Enterprise Frameworks Project: Analytical CRM Housing Analysis

This project is an analytical CRM project that examines the relationship between house prices and various factors, including construction costs, the number of loans, and the value of mortgage loans. Using data from the Department of Housing, Planning, and Local Government of Ireland, the study aims to determine the impact of these factors on house prices, aiding decision-making for suppliers and consumers. The project employs data visualization tools like scatter plots and correlation analysis. It utilizes a multiple regression model to analyze the relationship between the independent variables (cost of construction, number and value of loans) and the dependent variable (house prices). The project sets a null hypothesis that there is no relationship and an alternative hypothesis stating that there is a relationship, with a significance level of 0.05. The regression findings are presented in a formula, and relevant references are included.

Analytical CRM: Housing
Name:
Institution:

Introduction
 The following study seeks to exhibit the relationship between the house
prices and various factors, which include cost of construction, number
and value of mortgage loans.
 The data set used in this study was sourced from the department of
Housing, Planning, and Local Government of Ireland website.
 Notably, the dataset incorporates five variables, which include year
(2000 to 2016), prices in thousands, number of loans, value of loans in
millions, and cost of contrition in thousands.

Goals and Objectives of the project
 The general objective of the study is to find the extend at
which the above factors have an impact on the prices of
houses and how this can aid in decision making by suppliers
and consumers at large.
 Moreover, the study incorporates three specific goals, which
include finding the magnitude of each factor on the prices,
fitting a model, and determining the strength of association
between the factors and the prices.

Strategies
 It is essential to incorporate various data visualizing tool in a study to
aid in exhibiting various characteristics associated with the dataset.
Therefore, the study used a scatter plot, which is a graphical tool that
exhibits the association between quantitative variables.
 Correlation is useful for determining the strength and direction of the
association between two variables, in this case, cost of construction,
number and value of loans approved and house prices.
 The adequate method to show this relationship is the regression
model, specifically the multiple regression model since it incorporates
numerous independent variables. Regression is predictive technique
that exhibits the level of association between the explanatory and
response variables.

Hypothesis
 Null Hypothesis: There is no relationship between the
response variable and the explanatory variables
 Alternative hypothesis: There is no relationship between
the response variable and the independent variables
 Significance level: 0.05
 Decision rule: If the p-value is less than 0.05, we reject
the null hypothesis and conclude there is a relationship.

Regression Findings
House prices = 99.54 – 0.00346 Number of Loans + 0.01955 Value of loans + 0.6837 Costs
Coefficien
ts
Standard
Error t Stat P-value
Intercept 99.54965 138.5679
0.71841775
2
0.48521222
2
Number of
Loans -0.00346 0.001917
-
1.80640728
9
0.09404790
7
Value of
Loans 0.019555 0.007331 2.66753189
0.01935506
3
Costs 0.683637 0.66243
1.03201368
7
0.32088407
3

References
[1] Pettinger, T. (2017, November 27). Factors affecting supply and
demand of housing. Retrieved from Economics Help Website:
https://www.economicshelp.org/blog/15390/housing/factors-affecting-
supply-and-demand-of-housing/
[3] Hayes, A. (2019, June 2019). Correlation. Retrieved from Investopedia
Website: https://www.investopedia.com/terms/c/correlation.asp