Overview: FHFA HPI in one sentence

The FHFA House Price Index (HPI) tracks repeat-sales price changes for single-family properties financed by conforming mortgages. From 1991 through 2025 the long-run average for the index is ~207.97 points, while 2025 recorded a maximum near 436.8, illustrating a clear multi-decade appreciation trend.

Why the FHFA HPI matters to investors

The FHFA HPI is not a speculative snapshot: it is a methodologically robust indicator that captures actual resale price dynamics over time. Because it focuses on repeat sales, it filters out changes due to property mix and therefore offers a cleaner measure of underlying price movement.

For portfolio managers and high‑net‑worth investors, the HPI provides a durable metric to model real estate appreciation, stress-test scenarios and calibrate long-run return assumptions for residential allocations.

Technical context and implications

Three technical factors underpin the HPI’s signal:

1. Repeat‑sales methodology: reduces composition bias and better isolates price movement for similar properties.
2. Conforming loan coverage: the index reflects transactions tied to the conforming mortgage market, which represents a broad segment of residential activity across diverse US metros.
3. Long‑term trend capture: multi-decade series smooths short-term volatility coming from temporary demand shocks or local microcycles.

Together these characteristics make FHFA HPI a preferred input for scenario modeling, correlation analyses with other asset classes and Monte Carlo simulations that inform allocation decisions.

Historical resilience: recovery after downturns

Historic episodes — including the early 1990s recession, the 2008 financial crisis and localized corrections — show meaningful short-term drawdowns followed by multi-year recoveries. The HPI’s long-run upward drift illustrates how residential real estate historically reclaims lost ground as macro fundamentals normalize.

Investors should model downside scenarios, but also account for typical recovery profiles observed in FHFA and similar indices.

Practical investor applications

How to use FHFA HPI within an investment framework:

• Incorporate HPI trends into expected return models for residential allocations.
• Use the index to calibrate stress tests over 3–5 year horizons.
• Cross-reference with regional price indices (Case‑Shiller, local MLS data) for micro‑allocation decisions.

Data sources & next steps

Primary sources for FHFA HPI and complementary datasets include the FHFA website, FRED (Federal Reserve Bank of St. Louis), and major analytics providers (CoreLogic, S&P Case‑Shiller). For detailed modeling, download series from FHFA or FRED and integrate into time-series toolsets (R, Python, or Excel).