ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Sun City Georgetown

Georgetown, TX 78633
Resale homes · closed Apr 25 – Oct 21, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 22, 2026 · 7:00 PM CDT
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
304
single-family resale homes
Median net sale price
$366,156
average $407,775
Median net price per SF
$210
average $214
Median days on market
43 days
average 74 days
1. SummaryResale homes · 304 sales · April 25, 2026 – October 21, 2026

This study looks at the 304 resale homes sold in Sun City Georgetown in the 180 days to October 21, 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 sold304 resale homes · colored by price range

Each dot is one resale home, colored by its price range. 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 · 76 sales$310K–$364K · 76 sales$368K–$462K · 76 salesOver $462K · 76 sales
3. Quartiles by net sale priceResale homes · 304 sales · April 25, 2026 – October 21, 2026

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

2026-10-22T19:01:03.338563 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 76 76 76 76 304 sales Sales per group Under $310K: 76 sales (25%) $310K–$364K: 76 sales (25%) $368K–$462K: 76 sales (25%) Over $462K: 76 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–$364K$368K–$462KOver $462K
Higher-priced homes Over $462K
  • 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–$364K$368K–$462KOver $462KReal difference?
Net price per SF$195 per SFLowest$203 per SF$214 per SF$244 per SFHighest✓ large
Living area1,420 SFSmallest1,671 SF1,972 SF2,470 SFLargest✓ large
Year built2006200620062008unclear
Days on market56 days50 days27 days25 daysunclear
Discount from original list8.2%Biggest6.6%3.8%Smallest4.4%✓ medium
Price cut before sale55%55%36%37%unclear
Seller paid closing costs or repairs64%59%55%59%unclear
Has a view47%51%46%Fewest79%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 $462K sell for about $48 per SF (25%) more than homes under $310K

2026-10-22T19:01:03.366951 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 76 sales $310K– $364K 76 sales $368K– $462K 76 sales Over $462K 76 sales $0 $100 $200 $195 $203 $214 $244 Net price per SF, average

The average is $244 per SF for homes over $462K and $195 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 $462K are about 1,050 SF (74%) larger than homes under $310K

2026-10-22T19:01:03.389029 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 76 sales $310K– $364K 76 sales $368K– $462K 76 sales Over $462K 76 sales 0 1,000 2,000 1,420 1,671 1,972 2,470 Living area (SF), average

The average is 2,470 SF for homes over $462K and 1,420 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

2026-10-22T19:01:03.411810 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 76 sales $310K– $364K 76 sales $368K– $462K 76 sales Over $462K 76 sales 2000 2005 2010 2015 2006 2006 2006 2008 Year built: median (dot) and middle half (bar)

The median is 2008 for homes over $462K and 2006 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

2026-10-22T19:01:03.433572 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 76 sales $310K– $364K 76 sales $368K– $462K 76 sales Over $462K 76 sales 0 20 40 60 56 50 27 25 Days on market, median

The median is 25 days for homes over $462K and 56 days for homes under $310K. It falls step by step from the lowest range to the highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Homes over $462K sell about 4 percentage points closer to their first asking price than homes under $310K

2026-10-22T19:01:03.455573 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 76 sales $310K– $364K 76 sales $368K– $462K 76 sales Over $462K 76 sales 0.0% 2.5% 5.0% 7.5% 8.2% 6.6% 3.8% 4.4% Discount from original list, average

The average is 4.4% for homes over $462K and 8.2% 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

2026-10-22T19:01:03.485111 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 64 47 55 59 51 36 55 46 37 59 79 Under $310K $310K–$364K $368K–$462K Over $462K

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

  • 37% of homes over $462K had a price cut before sale, against 55% of homes under $310K. Defined in 8.1.7.
  • 59% of homes over $462K had the seller pay closing costs or repairs, against 64% of homes under $310K. Defined in 8.1.8.
  • 79% of homes over $462K have a view, against 47% 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 $310K76 sales$310K–$364K76 sales$368K–$462K76 salesOver $462K76 salesAll304 salesReal difference?
Net sale price average$274,125$334,812$418,200$603,965$407,775by design
Net price per SF average$195$203$214$244$214✓ large
Living area average1,4201,6711,9722,4701,883✓ large
Year built median20062006200620082006unclear

