🏢 MSA Investment Ranking Dashboard

Interactive Analysis of US Metropolitan Statistical Areas Investment Potential

Total MSAs

-

Avg Score

-

Top Score

-

Year

2024

Investment Score Distribution

Mainland US MSA Heat Map

MSA-level score by principal city (each dot represents one MSA)

Tip: Scroll to zoom, drag to pan, click a city dot for MSA details

Top 20 MSAs Ranking

Rank MSA Name Population Score Actions

Investment Score: Interactive Weighting System

Adjust the weights below, then click Update Index Weights to apply ranking changes

ℹ️ Two-level weighting system: Step 1 (sub-factors): For indices with multiple factors (Economic, Supply), use the sub-factor sliders to control how each raw field is weighted within its index. Values are min-max normalized across all MSAs before weighting ( = lower raw value is better; = higher is better).
Step 2 (index-level): Adjust the six index weights below to set your cross-index investment priorities. The total must equal 100%, then click Update Index Weights to apply.
Formula: raw sub-factor → min-max [0,1] → sub-factor weighted sum → index value → index weighted sum → min-max scaled to 0–100.

📊 Configure Weights

20%
⚙ Sub-Factor Weights (auto-normalized within index)
Employment Rate ↑ 25%
Employment Growth ↑ 25%
Population Growth ↑ 25%
Income Growth ↑ 25%
Factor Total Weight 100%
Please ensure factor weights sum to 100%.
15%
15%
⚙ Sub-Factor Weights (auto-normalized within index)
New Multifamily Unit ↓ 50%
Vacancy Rate ↓ 50%
Factor Total Weight 100%
Please ensure factor weights sum to 100%.
15%
15%
20%
Total Weight: 100%

🎯 Updated Rankings

Rank MSA Name New Score Change

📋 Factor Descriptions & Formulas

📈 Economic Index

Evaluates strength and growth potential of an MSA's economic fundamentals.

Employment Rate
\[ \text{Employment Rate} = \frac{\text{Employed}}{\text{Labor Force Population}} \]
Employment Growth
\[ \text{Employment Growth} = \frac{\text{Employed}_{t} - \text{Employed}_{t-1}}{\text{Employed}_{t-1}} \]
Population Growth
\[ \text{Population Growth} = \frac{\text{Pop}_{t} - \text{Pop}_{t-1}}{\text{Pop}_{t-1}} \]
Income Growth
\[ \text{Income Growth} = \frac{\text{Income}_{t} - \text{Income}_{t-1}}{\text{Income}_{t-1}} \]

Data Source: MSA-level data derived from U.S. Census Bureau datasets

🛡️ Housing Affordability Index

Evaluates local housing affordability pressure relative to income.

Rent-to-Income Ratio
\[ \text{Rent-to-Income} = \frac{\text{Median Rent}}{\text{Median Income}} \]

Data Source: MSA-level data derived from U.S. Census Bureau datasets

🏗️ Supply Demand Index

Evaluates local supply-demand balance through multifamily pipeline growth and vacancy tightness.

New Multi Units
\[ \text{New Multi Units} = \text{Total Multi Units}_{t} - \text{Total Multi Units}_{t-1} \]
Vacancy Rate
\[ \text{Vacancy Rate} = \frac{\text{Vacant Units}}{\text{Total Units}} \]

Data Source: MSA-level data derived from U.S. Census Bureau datasets

💰 Pricing Power Index

Measures ability of landlords to raise rents over time.

Rent Growth
\[ \text{Rent Growth} = \frac{\text{Rent}_{t} - \text{Rent}_{t-1}}{\text{Rent}_{t-1}} \]

Data Source: MSA-level data derived from U.S. Census Bureau datasets

📊 Valuation Index

Measures conversion-related value uplift by comparing multifamily and hotel valuation implied by the same NOI stream.

