  Real estate prices have a significant impact on individuals, families,
businesses, and governments. The general objective of real estate price
prediction is to identify and exploit socioeconomic patterns arising from real
estate transactions over multiple aspects, ranging from the property itself to
other contributing factors. However, price prediction is a challenging
multidimensional problem that involves estimating many characteristics beyond
the property itself. In this paper, we use multiple sources of data to evaluate
the economic contribution of different socioeconomic characteristics such as
surrounding amenities, traffic conditions and social emotions. Our experiments
were conducted on 28,550 houses in Beijing, China and we rank each
characteristic by its importance. Since the use of multi-source information
improves the accuracy of predictions, the aforementioned characteristics can be
an invaluable resource to assess the economic and social value of real estate.
Code and data are available at: https://github.com/IndigoPurple/PATE
