The U.S. housing market in the first half of 2026 will not be decided by headlines. It will be decided by affordability and timing.
This project focused on forecasting median U.S. home prices through June 2026 using 10 years of macroeconomic data. Rather than chasing a single outcome, three scenarios were modeled to understand ranges, probabilities, and decision windows.
The goal was not precision for its own sake. It was to understand where the market is likely to land, how wide the range of outcomes is, and when the critical decision windows are likely to occur.
Why Scenario-Based Modeling?
Rather than chasing a single forecast number, this model was designed around decision windows and probability ranges. A single forecast can be wrong in one direction. Three scenarios force the analyst to think about what has to be true for each outcome to occur.
The model incorporates multiple macro signals to capture how rates, inflation, and income growth interact with housing demand. Each signal is weighted based on its historical correlation with median home prices over the past 10 years.
What Makes This Useful
- Scenario-based thinking instead of single point forecasts
- Conservative assumptions built into the base case
- Focus on decision timing, not just price predictions
- Transparent data sources — all from public government agencies
- Replicable methodology — anyone can rebuild this with the same data
Macro Signals Used
- 30-year fixed mortgage rate trajectory
- Federal funds rate forward curve
- CPI inflation (shelter component vs overall)
- Wage growth vs home price growth ratio
- Housing affordability index (HAI)
- Inventory levels (months of supply)
- GDP growth and consumer spending trends
- Historical median home price cycles (10 years)