3.10 The numbers: selling

Under $310K76 sales$310K–$364K76 sales$368K–$462K76 salesOver $462K76 salesAll304 salesReal difference?
Days on market median5650272543unclear
Discount from original list average8.2%6.6%3.8%4.4%5.7%✓ medium
Price cut before sale55%55%36%37%46%unclear
Seller paid closing costs or repairs64%59%55%59%60%unclear
Has a view47%51%46%79%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 · 304 sales · April 25, 2026 – October 21, 2026

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

2026-10-22T19:01:03.505887 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 76 75 78 75 304 sales Sales per group Under 1,497 SF: 76 sales (25%) 1,497–1,822 SF: 75 sales (25%) 1,845–2,091 SF: 78 sales (26%) Over 2,091 SF: 75 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 SF1,497–1,822 SF1,845–2,091 SFOver 2,091 SF
Larger homes Over 2,091 SF
  • Sell for much more.
Smaller homes Under 1,497 SF
  • Sell for much less.

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 SF1,497–1,822 SF1,845–2,091 SFOver 2,091 SFReal difference?
Net sale price$285KLowest$339K$420K$588KHighest✓ large
Net price per SF$209 per SF$205 per SF$214 per SF$228 per SFunclear
Year built2010200520052006unclear
Days on market54 days31 days28 days47 daysunclear
Discount from original list7.1%5.4%5.2%5.1%unclear
Price cut before sale55%44%41%43%unclear
Seller paid closing costs or repairs63%61%56%57%unclear
Has a view50%55%54%65%unclear

4.3 Homes over 2,091 SF sell for about $303K (106%) more than homes under 1,497 SF

2026-10-22T19:01:03.527273 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 76 sales 1,497– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 75 sales $0K $200K $400K $600K $285K $339K $420K $588K Net sale price, average

The average is $588K for homes over 2,091 SF and $285K 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 Net price per SF: no clear difference

2026-10-22T19:01:03.550022 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 76 sales 1,497– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 75 sales $0 $100 $200 $209 $205 $214 $228 Net price per SF, average

The average is $228 per SF for homes over 2,091 SF and $209 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.

– No clear difference · could be chance

4.5 Year built: no clear difference

2026-10-22T19:01:03.573303 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 76 sales 1,497– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 75 sales 2000 2005 2010 2015 2010 2005 2005 2006 Year built: median (dot) and middle half (bar)

The median is 2006 for homes over 2,091 SF and 2010 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

2026-10-22T19:01:03.596032 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 76 sales 1,497– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 75 sales 0 20 40 60 54 31 28 47 Days on market, median

The median is 47 days for homes over 2,091 SF and 54 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

2026-10-22T19:01:03.618798 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,497 SF 76 sales 1,497– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 75 sales 0.0% 2.5% 5.0% 7.5% 7.1% 5.4% 5.2% 5.1% Discount from original list, average

The average is 5.1% for homes over 2,091 SF and 7.1% for homes under 1,497 SF. It falls step by step from the lowest range to the 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

2026-10-22T19:01:03.645400 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 63 50 44 61 55 41 56 54 43 57 65 Under 1,497 SF 1,497–1,822 SF 1,845–2,091 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 55% of homes under 1,497 SF. Defined in 8.1.7.
  • 57% of homes over 2,091 SF had the seller pay closing costs or repairs, against 63% of homes under 1,497 SF. Defined in 8.1.8.
  • 65% of homes over 2,091 SF have a view, against 50% 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 SF76 sales1,497–1,822 SF75 sales1,845–2,091 SF78 salesOver 2,091 SF75 salesAll304 salesReal difference?
Net sale price average$284,775$339,128$420,351$587,985$407,775✓ large
Net price per SF average$209$205$214$228$214unclear
Living area average1,3701,6561,9612,5501,883by design
Year built median20102005200520062006unclear

4.10 The numbers: selling

Under 1,497 SF76 sales1,497–1,822 SF75 sales1,845–2,091 SF78 salesOver 2,091 SF75 salesAll304 salesReal difference?
Days on market median5431284743unclear
Discount from original list average7.1%5.4%5.2%5.1%5.7%unclear
Price cut before sale55%44%41%43%46%unclear
Seller paid closing costs or repairs63%61%56%57%60%unclear
Has a view50%55%54%65%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 · 304 sales · April 25, 2026 – October 21, 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.

