Relative House Price Dynamics Across Euro Area and US Cities: Convergence or Divergence?

Hiebert, Paul; Roma, Moreno

doi:10.2139/ssrn.1616616

Cited by 10 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If such is the case, a stagnant housing market can lead to stagnant consumption and consequently a contraction in a city's economy. As a consumption good, housing has a strong non-traded component (i.e., land and labor) and a small traded component (e.g., construction materials) (Hiebert and Roma, 2010). The nontraded component can limit the prospect of house price convergence across geographic regions.…”

Section: Introductionmentioning

confidence: 99%

Convergence and spillover of house prices in Chinese cities

2016

View full text Add to dashboard Cite

The issue of house price convergence in 34 Chinese cities is investigated. We augmented the convergence model with contemporaneous spatial dependence in house prices and found that price convergence and positive spatial spillover are both present. We explicitly addressed the endogeneity problem by introducing a Bayesian instrumental variable setup, which was estimated with particle filtering techniques. From a growth poles perspective, the empirical evidence indicates that the spread effect in regional house prices outweighs the backwash effect. The identified positive spatial spillover has two effects on the growth of house prices in Chinese cities. First, the spillover elevates the trajectories of the steady-state growth paths of house prices. Second, the spillover narrows the gaps between the growth paths of house prices in neighboring cities. Shocks to the socioeconomic variables of a city generate their own effects on domestic house prices that dominate the effects arising from cross-city price feedbacks, thus mitigating the prospect of level convergence. Our findings also suggest a collaborating role between time and spatial dependence parameters. The identification of inter-city spillover, which is a conditioning factor for regional house price convergence, offers implications to policies that are most likely to be effective in reducing regional disparity.

show abstract

Section: Introductionmentioning

confidence: 99%

Convergence and spillover of house prices in Chinese cities

2016

View full text Add to dashboard Cite

show abstract

“…For example, it is important to take into account the role of money, mortgages and migration (Taltavull de La Paz and White, 2012) and the connection between house price and household income, population and the structure of production (Taltavull de La Paz, 2003). On the other hand, great attention has been paid to examining the location choices of households (Chen and Lin, 2012; Clark and Huang, 2003; Zhu, 2013) and a spatial difference in house price (Gathergood, 2011; Hiebert and Roma, 2010). Besides, it is also necessary to better understand households’ aspirations and expectations and changes in housing demand (Maclennan, 1982; Maclennan and Wood, 1982; Marsh and Gibb, 2011; Rae, 2015).…”

Section: Methodsmentioning

confidence: 99%

A systematic analysis of the spatial migration patterns and the linkages between market components in Greater Manchester, United Kingdom

Doan

Yudono

2021

IJHMA

View full text Add to dashboard Cite

Purpose This paper aims to bring together research on housing market area, submarket and household migration into a systems approach that helps us gain a better understanding of the structure and dynamics of a housing market and identify housing problems for a large metropolitan area. Design/methodology/approach The paper uses a geographic information system (GIS)-based method with simple quantitative techniques, including spatial analysis, location analysis, house price clustering and cross-tabulation. The analysis is based on migration data from the 2011 Census, house price data from the Land Registry in 2011 for Greater Manchester at the ward level and the output areas level. Findings The results show that different submarkets and housing market areas had different patterns of spatial migration and connections with other areas. Through a systematic analysis of migration and house price in combination, it also found a close connection between destination submarkets and the ages of migrants and identified specific problematic patterns for a large metropolitan area. Research limitations/implications The interactions between the owner-occupied sector and the social and private rented sectors are arguably an important omission from the analysis. Also, it is acknowledged that clustering ward units based on price differentials is subject to distortions in terms of specification, size and shape. Moreover, the use of the large samples may result in very small p-values, leading to the problem of the rejection of the predefined hypothesis. Practical implications A systematic analysis of migration and house price in combination may be used to gain a better understanding of the housing market dynamics and identify housing problems systematically for a large metropolitan. It may help to identify low-demand areas, high-demand areas and assist planners with decisions in allocating suitable land for new housing constructions. Social implications The GIS-based method introduced in the paper could be considered as an effective approach to provide a better basis for determining policy interventions and public investment designed to allocate land resources effectively and improve transport systems to change existing problematic migration patterns. Originality/value This paper fills a gap in the international literature in relation to adopting a systems approach that analyses migration and house price data sets in combination to systematically explore migration patterns and linkages and identify housing problems for a large metropolitan area. This systems approach can be applied in any metropolitan area where migration and house price data are available.

show abstract

“…In contrast, Miles (2017) and Tsai (2018) find a relative lack of such synchronization in international housing markets. Further, there is evidence that even within a country the housing market may not be fully integrated (see, Miles (2015) and Hernández-Murillo et al (2017) for the U.S., Hiebert and Roma (2010) for Europe and the U.S., Klarl (2018) for Netherlands and Funke et al (2018) for the U.K. housing market. ).…”

Section: Introductionmentioning

confidence: 99%

(A)Synchronous Housing Markets of Global Cities

Bhatt

Kishor

2022

J Real Estate Finan Econ

View full text Add to dashboard Cite

In this paper we examine house price synchronization in 15 global cities using real house price data from 1995:Q1-2020:Q2. We find that although there is evidence for bilateral positive phase synchronization, there is no evidence for an integrated global housing market for our sample of cities. Using a hierarchical clustering approach, we identify three clusters of cities with similar housing price cycles that are not solely determined by geographic proximity. We interpret this finding as suggestive of a rather segmented housing market for the global cities in our sample. Using a dynamic factor model with time-varying stochastic volatility we decompose a city's real housing price growth into a global component, a clusterbased component, and an idiosyncratic component. For most cities in our sample, the global component plays a minor role, whereas the cluster-based factor explains a large fraction of the observed variation in real house price growth with its contribution peaking during the Great Recession of 2007-09.

show abstract

Relative House Price Dynamics Across Euro Area and US Cities: Convergence or Divergence?

Cited by 10 publications

References 22 publications

Convergence and spillover of house prices in Chinese cities

Convergence and spillover of house prices in Chinese cities

A systematic analysis of the spatial migration patterns and the linkages between market components in Greater Manchester, United Kingdom

(A)Synchronous Housing Markets of Global Cities

Contact Info

Product

Resources

About