‘Ripple’ Effects in South African House Prices

Balcılar, Mehmet; Beyene, Abebe D.; Gupta, Rangan; Seleteng, Monaheng

doi:10.1177/0042098012458551

Cited by 59 publications

(46 citation statements)

References 68 publications

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“…A number of studies have been conducted for the South African housing sector focusing on forecasting housing prices, impact of monetary policy on housing prices, "ripple" effects and convergence, and the impact of housing prices on consumption (a few recent examples: Burger and van Rensburg, 2008;Gupta & Das, 2008;Das, Gupta, & Kabundi, 2009;Gupta, Jurgilas, & Kabundi, 2010;Das, Gupta, & Kaya, 2010;Balcilar, Gupta, & Shah, 2011;Balcilar, Beyene, Gupta, & Seleteng, forthcoming; Das, Gupta and Kanda, 2011), however, none of the papers have shed light into the relationship between housing and general prices in South Africa to date.…”

Section: Introductionmentioning

confidence: 99%

The Long-Run Relationship Between House Prices and Inflation in South Africa: An Ardl Approach

Inglesi‐Lotz

Gupta

2013

International Journal of Strategic Property Management

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This paper investigates whether house prices provide a suitable hedge against inflation in South Africa by analysing the long-run relationship between house prices and the prices of non-housing goods and services. Quarterly data series are collected for the luxury, large middle-segment, medium middle-segment, small middle-segment and the entire middle segment of house prices, as well as, the consumer price index excluding housing costs for the period 1970:Q1-2011:Q1. Based on autoregressive distributed lag (ARDL) models, the empirical results indicate long-run cointegration between the house prices of all the segments and the consumer price index excluding housing costs. Moreover, the long-run elasticity of house prices with respect to prices of nonhousing goods and services, i.e., the Fisher coefficient is greater than one for the luxury segment, virtually equal to one for the small middle-segment, and less than one for the large and medium middle-segments, as well as the affordable segments. More importantly though, the estimated Fisher coefficients are not statistically different from unity -a result consistent with the proposed theoretical framework relating housing prices and consumer prices excluding housing expenditure. In general, we infer that house prices in South Africa provide a stableinflation hedge in the long-run.

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Section: Introductionmentioning

confidence: 99%

The Long-Run Relationship Between House Prices and Inflation in South Africa: An Ardl Approach

Inglesi‐Lotz

Gupta

2013

International Journal of Strategic Property Management

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“…Canarella et al (2012), also studied the house price ripple effect in the USA by combining the generalised least squares version of the ADF with non-linear unit root tests and other procedures that control for structure breaks. The Bayesian and panel seemingly unrelated regressions augmented Dickey-Fuller (SURADF) methods for testing unit roots have also been used by a section of the housing literature (Balcilar et al, 2012;Lee and Chien, 2011;Holmes, 2007).…”

Section: The Empirical Literaturementioning

confidence: 99%

“…This impression has stimulated our interest in the notion that Amsterdam house price development ripples to other Dutch regional housing markets. The ripple effect is conceptually a market phenomenon in which house price shocks in one region spread out their influence to house prices in other parts of the country (Meen, 1999;Nanda and Yeh, 2014;Balcilar et al, 2012). It manifests itself by way of house prices appreciating (down-turning) in one location, and subsequently appreciating (down-turning) in other regions (Giussani and Hadjimatheou, 1991).…”

Section: Introductionmentioning

confidence: 99%

Amsterdam house price ripple effects in The Netherlands

Teye

Knoppel

Haan

et al. 2017

JERER

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Purpose -This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.Design/methodology/approach -The paper considers the ripple effect as a lead-lag effect and a longrun convergence between the Amsterdam and regional house prices. Using the real house prices for secondhand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda-Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.Findings -The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1753-9269.htm Research limitations/implications -The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.Originality/value -This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors' knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

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“…An extensive review of the literature is provided by Balcilar et al (2013) and most recently by Nanda and Yeh (2014) and Gong et al (2016).…”

Section: Extant Literaturementioning

confidence: 99%

Spatial and Temporal House Price Diffusion in the Netherlands: A Bayesian Network Approach

Teye¹,

Ahelegbey²

2017

24th Annual European Real Estate Society Conference

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Following the 2007-08 Global Financial Crisis, there has been a growing research interest on the spatial interrelationships between house prices in many countries. This paper examines the spatio-temporal relationship between house prices in the twelve provinces of the Netherlands using a recently proposed econometric modelling technique called the Bayesian Graphical Vector Autoregression (BG-VAR). This network approach is suitable for analysing the complex spatial interactions between house prices. It enables a data-driven identification of the most dominant provinces where temporal house price shocks may largely diffuse through the housing market. Using temporal house price volatilities for owner-occupied dwellings from 1995Q1 to 2016Q1, the results show evidence of temporal dependence and house price diffusion patterns in distinct sub-periods from different provincial housing sub-markets in the Netherlands. In particular, the results indicate that Noord-Holland was most predominant from 1995Q1 to 2005Q2, while Drenthe became most central in the period 2005Q3-2016Q1.JEL classification: C11; C15; C32; C52; R20; R32

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‘Ripple’ Effects in South African House Prices

Cited by 59 publications

References 68 publications

The Long-Run Relationship Between House Prices and Inflation in South Africa: An Ardl Approach

The Long-Run Relationship Between House Prices and Inflation in South Africa: An Ardl Approach

Amsterdam house price ripple effects in The Netherlands

Spatial and Temporal House Price Diffusion in the Netherlands: A Bayesian Network Approach

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