2026-10-22T19:01:03.666699 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 77 67 82 78 304 sales Sales per group Before 2000: 77 sales (25%) 2000–2005: 67 sales (22%) 2006–2014: 82 sales (27%) After 2014: 78 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 20002000–20052006–2014After 2014
Newer homes After 2014
  • Are more likely to have a view.
  • Sell for more per square foot.
Older homes Before 2000
  • 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 20002000–20052006–2014After 2014Real difference?
Net sale price$383K$422K$394K$434Kunclear
Net price per SF$207 per SFLowest$209 per SF$213 per SF$226 per SFHighest✓ medium
Living area1,849 SF1,953 SF1,836 SF1,908 SFunclear
Days on market44 days40 days25 days58 daysunclear
Discount from original list6.4%5.6%5.1%5.8%unclear
Price cut before sale51%40%37%55%unclear
Seller paid closing costs or repairs56%64%61%58%unclear
Has a view43%Fewest46%79%Most53%✓ small

5.3 Net sale price: no clear difference

2026-10-22T19:01:03.687816 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 77 sales 2000– 2005 67 sales 2006– 2014 82 sales After 2014 78 sales $0K $200K $400K $383K $422K $394K $434K Net sale price, average

The average is $434K for homes built after 2014 and $383K for homes built before 2000. 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 $19 per SF (9%) more than homes built before 2000

2026-10-22T19:01:03.710703 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 77 sales 2000– 2005 67 sales 2006– 2014 82 sales After 2014 78 sales $0 $100 $200 $207 $209 $213 $226 Net price per SF, average

The average is $226 per SF for homes built after 2014 and $207 per SF for homes built before 2000. 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

2026-10-22T19:01:03.732270 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 77 sales 2000– 2005 67 sales 2006– 2014 82 sales After 2014 78 sales 0 1,000 2,000 1,849 1,953 1,836 1,908 Living area (SF), average

The average is 1,908 SF for homes built after 2014 and 1,849 SF for homes built before 2000. It does not move in a straight line; homes built 2000–2005 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-22T19:01:03.754237 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 77 sales 2000– 2005 67 sales 2006– 2014 82 sales After 2014 78 sales 0 20 40 60 44 40 25 58 Days on market, median

The median is 58 days for homes built after 2014 and 44 days for homes built before 2000. 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

2026-10-22T19:01:03.776574 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 77 sales 2000– 2005 67 sales 2006– 2014 82 sales After 2014 78 sales 0.0% 2.0% 4.0% 6.0% 6.4% 5.6% 5.1% 5.8% Discount from original list, average

The average is 5.8% for homes built after 2014 and 6.4% for homes built before 2000. It does not move in a straight line; homes built before 2000 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

2026-10-22T19:01:03.802433 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% 51 56 43 40 64 46 37 61 79 55 58 53 Before 2000 2000–2005 2006–2014 After 2014

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

  • 55% of homes built after 2014 had a price cut before sale, against 51% of homes built before 2000. Defined in 8.1.7.
  • 58% of homes built after 2014 had the seller pay closing costs or repairs, against 56% of homes built before 2000. Defined in 8.1.8.
  • 53% of homes built after 2014 have a view, against 43% of homes built before 2000. 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 200077 sales2000–200567 sales2006–201482 salesAfter 201478 salesAll304 salesReal difference?
Net sale price average$383,404$422,224$394,158$433,739$407,775unclear
Net price per SF average$207$209$213$226$214✓ medium
Living area average1,8491,9531,8361,9081,883unclear
Year built median19972003200820192006by design

5.10 The numbers: selling

Before 200077 sales2000–200567 sales2006–201482 salesAfter 201478 salesAll304 salesReal difference?
Days on market median4440255843unclear
Discount from original list average6.4%5.6%5.1%5.8%5.7%unclear
Price cut before sale51%40%37%55%46%unclear
Seller paid closing costs or repairs56%64%61%58%60%unclear
Has a view43%46%79%53%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 · 304 sales · April 25, 2026 – October 21, 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.

