  With everyone trying to enter the real estate market nowadays, knowing the
proper 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 and
demographic information to predict property prices. In this investigation, we
attempted to improve upon past research. So we decided to explore a unique
approach: we wanted to determine if mobile location data could be used to
improve the predictive power of popular regression and tree-based models. To
prepare our data for our models, we processed the mobility data by attaching it
to individual properties from the real estate data that aggregated users within
500 meters of the property for each day of the week. We removed people that
lived within 500 meters of each property, so each property's aggregated
mobility data only contained non-resident census features. On top of these
dynamic census features, we also included static census features, including the
number of people in the area, the average proportion of people commuting, and
the number of residents in the area. Finally, we tested multiple models to
predict real estate prices. Our proposed model is two stacked random forest
modules combined using a ridge regression that uses the random forest outputs
as predictors. The first random forest model used static features only and the
second random forest model used dynamic features only. Comparing our models
with and without the dynamic mobile location features concludes the model with
dynamic mobile location features achieves 3/% percent lower mean squared error
than the same model but without dynamic mobile location features.
