| # | Metro | Region | Pop | Price | P/I | Appreciation | Value/$ | Opp Gap | Top Archetype | Confirm |
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Click a metro to see detail breakdown
| # | Metro | Region | Pop | Price | P/I | Appreciation | Value/$ | Opp Gap | Top Archetype | Confirm |
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This model scores 166 US metros on a single question: where is buyer demand building that the market hasn't priced in yet?
It doesn't rank "nice places to live." It builds an engine that simulates why people move, where they flow, and whether local supply can absorb them — or whether prices have to rise instead.
A tech worker in San Francisco sees that Raleigh offers 70% of the career depth at 45% of the housing cost. That value gap creates pressure. Pressure creates flow — people actually move. Flow hits supply constraint — can the metro build fast enough to absorb the new demand? If yes, you get sprawl (Houston, Dallas). If no, you get appreciation (Raleigh, Huntsville). Then appreciation erodes the original pressure — Austin's value ratio was enormous in 2019, flow poured in, prices rose 45%, and now the gap has compressed. The alpha lives in metros where pressure is building but flow hasn't fully arrived yet.
Appreciation 89 · Value 68 · Opp Gap -9 · P/I 4.3 · Elasticity 2.0
Strong structural demand from defense/aerospace employment, affordable at 4.3x income, decent schools and safety. But the opportunity gap is -9: trailing data already shows the inflows. The market has noticed Huntsville. It's still a good metro — it's just no longer undiscovered.
Appreciation 39 · Value 83 · Opp Gap +29 · P/I 4.1 · Elasticity 1.5
Very high value — strong schools, diversified economy, affordable for a major metro. Moderate supply constraint. The +29 gap means the model sees structural attractiveness that trailing data hasn't caught up to yet. Nobody on Twitter is pumping Minneapolis — which is exactly why the opportunity exists.
Value Score answers "is this a nice place to live for the money?" It's a snapshot, not a prediction. Eight buyer archetypes each evaluate quality (schools, safety, climate, culture, walkability, healthcare) divided by total cost (price-to-income adjusted for state tax and insurance burden). Boston is amazing but costs 6.9x income — low value. Huntsville is decent at 4.3x — good value. When you select an archetype lens, the value score shows that specific buyer type's quality/cost ratio rather than the blended average.
Appreciation Score answers "will prices go up?" It takes the migration engine's demand estimate and passes it through supply constraint. High demand + low elasticity = appreciation. High demand + high elasticity = sprawl. Three components: (1) demand volume relative to existing housing stock, (2) supply constraint from geographic barriers, permit rates, and vacancy, and (3) affordability runway — how much room prices have to run before hitting income ceilings.
You don't see a migration score directly, but it drives everything. Eight buyer archetypes — Remote Tech, Young Professional, Family, Retiree, Downsizer, Cost Refugee, Investor, International — each evaluate every metro through sigmoid gates. These are smooth dealbreaker thresholds: fail one gate badly and your score collapses, but there's no hard cliff. A metro with safety 38 doesn't hit a wall — it takes a steep but continuous penalty.
Each archetype has different gates. Remote Tech workers care most about culture, walkability, and climate. Families weight schools and safety heavily. Cost Refugees are primarily driven by affordability. Retirees gate on climate, healthcare, and tax burden. The model doesn't assume all buyers want the same thing — it simulates eight different decision processes and aggregates the flow.
People don't move to "nice" places. They move to places that are getting nicer on the margins. A wide pressure trend (0.50–1.48x) makes trajectory compete with static quality in the migration engine. Economic vitality — job growth, income growth, population growth, tech sector share — drives this trajectory signal. A metro that's merely adequate but improving fast pulls more migration than a great metro that's stagnant.
A maturation discount reduces trajectory credibility when a metro has already had a big price run-up. Boise's measured growth metrics are partly the boom itself, not a predictor of the next one. The model adjusts for this, which is why Boise at appreciation 34 is lower than its raw vitality numbers would suggest.
Based on Albert Saiz's research quantifying geographic constraints — water, mountains, wetlands — plus regulatory barriers. This is the single most validated predictor of metro-level appreciation in real estate economics. Cities that can't build see prices absorb demand shocks. Cities that can build see supply absorb them instead.
Elasticity enters through a blend: 55% geographic constraint (the Saiz index), 25% permit rate activity, 20% vacancy rate. Even a highly elastic metro isn't perfectly efficient at building — permits take time, land acquisition has friction. But the model gives elastic metros like Dallas (2.3) and Houston (2.5) substantially less appreciation credit than constrained metros like Boston (0.8) or DC (0.9) for the same amount of inbound demand.
This is the model's most useful signal. It's the spread between the Migration Score (forward-looking, built from structural fundamentals) and the Confirmation Score (backward-looking, built from trailing migration data, job growth, population growth, and permits).
Positive gap = structural demand that trailing data hasn't shown yet. The market is underpricing this metro. Negative gap = trailing data shows inflows the model can't justify from fundamentals alone. The market may be riding narrative or pandemic-era momentum.
The bottom of the gap list is almost certainly right: Boise (-44), Cape Coral (-76), Phoenix (-45) have all had their "discovery moment" — trailing data screams hot market, but affordability runway, insurance costs, and elasticity say the music is stopping. The top of the list is where sophisticated investors look: metros where the math says people should move but the crowd hasn't arrived yet.
Instead of individual sliders, the model uses coherent macro scenarios that change multiple parameters simultaneously. Base Case: current trends continue. RTO Wins: remote work collapses, urban cores recover. Tax Migration: remote work + tax competition accelerates. Climate Reckoning: insurance markets break in the Sun Belt. AI Concentration: tech jobs concentrate in 3-5 metros.
Rankings don't change as much across scenarios as you'd expect — structurally constrained, affordable metros with diversified economies tend to score well under all five futures. That's a feature, not a bug. If a metro ranks well only under one scenario, it's a fragile bet.
High appreciation ≠ "nice place to live." San Francisco is world-class. It scores 0 because at 10.4x income there is zero affordability runway. The model measures where prices have room to rise, not where you'd enjoy your weekends.
Trust the extremes. Scores above 70 and below 10 are high-conviction signals. Be skeptical of exact rankings in the 25–60 band — that spread is within the noise of estimated inputs. Use scenarios to stress-test: if a metro holds up across all five, that's a robust bet.
Interest rates. A move from 7% to 5% would lift every constrained metro 15-25% regardless of demand quality. The model ranks relative winners — which metros outperform peers — not absolute returns. Relative rankings are robust to rate changes because rates affect everything roughly equally.
Metro-level data is estimated from public sources. Individual values may be ±15-20% for top metros, wider for smaller ones. Climate risk enters through insurance cost trends, not as a direct penalty — because actual buyers respond to monthly payments, not FEMA maps. Supply elasticity values are informed by Saiz (2010) and may not reflect recent regulatory changes.
This is a screening tool. Metros that score well deserve deeper research, not blind investment.