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 R0.82397737
R Square0.6789387
Adjusted R Square0.678617
Standard Error71381.3565
Observations1000
  
ANOVA 
 df
Regression1
Residual998
Total999
  
 Coefficients
Intercept104467.536
X Variable – square feet112.701097

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 
      
Mean342364.9Mean168.692Mean2110.87
Standard Error3981.744Standard Error1.307016Standard Error29.11123
Median318000Median165.8728Median1880.5
Mode270600Mode#N/AMode1847
Standard Deviation125913.8Standard Deviation41.33146Standard Deviation920.5778
Sample Variance1.59E+10Sample Variance1708.29Sample Variance847463.4
Kurtosis4.933758Kurtosis0.60014Kurtosis6.688469
Skewness1.902978Skewness0.649056Skewness2.525366
Range852300Range273.2408Range5415
Minimum135300Minimum70.69983Minimum1101
Maximum987600Maximum343.9406Maximum6516
Sum3.42E+08Sum168692Sum2110870
Count1000Count1000Count1000

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

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