Project 5 — Migration & Housing Trends¶

Goal¶

"Where are people moving in and out of [your metro area], and how do those flows align with housing-price changes?"

Tell a temporal story by combining migration flows (origin → destination, by county) with housing-price trends (Zillow ZHVI or similar) at the tract or county level.

Data needed¶

Tools used¶

• ArcGIS Pro: Add Join, Calculate Field, Spatial Join
• Time-aware layers, animations
• Layout multi-frame
• Charts (in pop-ups or dashboard)

Workflow¶

Step 1 — Set up¶

1. Project: migration_housing_<metro>.
2. Project to NAD83 / Conus Albers (EPSG:5070) for area-correct visuals.
3. Filter counties to your metro CBSA.

Step 2 — Compute net migration¶

For each county:

def net_migration(in_flow, out_flow):
    if in_flow is None: in_flow = 0
    if out_flow is None: out_flow = 0
    return in_flow - out_flow

Add field NET_MIG_2022. Repeat for several years.

Step 3 — Compute housing change¶

For each county:

def pct_change(now, then):
    if now is None or then is None or then == 0: return None
    return ((now - then) / then) * 100

Add field ZHVI_CHG_2018_2024 = pct_change(!ZHVI_2024!, !ZHVI_2018!).

Step 4 — Map both¶

Map A — Net migration (diverging)¶

1. Symbology: Graduated Colors on NET_MIG_2022.
2. Method: Manual (negative quantiles + 0 + positive quantiles).
3. Color scheme: Diverging RdBu — losses red, gains blue.

Map B — Housing-price change¶

1. Symbology: Graduated Colors on ZHVI_CHG_2018_2024.
2. Sequential or diverging palette (depending on whether any counties saw declines).
3. Match the 0% midpoint to neutral.

Step 5 — Detect patterns¶

Add a categorical field:

def category(net_mig, price_chg):
    if net_mig is None or price_chg is None: return "Unknown"
    if net_mig > 0 and price_chg > 0: return "Hot growth"
    if net_mig > 0 and price_chg <= 0: return "Bargain growth"
    if net_mig < 0 and price_chg > 0: return "Pricing out"
    if net_mig < 0 and price_chg < 0: return "Decline"
    return "Mixed"

Symbolize a third map by CATEGORY — this is the headline.

Step 6 — Time animation¶

If your data is a long table (year per row), create a time-aware feature class:

1. Right-click layer → Properties → Time → Filter Layer Content based on attribute values.
2. Set Time Field = YEAR.
3. Open the Time ribbon → animate.
4. Export as MP4 / GIF for your portfolio.

Step 7 — Layout / Story Map¶

A 4-section Story Map:

1. Set the scene: "Where are Americans moving?"
2. Net migration map (animated).
3. Housing change map.
4. Combined category map + 1-paragraph takeaway.

Skills learned¶

• Time-aware data
• Animation export
• Diverging vs sequential palettes
• Cross-source data joins (Zillow + Census)
• Categorical synthesis from multiple variables

Portfolio value¶

A real-estate / urban-planning power move. Combines analysis with storytelling and a topical question (post-COVID migration, remote work, housing affordability).

Stretch goals¶

• Add flow lines between origin and destination counties (use XY To Line + flow line styling).
• Use migration_flows OD table to map where people came from for any one county.
• Layer rent data alongside home values.
• Build a dashboard with toggle between metros for comparison.

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