Housing prices prediction with deep learning: an application for the real estate market in Taiwan

The housing market is increasing huge, predicting housing prices is not only important for a business issue, but also for people. However, housing price fluctuations have a lot of influencing factors. Also, there is a non-linear relationship between housing prices and housing factors. Most econometric or statistical models cannot capture non-linear relationships yet. Therefore, we propose housing price prediction models based on deep learning methods, which can capture non-linear relationships. In this work, we construct a dataset, including the housing attributes data and macroeconomic data in Taiwan from January 2013 to December 2018. The housing attributes data includes two types of housing transactions, which are “land + building” (Type1) and “land + building + park” (Type2). Macroeconomic data includes housing investment demand ratio, owner-occupier housing ratio, housing price to income ratio, housing loan burden ratio, and housing bargaining space ratio. Then, this dataset is utilized to evaluate the prediction methods based on deep learning algorithms BPNN and CNN to predict housing prices. Experimental results show that CNN with housing features has the best prediction effect. This study can be used to develop targetted interventions aimed at the housing market.