Analysis of Real Estate Market for House and Unit Price

Running head: BASIC BUSINESS STATISTICS
Basic Business Statistics
Name of the Student:
Name of the University:
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Table of Contents
Introduction................................................................................................................................2
Data collection and manipulation...............................................................................................2
Analysis of house and unit price for both the states...................................................................3
Analysis of real estate of state A for unit price......................................................................3
Analysis of real estate of state A for house price...................................................................4
Analysis of real estate of state B house price.........................................................................4
Analysis of real estate of state B unit price............................................................................5
Conclusion..................................................................................................................................6
Reference and Bibliography.......................................................................................................7
Appendices.................................................................................................................................9

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Introduction
The report analyses the real estate market to predict the price of the houses depending
on the area of the house, number of bedrooms and bathrooms and garages. Here, the
consultant has used the data of state A and State B that contains the information about the
unit price and house price of non-capital cities and non-capital towns (Lin and Maras 2018).
The data is the sample of non-capital cities of State A and State B collected by Safe-As-
House Real Estate which is the large company in real estate industry at national level. The
data includes the following variables:
1. Price of the house and unit and stored in $000 unit,
2. Internal area in m2 unit,
3. Number of bedrooms,
4. Number of bathrooms and
5. Number of garages.
The consultant has used the data to form a model that can calculate the house price and unit
price (Frank, McKenna and Peck 2018). The objective of the analysis is to estimate the price
of residential for both the states A and B.
The report discusses about constructing the proper form of data before the analysis.
The analysis needs descriptive statistics of the variables to understand the characteristics of
the variables. So, the descriptive statistics is also calculated and discussed for the both the
states. Then there is a brief discussion of regression analysis for both the states. Finally, there
is a conclusion part which summarises the major findings of the analysis.
Data collection and manipulation
The collected data is separated according to the residential areas. For example, data is
separated for costal town 1, costal town 2, costal city 1, costal city 2, regional city 1 and

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regional city 2 for the state A and B. These all data is merged in two data frames. One is for
state A and the other one is for state B. Then each data frame is split in two parts one is for
the house and the other one is for unit (Kang et al. 2018). This will provide the easy
estimation process and robust estimation result.
Analysis of house and unit price for both the states
In this process, summary statistics is computed for both house and unit for each state.
It includes the location, shape and spread of the variable. Then a regression analysis is
performed and the model is constructed. It shows the important variables that are needed to
predict the prices. This is explained briefly with the tables in the below section:
Analysis of real estate of state A for unit price
The table 1 presents statistical description of the variables. The average unit price in
state is $320339.7 with a variance of $21024299.9 (Salkind and Frey 2019). The average
internal area is 96.3657 m2. This implies that the average unit price in state A is around
$320339.7 for an average of 96.3657 m2 internal area, 2 bedrooms, 1 bathroom and 1 garages
(Ampudia and Mayordomo, 2018). However the medina value shows that 50% of the unit
prices are below $280000 which has 2 bedrooms, 1 bathroom and 1 garage. From the
regression analysis following model is obtained:
unit price∈ state A=−95.2345+ ( 1.1974∗internal area ) + ( 40.3937∗bedrooms ) + ( 140.5857∗bathrooms ) +(19.1
The regression result shows that it can predict the unit price of State A with 54.64%
accuracy (adjusted R2 = 0.5464) depending on the internal area, number of bedroom,
bathroom and garages. The ANOVA result shows that the included independent variables are
important in case of predicting the house price (F (38.6376, 125) =0.000). The table shows
the significance level of independent variables (Sethapramote, Lu, and Thepmongkol 2019).