2026-10-22T19:01:03.821172 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 170 128 298 sales Sales per group View: 170 sales (57%) No view: 128 sales (43%)
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$440KHighest$366KLowest✓ small
Net price per SF$222 per SFHighest$204 per SFLowest✓ medium
Living area1,941 SF1,804 SFunclear
Year built20072003unclear
Days on market25 daysFastest52 daysSlowest✓ small
Discount from original list5.1%6.5%unclear
Price cut before sale39%54%unclear
Seller paid closing costs or repairs61%58%unclear

6.3 Homes with a view sell for about $74K (20%) more than homes without a view

2026-10-22T19:01:03.837732 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 128 sales $0K $200K $400K $440K $366K Net sale price, average

The average is $440K for homes with a view and $366K for homes without a view. Defined in 8.1.1.

✓ Real difference · small

6.4 Homes with a view sell for about $18 per SF (9%) more than homes without a view

2026-10-22T19:01:03.855410 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 128 sales $0 $100 $200 $222 $204 Net price per SF, average

The average is $222 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

2026-10-22T19:01:03.895574 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 128 sales 0 500 1,000 1,500 2,000 1,941 1,804 Living area (SF), average

The average is 1,941 SF for homes with a view and 1,804 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-22T19:01:03.915711 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 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 28 days faster than homes without a view

2026-10-22T19:01:03.934081 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 128 sales 0 20 40 60 25 52 Days on market, median

The median is 25 days for homes with a view and 52 days for homes without a view. Defined in 8.1.5.

✓ Real difference · small

6.8 Discount from original list: no clear difference

2026-10-22T19:01:03.951586 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 128 sales 0.0% 2.0% 4.0% 6.0% 5.1% 6.5% Discount from original list, average

The average is 5.1% 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

2026-10-22T19:01:03.970246 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 39 61 54 58 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 54% 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 58% 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

View170 salesNo view128 salesAll298 salesReal difference?
Net sale price average$439,747$365,837$408,000✓ small
Net price per SF average$222$204$214✓ medium
Living area average1,9411,8041,882unclear
Year built median200720032006unclear

6.11 The numbers: selling

View170 salesNo view128 salesAll298 salesReal difference?
Days on market median255243✓ small
Discount from original list average5.1%6.5%5.7%unclear
Price cut before sale39%54%46%unclear
Seller paid closing costs or repairs61%58%59%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 · April 25, 2026 – October 21, 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$420,609$365,495
Net price per SF$243$239
Living area (SF)1,7281,550
Days on market8240
Discount from original list7.0%6.0%
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
304 resale sales. Average $407,775; median $366,156; standard deviation $153,975; middle half (25th–75th percentile) $309,375–$462,125; lowest $203,450, highest $1,588,000. 181 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
304 resale sales. Average $214; median $210; standard deviation $33; middle half (25th–75th percentile) $194–$231; lowest $134, 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
304 resale sales. Average 1,883; median 1,845; standard deviation 486; middle half (25th–75th percentile) 1,495–2,091; 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
304 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
304 resale sales. Average 74; median 43; standard deviation 94; middle half (25th–75th percentile) 9–94; lowest 0, highest 564. 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
304 resale sales. Average 5.7%; median 4.6%; standard deviation 5.7%; middle half (25th–75th percentile) 1.5%–8.6%; lowest −6.2%, highest 31.3%. 12 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
139 of 304 resale sales (45.7%).

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
181 of 304 resale sales (59.5%).

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
170 sales with a view, 128 without, 6 unclassified. Most common View entries: Park/Greenbelt 133, Neighborhood 98, None 64, Trees/Woods 61, Golf Course 32, Hill Country 14, See Remarks 13, Panoramic 7.

