Richardson Heights Residential Subdivision Regression Analysis:
Days on Market vs. Final Sales Price

I. RESULTS
No underlying model assumptions required for this regression analysis were violated. Each of the observations was independent. Scatterplots and correlations coefficients for all multivariate combinations were evaluated and the use of a linear function form of final sales prices was determined to be justified. The final sales price is a normally distributed continuous random variable with randomly distributed residuals.

A complete documentation of the stepwise regression is included in the reseach paper. Model 3 has the highest coefficient of determination, explaining 67.7% of the variation in final selling price. Models 1, and 2 were rejected because they have a significantly lower level of explanatory power, at 66% and 67% respectively.


II. CONCLUSIONS
The additional independent variables are both feasible and sensible to include in the model as the size and number of bedrooms of homes are known by non-statisticians to be sufficiently correlated to its final selling price (see scatterplots below). Statistically speaking, their t-statistics are significant and validate the simpler model results that size alone explains 66 % of the variability in final selling price, and coupled with more than 3 bedrooms adds another 0.01% of variance explanation.

In conclusion, the Richardson research study demonstrates that size, more than 3 bedrooms, and the days on the market does impact the final sales price.

Jerry Burbridge, Barrett Shepherd, Ali Samee