ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Sun City Georgetown

Georgetown, TX 78633
Resale homes · closed May 11 – Nov 6, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
November 7, 2026 · 6:42 AM CST
MARKET STUDY All analysis covers resale homes only. New construction appears once, in its own section. Click any dot on the map for that sale.
Resale sales, 180 days
308
single-family resale homes
Median net sale price
$374,125
average $411,865
Median net price per SF
$212
average $215
Median days on market
42 days
average 71 days
1. SummaryResale homes · 308 sales · May 11, 2026 – November 6, 2026

This study looks at the 308 resale homes sold in Sun City Georgetown in the 180 days to November 6, 2026. New construction is left out and shown on its own in section 7. The homes are compared four ways: by price, by living area, by year built and with or without a view. The first three are split into four equal groups (quartiles), each named by its range.

Each comparison has its own section (3–6) with everything about it in one place: the key differences, a table of every measure at a glance, the charts and the numbers.

2. Where the resale homes sold308 resale homes · colored by price range

Each dot is one resale home, colored by its price range. The map is drawn around the middle 99% of sales; 4 sales with map points far outside the area are left off it (they stay in every figure). Use the buttons to show one price range at a time. Click a dot for that sale: address, close date, original and last list price, net sale price, living area, net sale per SF and days on market. Scroll or pinch to zoom.

Under $310K · 77 sales$310K–$374K · 77 sales$374K–$468K · 77 salesOver $468K · 77 sales
3. Quartiles by net sale priceResale homes · 308 sales · May 11, 2026 – November 6, 2026

Sales ranked by net sale price and cut into four equal groups. This is the usual way to read a market by price.

2026-11-07T06:42:16.528603 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 77 77 77 77 308 sales Sales per group Under $310K: 77 sales (25%) $310K–$374K: 77 sales (25%) $374K–$468K: 77 sales (25%) Over $468K: 77 sales (25%)
Bottom line

3.1 Key differences

The top group against the bottom group. Only real differences are listed. Section 8 explains each measure.

Under $310K$310K–$374K$374K–$468KOver $468K
Higher-priced homes Over $468K
  • Are much larger.
  • Sell for more per square foot.
  • Are more likely to have a view.
  • Sell closer to their first asking price.
Lower-priced homes Under $310K
  • Are much smaller.
  • Sell for less per square foot.
  • Are less likely to have a view.
  • Sell further below their first asking price.

3.2 At a glance

Shaded rows are real differences; the darkest cell is the highest. Gray rows show no clear difference. Prices are averages; days on market and year built are medians.

Under $310K$310K–$374K$374K–$468KOver $468KReal difference?
Net price per SF$197 per SFLowest$202 per SF$217 per SF$246 per SFHighest✓ large
Living area1,411 SFSmallest1,687 SF1,964 SF2,488 SFLargest✓ large
Year built2005200520072007unclear
Days on market52 days44 days27 days30 daysunclear
Discount from original list7.7%Biggest6.9%3.5%Smallest4.3%✓ medium
Price cut before sale55%52%36%35%unclear
Seller paid closing costs or repairs68%58%55%60%unclear
Has a view45%Fewest49%51%81%Most✓ small

How to read this section. Each heading gives the finding. The gold box says whether the gap is real (✓, with its size) or could be chance (–). The section ends with its numbers; the tests are in section 8.3.

3.3 Homes over $468K sell for about $49 per SF (25%) more than homes under $310K

2026-11-07T06:42:16.559485 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $374K 77 sales $374K– $468K 77 sales Over $468K 77 sales $0 $100 $200 $197 $202 $217 $246 Net price per SF, average

The average is $246 per SF for homes over $468K and $197 per SF for homes under $310K. It rises step by step from the lowest range to the highest. Defined in 8.1.2.

✓ Real difference · large

3.4 Homes over $468K are about 1,080 SF (76%) larger than homes under $310K

2026-11-07T06:42:16.582547 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $374K 77 sales $374K– $468K 77 sales Over $468K 77 sales 0 1,000 2,000 1,411 1,687 1,964 2,488 Living area (SF), average

The average is 2,488 SF for homes over $468K and 1,411 SF for homes under $310K. It rises step by step from the lowest range to the highest. Defined in 8.1.3.

✓ Real difference · large

3.5 Year built: no clear difference

2026-11-07T06:42:16.607253 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $374K 77 sales $374K– $468K 77 sales Over $468K 77 sales 2000 2005 2010 2015 2005 2005 2007 2007 Year built: median (dot) and middle half (bar)

The median is 2007 for homes over $468K and 2005 for homes under $310K. It rises step by step from the lowest range to the highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-11-07T06:42:16.630804 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $374K 77 sales $374K– $468K 77 sales Over $468K 77 sales 0 20 40 60 52 44 27 30 Days on market, median

The median is 30 days for homes over $468K and 52 days for homes under $310K. It does not move in a straight line; homes under $310K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Homes over $468K sell about 3 percentage points closer to their first asking price than homes under $310K

2026-11-07T06:42:16.654071 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $374K 77 sales $374K– $468K 77 sales Over $468K 77 sales 0.0% 2.5% 5.0% 7.5% 7.7% 6.9% 3.5% 4.3% Discount from original list, average

The average is 4.3% for homes over $468K and 7.7% for homes under $310K. It does not move in a straight line; homes under $310K are highest. Defined in 8.1.6.

