.

In order to assess the impact of the interdependence between the variables and their impact on the prices of the houses, the researchers have undertaken the regression and the results are explained below.

In this regression model, price is the dependent variable and the rest of attributes are considered to be the independent variables. This will help in explaining the pricing structure, which is practiced by the people of Mee City when they make decision to purchase a house.

Table 3: regression Model Summary

(Source: Extracted from Excel File)

The above table depicts the regression model, the R-square of the model explains its goodness of fit that how close the variables are to the trend line (Sharma 2009; M. J. Schniederjans, D. G. Schniederjans and Starkey 2014). In this case, the value of the R-square is 0.83 explaining that almost 83% of the variations in the selling prices of the houses in the Mee City has been explained by the variations in the independent variables.

The following ANOVA table shows the overall significance of the chosen statistical model.

Table 4: ANOVA Table

(Source: Extracted from Excel File)

The significance of F-statistics is close to zero, which is less than 0.05 making the chosen model statistically significant and fit for analyzing the changes in the dependent and independent variables. The regression coefficients are presented in the following table.

Table 5: Regression Coefficients

(Source: Extracted from Excel File)

The above table shows that the p-value significance of the independent variables is lower than the common alpha level, i.e., 0.05. This shows that independent variables are statistically significant in bringing about changes in the selling price of the houses in the Mee City.

Validating Hypothesis

The p-value significance is of square footage of the houses is less than 0.05 making it statistically significant and thus, the alternate hypothesis that size of the house is related to the prices has to be accepted. The same goes for the number of bedrooms and bathrooms, offers made on the houses, the construction using brick and most importantly the neighborhood of the houses.

The above results explain that in the Mee City, as the size of the houses increase and they are built in more prestigious neighborhood, the house offers better amenities with more bedroom and bathrooms. This directly increases the selling prices of the houses. Therefore, it can be stated that traditional neighborhood houses made from brick, as the primary construction material will be less costly in comparison to the ones situated in the newer neighborhood. The change in neighborhood also involves shifting to bigger and better housing in the Mee City. This shows that people with low income will be living in the traditional neighborhood while the modern homes in the newer neighborhood will be inhabited by the affluent section in the city, which drive its culture and further development of the region. On the other hand, this analysis also explains that the factors that affect the purchasing intention of the people are governed by the size, less number of offers, use of less brick in the construction and the neighborhood choice. The willingness to pay higher price also tells about their preferences, which answer the second research question.

Conclusion and Recommendations

The research into the pricing structure of the houses in the Mee City helped in understanding the preferences of the people while making the purchasing decision for houses. Furthermore, the hypotheses have been formulated and tested with appropriate statistical analysis to explain the relationship between pricing and neighborhood. It further reflects on the relationship between the selling price of the houses and the factors that contributes towards making the choice of purchasing house in Mee City.

The distribution of the neighborhood has explained that income structure of the people will be clearly visible from the neighborhood. This might deter the development of the areas based on the use of resources and spending pattern. The government in the city should ensure that people are able to purchase their desired home at a good neighborhood. The availability of better building materials will be able to lower the prices of the house.

The sample size is adequate to represent the perspective of the house owners but the research could not be extensive including the perspective of builders and the way a balance can be brought between the two neighborhoods. This can be taken up in future research where the role of government and other macro-economic variables can be studied. Furthermore, the researcher face time and cost constraint, which prevented expansion of the scope of the research.

Reference List

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Gentles, S. J., Charles, C., Ploeg, J., and McKibbon, K. A. (2015). ‘Sampling in qualitative research: Insights from an overview of the methods literature’. The Qualitative Report, 20(11), 1772.

Koopman, M. (2012). Economic Analysis of Neighbourhood Quality, Neighbourhood Reputation and the Housing Market. Amsterdam: IOS Press.

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Sharma, R. K. (2009). Demand Management: Supply Constraints and Inflation. New Delhi: Global India Publications.

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