How DMV Appreciation Projections Works
This model scores 293 ZIP codes across the DC–Baltimore region on a single question: how much buyer demand flows to each area, and from whom?
It doesn't bolt together separate scores. It builds one integrated engine where every belief about the market shapes how demand is calculated.
The Core Idea: People Shop in Corridors
A family priced out of Kensington doesn't compare every ZIP in the region. They look up the 270 corridor: Rockville → Gaithersburg → Germantown → maybe Frederick. That's their corridor — the set of areas that share commute direction, school system, and social geography.
The model defines 24 corridors (MoCo-270, NoVA-66, PG-South, Balt-North, Shore, etc.) and routes buyer demand through them. Most demand stays captive within a corridor. But when value gaps get large enough — say, Catonsville SFH at $365k vs Germantown TH at $480k — some buyers cross corridors. The model calculates how much leakage happens based on the size of the gap and the switching costs between corridors.
Example: Catonsville
Score 85 · 12 buyer types · 53% captive demand
Strong within Balt-North corridor (captive families, investors), PLUS cross-corridor pull from 270 and PG buyers who discover the value gap. Brick SFH at $365k is a product that barely exists inside the Beltway.
Example: Gaithersburg
Score 30 · 8 buyer types · 76% captive demand
Most demand is corridor-locked MoCo shoppers priced out of Rockville/Kensington. Lower score because at $475k for a TH, the per-dollar value is weaker. But the captive pool is real — those buyers won't cross to Baltimore.
23 Buyer Archetypes
The model simulates 23 types of buyers, each with different budgets, product preferences, and priorities: DC families, first-timers, federal workers, military, investors, retirees, remote workers, urban professionals, and more.
Each archetype defines a budget ceiling, corridors they shop (weighted by how natural each corridor is for them), and priorities — how much they weight schools, safety, walkability, product quality, and access, all measured per dollar.
Per-Dollar Value Metrics
The model doesn't ask "are schools good?" It asks "how good are schools for what you're paying?" Everything is measured relative to price.
This is why Parkville ($270k, schools 65, SFH) scores well and Bethesda ($1.1M, schools 88) scores low. Per-dollar metrics include schools/$100k, safety/$100k, lifestyle/$100k, and a product grade — what housing type you actually get at each price point.
Thesis Dials
Beliefs about the market don't add or subtract points. They reshape how the engine runs:
| Dial | What It Does | Default |
|---|
| Corridor Lock | How captive buyers are to their corridor. Lower = more cross-shopping. | 72% |
| Remote Pool | Share of buyers with no commute constraint. | 5% |
| Product Premium | How much buyers reward SFH at a TH-dominant price. | 1.35 |
| School Floor | Below this, family buyers start penalizing the area. | 65 |
| Safety Floor | Below this, most buyers penalize the area. | 40 |
| Brick Mult | Quality advantage for brick stock (ARV, maintenance). | 1.25 |
| Lifestyle Disc | How much emerging lifestyle demand matters. | 1.20 |
| Investor Agg | How active is investor demand right now. | 1.00 |
Moving a dial changes how all 23 buyer types evaluate all 293 ZIPs simultaneously.
What the Score Means
The score (0–100) is how much total buyer demand routes to each ZIP relative to the region. It blends percentile rank with absolute demand strength.
High score = many buyer types, strong per-dollar value within and across corridors. Low score = few buyer types, weak per-dollar value, or priced above most budgets.
High score ≠ "nice area." Bethesda is a great place to live. It scores 17 because it's above almost every archetype's budget and the per-dollar value is poor. The model measures demand concentration, not quality of life.
How to Use It
Trust the extremes. Scores above 80 and below 20 are high-conviction signals. Be skeptical of exact rankings in the 40–70 band — that spread is within the noise of estimated weights.
Use the dials to stress-test. If a ZIP scores well across many dial settings, that's a robust bet. If it collapses when you move one dial, it's a fragile bet that depends on one belief being right.
Click any ZIP to see the full decomposition: which buyer types route there, how much is captive vs cross-corridor, and the per-dollar metrics driving the score.
What's Not in the Model
Real commute times (uses distance), actual rent data, zoning/development pipeline, crime trends, interest rate sensitivity, or any time-series data. School scores are ZIP-level averages. Prices are estimates. The architecture is sound; the inputs are approximate. Use this to identify where to look, not to make final decisions.