✓ Real difference · medium

3.8 How often: price cut before sale, seller paid closing costs or repairs, has a view

2026-11-07T06:42:16.685283 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs Has a view 0% 25% 50% 75% 100% 55 68 45 52 58 49 36 55 51 35 60 81 Under $310K $310K–$374K $374K–$468K Over $468K

Each bar is the share of homes in the group, in percent.

  • 35% of homes over $468K had a price cut before sale, against 55% of homes under $310K. Defined in 8.1.7.
  • 60% of homes over $468K had the seller pay closing costs or repairs, against 68% of homes under $310K. Defined in 8.1.8.
  • 81% of homes over $468K have a view, against 45% of homes under $310K. Defined in 8.1.9.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: ✓ Real difference · small

3.9 The numbers: price and size

Under $310K77 sales$310K–$374K77 sales$374K–$468K77 salesOver $468K77 salesAll308 salesReal difference?
Net sale price average$274,714$336,953$421,649$614,145$411,865by design
Net price per SF average$197$202$217$246$215✓ large
Living area average1,4111,6871,9642,4881,888✓ large
Year built median20052005200720072006unclear

3.10 The numbers: selling

Under $310K77 sales$310K–$374K77 sales$374K–$468K77 salesOver $468K77 salesAll308 salesReal difference?
Days on market median5244273042unclear
Discount from original list average7.7%6.9%3.5%4.3%5.6%✓ medium
Price cut before sale55%52%36%35%44%unclear
Seller paid closing costs or repairs68%58%55%60%60%unclear
Has a view45%49%51%81%56%✓ small

Prices, price per SF, living area and discount are averages; days on market and year built are medians. The test behind each verdict is in 8.3.1.

4. Quartiles by living areaResale homes · 308 sales · May 11, 2026 – November 6, 2026

Sales ranked by living area and cut into four equal groups, from the smallest homes to the largest.

2026-11-07T06:42:16.707554 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 77 77 77 77 308 sales Sales per group Under 1,497 SF: 77 sales (25%) 1,497–1,845 SF: 77 sales (25%) 1,848–2,091 SF: 77 sales (25%) Over 2,091 SF: 77 sales (25%)
Bottom line

4.1 Key differences

The top group against the bottom group. Only real differences are listed. Section 8 explains each measure.

Under 1,497 SF1,497–1,845 SF1,848–2,091 SFOver 2,091 SF
Larger homes Over 2,091 SF
  • Sell for much more.
  • Sell for more per square foot.
Smaller homes Under 1,497 SF
  • Sell for much less.
  • Sell for less per square foot.

4.2 At a glance

Shaded rows are real differences; the darkest cell is the highest. Gray rows show no clear difference. Prices are averages; days on market and year built are medians.

Under 1,497 SF1,497–1,845 SF1,848–2,091 SFOver 2,091 SFReal difference?
Net sale price$283KLowest$345K$423K$596KHighest✓ large
Net price per SF$208 per SF$207 per SFLowest$215 per SF$231 per SFHighest✓ small
Year built2008200620052006unclear
Days on market49 days31 days27 days47 daysunclear
Discount from original list7.1%5.2%5.2%5.0%unclear
Price cut before sale53%44%38%43%unclear
Seller paid closing costs or repairs66%60%58%56%unclear
Has a view49%53%55%69%unclear

4.3 Homes over 2,091 SF sell for about $313K (110%) more than homes under 1,497 SF

2026-11-07T06:42:16.731478 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 77 sales 1,497– 1,845 SF 77 sales 1,848– 2,091 SF 77 sales Over 2,091 SF 77 sales $0K $200K $400K $600K $283K $345K $423K $596K Net sale price, average

The average is $596K for homes over 2,091 SF and $283K for homes under 1,497 SF. It rises step by step from the lowest range to the highest. Defined in 8.1.1.

✓ Real difference · large

4.4 Homes over 2,091 SF sell for about $22 per SF (11%) more than homes under 1,497 SF

2026-11-07T06:42:16.755243 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 77 sales 1,497– 1,845 SF 77 sales 1,848– 2,091 SF 77 sales Over 2,091 SF 77 sales $0 $100 $200 $208 $207 $215 $231 Net price per SF, average

The average is $231 per SF for homes over 2,091 SF and $208 per SF for homes under 1,497 SF. It does not move in a straight line; homes over 2,091 SF are highest. Defined in 8.1.2.

