Housing Study
- No major demand signals available.
The total market is occupied units + for sale/rent + sold/rented but not occupied.
Methodology
What is being projected?
We project the change in owner-occupied households over the next five years for each county.
What data goes into the model?
The model uses recent housing market and local economic signals, including home prices, sales activity, recent household change, employment change, and domestic and international migration.
How does the model work?
For each county, we create a five-year “window.” We look at what the county looked like in one year, then measure how much owner households changed five years later. The model learns from many of those past windows.
Why use a rolling five-year model?
It lets the model learn from multiple time periods instead of just one. That helps capture how different local conditions have shaped growth over time.
How was it tested?
We used leave-one-window-out validation. That means we trained the model on earlier five-year periods, held one later period out, and tested how well it predicted that unseen period. Then we repeated that across the available windows.
What does that validation tell us?
It shows how the model performs on real historical periods it did not train on. That gives us a better sense of how it may perform in a future forecast.
What is the final forecast based on?
After testing, we fit the selected model on all valid historical five-year windows, then apply it to the most recent year of county data to project the next five-year change.
Why do the forecasts differ across counties?
Because the inputs differ. Counties with stronger recent migration, price growth, market activity, or household momentum may project differently than counties with weaker or negative recent trends.
Are these projections just a continuation of the last trend?
No. The model considers recent trend, but it also weighs market conditions, migration, and employment. So the forecast is based on several signals together, not just a straight-line extension.
What are the “drivers” shown in the dashboard?
They are grouped model contributions that help explain which factors pushed a county’s projection up or down the most, such as migration, employment, market conditions, or recent trend.