Report Purpose
The real estate market is a constantly changing industry, with numerous factors influencing the selling prices of properties. One important consideration is the size of a building, as larger structures tend to command higher prices. In this report, the relationship between the square footage of a building and its selling price has been analyzed with the help of data obtained from the East North Central Region. Through this analysis, the report seeks to uncover trends and insights and provide valuable information on the relationship between the selling price of properties and their size in square feet.
Estate Size-Price Analysis
Representative Data Sample
After randomly selecting a sample of 30 items out of the 100 items from the East North Central region, the sample mean, median, and standard deviation of the listing price and the square foot variables are as follows;
Listing Price Variable
The Square Foot Variable
Data Analysis
Table 1 – Analysis of Sample and National Data
Source: D.M. Pan Real Estate Company, 2019.
The results of the regional sample show lower prices compared to the national market, which does not accurately reflect national statistics. The mean price has a variance of $97812, the median price is $91850, and the standard deviation is 35736 (D.M. Pan Real Estate Company, 2019). The square footage variable also reveals a similar trend, with a lower variance in the mean square footage of 302, a median of 164, and a standard deviation of 157 (D.M. Pan Real Estate Company, 2019). Overall, the data from the sample region suggests lower prices. This may be due to the smaller square footage compared to the national market, where properties have larger capacities and higher prices.
To obtain the sample population of the East North Central region, a random number was assigned to each of the 100 items in the population using the “Rand()” function in Excel. The population list was then sorted based on these random numbers to ensure a random order. Finally, the top 30 members of the sorted list were selected as the sample.
Scatterplot
Pattern
In the above graph, the y-variable represents the selling price of the properties. The dependent variable forms the basis for measuring how a change in the independent variable will affect it. On the other hand, the x-variable represents the square foot area, which is the variable under test, to determine how much it affects the dependent variable, thus providing a conclusion. The association between these two variables can take two forms: linear or nonlinear. For instance, the relationship between the variables is linear in this case.
In the graph above, most data is concentrated in one area, with only two points sparsely distributed. These points, known as outliers, are distinct from the general population. In this case, the two isolated points represent square footage typically larger than the average under study. This phenomenon may be caused by natural variability in the data or errors in measurement, such as using a flawed instrument or an incorrect unit of measurement.
Using the regression equation y = 94.533x + 60859, the estimated price for a property with 1,800 square feet is $231018.4. This equation is based on data about house size and price and captures the statistical relationship between these variables. By inserting a value for the property size, in this case, 1,800 square feet, the equation estimates the likely price of the property.
References
D.M. Pan Real Estate Company. (2019). Real estate county data [Data set]. Real Estate Data Spreadsheet
D.M. Pan Real Estate Company. (2019). Summary Statistics for MAT 240 Real Estate Data (for dataset in Modules 2, 3, and 4) [Infographic]. National Summary Statistics and Graphs Real Estate Data PDF