✓ Real difference · small

4.5 Year built: no clear difference

2026-11-07T06:42:16.779717 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 77 sales 1,497– 1,845 SF 77 sales 1,848– 2,091 SF 77 sales Over 2,091 SF 77 sales 2000 2005 2010 2015 2008 2006 2005 2006 Year built: median (dot) and middle half (bar)

The median is 2006 for homes over 2,091 SF and 2008 for homes under 1,497 SF. It does not move in a straight line; homes under 1,497 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-11-07T06:42:16.804174 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 77 sales 1,497– 1,845 SF 77 sales 1,848– 2,091 SF 77 sales Over 2,091 SF 77 sales 0 20 40 49 31 27 47 Days on market, median

The median is 47 days for homes over 2,091 SF and 49 days for homes under 1,497 SF. It does not move in a straight line; homes under 1,497 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-11-07T06:42:16.827117 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 77 sales 1,497– 1,845 SF 77 sales 1,848– 2,091 SF 77 sales Over 2,091 SF 77 sales 0.0% 2.5% 5.0% 7.5% 7.1% 5.2% 5.2% 5.0% Discount from original list, average

The average is 5.0% for homes over 2,091 SF and 7.1% for homes under 1,497 SF. It does not move in a straight line; homes under 1,497 SF are highest. Defined in 8.1.6.

– No clear difference · could be chance

4.8 How often: price cut before sale, seller paid closing costs or repairs, has a view

2026-11-07T06:42:16.855803 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs Has a view 0% 25% 50% 75% 100% 53 66 49 44 60 53 38 58 55 43 56 69 Under 1,497 SF 1,497–1,845 SF 1,848–2,091 SF Over 2,091 SF

Each bar is the share of homes in the group, in percent.

  • 43% of homes over 2,091 SF had a price cut before sale, against 53% of homes under 1,497 SF. Defined in 8.1.7.
  • 56% of homes over 2,091 SF had the seller pay closing costs or repairs, against 66% of homes under 1,497 SF. Defined in 8.1.8.
  • 69% of homes over 2,091 SF have a view, against 49% of homes under 1,497 SF. Defined in 8.1.9.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: – No clear difference · could be chance

4.9 The numbers: price and size

Under 1,497 SF77 sales1,497–1,845 SF77 sales1,848–2,091 SF77 salesOver 2,091 SF77 salesAll308 salesReal difference?
Net sale price average$283,206$345,380$422,965$595,909$411,865✓ large
Net price per SF average$208$207$215$231$215✓ small
Living area average1,3651,6661,9682,5521,888by design
Year built median20082006200520062006unclear

4.10 The numbers: selling

Under 1,497 SF77 sales1,497–1,845 SF77 sales1,848–2,091 SF77 salesOver 2,091 SF77 salesAll308 salesReal difference?
Days on market median4931274742unclear
Discount from original list average7.1%5.2%5.2%5.0%5.6%unclear
Price cut before sale53%44%38%43%44%unclear
Seller paid closing costs or repairs66%60%58%56%60%unclear
Has a view49%53%55%69%56%unclear

Prices, price per SF, living area and discount are averages; days on market and year built are medians. The test behind each verdict is in 8.3.2.

5. Quartiles by year builtResale homes · 308 sales · May 11, 2026 – November 6, 2026

Sales ranked by year built and cut into four groups, from the oldest to the newest. Homes built in the same year always stay in the same group, so group sizes can differ a little.

2026-11-07T06:42:16.879030 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 73 75 81 79 308 sales Sales per group Before 1999: 73 sales (24%) 1999–2005: 75 sales (24%) 2006–2014: 81 sales (26%) After 2014: 79 sales (26%)
Bottom line

5.1 Key differences

The top group against the bottom group. Only real differences are listed. Section 8 explains each measure.

Before 19991999–20052006–2014After 2014
Newer homes After 2014
  • Are more likely to have a view.
  • Sell for more per square foot.
Older homes Before 1999
  • Are less likely to have a view.
  • Sell for less per square foot.

5.2 At a glance

Shaded rows are real differences; the darkest cell is the highest. Gray rows show no clear difference. Prices are averages; days on market and year built are medians.

Before 19991999–20052006–2014After 2014Real difference?
Net sale price$379K$432K$398K$437Kunclear
Net price per SF$208 per SFLowest$212 per SF$214 per SF$226 per SFHighest✓ medium
Living area1,822 SF1,972 SF1,842 SF1,915 SFunclear
Days on market41 days40 days25 days58 daysunclear
Discount from original list6.6%5.2%5.0%5.7%unclear
Price cut before sale48%40%36%54%unclear
Seller paid closing costs or repairs56%67%60%57%unclear
Has a view47%45%Fewest78%Most54%✓ small

5.3 Net sale price: no clear difference

2026-11-07T06:42:16.902424 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1999 73 sales 1999– 2005 75 sales 2006– 2014 81 sales After 2014 79 sales $0K $200K $400K $379K $432K $398K $437K Net sale price, average

The average is $437K for homes built after 2014 and $379K for homes built before 1999. It does not move in a straight line; homes built after 2014 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Homes built after 2014 sell for about $18 per SF (9%) more than homes built before 1999

2026-11-07T06:42:16.929429 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1999 73 sales 1999– 2005 75 sales 2006– 2014 81 sales After 2014 79 sales $0 $100 $200 $208 $212 $214 $226 Net price per SF, average

The average is $226 per SF for homes built after 2014 and $208 per SF for homes built before 1999. It rises step by step from the lowest range to the highest. Defined in 8.1.2.