Value Creation
\[ \text{Value Creation} = \text{Annual Rent} \times (1 - \text{Operating Expenditure Ratio}) \times \left(\frac{1}{\text{MF Cap Rate}} - \frac{1}{\text{Hotel Cap Rate}}\right) \]

Data Source: MSA-level Annual rent inputs are derived from U.S. Census Bureau datasets. State-level cap rates and operating expenditure ratios are compiled from publicly available market data and industry reports, curated via Gemini-assisted prompt engineering

🏛️ Regulatory Market Index

Captures policy and execution feasibility for hotel-to-multifamily conversion at the state level.

Hotel Conversion Relevance

We convert state letter ratings into a monotonic [0,1] score to preserve rank order while allowing non-linear spacing between tiers. The mapping is intentionally non-equidistant: lower tiers (F, D-, D) receive steeper penalties to reflect higher execution and policy risk, while top tiers (A, A+) receive an additional premium for stronger conversion feasibility.

State Rating Hotel Conversion Relevance Score
A+1.00
A0.90
A-0.84
B+0.78
B0.66
B-0.61
C+0.57
C0.52
C-0.40
D+0.33
D0.25
D-0.12
F0.00

Data Source: State-level hotel conversion relevance ratings are based on policy and execution feasibility for hotel-to-multifamily conversion, synthesized from public policy materials via Gemini-assisted prompt engineering

Raw Data: All Factors & Metrics

Complete dataset showing MSA raw data (population, housing, employment, etc.) and 10 investment factors organized by 6 key dimensions

📈 Economic Factors

  • Employment Rate - Employed / Labor Force (%)
  • Employment Growth - Year-over-year employment change (%)
  • Pop Growth - Year-over-year population change (%)
  • Income Growth - Year-over-year income change (%)

Data Source: U.S. Census Bureau and datasets

🛡️ Housing Affordability Factor

  • Rent/Income - Monthly rent / monthly income (%)

Data Source: U.S. Census Bureau datasets

🏗️ Supply Demand Factors

  • New Multi-Units - Year-over-year increase in multi-family units
  • Vacancy Rate - Vacant units / total housing units (%)

Data Source: U.S. Census Bureau datasets

💰 Pricing Power Factor

  • Rent Growth - Year-over-year rent change (%)

Data Source: U.S. Census Bureau datasets

📊 Valuation Factor

  • Value Creation - (Annual Rent × (1 - OPEX%))*(1 / MF Cap Rate - 1 / Hotel Cap Rate) ($)

Data Source: Annual rent inputs are derived from U.S. Census Bureau datasets. MSA-level cap rates and operating expenditure ratios are compiled from publicly available market data and industry reports, curated via Gemini-assisted prompt engineering

🏛️ Regulatory Market Factor

  • Hotel Conversion Relevance - Mapped score from Conversion Category

Data Source: State-level hotel conversion relevance ratings are based on policy and execution feasibility for hotel-to-multifamily conversion, synthesized from public policy materials via Gemini-assisted prompt engineering

🏛️ State-Level Market Inputs

The following data are state-level Operating Expenditure Ratio, Cap Rate, and Effective Tax Rate inputs.

Operating Expenditure Ratio (OER)

The Operating Expenditure Ratio in real estate measures the proportion of a property’s operating costs relative to its income. It is defined as:

\[ \text{Operating Expenditure Ratio} = \frac{\text{Operating Expenses}}{\text{Gross Operating Income}} \]

Operating expenses include costs such as maintenance, property management, utilities, insurance, and taxes, but exclude financing and depreciation. This ratio indicates how efficiently a property is managed, specifically how much cost is required to generate each dollar of income. A lower ratio suggests better operational efficiency, while a higher ratio indicates greater cost pressure. It is most meaningful when compared across similar property types.

Cap Rates

Cap rate, or capitalization rate, measures the expected return of a real estate investment based on its income. It is defined as:

\[ \text{Cap Rate} = \frac{\text{Net Operating Income}}{\text{Property Value}} \]

Net operating income represents income after operating expenses but before financing costs. The cap rate reflects how income relates to property value, with higher cap rates indicating higher risk and return, and lower cap rates suggesting more stable, lower-risk assets.

