  Urban house prices are strongly associated with local socioeconomic factors.
In literature, house price modeling is based on socioeconomic variables from
traditional census, which is not real-time, dynamic and comprehensive. Inspired
by the emerging concept of "digital census" - using large-scale digital records
of human activities to measure urban population dynamics and socioeconomic
conditions, we introduce three typical datasets, namely 311 complaints, crime
complaints and taxi trips, into house price modeling. Based on the individual
housing sales data in New York City, we provide comprehensive evidence that
these digital census datasets can substantially improve the modeling
performances on both house price levels and changes, regardless whether
traditional census is included or not. Hence, digital census can serve as both
effective alternatives and complements to traditional census for house price
modeling.
