Housing market cycles in large urban areas

Abstract

This paper examines the asymmetric behaviour of house prices in large metropolitan areas. Using a sample of large cities, in several countries, it is shown that real estate prices cycles are largely nonlinear. It is found that dynamic asymmetries in the housing market cycle can well be modelled using a logistic smooth transition model (LSTAR). Further, it is shown that the LSTAR model has better forecasting properties with respect to a linear autoregressive model.

Global Cities and Local Challenges: Booms and Busts in the London Real Estate Market

“…From the methodological point of view the estimation results of the GSTAR model allow us to support most of the findings in the previous literature about the nonlinear behaviour of the housing markets in large metropolitan areas (see for example Alqaralleh and Canepa 2020;Miles 2008;Cabrera et al 2011). However, looking at the results of the Pagan and Sossounov (2003) algorithm it is clear that the type of transition function commonly adopted in threshold models such as STAR-type models may be suitable for estimating house price dynamics at a higher level of aggregation (e.g.…”

“…In Crawford and Fratantoni (2003) a similar model was found to fit large cities well. Finally, Alqaralleh and Canepa et al (2020) found that the model in Eq. (6) well captured asymmetries in the housing market cycle (see also Kim and Bhattacharya 2009).…”

Housing Price Cycles in Poland – The Case of 18 Provincial Capital Cities in 2000–2020

“…In foreign literature, research on housing cycles is conducted at the country level (Bracke, 2013;Chang, 2010Chang, , 2020Igan & Loungani, 2012), for regions (André et al, 2019;Chowdhury & Maclennan, 2014) and at the city level (Akimov et al, 2015;Alqaralleh & Canepa, 2020;Cunningham & Kolet, 2011;Fan et al, 2019;Li et al, 2020). These tests concern the identification of cycles and their characteristics, synchronisation of cycles or duration dependence.…”

Second Order Time Dependent Inflation Persistence in the United States: a GARCH-in-Mean Model with Time Varying Coefficients

“…Asymmetric behavior in house price dynamics is introduced in the model by equations First, the model in (1) with 1 = 2 = in the transition function in equations (3)- (4) implies that the GSTAR model reduces to a one-parameter symmetric logistic STAR model (see Teräsvirta, 1994). However, with respect to the STAR model a clear advantage of the indicator functions in equations (3)-(4) is that slope parameters are not constrained.…”