The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R² of 0.8192, performs better in the mass appraisal modeling of real estate. The comparison of different models provides a useful benchmark for policy makers regarding the mass appraisal process of urban centers. The finding also highlights the spatial characteristics of price-related parameters in high-density residential areas, providing an efficient evaluation approach for planning, land management, taxation, insurance, finance and other related fields.

This paper proposes a theoretical framework to analyse the regionally heterogeneous responses of housing prices and inflation to the monetary aggregates shock and the trans-regional interaction of housing prices and inflation, which has seldom been discussed in previous literature. Using a GVAR (Globe Vector Autoregression) model, evidence based on China’s 35 major cities for this framework is provided. The results show that (1) the housing price shocks have weak positive influence on CPIs (consumer price index); (2) the housing price shocks, especially the shocks in first-tier cities and eastern cities, have strong positive influence on domestic housing price dynamics and housing prices of other cities; (3) monetary aggregates shock has strong influence on the housing prices of first-tier cities and eastern cities, while weak influence on that of central and western cities. CPIs are barely influenced by monetary aggregates shocks. The empirical results are in accordance with the theoretical explanation. Based on empirical results, this paper proposes policy recommendations for stabilising housing prices.