Differences in housing price dynamics across cities: A comparison of different panel model specifications

Abstract

This study compares a conventionally used panel data model -that does not allow for regional variations in housing price dynamics -with panel models that let the dynamics differ across regions. We concentrate on examining the momentum dynamics and the reversion speed towards the fundamental price level. Based on data over 1988-2012, the results indicate that the regional differences are generally quite small in the Finnish market. Nevertheless, in several cities the dynamics differ significantly from those indicated by the baseline model that does not allow for regional variation. In addition, the long-term coefficient on income considerably varies across regions, the coefficient being greater in the more supply-constrained cities. The results indicate that the use of panel models that assume similar housing price dynamics across regions can lead to flawed conclusions being drawn.

Inflation-hedging properties of regional Chinese real estate market: evidence from 35 cities in China

“…In addition to regional factors, housing markets are well known for being heterogeneous (Clayton, Miller and Peng, 2010), particularly in terms of fundamentals causing substantial regional differences in house prices (Oikarinen and Engblom, 2015). The aggregate demand and supply for housing is elastic (Clayton, Miller and Peng, 2010).…”

“…It builds a system of two equations-the original equation and the transformed one-and is known as system GMM. See ( Oikarinen and Engblom, 2015) for discussion. 15 We experiment with various possible orderings among the three variables and find results regarding different relationships between regional real estate returns and inflation among the three regions are robust to orderings selected.…”

U.S. Metropolitan House Price Dynamics

“…These early studies generally ignore complications regarding the suitability of the estimators for non-stationary and cross-sectionally dependent data. Anundsen and Heebøll (2014), Oikarinen and Engblom (2016), and Lai and Van Order (2017) represent more recent studies using panel models that allow for heterogeneity in slope coefficients. Anundsen and Heebøll (2014) explore differences across the largest 100 U.S. Metropolitan Statistical Areas (MSAs) using several methods, including the mean group and pooled mean group estimators of Pesaran and Smith (1995) and Pesaran et al (1999).…”

“…Anundsen and Heebøll (2014) explore differences across the largest 100 U.S. Metropolitan Statistical Areas (MSAs) using several methods, including the mean group and pooled mean group estimators of Pesaran and Smith (1995) and Pesaran et al (1999). Oikarinen and Engblom (2016) investigate differences in price dynamics across Finnish cities. They estimate long-term dynamics separately for each city instead of relying on a panel estimator, but use fixed interaction effects to allow for different short-term parameter estimates across cities.…”

Quantitative Estimation and Spatiotemporal Characteristic Analysis of Price Deviation in China's Housing Market

“…The assumptions of spatial and temporal stationarity are not realistic, and the parameters tend to vary over the study area, especially within large markets. [33][34][35]. The geographically weighted regression (GWR) is a common method for capturing spatial heterogeneity [36], and it outperforms the spatial expansion method in terms of explanatory power and predictive accuracy [37].…”