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Selling Price Analysis for D.M. Pan National Real Estate Company 4

Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company

[Sidney Madison]

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 1

Southern New Hampshire University

Introduction

This report consists of data on listing price which are collected from more than 8 regions on square feet prices. The real estate industry heavily uses linear regression to estimate home prices, as cost of housing is currently the largest expense for most families. Additionally, in order to help new homeowners and home sellers with important decisions, real estate professionals need to go beyond showing property inventory. They need to be well versed in the relationship between price, square footage, build year, location, and so many other factors that can help predict the business environment and provide the best advice to their clients The main purpose of this report was to check the association between listing price and square feet prices, how both were related how price change by changing the estate by square feet.

Representative Data Sample

Table : Descriptive of data

	Median Listing Price	Median square feet
N		30	30
Mean	$346,198.6333	1992.3000
Median	$285,731.5000	2001.0000
Std. Deviation	$200,873.11258	297.36476

Above descriptive table show the results of mean, median and std. deviation of the data selling prices of national real estate company. There are 30 simple random sample selected from a data of country state data for 2019. In which data of different region and states are provided according to the listing prices of square feet of estates.

Data Analysis

I select sample of 30 from 978 observations, sample were selected by simple random sample. Samples were selected by stratified random sampling(make small groups of data) , given data collected from total 8 regions each region were taken as strata such as East South Central taken as 1st strata, South Atlantic as 2nd strata and other all. From each strata 4 samples were selected but data from two region were small so from Mid-Atlantic and Mountain 3 samples were selected from each. Sample from each strata selected by random number generated by using excel. In this way samples were selected randomly and each sample had equal chance to being selected.

Scatterplot

Above scatter plot show the result of current study also regression line draw on the graph which the current study design. Scatter plot draw between Median listing price and Median square feet. Median listing price as taken as response variable and median square feet taken as predictor variable.

The Pattern

In above graph median listing price taken as response variable and listing square feet taken as predictor variable. Graph were draw between Z-predictor and S-residual to show the relationship and association between both variables. Graph also show the trend line, also show outliers (errors). Listing square feet is predictor and independent variable listing price is response variable and depend on median square feet.

Scatter plot show there is linear association between x and y variables. Means both variables were related linearly and by changing one unit in one variable it also change in other variable.

As we see in graph there were many outliers in data all those points which were above or below from trend line known as outliers. Outliers are occurs due to three reasons due to natural variation, error occur while experimenting, and during data entry by typing wrong value. Outliers represent error in data.

Prediction

As regression line get by analysis is as follow

Y=138268.365+104.367 X1

Y is response variable which is name as Median listing price and X1 predictor variable named as median square feet which is given here as 1200 square feet than model become

Y=138268.365+104.367 (1200)

Y=263,508.765

By increasing 1200 square feet listing price will change and become 263508.765.there is an prediction by using regression equation.