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

Selling Price Analysis for D.M. Pan National Real Estate Company 2

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

Kayla Murphy

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

Southern New Hampshire University

Introduction

This study was carried out by real estate industry to determine competitive advantage of smart businesses. Statistical test such as regression analysis and descriptive statistics were used where regression analysis was used to determine the relationship between the selling price of properties and their size in square feet while descriptive statistics was used to summarize the data. A random sample of 30 real estates were selected where their median listing price and median square feet were recorded to carry out the study.

Representative Data Sample

Results from table 1 showed the results of 30 random samples from Texas region

Table 1

Descriptive statistics

According to Nick (2007), descriptive statistics is a statistical measure that summarizes data into measures of center and central tendency table below showed the summary of median listing price and median square feet.

Table 2

Results from the above table showed that the mean of the median listing price was $274900 while that of median square feet was 1598.5. The median value of the listing price was $270000 with the median of square feet being 1612.5. Standard deviation can be described as a measure of variation of a given data value from the mean. The standard deviation of median listing price was 42842.4 while that of median square feet was 278.336.

Data Analysis

From the descriptive statistics it was clear that the average median listing price of $274900 from the study was lower compared to that of national market that was approximately $284600.The sample is a reflective of the national market since the mean value is almost the same to that of the national market. The sample was made random by selecting the various samples from different strata’s. This included dividing the target population into different groups called strata’s and randomly selecting the 30 samples from the various groups. A sample of 30 was large enough to be a good representation of the population.

Scatterplot

The Pattern

From the scatter plot, median square feet was the predictor variable while the median listing price was the response variable. This was because median square feet was independent thus it was used to predict the selling price of the real estates.

The graph showed that there existed a positive relationship between median square and median listing price thus an increase in area of real estates would lead to an increase in the selling price. There existed no outliers from the data set since the values are concentrated in the same area.

The regression equation from the graph was,

y = 8.7399x + 260929

Having an area of 1200 square feet, the selling price would be

Median listing price = 8.7399(1200) + 260929

=$271416.88

The regression equation was positive indication that increase in independent variable would lead to an increase in the dependent variable.

References

Nick, Todd G. (2007). “Descriptive Statistics”. Topics in Biostatistics. Methods in Molecular Biology. 404. New York: Springer. pp. 33–52.

Phillip D. (2010). “Upper and Lower Bounds for the Sample Standard Deviation”. Teaching Statistics. 2 (3): 84–86

Scatter plot of median listing price v/s median square feet

median listing price($)

1500 1300 1600 1650 1700 1200 1350 2000 1850 1750 1640 1625 1550 1150 1350 1700 1850 1400 1450 1250 1540 1260 1360 1900 1870 2100 1270 1670 1980 2140 226000 314000 215000 354000 245000 257000 305000 317000 267000 268000 271000 229000 321000 216000 290000 279000 320000 211000 269000 225000 316000 328000 335000 327000 238000 240000 237000 302000 301000 224000

median square feet

median listing price

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