Today real estate market has become very popular, but the housing recovery has pushed up home prices nearly everywhere. In Taizhong, Taiwan has not been the exception, real estate market has expanded in the past couple years to the point where it is attracting interest, not only from other parts of Taiwan but also other parts of the world. An accurate prediction on the house price is important to prospective homeowners, developers, investors, appraisers, tax assessors and other real estate market participants, such as, mortgage lenders and insurers. People who are looking to buy a new place tend to be more conservative with their budget.
The goal of our project is to use previous transactions data to predict house prices and provide to consumers the information they need, help professionals build their businesses, and create additional value in adjacent markets. The days of calling a local Realtor or hiring an expensive appraiser just to find out what a home is worth are falling behind. In the first step we obtained data from the Ministry of Interior of Taiwan. Followed by visualizing the raw data and finding the relationship between predictors. Selection of variables using domain knowledge and including external data such as national economic growth, latitude and longitude of houses, distance to nearest future MRT station and other derivatives.
We considered several methods to approach the lowest error. First a multiple linear regression and KNN algorithm. Second Random Trees and Neural Nets. The first pair showed better performance so we continued improving those methods. The low error measure in the MLR showed promising opportunity for an accurate prediction system. We consider the model can be further improved with more data. Since online services are becoming the new business platform, we encourage the application of this system on an automatized service with costumer interaction.