Real Estate Market Analysis

Running head: REAL ESTATE MARKET ANALYSIS
Real Estate Market Analysis
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REAL ESTATE MARKET ANALYSIS
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Table of Contents
Introduction................................................................................................................................2
Data formation...........................................................................................................................2
Data analysis..............................................................................................................................2
Descriptive statistics for State A................................................................................................3
Predictive models for State A....................................................................................................4
Descriptive statistics for State B................................................................................................6
Predictive models for State B.....................................................................................................7
Conclusion..................................................................................................................................9
Reference..................................................................................................................................10

REAL ESTATE MARKET ANALYSIS
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Introduction
There is a lot of sectors in which the data analysis is used as tool to predict prices,
sales and production. It is very efficient in terms of predicting as it uses statistical measures
and past data which analyses the past information captured in the data. Here, it will be used in
predicting price of houses and units by using some statistical techniques and developing
models. The data is contains the information of real estate market in non-capital cities and
towns of State A and State B. The data is collected by a large national real estate company,
named Safe-As-House Real Estate. The sample has data on the recent residential sales from a
sample of non-capita cities and towns in these states. The variables on which the data is
stored in the sample, are price of the house, internal area of the house, number of bedrooms,
bathrooms, garages and the type of the residential. The variable price is stored in $000 units,
internal are is measured in m2, bedroom, bathrooms and garages shows the number and the
type of residential contains whether the residential is house or unit. The objective is to
develop a model in order to predict the house price and unit price for the two states.
Data formation
First of all the data from the state A is collected and aligned in each variable as they
were scattered across different towns and cities. Now, as the aim is to predict house price and
unit price the data is split into two sub groups according to the type of the resident. The same
process is followed for the data set of state B.
Data analysis
The first step of analysis is to check the descriptive statistics of all the variables in
order to check the missing vales and their distribution. Hence, the mean, median, mode,
standard deviation, skewness, kurtosis, range, minimum and maximum values are calculated
for each variable for two different types of residential across two states (Salkind and Frey

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2019). These statistics is described in detailed and presented in tables for descriptive statistics
and regression result.
Descriptive statistics for State A
Table 1: Descriptive statistics of the variables for unit price of State A
Statistics Price $000
Internal Area
m^2 Bedrooms Bathrooms Garages
Mean 320.34 96.37 2.31 1.30 1.25
Standard Error 12.92 1.89 0.06 0.04 0.04
Median 280 90.95 2 1 1
Mode 225 90.4 2 1 1
Standard Deviation 145.00 21.26 0.64 0.48 0.47
Sample Variance 21024.30 452.06 0.41 0.23 0.22
Kurtosis 3.18 0.42 0.25 -0.24 1.83
Skewness 1.61 0.24 0.37 1.10 1.66
Range 742 115.3 3 2 2
Minimum 148 49.7 1 1 1
Maximum 890 165 4 3 3
Sum 40362.8 12142.08 291 164 157
Count 126 126 126 126 126
In the table 1, the distribution of all the variables are positively skewed as the mean
value of the variables are greater than the median of the variables (Bonner 2018). The
average price of a unit in state A across different town and cities is $320340 where the
average internal area of the unit is 96 m2.
Table 2: Descriptive statistics of the variables for house price of State A
Statistics Price $000
Internal
Area m^2 Bedrooms Bathrooms Garages
Mean 479.57 166.29 3.56 1.75 1.81
Standard Error 11.46 2.67 0.05 0.04 0.05
Median 440 162.1 3 2 2
Mode 299 194.5 3 2 2
Standard Deviation 197.42 46.03 0.88 0.70 0.78
Sample Variance 38973.61 2118.81 0.78 0.50 0.61
Kurtosis 4.47 0.90 6.33 0.06 3.17
Skewness 1.77 0.74 1.49 0.62 0.59
Range 1202 270.6 7 3 6