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Data Analysis of American House Price

Extracts from this document...

Introduction

American House Price Analysis

Table of contents

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1. Introduction _________________________________________________________________

2. Executive Summary ______________________________

3. Introduction ____________________________________

4. Statistical Analysis _______________________________

4.1 - Overall Distribution of the house price  ________________________________________

4.2 - Investigation of Factors affecting the house price   ______________________________

4.3 - Investigation of Factors affecting the house price  ________________________________

5. Conclusion ____________________________________

6. Recommendations _______________________________

Notes   ____________________________________________


  1. Terms of Reference

This report is the outcome of an analysis and investigation of American house price in order to consider which factor influence the price. It is submitted as my project for Essential Data Analysis module on the Business Studies Programme.

  1. Executive Summary

The data was investigated using the software Minitab ver. 14. This program is very useful for analyzing big data set faster and easier. Through Minitab were created a graph for each requested point. In order o make the graph more understandable, it is provided a table with the more relevant statistic information. This allows a more comprehensive and understandable reading of the report and an easier and more efficient comparison among 2 or more variables in order to make a proper analysis. Correlation and Regression analysis was applied in order to establish the relationship between the price with the size and the distance to the nearest large town.

The data set given as a sample to analyse contain data collected of 100 houses in America from 5 different township numbered from 1 to 5.

...read more.

Middle

100. As a result, 45% of the houses analysed does not have a pool.

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The Graph 3 shows the proportion of the houses with a pool and a garage. By looking at the table it is clear that the majority of the houses with a pool have also a garage, with 58,18% (32 out of 55 houses with a pool); while 41,82% (23 out of 55) houses with a pool do not have a garage.

However, for houses without a pool, the proportion of houses without a garage is higher than houses with a pool where 82,22% (37 out of 45 houses) do not have a pool nor a garage.

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It is evident from Graph 4 that the proportion of the houses with a pool is not the same in all the 5 townships.

In township 5, all the houses (100%) have a pool; followed by township 4 with a 94,4% of the houses. On the other extreme there is township 1 with only 13,33%  (2 out of 15 houses) have a pool, followed by township 2 with 22,22% (6 out of 27 houses). As table 4 shows the proportion of houses with a pool are in ascending order with the number of township: township 1 has the lowest percentage and township 5 has the highest. This could be a coincidence.

However, on the overall distribution, township 4 has the highest percentage of houses with a pool, with 32,73% (18 out of 55 total house

...read more.

Conclusion

There is a slightly higher demand for houses with a pool.If the investor decides to buy a house with a pool, it is suggestible to have a garage as well. Otherwise it is more convenient to have a house without any of the two.If the house is in township 3 to 5, it is highly recommend to have a pool, especially for the last one.The pool will make a huge difference for the value of the house. The value will rise by about 75% if it has a pool.The bigger the house is, the more it values. However, houses with less than 1900sqrft are not very demanded. There is a medium demand for houses with a bigger size.Desirability scale 6 has a highest average and median price and it had a good demand.It is not relevant the distance between the house and a big city.

NOTES:

All the figures used to refer to the price are expressed as thousands of dollars ($ ,000).

In order to determinate the demand, it has been used the assumption that the higher frequency has a higher demand. For example: in township 5 all the houses have a pool. It implies that everybody in that area demand and is willing to buy a house with a pool. Thus there is a very high demand.

...read more.

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