Hotel Cap Rate and Multifamily Cap Rate further capture state-level return expectations and pricing conditions across asset classes. They enable cross-asset valuation comparisons and help identify relative pricing tightness within different markets.

Effective Tax Rates

The effective tax rate measures the proportion of a company's pre-tax income that is paid in taxes, reflecting its actual tax burden. It is defined as:

\[ \text{Effective Tax Rate} = \frac{\text{Income Tax Expense}}{\text{Pre-Tax Income}} \]

Unlike statutory tax rates, the effective tax rate captures the impact of deductions, credits, and other tax planning strategies. A lower effective tax rate may indicate efficient tax management or favorable tax treatments, while a higher rate suggests a greater tax burden.

In real estate analysis, effective tax rates can vary across regions and asset types due to differences in local tax policies. As a result, they provide useful insight into after-tax returns and help improve the accuracy of cross-market investment comparisons.

Data Sources: Publicly available information retrieved via Gemini AI

🌿 Average HERS Index Score:

The Home Energy Rating System (HERS) Index is the industry standard by which a home's energy efficiency is measured. It's also the nationally recognized system for inspecting and calculating a home's energy performance. https://www.hersindex.com/hers-index/understanding-hers-index/

A certified RESNET Home Energy Rater assesses the energy efficiency of a home, assigning it a relative performance score (the HERS® Index Score). The lower the number, the more energy efficient the home. A home built to 2006 energy efficiency standards scores 100.

A home with a HERS® Index Score of 70 is 30% more energy efficient than the RESNET Reference Home. A home with a HERS® Index Score of 130 is 30% less energy efficient than the RESNET Reference Home.

Due to inconsistency in HERS score availability and comparability across markets, this metric is not included in the 6-dimensional MSA ranking system. It is retained in the raw dataset for users' reference.

Detailed MSA Analysis

Select an MSA from the ranking table to view detailed analysis.

6-Dimensional Market Comparison

Compare markets across 6 key investment dimensions

✦ Powered by Gemini AI

🏙 ReZone Intel

Select MSAs from the ranked list, enter your Gemini API key, and generate a full commercial-to-residential intelligence report in seconds.

1 · API Configuration

Get a free key at aistudio.google.com → Create API Key. One key works for all models below.

Recommended
2.5
Stable
Fast and reliable. Best for most reports.
Newer
3
Balanced
Higher quality than 2.5 but may be slower.
Most Powerful
3.1
Advanced
Strongest reasoning, and usually the slowest.
2 · Select MSA Cities (up to 15)
0 selected
Loading MSA data...

Select 1–15 cities to generate a report.

⚠️ Security: Your API key is sent only to Google's API — never to any third party. For a public deployment, move the API call to a backend server.
How It Works
1

Enter API Key

Paste your Gemini API key from Google AI Studio. It stays in your browser and is only sent to Google's API.

2

Search & Select MSAs

All 181 ranked MSAs are listed by investment score. Search by name, then tick the checkboxes for the cities you want to analyze (1–15).

3

Gemini Researches

Gemini AI generates structured market intelligence for each selected city — pipeline data, active projects, policy timelines, financial mechanisms, and news.

4

Interactive Report Renders

A full dashboard appears inline — KPI overview, pipeline bars, market scorecard, and per-city deep profiles. All in ~15–40 seconds.

Quick Select Shortcuts
Custom Prompt (advanced)

Modify the prompt sent to Gemini. Leave unchanged to use the default.

Using default prompt
⚙ Generating Report

Researching your markets...

Gemini is analyzing each city. This typically takes 15–40 seconds.

Starting...
✦ AI-Generated Intelligence Report

Market Intelligence

AI-generated intelligence. Verify figures before investment decisions.

Pipeline Volume
Units in Active Conversion Pipeline
Market Scorecard
Overview Matrix
CityVacancyPipelineYoYStatus
⚡ AI-Generated: Content generated by Gemini AI. Sources listed are suggested references — verify independently. Not financial or investment advice.

City Intelligence Reports

AI-researched profiles — catalysts, projects, policy timelines, financial mechanisms, and sourced intelligence.

Select City