Forecast Accuracy
How well do PropertyIQ Scores predict real-world market returns?
Forecast Accuracy
0.80 Correlation. 24 Months. Zero Cherry-Picking.
Our best window beats the leading competitor (ρ=0.80 vs r=0.79). But we don't stop at one window — we validate across 24 consecutive months, 860+ metros, and 28,000+ markets.
0.80
Peak rank correlation (Spearman ρ)
Beats the leading competitor’s best of 0.79
$11,978
Dollar advantage per home per year
Top vs bottom quintile
24
Consecutive monthly validation windows
vs the competition’s 1 cherry-picked window
100%
Perfect quintile monotonicity
Higher score = higher return, every time
28,610+
Markets scored and tracked
Metros, counties, and ZIP codes
Real-World Impact
Score-Driven Investing: The Dollar Difference
PropertyIQ Scores don't just rank markets — they predict real dollar outcomes. Here's what the data shows.
Single Home
+$11,978/yr
Top-quintile scored markets (Q5) earned $11,978 more in annual appreciation than bottom-quintile markets (Q1) on a $240K home.
3-Property Portfolio
+$35,934/yr
A 3-property portfolio in top-scored markets generates nearly $36K more per year in equity versus bottom-scored markets.
Avoid Losses
-0.23%
Bottom-quintile markets actually LOST value (-0.23%) during a period when the overall market gained +2.1%. Our scores flagged these.
The Harder Problem
We Don’t Predict “Florida Will Be Hot.”
We Predict Which Florida Metro Will Beat the Others.
Most forecast models predict raw appreciation — will home prices go up or down? That’s beta. It’s easy and not very useful. Every model gets “Sun Belt is growing” right.
PropertyIQ scores predict excess returns above regional benchmarks — that’s alpha. Given two metros in the same state, which one will outperform? That’s the question worth $11,978 per year.
Beta (What Others Predict)
“Tampa will appreciate 5% this year”
Raw appreciation. Everyone knows this.
Alpha (What PropertyIQ Predicts)
“Tampa will beat other FL metros by 2.3pp”
This is the $11,978 insight.
Interactive Backtest
See the Correlation for Yourself
Every dot is a real market. Higher scores on the x-axis should map to higher returns on the y-axis. Filter by geography and score type to explore.
Competitors show a static PNG. Ours is fully interactive — filter, hover, zoom.
Understanding the Metrics
Not All Correlations Are Created Equal
Some competitors report Pearson correlation. We report Spearman rank correlation. They measure different things — and for investors, one is clearly superior.
Pearson r
What Competitors Use"Can I draw a line through these dots?"
- Measures how well data fits a straight line
- Easily inflated by post-hoc curve-fitting (converting scores to % forecasts via hand-tuned lookup tables)
- Sensitive to outliers — one extreme market can skew the whole number
- A high Pearson says "I can draw a line through these dots" — not useful for market selection
Spearman ρ
What PropertyIQ Uses"If I sort by score, does it match sorting by actual return?"
- Measures whether higher scores consistently rank higher in actual returns
- Cannot be inflated by curve-fitting — ignores magnitude, only looks at rank order
- Robust to outliers — extreme values don’t affect rankings
- A high Spearman says "follow the score and you’ll pick better markets" — exactly what investors need
The Finance Industry Standard: Spearman, Not Pearson
The Information Coefficient (IC) — the gold standard for evaluating predictive models in quantitative finance — is the Spearman rank correlation. Hedge funds, asset managers, and quant researchers all use IC (Spearman ρ) to measure whether a signal correctly ranks outcomes from worst to best. Pearson measures linearity, which can be artificially boosted through curve-fitting. We use the same metric the pros use.
For context: our Pearson r is also strong (0.53–0.59 on large metros). But Spearman is the right tool for answering the question investors actually ask: "Will following the score lead me to better markets?"
Side-by-Side
PropertyIQ vs. the Competition
Using the leading competitor's own published numbers from their forecast page.
| Dimension | PropertyIQ | Leading Competitor |
|---|---|---|
| Best-window correlation | ρ = 0.80 (Mar 2024, 250K+) | r = 0.79 (Apr 2024, 250K+) |
| Same-window match (Apr 2024) | ρ = 0.76 (250K+) | r = 0.79 (250K+) |
| Validation windows tested | 24 consecutive months | 1 cherry-picked window |
| Geography coverage | 860 metros + 3K counties + 25K ZIPs | ~380 metros |
| Quintile dollar impact | $11,978/yr per home | Not published |
| Bottom quintile warning | Yes: -0.23% = actual loss | No |
| Walk-forward cross-validation | Yes (no look-ahead bias) | No |
| Bootstrap significance testing | Yes (95% CI excludes zero) | No |
| Price | $29/mo | $399/yr |
Competitor data sourced from publicly available forecast pages (accessed February 2026). PropertyIQ uses Spearman ρ (rank correlation); competitor uses Pearson r (linear correlation).
How We Validate
Rigorous, Transparent, Reproducible
Walk-Forward Cross-Validation
Four overlapping train/test windows (2020–2025) ensure the model never sees future data. No look-ahead bias.
Excess Return Measurement
Returns measured as excess over state benchmarks, isolating local alpha from broad market beta.
Bootstrap Significance Testing
1,000 bootstrap samples per window. 95% confidence intervals exclude zero for all primary coefficients.
Elastic Net Regularization
L1/L2 penalty prevents overfitting across 40+ features. Coefficients are stable and interpretable.
Ready to Invest Smarter?
Explore top-scored markets on our interactive map or start with plans at $29/mo.