An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa

Abstract

a b s t r a c t a r t i c l e i n f oThis paper tests whether housing prices in the five segments of the South African housing market, namely large-middle, medium-middle, small-middle, luxury and affordable, exhibit non-linearity based on smooth transition autoregressive (STAR) models estimated using quarterly data from 1970:Q2 to 2009:Q3. Findings point to an overwhelming evidence of non-linearity in these five segments based on in-sample evaluation of the linear and non-linear models. We next provide further support for non-linearity by comparing one-to four-quarters-ahead out-of-sample forecasts of the non-linear time series model with those of the classical and Bayesian versions of the linear autoregressive (AR) models for each of these segments, for the out-ofsample horizon 2001:Q1 to 2009:Q3, using the in-sample period 1970:Q2 to 2000:Q4. Our results indicate that barring the one-, two and four-step(s)-ahead forecasts of the small segment, the non-linear model always outperforms the linear models. In addition, given the existence of strong causal relationship amongst the house prices of the five segments, the multivariate versions of the linear (classical and Bayesian) and STAR (MSTAR) models were also estimated. The MSTAR always outperformed the best performing univariate and multivariate linear models. Thus, our results highlight the importance of accounting for non-linearity, as well as the possible interrelationship amongst the variables under consideration, especially for forecasting.

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

“…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.…”

Are house prices in South Africa really nonstationary? Evidence from SPSM-based panel KSS test with a Fourier function

“…In the same vein as Balcilar et al (2011), who highlight the existence of nonlinear behaviour in South…”

‘Ripple’ Effects in South African House Prices

“…Post 2000, this figure escalated to double digit numbers and reached as high as 30 percent during 2004-2006. This is due to a large number of factors, namely, the domestic financial market liberalization in 1985 that led to lowering/abolition of the credit-constraints, the creation of democratic South Africa in 1994 which resulted in higher labor market participation since for the previously disadvantaged community, increased and stable growth rate of over 5 percent since 1994 due to foreign direct investment and controlled inflation in wake of the the informal inflation targeting in 1990 and the formal inflation targeting in 2000 (Kasai and Gupta 2010;Balcilar et al, 2011;Das et al, 2011 2 -79m 2 ), middle (R430,000 to R3,1million) and luxury (R3,1 million to R11.5 million). The data is further subdivided for the middle segment of the housing market, based on sizes (square meters), into small (80m 2 -140m 2 ), medium (141m 2 -220m 2 ) and large (221m 2 -400m 2 ).…”

“…Note, over and above accounting for structural changes, the non-linear unit root tests allow shift in the autoregressive parameters, that is the dynamics of the model also changes, since the persistence is allowed to change as well. Moreover, Balcilar et al, (2011) has highlighted that prices of the five segments of the South African housing market are characterized by non-linearity. Given this, it is well-known that linear unit root tests have low power and leads to wrong conclusions regarding stationarity at times.…”

“…The motivation for the analysis emanates not only due to the lack of any studies analyzing ripple effect in the South African housing market, but more generally, because of the importance of analyzing the behavior of regional house prices due to boom and bust cycles undergone by many local housing markets across the world (Canarella et al, 2010), including South Africa (see for example, Gupta and Das, 2008;Das et al, 2011;Gupta et al, 2010;Balcilar et al, 2011). Moreover, with most analysts attributing the recent world-wide financial crisis emerging from the collapse of house prices, the need for careful investigation of the time-series properties of the house prices, to check if house price shock in one metropolitan area ripples over to other areas and the economy as a whole, cannot be underestimated.…”