✓ Real difference · medium

5.5 Living area: no clear difference

2026-11-07T06:42:16.954470 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1999 73 sales 1999– 2005 75 sales 2006– 2014 81 sales After 2014 79 sales 0 1,000 2,000 1,822 1,972 1,842 1,915 Living area (SF), average

The average is 1,915 SF for homes built after 2014 and 1,822 SF for homes built before 1999. It does not move in a straight line; homes built 1999–2005 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-11-07T06:42:16.980565 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1999 73 sales 1999– 2005 75 sales 2006– 2014 81 sales After 2014 79 sales 0 20 40 60 41 40 25 58 Days on market, median

The median is 58 days for homes built after 2014 and 41 days for homes built before 1999. It does not move in a straight line; homes built after 2014 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-11-07T06:42:17.004745 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1999 73 sales 1999– 2005 75 sales 2006– 2014 81 sales After 2014 79 sales 0.0% 2.0% 4.0% 6.0% 6.6% 5.2% 5.0% 5.7% Discount from original list, average

The average is 5.7% for homes built after 2014 and 6.6% for homes built before 1999. It does not move in a straight line; homes built before 1999 are highest. Defined in 8.1.6.

– No clear difference · could be chance

5.8 How often: price cut before sale, seller paid closing costs or repairs, has a view

2026-11-07T06:42:17.033606 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs Has a view 0% 25% 50% 75% 100% 48 56 47 40 67 45 36 60 78 54 57 54 Before 1999 1999–2005 2006–2014 After 2014

Each bar is the share of homes in the group, in percent.

  • 54% of homes built after 2014 had a price cut before sale, against 48% of homes built before 1999. Defined in 8.1.7.
  • 57% of homes built after 2014 had the seller pay closing costs or repairs, against 56% of homes built before 1999. Defined in 8.1.8.
  • 54% of homes built after 2014 have a view, against 47% of homes built before 1999. Defined in 8.1.9.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: ✓ Real difference · small

5.9 The numbers: price and size

Before 199973 sales1999–200575 sales2006–201481 salesAfter 201479 salesAll308 salesReal difference?
Net sale price average$379,068$432,352$397,688$437,257$411,865unclear
Net price per SF average$208$212$214$226$215✓ medium
Living area average1,8221,9721,8421,9151,888unclear
Year built median19972002200820192006by design

5.10 The numbers: selling

Before 199973 sales1999–200575 sales2006–201481 salesAfter 201479 salesAll308 salesReal difference?
Days on market median4140255842unclear
Discount from original list average6.6%5.2%5.0%5.7%5.6%unclear
Price cut before sale48%40%36%54%44%unclear
Seller paid closing costs or repairs56%67%60%57%60%unclear
Has a view47%45%78%54%56%✓ small

Prices, price per SF, living area and discount are averages; days on market and year built are medians. The test behind each verdict is in 8.3.3.

6. View and no viewResale homes · 308 sales · May 11, 2026 – November 6, 2026

Sales where the MLS lists a view (greenbelt or park, trees, golf course, hill country, water and similar) against those listing none or only the neighborhood. 6 sales whose MLS view entry is only “See Remarks” are left out.

2026-11-07T06:42:17.056107 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 174 128 302 sales Sales per group View: 174 sales (58%) No view: 128 sales (42%)
Bottom line

6.1 Key differences

The top group against the bottom group. Only real differences are listed. Section 8 explains each measure.

ViewNo view
Homes with a view MLS lists a view
  • Sell for more per square foot.
  • Sell for more.
  • Sell faster.
Homes without a view no view listed
  • Sell for less per square foot.
  • Sell for less.
  • Take longer to sell.

6.2 At a glance

Shaded rows are real differences; the darkest cell is the highest. Gray rows show no clear difference. Prices are averages; days on market and year built are medians.

ViewNo viewReal difference?
Net sale price$447KHighest$365KLowest✓ small
Net price per SF$224 per SFHighest$204 per SFLowest✓ medium
Living area1,956 SF1,792 SFunclear
Year built20072003unclear
Days on market26 daysFastest51 daysSlowest✓ small
Discount from original list4.9%6.5%unclear
Price cut before sale39%52%unclear
Seller paid closing costs or repairs61%59%unclear

6.3 Homes with a view sell for about $82K (23%) more than homes without a view

2026-11-07T06:42:17.078136 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 174 sales No view 128 sales $0K $200K $400K $447K $365K Net sale price, average

The average is $447K for homes with a view and $365K for homes without a view. Defined in 8.1.1.

✓ Real difference · small

6.4 Homes with a view sell for about $20 per SF (10%) more than homes without a view

2026-11-07T06:42:17.103932 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 174 sales No view 128 sales $0 $100 $200 $224 $204 Net price per SF, average

The average is $224 per SF for homes with a view and $204 per SF for homes without a view. Defined in 8.1.2.

