Can Investors Hedge Residential Price Dynamics?

The paper examines whether residential real estate prices hedge inflation in Australia’s capital cities by analyzing the relationship between house price indices (HPI) and consumer price indices (CPI). Motivated by theory and prior evidence that real estate can hedge inflation, the authors highlight that Australian HPI growth has historically outpaced CPI growth. Prior studies mostly consider national aggregates or commercial property; the authors aim to provide city-level evidence on short-run and long-run relationships between HPI and CPI across eight Australian capitals from 1998 to 2008. Understanding these relationships is important for investors seeking inflation hedges and for policymakers monitoring housing market dynamics relative to consumer prices.

Foundational work by Fama and Schwert (1977) suggested real estate as a complete hedge against inflation. Subsequent studies report mixed evidence: Matysiak et al. (1996) find commercial property does not hedge inflation in the short run but shows positive correspondence in the long run. Newell (1996) reports heterogeneity in hedging across Australian commercial property types and cities, with perfect hedges against actual inflation in several cities and varied results for expected/unexpected inflation. Liu et al. (1997) show international differences in real estate securities’ inflation hedging. Barber et al. (1997) find UK commercial property can hedge inflation in some forms. Glascock et al. (2002) relate REIT returns and inflation through monetary policy effects. Kolari (2002) provides evidence of a long-run relationship between house prices and non-housing goods/services prices. Abelson et al. (2005) show CPI positively and significantly impacts Australian housing dynamics at the national level. Newell (2007) documents strong risk-adjusted returns for industrial property, and Brown et al. (2008) find wealth-related factors drive Australian residential investment. The present study addresses a gap by focusing on regional (city-level) residential markets and distinguishing short-run from long-run relationships between HPI and CPI.

Data: Quarterly HPIs (established houses, ABS Cat. 6416.0) and CPIs (ABS Cat. 6401.0) for Australia’s eight capital cities from March 1998 to March 2008, base 1989–90 = 100. The HPI for established houses is used (not contributing to CPI), and CPI covers eleven expenditure groups. Descriptive statistics note higher average HPI in Darwin and higher volatility in Perth; CPI levels and volatility vary little across cities.

Stationarity: Augmented Dickey–Fuller (ADF) tests assess unit roots. HPIs are non-stationary in levels; most become stationary at first differences except Brisbane and Darwin. CPIs are stationary at first differences in all cities.

Short-run model: An autoregressive distributed lag (ADL) model on first differences estimates short-run dynamics: ΔHPI_t = c + Σ α_i ΔHPI_{t−i} + Σ β_i ΔCPI_{t−i} + μ_t. Optimal lag lengths (1–4) are selected using Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC), prioritizing SBC to preserve degrees of freedom. Selected lags: 1 for Adelaide, Brisbane, Canberra; 2 for Hobart, Melbourne, Perth, Sydney; 3 for Darwin.

Long-run model: An error correction model (ECM) includes lagged levels to test cointegration/long-run relationships: ΔHPI_t = c + Σ α_i ΔHPI_{t−i} + Σ β_i ΔCPI_{t−i} + δ HPI_{t−1} + φ CPI_{t−1} + μ_t. Significance of δ and φ (t-tests) indicates a long-run relationship when both differ from zero. Model estimation uses standard t-statistics for inference.

• Stationarity: HPIs non-stationary in levels; at first differences, most are stationary except Brisbane and Darwin. CPIs are I(1) across all cities (stationary at first difference).
• Lag selection (SBC): Lag 1 (Adelaide, Brisbane, Canberra); Lag 2 (Hobart, Melbourne, Perth, Sydney); Lag 3 (Darwin).
• Short-run dynamics (ADL, Table 5):
  • Strong own-dynamics: α significant indicating HPI changes are influenced by their own past values.
    • Adelaide, Brisbane, Canberra, Sydney: positive α1 significant (p ≤ 0.0123).
    • Perth: α1 positive (p < 0.001), α2 negative (p = 0.0541).
    • Hobart and Melbourne: α2 significant positive (p = 0.0027 and p = 0.0089, respectively); α1 not significant.
    • Darwin: α3 positive significant (p = 0.0095).
  • HPI–CPI short-run links: β coefficients are generally not significant at 5%. Weak significance at 10% in Hobart (β1 p = 0.0638) and Melbourne (β1 p = 0.0508). Conclusion: No robust short-run correlation between HPI and CPI in most cities.
• Long-run relationships (ECM, Table 6):
  • Significant long-run relationships (both δ and φ significant) in: Adelaide (pδ = 0.0403; pφ = 0.0193), Brisbane (0.0177; 0.0078), Canberra (0.0082; 0.0062), Hobart (0.0229; 0.0080), Melbourne (0.0997; 0.0724 at 10% level), Sydney (0.0296; 0.0604 at 10% level).
  • No long-run relationship in Darwin (δ 10% level significant, φ not significant) and Perth (δ not significant, φ marginal at 10%). Overall: Short-run CPI movements do not explain HPI changes in most cities, but long-run cointegration exists in most capitals, with heterogeneity in strength across cities.

The study’s central question—whether residential property hedges inflation—is addressed by testing HPI–CPI linkages over short and long horizons. Results show that CPI changes do not contemporaneously or near-term drive HPI changes, indicating limited effectiveness of residential property as a short-term inflation hedge across Australian capital cities. However, significant long-run relationships in Adelaide, Brisbane, Canberra, Hobart, Melbourne, and Sydney suggest that over longer horizons, residential prices co-move with consumer prices, consistent with inflation-hedging characteristics. The absence or weakness of long-run linkages in Darwin and Perth implies city-specific structural factors may decouple housing from general price levels. These findings underscore the importance of horizon and location in using residential property for inflation protection and contribute regional evidence that complements national-level studies.

The paper demonstrates that Australian capital city house prices exhibit strong own-dynamics in the short run but generally lack short-run correlation with CPI, implying residential property is not an effective short-term inflation hedge. In contrast, long-run relationships between HPI and CPI exist in most cities (Adelaide, Brisbane, Canberra, Hobart, Melbourne, Sydney), albeit weaker in Melbourne and Sydney, while Darwin and Perth lack robust long-run links. For investors, this implies that inflation hedging via residential property is horizon- and city-dependent: long-term strategies may target cities with proven long-run relationships, while Darwin and Perth may be less suitable for inflation-hedging purposes. The study contributes city-level evidence on housing–inflation dynamics over 1998–2008 and highlights heterogeneity across markets.