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You find a place you love, you prove you can afford it, and the bank applies
the same rules to everyone.
That's the American Dream.
Across U.S. counties, loan approvals and amounts shift dramatically
with race, income, and age — even when families look similar on paper.
The numbers tell a story of inequality. Behind every mortgage approval or denial,
there's a pattern.
Some communities face barriers that others don't.
We built this explorer to compare the reality of a family in a county of your
choice versus a family in San Diego.
Let's see how their paths diverge.
Every family starts with a budget. They save for a down payment, expecting to find a starter home that fits their means.
BUT...In many American counties, that 'starter home' has vanished. Prices have risen so high that the entry-level tier is no longer accessible to the average earner.
Do the loan patterns in feel more expensive or more affordable compared to San Diego?
Homeownership is the primary way American families build wealth across generations. It is the milestone young adults strive for.
BUT...As inventory shrinks and costs rise, the ladder is being pulled up. In many places, the market is no longer driven by young families growing, but by established wealth consolidating.
Does the age mix resemble San Diego, or does one county lean toward a different stage of life?
The Fair Housing Act was signed over 50 years ago to ensure that creditworthiness—not race—decides who gets a loan.
BUT...The shadow of redlining persists. In county after county, we see vast disparities in who actually walks away with the keys.
When the proportions shift between and San Diego, which groups are missing?
A steady job and a middle-class income used to be enough to buy a piece of the American Dream.
BUT...In the modern economy, housing costs have decoupled from wages. A 'good income' in one county might be poverty-level purchasing power in another.
How does the income landscape in line up with San Diego?
We have looked at race. We have looked at income.
BUT...Neither tells the whole story alone. High-earning minority families often face barriers that low-earning white families do not.
This exposes the 'missing' borrowers. It shows the share of loans going to specific Race/Income combinations. Look for the empty squares.
Enter your info below to see your consolidated market position in vs San Diego.
Based on your inputs, this analysis will compare you against the actual mortgage market in and San Diego County. Please enter your details above.
Our one major takeaway is that high diversity numbers can mask low economic equity. The presence of minority homeowners does not automatically equate to financial equality. True housing justice isn't just about 'access' to a mortgage; it is about ensuring that access successfully translates to generational wealth, which our data shows is still blocked by systemic barriers. This project succeeds because it moves beyond static statistics and uses a comparative narrative. By visualizing the journey of two specific families against the backdrop of millions of HMDA records, the visualization forces the user to see the 'invisible walls' of income and geography. It effectively contrasts the visual appearance of diversity (on the map) against the harsh reality of economic stagnation, making the gap between 'having a home' and 'building wealth' undeniable.
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Project Overview & Methodology
Watch the full walkthrough on YouTube.
Access the GitHub repo here .
This project visualizes how access to home purchase mortgages varies across neighborhoods and demographic groups in the United States. We used tract-level HMDA summary data to study who receives mortgages, how many loans originate in each neighborhood, and how large those loans are relative to borrower income.
We calculated indicators such as minority share, low-income borrower share, and loan-to-income ratios to compare where credit is concentrated and where access is limited. By pairing this analysis with FRED macro data, we connected national economic trends—like rising interest rates—to local neighborhood outcomes.
This project is a data-driven exploration. The findings here come entirely from the numbers reported in HMDA and FRED. We do not model or adjust for policy decisions, lender practices, or household wealth. HMDA data reflects only applications and originations.
The goal of this project is to visualize patterns, not to diagnose causes. The charts help surface differences between places, but they should not be used as evidence of discrimination or policy impact without deeper analysis.