The Ripple Effect at an inter-suburban level in the Sydney Metropolitan area

Abstract

PurposeThe purpose of this study is to investigate the Ripple Effect of house prices at an inter-suburban level of analysis in the Sydney metropolitan area. By doing this, more practical information of price transmission can be provided to improve residential real estate purchasing decisions of market participants. Equity from residential real estate is a major component of household wealth and is frequently used to improve and upgrade homes. With the ever-increasing prices of real estate in Sydney making more efficient purchasing decisions can grow this wealth quicker allowing a household to obtain financial related goals at a quicker pace.Design/methodology/approachUsing a two-stage sampling technique strings of adjoining suburbs from different Sydney regions were analysed using a combination of price graphs, Engle–Granger and Johansen co-integration techniques and Granger causality tests.FindingsPairwise co-integration was lacking throughout the various suburb strings, whereas multivariate co-integration was found in the lower priced areas further from the central business district, as these areas also experience less price volatility. The geographical location of suburbs therefore plays an important role in the ability to predict an individual suburb’s price movements. For a prominent Ripple Effect to exist at this level the best conditions would consist of a singular demand centre with restricted geographical space to which this demand can spread. Causal pathways were subsequently mapped for each suburb string identifying price transmission pathways and confirming support that while the standard Ripple Effect does not exist at an inter-suburban level, it is still possible to predict price movements by considering the price behaviour of surrounding suburbs.Originality/valueThis paper adds to the literature by examining the Ripple Effect across different suburbs in Sydney. This is done via an extensive search through the literature and analysing recent real estate data.