✓ Real difference · medium

6.5 Living area: no clear difference

2026-11-07T06:42:17.187907 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 174 sales No view 128 sales 0 500 1,000 1,500 2,000 1,956 1,792 Living area (SF), average

The average is 1,956 SF for homes with a view and 1,792 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-11-07T06:42:17.211333 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 174 sales No view 128 sales 2000 2004 2008 2012 2016 2007 2003 Year built: median (dot) and middle half (bar)

The median is 2007 for homes with a view and 2003 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Homes with a view sell 26 days faster than homes without a view

2026-11-07T06:42:17.236156 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 174 sales No view 128 sales 0 20 40 60 26 51 Days on market, median

The median is 26 days for homes with a view and 51 days for homes without a view. Defined in 8.1.5.

✓ Real difference · small

6.8 Discount from original list: no clear difference

2026-11-07T06:42:17.260520 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 174 sales No view 128 sales 0.0% 2.0% 4.0% 6.0% 4.9% 6.5% Discount from original list, average

The average is 4.9% for homes with a view and 6.5% for homes without a view. Defined in 8.1.6.

– No clear difference · could be chance

6.9 How often: price cut before sale, seller paid closing costs or repairs

2026-11-07T06:42:17.286121 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 39 61 52 59 View No view

Each bar is the share of homes in the group, in percent.

  • 39% of homes with a view had a price cut before sale, against 52% of homes without a view. Defined in 8.1.7.
  • 61% of homes with a view had the seller pay closing costs or repairs, against 59% of homes without a view. Defined in 8.1.8.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance

6.10 The numbers: price and size

View174 salesNo view128 salesAll302 salesReal difference?
Net sale price average$447,129$364,643$412,168✓ small
Net price per SF average$224$204$216✓ medium
Living area average1,9561,7921,887unclear
Year built median200720032006unclear

6.11 The numbers: selling

View174 salesNo view128 salesAll302 salesReal difference?
Days on market median265142✓ small
Discount from original list average4.9%6.5%5.6%unclear
Price cut before sale39%52%44%unclear
Seller paid closing costs or repairs61%59%60%unclear

Prices, price per SF, living area and discount are averages; days on market and year built are medians. The test behind each verdict is in 8.3.4.

7. New constructionBasic statistics · 12 sales · May 11, 2026 – November 6, 2026

Homes sold new by the builder in the same 180 days. They are left out of every other section and shown here as basic figures only: 12 sales, no groups, no tests.

Note: these are only the new-construction sales reported in ABOR MLS. Builders often sell homes without listing them in the MLS, so these figures do not necessarily represent all new-construction sales in Sun City Georgetown.
MeasureAverageMedian
Sales12
Net sale price$380,652$348,819
Net price per SF$236$239
Living area (SF)1,6271,453
Days on market9640
Discount from original list7.7%6.4%
Price cut before sale58% of sales
Seller paid closing costs or repairs50% of sales
Year built2024–2026

Same definitions as the rest of the study (section 8). New construction = NewConstructionYN true, or PropertyCondition “New Construction” or “Under Construction”.

8. AppendixMeasures, methods and tests

Each measure and method three ways: in plain words, as statistics, and with the data from this study.

8.1 Measures

8.1.1 Net sale price

In simple terms
What the seller actually kept: the closing price, less money the seller gave back to the buyer for closing costs or repairs. It is the truest price for comparing homes.
Statistically
Net = ClosePrice − ACTRIS_BuyersClsgCostPdbySell − ACTRIS_RepairsAmount (a blank counts as $0). Summarized by the arithmetic mean in the slices.
The data
308 resale sales. Average $411,865; median $374,125; standard deviation $156,193; middle half (25th–75th percentile) $309,875–$468,500; lowest $203,450, highest $1,588,000. 185 sales had a seller payment; their median payment was $2,000.

8.1.2 Net price per SF

In simple terms
Net sale price divided by the heated and cooled square feet. It puts homes of different sizes on the same footing: did buyers pay more for each square foot, not just for more square feet?
Statistically
psf = Net ÷ LivingArea, per sale; summarized by the mean of the per-sale ratios.
The data
308 resale sales. Average $215; median $212; standard deviation $33; middle half (25th–75th percentile) $195–$232; lowest $149, highest $346.

8.1.3 Living area

In simple terms
The heated and cooled space, as listed in the MLS. Garages and porches are not counted.
Statistically
RESO LivingArea, in square feet. Sales under 400 SF are treated as entry errors and left out.
The data
308 resale sales. Average 1,888; median 1,846; standard deviation 489; middle half (25th–75th percentile) 1,495–2,093; lowest 915, highest 4,866.

8.1.4 Year built

In simple terms
The year the home was finished, as listed in the MLS. It shows whether a group leans to older or newer phases of the community.
Statistically
RESO YearBuilt; summarized by the median, with the 25th–75th percentile (middle half) on the charts.
The data
308 resale sales. Average 2007; median 2006; standard deviation 9; middle half (25th–75th percentile) 1999–2015; lowest 1995, highest 2026.

