Spatial Analysis and Modeling of the Housing Value Changes in the U.S. during the COVID-19 Pandemic

Optimization of United States Residential Real Estate Investment through Geospatial Analysis and Market Timing

In this study, we examined the long-term spatial patterns and trends of home values within the United States from 1996 to 2019. The Zillow Home Value Index (ZHVI) data were obtained from the Zillow's housing data portal. The overall trends indicated both coasts of the United States having the highest concentration of hotspots. The ideal time to sell varied seasonally across the country. We used geo-statistical analysis techniques, such as cluster analysis and emerging hot spot analysis (EHSA), to reveal the average and long-term trends in spatial patterns of home values. Washington was the only state with sporadic hot spots. The Western United States showed consecutive hotspots, most notably in California, but in surrounding states as well. The Northeast, Mid-Atlantic, and Florida were dominated by oscillating hotspots. Texas and the Southeast display new hotspots that signal change, indicative of southward migration of population. Finally, the Midwest has been a historical cold spot, but Cleveland has improved to a diminishing cold spot, pointing to the revitalization of the region. The seasonal level analysis displayed southern and coastal states as those benefiting most from winter home sales, and the colder northern states captured the most value and sold quicker in the summer months. This intuitive analysis of the country's variation in ideal sale month reveals a local view of when to sell rather than a generalized view of the entire United States.

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

With the rise of urban agglomerations, regional divergence of China’s real estate market has gradually intensified. City-specialized policies have become the main emphasis for promoting the healthy development of the regional real estate market. By adopting a gravity model, social net-work analysis, and impulse response analysis, this paper examines the spatial-temporal heterogeneity of housing prices’ association in the Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), the Yangtze River Delta Urban Agglomeration (YRDUA), and the Pearl River Delta Urban Agglomeration (PRDUA), which are the most developed urban agglomerations in China. Meanwhile, the formation mechanism of the housing prices’ association network and spillover effect in urban agglomeration were theoretically analyzed. This paper found that (1) significant aggregation phenomena of housing prices were observed in the urban agglomerations; (2) characteristics of overall and individual networks were dynamically heterogeneous. In the BTHUA and the PRDUA, the associations of housing prices were polarized and sparse, while they were more linked and complex in the YRDUA; (3) polycentric network structure has been demonstrated in the urban agglomerations and the spillover effects of central cities varied in intensity and breadth on responding cities and persisted during the lag period. Accordingly, several policy recommendations have been made.