Which Southeast metros are genuinely strong after affordability, growth, and labor-market fundamentals are evaluated together?
This project evaluates 105 metros across 11 Southeast states using two views: an overall score that blends affordability, growth, and labor-market depth, and a balance score that requires growth and labor-market strength to agree. The strongest markets are not simply the cheapest or the fastest-appreciating. They are the metros where price levels still make sense relative to income, where population and home-price momentum are real, and where the labor market is broad enough to sustain demand. Huntsville, AL ranks first on both the overall score and the balance score, making it the clearest example of a market that performs well without a major tradeoff.
Metro areas across the Southeast only, compared on affordability, growth, and labor-market strength.
Listing-level pricing, cap rates, neighborhood comparisons, or deal-specific underwriting inputs.
This page is a portfolio-style summary of the full notebook, which carries the project through ETL, cleaning, EDA, feature engineering, scoring, sensitivity checks, and stakeholder interpretation.
Two rankings, two ways to read the market.
The overall score is the broad screening view. It asks which metros look strongest when affordability, growth, and labor-market depth are weighted almost evenly. The balance score is the stricter view. It asks which metros remain compelling when strong growth and strong labor-market conditions are both required at the same time. In practice, the overall list tends to surface metros that are exceptionally affordable, while the balance list surfaces metros where momentum and economic depth reinforce each other. Markets that appear in both are the most defensible all-around picks in the dataset.
Top 10 Overall
Approximately equal-weight composite: Affordability (33%) · Growth (34%) · Labor Market (33%)
| # | Metro | Score |
|---|---|---|
| 1 | Huntsville, AL | |
| 2 | Florence-Muscle Shoals, AL | |
| 3 | Decatur, AL | |
| 4 | Fayetteville-Springdale-Rogers, AR | |
| 5 | Bowling Green, KY | |
| 6 | Warner Robins, GA | |
| 7 | Birmingham, AL | |
| 8 | Montgomery, AL | |
| 9 | Spartanburg, SC | |
| 10 | Lexington-Fayette, KY |
Top 10 by Balance Score
Geometric mean of Growth and Labor Market. A metro must be strong on both to rank here.
| # | Metro | Balance |
|---|---|---|
| 1 | Huntsville, AL | |
| 2 | Fayetteville-Springdale-Rogers, AR | |
| 3 | Nashville, TN | |
| 4 | Charlotte-Concord-Gastonia, NC-SC | |
| 5 | Auburn-Opelika, AL | |
| 6 | Bowling Green, KY | |
| 7 | Florence-Muscle Shoals, AL | |
| 8 | Gainesville, GA | |
| 9 | Birmingham, AL | |
| 10 | Knoxville, TN |
What the results say about the region.
How the scoring framework works.
The score is built to be transparent, reproducible, and auditable. It combines a current affordability snapshot, a multi-year growth window from 2019 to 2023, and current labor-market depth. Every metric is converted to a percentile rank before weighting, so no large-scale variable dominates simply because of its units. The goal is not to create a black-box winner. It is to show exactly why one metro rises above another.
Three-pillar scoring
The overall score blends all three pillars into one broad screening metric. The balance score takes the geometric mean of Growth and Labor Market only, excluding affordability, so it isolates which metros are strong on both economic dimensions simultaneously. That second ranking is what makes the project more than a simple affordability list or hot-market list. The percentages shown inside each pillar are internal weights, not household spending shares.
Unemployment rate can stay low when workers leave the labor force entirely. Employment-to-Population avoids that problem. If people exit the local economy, E/Pop falls even when the unemployment headline stays flat.
The balance score catches a problem that a simple average can miss. A metro can look strong overall because it is very cheap, even if growth is only average. The geometric mean pushes those one-sided cases down and lifts markets that are genuinely strong on both growth and labor-market stability.
The Census universe begins with Southeast metropolitan and micropolitan CBSAs, and the drop from 156 loaded areas to 105 scored areas is driven mainly by cross-source overlap and complete-case filtering rather than ACS alone. The percentile approach keeps unlike variables comparable, but it also compresses real magnitude differences, income growth is nominal rather than inflation-adjusted, and the framework is presented as a screening tool rather than a backtested forecast.
The strongest upgrades for a v2 are historical backtesting, alternative standardization and weighting tests, external benchmark comparison, and deeper supply-side inputs such as permits, rate-adjusted affordability, sub-market vacancy, and wage growth by sector.
How different stakeholders can use the rankings.
This is a first-pass screening tool. It narrows the field, clarifies the tradeoffs, and tells you where to look first. It does not replace local research on listings, cap rates, underwriting standards, or employer news. It helps you ask better next-step questions. The key is to match the ranking lens to the decision you are actually making: the overall list is better for broad screening, while the balance list is better when resilience matters more than simple cheapness.
Start with Huntsville if you want the strongest all-around market in the sample. It offers the clearest mix of reasonable entry cost, labor-market strength, and forward-looking growth support. If maximum purchase affordability matters more than upside, Florence-Muscle Shoals and Decatur deserve a look, but they should be read as lower-cost options, not necessarily the strongest long-run markets.
For buyers, the main question is whether you want the cheapest market today or the strongest long-run combination of value and fundamentals. The overall ranking helps with the first screen. The balance ranking helps separate genuinely resilient metros from places that are simply inexpensive because growth has been weaker.
Use the balance score list as your primary filter. Huntsville and Fayetteville-Springdale-Rogers are especially valuable because price momentum and labor-market depth are both high at the same time. That lowers the chance that appreciation is being driven mainly by speculative heat.
For investors, this framework is best used as a demand-quality screen. It tells you where appreciation appears to be supported by population growth, employment depth, and income strength. It does not replace deal underwriting, so the next step is still local validation with rent comps, cap rates, vacancy, and current supply.
Treat the labor-market pillar, especially E/Pop ratio and income growth, as the primary credit-quality signal. A market that looks attractive only because it is cheap can still be fragile if employment depth and income formation are weak.
For credit and risk work, the useful distinction is between markets that are affordable and healthy versus markets that are affordable because demand is stagnant. High affordability paired with a weak balance score is a flag for more conservative underwriting rather than a reason to lean in.
The balance top five are the regional benchmark for what sustained, two-dimensional growth looks like. They provide a more useful comparison set than headline metros alone because they show where growth and labor-market depth are reinforcing each other instead of pulling apart.
For public-sector readers, the value is diagnostic. If a metro underperforms, the framework helps show whether the binding constraint is weak growth, shallow labor-market depth, or affordability pressure created by rapid appreciation. That makes the ranking more useful as a policy lens than as a vanity table.
Data, scope, and reproducibility.
The project is reproducible from public source data, not proprietary feeds. Census provides the affordability and demographic backbone, FHFA provides metro-level house price momentum, and BLS provides labor-market depth. Those pulls are stitched together in a reusable ingestion layer, transformed to a metro-level analysis dataset, and then scored in the notebook.
data_ingestion.py and
southeast_housing_analysis.ipynb. The ingestion layer is written to be reusable
across other geographies; it is not hard-coded to the Southeast. The notebook then carries the project
through cleaning, EDA, feature engineering, sensitivity checks, ranking, and stakeholder interpretation.
In other words, this page is the summary, not the only evidence. The full project remains inspectable end to end.