8.1.5 Days on market

In simple terms
How many days the home was on the market before the buyer's offer was accepted, counting any earlier listing periods (relists). This is the days on market agents and clients quote.
Statistically
CDOM = max(CumulativeDaysOnMarket, DaysOnMarket, PurchaseContractDate − ListingContractDate). The MLS counter can freeze at a price change, so the contract-date gap is a floor. Summarized by the median, because a few homes that sat for months pull an average up.
The data
308 resale sales. Average 71; median 42; standard deviation 96; middle half (25th–75th percentile) 9–90; lowest 0, highest 645. 17 sales went under contract on their listing date (0 days).

8.1.6 Discount from original list

In simple terms
How far below the first asking price the home sold, after seller payments. It measures how much negotiating room sellers gave up. A negative figure means the home sold above its first asking price.
Statistically
Discount % = (OriginalListPrice − Net) ÷ OriginalListPrice × 100, per sale; summarized by the mean.
The data
308 resale sales. Average 5.6%; median 4.3%; standard deviation 5.8%; middle half (25th–75th percentile) 1.4%–8.4%; lowest −6.9%, highest 33.1%. 16 sales closed above their first asking price.

8.1.7 Price cut before sale

In simple terms
The asking price was lowered at least once before the home went under contract.
Statistically
Indicator: ListPrice at close < OriginalListPrice. Reported as the share of sales (%).
The data
137 of 308 resale sales (44.5%).

8.1.8 Seller paid closing costs or repairs

In simple terms
The seller paid some of the buyer's closing costs or repairs.
Statistically
Indicator: ACTRIS_BuyersClsgCostPdbySell + ACTRIS_RepairsAmount > 0. Reported as the share of sales (%).
The data
185 of 308 resale sales (60.1%).

8.1.9 Has a view

In simple terms
The MLS says the home looks over something: a greenbelt or park, trees, golf course, hill country, water and similar.
Statistically
View = the View field holds any entry other than “None”, “Neighborhood” or “See Remarks”. No view = only “None” and/or “Neighborhood”. A record whose only entry is “See Remarks” cannot be classified and is left out of the view slice.
The data
174 sales with a view, 128 without, 6 unclassified. Most common View entries: Park/Greenbelt 134, Neighborhood 101, Trees/Woods 64, None 60, Golf Course 35, Hill Country 16, See Remarks 13, Panoramic 10.

8.2 Methods

8.2.1 Data source

In simple terms
Every figure comes from the Austin Board of REALTORS® MLS, read live by ALEX. No figure is estimated or typed in.
Statistically
ABOR MLS via secure APIs, read from ALEX's copy of the MLS (refreshed every 15 minutes; this pull as of 2026-11-07 12:30 UTC), every field. Filter: StandardStatus = Closed, PropertyType = Residential, City = Georgetown, SubdivisionName contains any of “sun city georgetown”, “sun city”, “sun city texas”, “sun city george town”, “*sun city*” (case-insensitive). A record is kept when its subdivision name belongs to the Sun City Georgetown family, the same rule comparable sales use (it includes Sun City, Sun City Texas, proven adjacent and priced alike).
The data
Pulled November 7, 2026: 593 closed Sun City Georgetown records over the prior year, in ZIP 78633. 169 subdivision spellings kept, the most common: Sun City (93); Sun City Texas (65); Sun City Georgetown Neighborhood 04 (18); Sun City Georgetown (17); Sun City Georgetown Ph 04A Neighborhood 15 Pu (12); Sun City Georgetown Ph 02A Neighborhood 11 Pu (10); Sun City Georgetown Ph 02 Neighborhood 10 Pud (10); Sun City Georgetown Neighborhood 06 Amd B (10); Sun City Georgetown Neighborhood 07 (9); Sun City Georgetown Neighborhood 40 Pud (9); Sun City Georgetown Neighborhood 02 Amd B (8); Sun City Georgetown Ph 02 Neighborhood 09 Pud (8); and 157 more (324 records). No other names were found by the search.

8.2.2 Study window

In simple terms
The most recent 180 days of closed sales: recent enough to describe today's market, long enough to give each group plenty of sales.
Statistically
CloseDate from May 11, 2026 to November 6, 2026 inclusive.
The data
320 single-family sales in the window: 308 resale, 12 new construction.

8.2.3 Resale and new construction

In simple terms
A resale home was previously owned. New construction is sold by the builder and behaves differently, so it is kept out of every analysis and reported once on its own.
Statistically
New construction = NewConstructionYN true, or PropertyCondition contains “New Construction” or “Under Construction”. Everything else is resale.
The data
308 resale; 12 new construction.