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
Bridge Interactive RESO Web API (OData), ABOR dataset, every field returned. 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 georgetown nbhrd” (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 October 22, 2026: 585 closed Sun City Georgetown records over the prior year, in ZIP 78633. 168 subdivision spellings kept, the most common: Sun City (93); Sun City Texas (64); 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 (11); Sun City Georgetown Neighborhood 06 Amd B (10); Sun City Georgetown Neighborhood 07 (9); Sun City Georgetown Ph 02 Neighborhood 10 Pud (9); Sun City Georgetown Neighborhood 02 Amd B (8); Sun City Nbrhd 83 (8); Sun City Georgetown Neighborhood 40 Pud (8); and 156 more (318 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 April 25, 2026 to October 21, 2026 inclusive.
The data
316 single-family sales in the window: 304 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
304 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–$309,000 (76 sales); $310K–$364K: $309,500–$364,000 (76 sales); $368K–$462K: $368,313–$462,000 (76 sales); Over $462K: $462,500–$1,588,000 (76 sales)
Quartiles by living area: Under 1,497 SF: 915–1,490 SF (76 sales); 1,497–1,822 SF: 1,497–1,822 SF (75 sales); 1,845–2,091 SF: 1,845–2,091 SF (78 sales); Over 2,091 SF: 2,100–4,866 SF (75 sales)
Quartiles by year built: Before 2000: 1995–1999 (77 sales); 2000–2005: 2000–2005 (67 sales); 2006–2014: 2006–2014 (82 sales); After 2014: 2015–2026 (78 sales)
View and no view: View: MLS lists a view (170 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 $407,775, median $366,156. Days on market: average 74, median 43: 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
10 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 = 85.403< 0.0001p < 0.0063Yesε² = 0.282large
Living areaKruskal–WallisH = 226.323< 0.0001p < 0.0063Yesε² = 0.747large
Year builtKruskal–WallisH = 2.6730.4457p < 0.0063Unclearε² = 0.009negligible
Days on marketKruskal–WallisH = 12.1230.0070p < 0.0063Unclearε² = 0.040small
Discount from original listKruskal–WallisH = 31.853< 0.0001p < 0.0063Yesε² = 0.105medium
Price cut before saleChi-squareχ² = 11.1730.0108p < 0.0063UnclearCramér’s V = 0.192small
Seller paid closing costs or repairsChi-squareχ² = 1.3530.7169p < 0.0063UnclearCramér’s V = 0.067negligible
Has a viewChi-squareχ² = 22.263< 0.0001p < 0.0063YesCramér’s V = 0.271small

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 228.113< 0.0001p < 0.0063Yesε² = 0.753large
Net price per SFKruskal–WallisH = 11.9330.0076p < 0.0063Unclearε² = 0.039small
Year builtKruskal–WallisH = 9.4530.0239p < 0.0063Unclearε² = 0.031small
Days on marketKruskal–WallisH = 6.8430.0771p < 0.0063Unclearε² = 0.023small
Discount from original listKruskal–WallisH = 9.0730.0284p < 0.0063Unclearε² = 0.030small
Price cut before saleChi-squareχ² = 3.8530.2778p < 0.0063UnclearCramér’s V = 0.113small
Seller paid closing costs or repairsChi-squareχ² = 0.9830.8056p < 0.0063UnclearCramér’s V = 0.057negligible
Has a viewChi-squareχ² = 3.9630.2658p < 0.0063UnclearCramér’s V = 0.114small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 5.2830.1524p < 0.0063Unclearε² = 0.017small
Net price per SFKruskal–WallisH = 26.153< 0.0001p < 0.0063Yesε² = 0.086medium
Living areaKruskal–WallisH = 0.8930.8280p < 0.0063Unclearε² = 0.003negligible
Days on marketKruskal–WallisH = 9.9930.0186p < 0.0063Unclearε² = 0.033small
Discount from original listKruskal–WallisH = 1.9830.5763p < 0.0063Unclearε² = 0.007negligible
Price cut before saleChi-squareχ² = 7.0930.0692p < 0.0063UnclearCramér’s V = 0.153small
Seller paid closing costs or repairsChi-squareχ² = 1.2230.7492p < 0.0063UnclearCramér’s V = 0.063negligible
Has a viewChi-squareχ² = 26.353< 0.0001p < 0.0063YesCramér’s V = 0.294small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 13,745.00—0.0001p < 0.0063Yesrank-biserial r = 0.263small
Net price per SFMann–Whitney UU = 14,373.50—< 0.0001p < 0.0063Yesrank-biserial r = 0.321medium
Living areaMann–Whitney UU = 12,423.50—0.0361p < 0.0063Unclearrank-biserial r = 0.142small
Year builtMann–Whitney UU = 12,663.00—0.0154p < 0.0063Unclearrank-biserial r = 0.164small
Days on marketMann–Whitney UU = 8,228.50—0.0003p < 0.0063Yesrank-biserial r = 0.244small
Discount from original listMann–Whitney UU = 9,239.50—0.0259p < 0.0063Unclearrank-biserial r = 0.151small
Price cut before saleChi-squareχ² = 6.1810.0129p < 0.0063UnclearCramér’s V = 0.144small
Seller paid closing costs or repairsChi-squareχ² = 0.2310.6291p < 0.0063UnclearCramér’s V = 0.028negligible

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 304 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 October 22, 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.