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Q: Real Estate Pricing Regression ( No Answer,   1 Comment )
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 Subject: Real Estate Pricing Regression Category: Science > Social Sciences Asked by: vlad-ga List Price: \$10.00 Posted: 17 May 2006 17:41 PDT Expires: 16 Jun 2006 17:41 PDT Question ID: 729891
 ```If I set my OLS regression up as follows: yHousePrice = x1Lot Size + x2HomeSize + x3DummyVariableCity + x3DummyVariableHouseType + Errors It is clear that my regressors are endogenous and therefore I will run into a multicollinearity problem. No? What is the standard approach to correct for that? If I need to eschew a linear model, please explain why.```
 ```Regression can be problematic WHEN there is "Perfect" Collinearity. When there is multicollinearity, your regression may be BLUE. One problem that you may be referring to is that multicollinearity, which is high correlation between coefficients of variables, suggests the statistical significance of each coefficient may be false. In this regression, as far as I can notice, the correlation between variables is small. There may be some correlation between DummyVariableCity and DummyVariableHouseType. High collinearity can be observed when you consider variable from similar categories - say, HouseSize and NumberOfRooms (... well, not a good example, but I hope you got the gist of answer...) If there is multicollinearity, one solution to correct is to eliminate a variable from regression. If, with high chance, one variable can predicts the other variable, other variable can be omitted to avoid some trouble. Another solution may work for somesituation is to change the broadness of variable. For housing price, linear model should be fine unless your plotting data suggests logarithmic increments or exponential increments.```