Purpose
The purpose of this paper is twofold. First, the study aims to test whether expectations are adaptive in the Auckland housing market. The second purpose is to examine the interplay between expectations and Auckland housing prices.
Design/methodology/approach
In this study, two vector error correction models (VECM) are built: one VECM includes survey-based expectations and another one encompasses model-based expectations with the assumption that property investors’ expectations are adaptive. The paper goes on by comparing and examining the results of Granger causality tests and impulse response analyses.
Findings
The findings reveal that Auckland property buyers’ expectations are adaptive. In addition, this study provides some evidence of a feedback cycle between Auckland housing prices and expectations.
Research limitations/implications
This study posits that Auckland property buyers’ expectations in the next 12 months are based on three-year price movements with more emphasis being placed on recent price history. This assumption may not be an accurate reflection of true expectations.
Practical implications
This paper helps policymakers to deepen their understanding of Auckland property buyers by showing that their expectations form through the extrapolation of the past price trend.
Originality/value
The study possibly marks the first attempt to test and compare the relationship between housing prices and two forms of expectations: survey-based and model-based. Additionally, this study is probably the first one that empirically examines whether there is a feedback cycle between expectations and property prices in the Auckland housing market.
This research examines housing price volatility and its determinants in Auckland, New Zealand. It differs from the existing literature by dividing residential sales into four groups: leveraged investment (LI), leveraged owner‐occupancy (LO), unleveraged investment (UI) and unleveraged owner‐occupancy (UO). The housing price volatility of these groups is estimated using autoregressive conditional heteroscedasticity (ARCH) models. This study builds four vector autoregression (VAR) models to conduct Granger causality tests and impulse response analyses. It is found that the four volatility series respond differently to shocks. A decrease in housing prices is a significant determinant across all four groups of transactions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.