Real Estate Data Analysis
D. M. Pan Real Estate Company
The County Data for Real Estate for 2019 provided has been used in this report. The population sample has 1000 items (n = 1000). The listing price is the dependent or response variable y and the square feet is the independent or predictor variable x. A regression (Corporate Finance Institute) was done on the two variables and the following graph was obtained also showing the line of best fit.
Further analysis of the data gave information on table 1 below.
Table 1
| SUMMARY OUTPUT | |
| Regression Statistics | |
| Multiple R | 0.82397737 |
| R Square | 0.6789387 |
| Adjusted R Square | 0.678617 |
| Standard Error | 71381.3565 |
| Observations | 1000 |
| ANOVA | |
| df | |
| Regression | 1 |
| Residual | 998 |
| Total | 999 |
| Coefficients | |
| Intercept | 104467.536 |
| X Variable – square feet | 112.701097 |
Using this information, the equation for the line of best fit will be y = 112.7x + 104467.5. The value for r is 0.824. This means that there is a positive relationship between listing price and square feet. R squared gives 0.679 or 67.9 %. This shows that 67.9 % of listing price is determined by the area in square feet. Therefore, there is a strong relationship between listing price and square feet. The direction of the correlation is positive towards the right due to the positive value or r in the regression coefficient. The gradient of the regression equation is 112.7 which is a positive slope and the y intercept is 104467.5. This means that there is a cost involved before getting the cost of square feet. This initial cost must be added to the cost of the square feet before getting the total cost of the estate. This therefore must be the cost of land being valued before considering the footage of the house. From the regression above, the y intercept 104467.5 indicating that the cost of land is $104467.5. R squared in this regression is 0.679. This shows the extent to which the linear relationship between listing price and square feet is strong. With a maximum of 1, this shows that the two variables are strongly and positively related to each other in this region, and this has been the case on the overall in the United States. In this region also, the analysis shows that for every unit increase in square footage, the price increases by $112.7. This implies that for every 100 square feet, the price goes up by $11270. The graphs below show the frequencies obtained for the two variables.
From the frequency graph above, the square footage range that would best be used for the graph are between 1500 and 2500 square feet. Some further statistics for the real estate data are given in the table below (Simpli Learn).
| LISTING PRICE | $’s PER SQUARE FOOT | SQUARE FEET | |||
| Mean | 342364.9 | Mean | 168.692 | Mean | 2110.87 |
| Standard Error | 3981.744 | Standard Error | 1.307016 | Standard Error | 29.11123 |
| Median | 318000 | Median | 165.8728 | Median | 1880.5 |
| Mode | 270600 | Mode | #N/A | Mode | 1847 |
| Standard Deviation | 125913.8 | Standard Deviation | 41.33146 | Standard Deviation | 920.5778 |
| Sample Variance | 1.59E+10 | Sample Variance | 1708.29 | Sample Variance | 847463.4 |
| Kurtosis | 4.933758 | Kurtosis | 0.60014 | Kurtosis | 6.688469 |
| Skewness | 1.902978 | Skewness | 0.649056 | Skewness | 2.525366 |
| Range | 852300 | Range | 273.2408 | Range | 5415 |
| Minimum | 135300 | Minimum | 70.69983 | Minimum | 1101 |
| Maximum | 987600 | Maximum | 343.9406 | Maximum | 6516 |
| Sum | 3.42E+08 | Sum | 168692 | Sum | 2110870 |
| Count | 1000 | Count | 1000 | Count | 1000 |
References
Corporate Finance Institute. (n.d.). Retrieved January 25, 2024, from Corporate Finance Institute Website: http://www.corporatefinanceinstitute.com>resources
Simpli Learn. (n.d.). Retrieved January 24, 2024, from Simplilearn Website: http://www.simplilearn.com>excel-tutorial>regression
