traditional methodologies such as OLS or models that take into account of spatial autocorrelation can overstate the value of a marginal square foot for lower-priced homes but understate the effect on higher-priced homes. The quantile results provide some valuable insights to the different relationships that the explanatory variables have with selling price. For example, some variables such as square footage, lot size, bathrooms, and floor type have a greater impact as selling price increases. Other variables have a relatively constant effect on selling price across different price ranges. These include garage, exterior siding, sprinkler system, and distance to city center. Some other variables such as bedrooms and percentage of nonwhite population have a significant effect on selling price but there is no clear pattern of the effect across different price ranges. Lastly, the quantile regressions confirm that most variables showing no statistical significance under OLS or 2SLS remain not significant across the different price ranges. These results add to the body of research explaining house prices. Even though variations in the value of housing characteristics across different price ranges may have been considered intuitive beforehand, quantile regression provides a way to confirm these expectations.
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