8.2.4 Sales left out

In simple terms
A few records cannot be used as entered and are left out before any analysis.
Statistically
Excluded: PropertySubType other than Single Family Residence; no OriginalListPrice; LivingArea under 400 SF; seller payments above 10% of ClosePrice (treated as entry errors).
The data
1 sale(s) with no original list price

8.2.5 Quartiles and groups

In simple terms
Sales lined up from lowest to highest and cut into four groups of about equal size. Each group is named by its range, for example “Under $310K”.
Statistically
Sort by the slicing variable; cut k sits at round(nk/4), k = 1…3. A cut that would split tied values moves to the nearer edge of the tie. A group under 3 sales merges into its smaller neighbor. Range names use the next group's lowest value (“Under”) and the previous group's highest value (“Over”/“After”).
The data
Quartiles by net sale price: Under $310K: $203,450–$309,500 (77 sales); $310K–$374K: $310,000–$374,000 (77 sales); $374K–$468K: $374,250–$468,000 (77 sales); Over $468K: $470,000–$1,588,000 (77 sales)
Quartiles by living area: Under 1,497 SF: 915–1,490 SF (77 sales); 1,497–1,845 SF: 1,497–1,845 SF (77 sales); 1,848–2,091 SF: 1,848–2,091 SF (77 sales); Over 2,091 SF: 2,100–4,866 SF (77 sales)
Quartiles by year built: Before 1999: 1995–1998 (73 sales); 1999–2005: 1999–2005 (75 sales); 2006–2014: 2006–2014 (81 sales); After 2014: 2015–2026 (79 sales)
View and no view: View: MLS lists a view (174 sales); No view: None or Neighborhood (128 sales)

8.2.6 Average and median

In simple terms
The average adds the values and divides by how many; one very expensive home can pull it up. The median is the middle value, with half above and half below.
Statistically
Mean x̄ = (1/n) Σ xᵢ; median = 50th percentile. Price, price per SF, living area and discount use the mean; days on market and year built use the median (skewed or whole-year values).
The data
Net sale price: average $411,865, median $374,125. Days on market: average 71, median 42: the average is pulled up by a few long listings.

8.2.7 Kruskal–Wallis test

In simple terms
Checks whether a measure really differs across the four groups of a slice. It uses each sale's rank, not its value, so a few extreme sales cannot drive the answer.
Statistically
H = [12/(n(n+1))] Σ Rⱼ²/nⱼ − 3(n+1), compared with χ² on k − 1 degrees of freedom (Kruskal & Wallis, 1952). Effect size ε² = H/(n − 1): ≥ .01 small, ≥ .06 medium, ≥ .14 large (Tomczak & Tomczak, 2014). A slice is tested only when every group has at least 5 sales; smaller groups are described, not tested.
The data
Run on every measure in sections 3–5; each result is in 8.3.

8.2.8 Mann–Whitney U test

In simple terms
The same check when there are only two groups: view against no view.
Statistically
Two-sided U test (Mann & Whitney, 1947). Effect size = rank-biserial correlation |1 − 2U/(n₁n₂)|: ≥ .10 small, ≥ .30 medium, ≥ .50 large (Kerby, 2014; Cohen, 1988).
The data
Run on every measure in section 6; results in 8.3.

8.2.9 Chi-square test

In simple terms
Checks whether a yes/no share (price cut, seller payment, view) differs between groups more than chance would.
Statistically
Pearson χ² on the 2 × k table of counts, no continuity correction (Pearson, 1900). Effect size Cramér's V = √(χ²/n): ≥ .10 small, ≥ .30 medium, ≥ .50 large (Cramér, 1946).
The data
Run on the three shares in every slice; results in 8.3.

8.2.10 Real difference (significance)

In simple terms
“Real difference” means a gap this large would be very unlikely if the groups were really alike. Because many measures are checked in each slice, the bar is set higher than usual.
Statistically
Significant when p < 0.05 ÷ m, where m is the number of measures tested in that slice (Bonferroni; Dunn, 1961). The slicing variable itself differs by design and is not tested.
The data
Quartiles by net sale price: m = 8, threshold p < 0.0063; Quartiles by living area: m = 8, threshold p < 0.0063; Quartiles by year built: m = 8, threshold p < 0.0063; View and no view: m = 8, threshold p < 0.0063

8.2.11 Size of a difference

In simple terms
A difference can be real but small. The size word (small, medium, large) says how much the groups pull apart.
Statistically
ε² for Kruskal–Wallis, rank-biserial r for Mann–Whitney, Cramér's V for chi-square, with the thresholds above; below the small threshold the size is “negligible”.
The data
11 real differences across the four slices; sizes are listed in 8.3.

8.2.12 Rounding in the text

In simple terms
Headings and sentences round figures so they read easily; the tables at the end of each section give them in full.
Statistically
Prices in headings round to the nearest $1K; price per SF and days on market to the nearest whole number; living area to the nearest 10 SF; shares and percentage gaps to whole percents.
The data
Days on market is always shown in days.

8.3 Test results

Every test behind every gold box. Statistic, degrees of freedom (df), p-value, the threshold it must beat, and the size of the difference.

