The rise of real estate business

The rise of real estate business

 

 

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Contents Introduction 2 Hypotheses 3 CONCLUSION 3 RESULTS 4 Correlation table on the year built 4 Linear Regression Model 5 T STATS the values of t= -34.157 5 Regression summary: 5

 

 

 

 

 

 

 

 

 

 

Introduction

 

This study’s approach is based on an analysis of the data set. Is population increase causing the real estate industry to grow more quickly in the recent years? Before investing in such a rich and rapidly growing firm, thorough study is required. This market sells a wide range of homes. There are three types of stories: one, 1.5, and two. This data is intended to provide information if population gworth real affects the growth of real estate business.

Hypotheses

The purpose of this study is to investigate and conduct analytical tests on the growth of real estate business and the houses sold in various years, and which month of the year houses are most commonly sold. The hypothesis question raised are :

Is population growth directly proportional to the growth of real estate business in the recent years?

 

ANOVA in Excel is used to perform a variety of analyses. This design is used to do regression and correlation analysis to determine and display real estate market trends. The rate at which houses are being sold based on the increasing population growth.

CONCLUSION

 

The data was evaluated after 2930 samples were counted. A measure of central tendency was calculated based on the descriptive data. Various measures have been calculated, including the mean, mode, and median.

Year built.

Samples were taken from the 2930. The mode of 6 indicates that the majority of homes were sold during this month. The most recent residence to sell was on the 12th month. The minimal value obtained from the descriptive analysis is 1. 11 is the range. This number was derived by subtracting the greatest value from the minimum value.

 

Samples were taken from the 2930. The year 2005 is the mode, indicating that the majority of the residences were constructed in that year. The oldest home was constructed in 1872. The minimal value is 1877, according to the descriptive analysis. 138 is the range. This number was derived by subtracting the greatest value from the minimum value.

 

 

RESULTS

p has the value of 0. 1.733 is the standard deviation.

The criterion for significance is 0.05, which equates to a 95% confidence interval.

 

Correlation table on the year built

r=0.558 is the correlation.

This shows a modest relationship.

The year a property was built has a modest relationship with the price it is being sold for. There’s no reason to believe that an ancient house won’t sell for a specific price.

We may deduce from the correlation value that the years a house was built and the amount it was sold for are somewhat associated.

 

 

Linear Regression Model

T STATS the values of t= -34.157

Regression summary:

The R square value is 0.312, while the R value is 0.558.

The coefficient of determination R square = 0.312, and there is no change in significance values, indicating that the entire model is still statistically significant, rejecting the null hypothesis with p < 0.000 from the ANOVA table.

Reject Ho

Scatter diagram

 

There appears to be a positive relationship in the distribution based on the scatter plot. According to the graph’s trend, the data set has a significant positive linear connection.

There is no enough evidence to support the claim that increasing population growth is directly proportional to the rise and growth of real estate business. The null hypothesis is rejected.

 

 

Reference

Real Estate Market Size & Trends Report, 2022–2030. (2020). Realestate. Retrieved 2022, from https://www.grandviewresearch.com/industry-analysis/real-estate-market

 

Ltd, R. A. M. (2022). Real Estate Global Market Report 2022 by Type, Mode, Property Type. Research and Markets Ltd 2022. https://www.researchandmarkets.com/reports/5546258/real-estate-global-market-report-2022

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