Here are the dependent variable and all predictors used in the model. We can look at the map of sale prices and other interesting variables (not log-transformed)
To observe the relationships between variables more intuitively, we created a correlation matrix, showing the correlation between variables. Also, to see the variable distributions across neighborhoods with different income level, we picked three neighborhoods:Charlestown(rich),South Boston(middle Income),Mattapan(poor).The boxplots show the variable distribution across neighborhoods.z
To make sure the model is generalizable, we conducted spatial cross-validation, and the results show that the prediction power in low-income neighborhood is best.
Here are the dependent variable and all predictors used in the model. We can look at the map of sale prices and other interesting variables (not log-transformed)
Home Prices Prediction For Boston
Oct-Nov 2017
Home price prediction is a tricky task, because so many factors might influence the home prices, and it is hard to figure out the relationship between these factors and home prices.
We built a predictive model of home prices for Boston using OLS regression. For dependent variable, we used log-transformed sale price of 1286 properties in Boston. For independent variables, we introduced 36 predictors into the model and took into consideration different factors that were expected to be associated with home prices. Through out-sample prediction and cross-validation, we could ensure that the model is robust and relatively accurate.
Our final model could account for 81% variations in the log-transformed sale price. In training set, the root mean square error (RMSE) and mean absolute percent error (MAPE) are around 0.15 to 0.16 and 12%-13%, respectively. The relatively low RMSE and MAPE indicate that it is a good model. In test set, we have computed Global Moran's I and have found that there is no significant spatial auto-correlation, which means that the model will not perform better or worse in some specific areas.