8.3.1 Quartiles by net sale price

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net price per SFKruskal–WallisH = 93.733< 0.0001p < 0.0063Yesε² = 0.305large
Living areaKruskal–WallisH = 231.423< 0.0001p < 0.0063Yesε² = 0.754large
Year builtKruskal–WallisH = 3.1930.3633p < 0.0063Unclearε² = 0.010small
Days on marketKruskal–WallisH = 10.3630.0157p < 0.0063Unclearε² = 0.034small
Discount from original listKruskal–WallisH = 33.473< 0.0001p < 0.0063Yesε² = 0.109medium
Price cut before saleChi-squareχ² = 9.7230.0211p < 0.0063UnclearCramér’s V = 0.178small
Seller paid closing costs or repairsChi-squareχ² = 2.8630.4144p < 0.0063UnclearCramér’s V = 0.096negligible
Has a viewChi-squareχ² = 24.573< 0.0001p < 0.0063YesCramér’s V = 0.282small

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 233.223< 0.0001p < 0.0063Yesε² = 0.760large
Net price per SFKruskal–WallisH = 14.6230.0022p < 0.0063Yesε² = 0.048small
Year builtKruskal–WallisH = 6.1930.1026p < 0.0063Unclearε² = 0.020small
Days on marketKruskal–WallisH = 6.3630.0953p < 0.0063Unclearε² = 0.021small
Discount from original listKruskal–WallisH = 9.5930.0224p < 0.0063Unclearε² = 0.031small
Price cut before saleChi-squareχ² = 3.9330.2690p < 0.0063UnclearCramér’s V = 0.113small
Seller paid closing costs or repairsChi-squareχ² = 1.8830.5974p < 0.0063UnclearCramér’s V = 0.078negligible
Has a viewChi-squareχ² = 6.8230.0780p < 0.0063UnclearCramér’s V = 0.149small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 5.8530.1193p < 0.0063Unclearε² = 0.019small
Net price per SFKruskal–WallisH = 22.863< 0.0001p < 0.0063Yesε² = 0.074medium
Living areaKruskal–WallisH = 1.6130.6580p < 0.0063Unclearε² = 0.005negligible
Days on marketKruskal–WallisH = 9.4630.0238p < 0.0063Unclearε² = 0.031small
Discount from original listKruskal–WallisH = 3.0530.3835p < 0.0063Unclearε² = 0.010negligible
Price cut before saleChi-squareχ² = 6.6030.0857p < 0.0063UnclearCramér’s V = 0.146small
Seller paid closing costs or repairsChi-squareχ² = 2.1530.5421p < 0.0063UnclearCramér’s V = 0.084negligible
Has a viewChi-squareχ² = 21.793< 0.0001p < 0.0063YesCramér’s V = 0.266small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 14,411.50—< 0.0001p < 0.0063Yesrank-biserial r = 0.294small
Net price per SFMann–Whitney UU = 14,971.50—< 0.0001p < 0.0063Yesrank-biserial r = 0.344medium
Living areaMann–Whitney UU = 13,053.00—0.0106p < 0.0063Unclearrank-biserial r = 0.172small
Year builtMann–Whitney UU = 12,809.00—0.0255p < 0.0063Unclearrank-biserial r = 0.150small
Days on marketMann–Whitney UU = 8,737.50—0.0014p < 0.0063Yesrank-biserial r = 0.215small
Discount from original listMann–Whitney UU = 9,641.50—0.0463p < 0.0063Unclearrank-biserial r = 0.134small
Price cut before saleChi-squareχ² = 4.6610.0310p < 0.0063UnclearCramér’s V = 0.124small
Seller paid closing costs or repairsChi-squareχ² = 0.1710.6836p < 0.0063UnclearCramér’s V = 0.023negligible

8.4 Data notes

Left out of the analysis: 12 new-construction sales (basic statistics only, section 7); 1 sale(s) with no original list price. Statistics: Python, NumPy 2.5.3, SciPy 1.18.1.

8.5 References

  1. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Erlbaum.
  2. Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press.
  3. Dunn, O. J. (1961). Multiple comparisons among means. JASA, 56(293), 52–64.
  4. Kerby, D. S. (2014). The simple difference formula: an approach to teaching nonparametric correlation. Comprehensive Psychology, 3, 11.IT.3.1.
  5. Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. JASA, 47(260), 583–621.
  6. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.
  7. Pearson, K. (1900). On the criterion that a given system of deviations… Philosophical Magazine, 50(302), 157–175.
  8. Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited. Trends in Sport Sciences, 21(1), 19–25.

This is a market study of closed sales, not an appraisal or an opinion of value for any home. The analysis covers 308 closed MLS resale sales; the 12 new-construction sales appear only in their own section, as basic statistics. Sale and listing data: ABOR MLS via secure APIs, pulled November 7, 2026. Map data © OpenStreetMap contributors. ALEX Companies · Licensed by the Texas Real Estate Commission · Broker: Alexander Tiffany Southwest, LLC (ALEX Real Estate), #565194-BB · Diane Hart Alexander, Designated Broker, #574877-B.