Privacy Index Ranking — Georgetown

Updated

Which Georgetown neighborhoods have the highest privacy index score? All 7 neighborhoods ranked. City average: 6.33/10. How privacy index is measured →

Score: 8–10 6–8 4–6 2–4 0–2
Map showing the location of Georgetown neighborhoods
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Top Privacy Index Neighborhoods — Ranked

#1
North Lake Land8.5

Spacious lot separation provides significant seclusion compared to the Georgetown average. This neighborhood layout minimizes direct sightlines between adjacent properties.

#2
Fountainwood Estates7.5

Residential lots feature significant separation between homes, providing more privacy than the Georgetown average. According to iHuus data, the spacing between properties helps minimize direct views between neighbors.

#3
Lakewood Estates6.7

Large lot sizes and significant separation between homes provide a higher level of privacy than is typical for the Georgetown area, according to iHuus data.

#4
Georgetown Village6.2

#5
Sun City6.1

#6
Quail Valley6

#7
Terravista6

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Full Georgetown Privacy Index Ranking

City average:6.33— scores above this beat the city average
NeighborhoodScoreCity Rankvs. City Avg
North Lake Land8.5#1+2.2 ↑
Fountainwood Estates7.5#2+1.2 ↑
Lakewood Estates6.7#3+0.4 ↑
Georgetown Village6.2#4-0.1 ↓
Sun City6.1#5-0.2 ↓
Quail Valley6#6-0.3 ↓
Terravista6#7-0.3 ↓

iHuus Neighborhood Intelligence Insights

Get address-level neighborhood insights for Georgetown

  • Street-level scores — see how individual blocks compare, not just neighborhood averages
  • Micro-zone boundaries — identify quiet pockets, flood boundaries, and walkable clusters before you visit
  • 15 dimensions in full — walkability, noise, air quality, school quality, insurance coverage, population age profile, urban/rural index, and more
  • Boundary risk alerts — know if your address sits near a block boundary where scores drop, the kind of detail no listing will ever mention
  • School insights — individual school ratings and insights, not just district-level data
  • Compare before you commit — side-by-side data across neighborhoods, down to block precision

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