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With everyone trying to enter the real estate market nowadays, knowing theproper valuations for residential and commercial properties has become crucial.Past researchers have been known to utilize static real estate data (e.g.number of beds, baths, square footage) or even a combination of real estate anddemographic information to predict property prices. In this investigation, weattempted to improve upon past research. So we decided to explore a uniqueapproach: we wanted to determine if mobile location data could be used toimprove the predictive power of popular regression and tree-based models. Toprepare our data for our models, we processed the mobility data by attaching itto individual properties from the real estate data that aggregated users within500 meters of the property for each day of the week. We removed people thatlived within 500 meters of each property, so each property's aggregatedmobility data only contained non-resident census features. On top of thesedynamic census features, we also included static census features, including thenumber of people in the area, the average proportion of people commuting, andthe number of residents in the area. Finally, we tested multiple models topredict real estate prices. Our proposed model is two stacked random forestmodules combined using a ridge regression that uses the random forest outputsas predictors. The first random forest model used static features only and thesecond random forest model used dynamic features only. Comparing our modelswith and without the dynamic mobile location features concludes the model withdynamic mobile location features achieves 3/% percent lower mean squared errorthan the same model but without dynamic mobile location features.

Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data