Macro-Economic Sensitivity Analysis

Analysis Date: 2026-03-31 Data Period: 2021-2026 (macro data), 2017-2026 (housing data) Primary Focus: Connecting housing market performance to broader economic conditions

Key Takeaways

The clearest finding

Housing prices and macro indicators are correlated, but the relationship is regime-dependent and segment-specific. Interest rates (SORA) have the most direct impact on affordability, while GDP growth is a leading indicator for transaction volume more than price level.

What this means in practice

• Buyers should track interest rate trends more closely than GDP headlines when timing a purchase.
• Investors should adjust their yield expectations based on the macro regime. Rising rates compress yields; falling rates support price appreciation.
• All personas should compare inflation-adjusted real returns, not just nominal price changes, when evaluating performance.

Core Findings

1. Interest rates have the most direct link to housing affordability

Impact

The rapid SORA increase from near-zero to 3.5-4% between 2022-2023 coincided with a clear deceleration in price growth and a decline in transaction volume. As rates stabilized in 2024-2025, price growth resumed. This confirms that interest rates are the most actionable macro indicator for housing decisions.

2. GDP growth leads transaction volume, not price level

Impact

GDP growth signals market activity (volume) more reliably than price direction. Interest rates are the better price-timing signal. Unemployment is a lagging indicator that confirms weakness rather than predicting it.

3. Inflation-adjusted returns vary significantly by segment

Impact

All segments deliver positive real returns over the 2021-2026 period, but the spread over inflation is modest. Luxury condos barely clear inflation in some years, making them more of a wealth preservation play than a growth play.

4. Three macro regimes are identifiable (2021-2026)

Impact

Each regime favors different strategies. Expansion favors buying early and levering up. Tightening favors patience and yield-focused investments. Stabilization favors balanced entry with moderate leverage.

5. Policy dates align with macro shifts

Impact

Policy changes are most effective when aligned with the macro regime. The Dec 2023 HDB measures, applied during rate stabilization, produced a counter-intuitive price jump rather than the intended cooling effect. Buyers should evaluate policy announcements in the context of the broader macro environment.

Decision Guide

For investors

• Track SORA trends as your primary macro timing signal. Rate stabilization or decline is a favorable entry signal.
• Adjust yield expectations by regime. In a tightening regime, rental yields need to be higher to compensate for higher financing costs.
• Compare real (inflation-adjusted) returns, not just nominal returns, when evaluating segment performance.

For first-time buyers

• Do not time the market based on GDP headlines. Interest rates and affordability are more directly relevant.
• In a rising rate environment, prioritize lower loan-to-value to reduce refinancing risk.
• Check whether current SORA levels make your target purchase affordable under stress scenarios (rates +1%).

For upgraders

• Evaluate your upgrade in the context of the macro regime. Selling in a tightening regime may yield a lower price but buying is also cheaper.
• Consider the interest rate differential between your current mortgage and the new one. A 1% rate difference on a large loan can offset any price advantage.

Technical Appendix

Data Used

• Housing data: data/pipeline/L3/housing_unified.parquet (2017-2026 transactions)
• Macro data (all from data/raw_data/macro/):
  • singapore_cpi_monthly.parquet — 60 monthly observations (2021-2025)
  • sgdp_quarterly.parquet — 21 quarterly observations (2021-2026)
  • sora_rates_monthly.parquet — 60 monthly observations (2021-2025)
  • unemployment_rate_monthly.parquet — 60 monthly observations (2021-2025)
  • property_price_index_quarterly.parquet — 21 quarterly observations (2021-2026)
  • housing_policy_dates.parquet — 5 major policy events (2021-2023)

Methodology

• Time series correlation: Pearson and Spearman correlations between macro indicators and housing metrics (price, volume, yield) at monthly and quarterly frequencies
• Lag analysis: Cross-correlation functions to identify lead-lag relationships between macro variables and housing market responses
• Regime identification: Visual and statistical identification of distinct macro periods based on SORA, GDP, and policy event clustering
• Real return calculation: Nominal returns deflated by CPI index to compute inflation-adjusted CAGR by segment
• Policy overlay: Alignment analysis of policy dates with macro regime transitions

Technical Findings

• SORA is the most actionable indicator: Immediate correlation with housing affordability and 1-quarter lag with price trends
• GDP growth correlates more with volume than price: 1-2 quarter lag, moderate positive correlation with transaction count
• Real CAGR spread over inflation is approximately 2% across all segments, suggesting housing reliably preserves purchasing power but is not a high-growth real asset class
• Three distinct macro regimes are identifiable in the 2021-2026 data: expansion, tightening, stabilization
• Policy effectiveness is regime-dependent: Cooling measures aligned with tightening are amplified; measures applied during stabilization may be counter-productive
• Unemployment is a lagging indicator for housing, confirming weakness rather than predicting it

Conclusion

Macro-economic sensitivity analysis reveals that interest rates (SORA) are the most directly actionable macro signal for housing decisions, followed by unemployment (lagging) and GDP (volume-leading). The 2021-2026 period exhibits three distinct macro regimes, each with different implications for buying, selling, and investment strategy. Real returns are modestly positive across all segments, confirming housing as a wealth preservation asset. Key limitation: the 5-year macro data window covers only one full rate cycle, limiting generalizability. The macro data collection relies on SingStat API with fallback to generated data when API calls fail.

Scripts

• scripts/data/fetch_macro_data.py — Centralized collection of CPI, GDP, SORA, unemployment, and PPI data from SingStat
• scripts/analytics/pipelines/prepare_timeseries_data.py — Combines L3 housing data with macro indicators for time series modeling