Pools add value to homes

The goal of this study is to build a statistical model to capitalize swimming pools into housing prices and find the relationship between swimming pools and the values of residential properties. The data for this study comes from house transactions of Arlington area-83-1 near UTA.

All of the past studies found in the Literature Review section of this paper have found positive correlations with swimming pools. Pools do tend to add value to a home — about 7.7%, according to National Association of Realtors statistics. Methodologies and results from these past studies supported that the effect of swimming pools has a significant importance on property values. Therefore, the hypothesis of this paper is pools add value to homes.

The regression model has R square equal to 0.734 which means that 73.4% of the data is explained by this model. This number is confirmed by the Significance F change and Durbin Watson which is equal to 1.963. (very closed to 2) The model select seven independent variables. In the T statistic analysis, acres, Square ft, ages, and pools have significant level under 0.05. In 95% confidence Interval for B, the value of pools is between 2897.636 and 25447.888. In addition, In the ANOVA analysis, the F-statistic is equal to 131.347 with significant level. When checking multicollinearity, the VIF falls into reasonable range and collinearity diagnostics points out there is no multicollinearity problem. Generally, the Histogram graph looks fine. However, the scatterplot graph has homoskedasticity problem. Therefore, the natural log regression may help correct the flaw of the model. This approach considers a linear relationship by log-transformed dependent variable.

The purpose of this paper is to find the correlation among variables. The author of the paper followed the 5 steps of regression analysis beginning with looking at the data and ending with analyzing. If everything is followed correctly the ending analysis should produce valuable confidence and prediction intervals that can be used in structural modeling. Upon using the SPSS, the outcome of the analysis has shown that there is a positive correlation between the pools and sales prices.