What Forces Drive the Dynamic Interaction Between Regional Housing Prices?

Abstract

This paper examines the dynamic interaction between regional housing prices in the United States. We use the copula method to explore the dependent distribution of housing prices in ten metropolitan statistical areas (MSAs) in three regions. The results generally show that changes in time-varying correlation result from different trends in regional housing prices. We regress housing price dynamic correlation on regional economic variables, finding that the economic co-movement mechanism determines the housing price correlation in the Western and Great Lakes regions, while the migration mechanism drives the housing price correlation in the Eastern region. We also find that economic co-movement is the main force driving the housing price correlation between regions.

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

“…For example, Lam and Hui (2018) used panel data and constructed panel regression models to study the impact of market sentiment on the future returns of Hong Kong residential properties. However, the housing market is spatially correlated, with housing prices spreading across cities (Luo et al, 2007;Wu et al, 2017), but traditional panel regression models ignore the spatial correlation of housing markets and do not take into account the spatial effect of sentiment on housing prices, leading to potentially biased estimates from the resulting models. Spatial econometric models, which incorporate spatial factors into the model by constructing a spatial weight matrix, have been widely used in studies exploring housing price volatility (Vergos & Zhi, 2018).…”

Spatial analysis of housing prices in Tehran city

“…Therefore, we will review the past research to make the connection between theory and practice, as well as to get a better understanding of the theoretical gap. Based on the conducted research, some factors such as socio-economic variables (Mirkatouli et al , 2018; Ozus et al , 2007); proximity to various uses (parks, urban services, access to gardens and agricultural lands and commercial-administrative use) (Safdari Molan and Farhadi, 2019); location and quality of planning (Ozus et al , 2007); welfare facilities (Cai et al , 2020); construction cost, population growth, population density, climatic characteristics and household income (Manning, 1988); socio-demographic and environmental factors (Cellmer et al , 2020); population growth, income changes, construction costs and interest rates (Winker and Jud, 2002); adjacent to recreational sites (such as lakes, mountains and landscapes) (Hu et al , 2016; Wen et al , 2015); government policies (Yuan et al , 2018); geographical barriers (Bruyne and Hove, 2013); housing market plans (Liu, 2010); economic factors (Wu et al , 2017); land prices (Bourassa et al , 2011); environmental factors (Brasington, 2005); green space (Zhang and Dong, 2018); public and rail transportation (Yang et al , 2020; Vichiensan and Miyamoto, 2010); occupation and housing quality (Tomal, 2021); physical and spatial factors (Hai-Zhen et al , 2005; Liang et al , 2018); educational facilities (Wen et al , 2019); growth policies (such as intelligent growth) (Bagheri and Shaykh-Baygloo, 2021) are the most critical factors influencing housing price fluctuations. In Tehran, the above variables affect the zoning of housing prices and cause the price difference between different areas of Tehran to increase daily.…”

The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China

“…In recent years, some scholars have attempted to investigate the long-run convergence of housing prices within cities, albeit with mixed results [24,68,69]. Holmes et al [70] provided evidence for the existence of long-run property price convergence in London.…”