House Price Modeling with Digital Census

TL;DR

This paper demonstrates that digital census data, such as complaints and taxi trips, significantly enhance urban house price modeling accuracy, offering a real-time, dynamic alternative to traditional census data.

Contribution

Introduces digital census datasets into house price modeling, showing they improve accuracy and can replace or complement traditional census data.

Findings

Digital census data improves house price prediction accuracy.

Digital census can serve as an effective alternative to traditional census.

Incorporating digital data enhances modeling of house price changes.

Abstract

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.