ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Sun City Georgetown Neighborhood 04

Georgetown, TX 78633
Resale homes · closed May 5 – Oct 31, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
November 1, 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
303
single-family resale homes
Median net sale price
$359,800
average $408,979
Median net price per SF
$210
average $215
Median days on market
43 days
average 73 days
1. SummaryResale homes · 303 sales · May 5, 2026 – October 31, 2026

This study looks at the 303 resale homes sold in Sun City Georgetown Neighborhood 04 in the 180 days to October 31, 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 sold303 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–$360K · 75 sales$362K–$463K · 75 salesOver $463K · 76 sales
3. Quartiles by net sale priceResale homes · 303 sales · May 5, 2026 – October 31, 2026

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

2026-11-01T06:42:17.993744 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 77 75 75 76 303 sales Sales per group Under $310K: 77 sales (25%) $310K–$360K: 75 sales (25%) $362K–$463K: 75 sales (25%) Over $463K: 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–$360K$362K–$463KOver $463K
Higher-priced homes Over $463K
  • 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–$360K$362K–$463KOver $463KReal difference?
Net price per SF$196 per SFLowest$202 per SF$215 per SF$246 per SFHighest✓ large
Living area1,412 SFSmallest1,677 SF1,957 SF2,480 SFLargest✓ large
Year built2005200620072007unclear
Days on market52 days48 days28 days25 daysunclear
Discount from original list8.1%Biggest7.3%3.6%Smallest4.3%✓ medium
Price cut before sale55%55%36%37%unclear
Seller paid closing costs or repairs66%61%53%58%unclear
Has a view45%Fewest52%47%80%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 $463K sell for about $50 per SF (25%) more than homes under $310K

2026-11-01T06:42:18.024429 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $360K 75 sales $362K– $463K 75 sales Over $463K 76 sales $0 $100 $200 $196 $202 $215 $246 Net price per SF, average

The average is $246 per SF for homes over $463K and $196 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 $463K are about 1,070 SF (76%) larger than homes under $310K

2026-11-01T06:42:18.048939 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $360K 75 sales $362K– $463K 75 sales Over $463K 76 sales 0 1,000 2,000 1,412 1,677 1,957 2,480 Living area (SF), average

The average is 2,480 SF for homes over $463K and 1,412 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

2026-11-01T06:42:18.074398 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $360K 75 sales $362K– $463K 75 sales Over $463K 76 sales 2000 2005 2010 2015 2005 2006 2007 2007 Year built: median (dot) and middle half (bar)

The median is 2007 for homes over $463K 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

2026-11-01T06:42:18.098297 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $360K 75 sales $362K– $463K 75 sales Over $463K 76 sales 0 20 40 60 52 48 28 25 Days on market, median

The median is 25 days for homes over $463K and 52 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 $463K sell about 4 percentage points closer to their first asking price than homes under $310K

2026-11-01T06:42:18.122620 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $310K 77 sales $310K– $360K 75 sales $362K– $463K 75 sales Over $463K 76 sales 0.0% 2.5% 5.0% 7.5% 8.1% 7.3% 3.6% 4.3% Discount from original list, average

The average is 4.3% for homes over $463K and 8.1% 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

2026-11-01T06:42:18.155726 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 66 45 55 61 52 36 53 47 37 58 80 Under $310K $310K–$360K $362K–$463K Over $463K

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

  • 37% of homes over $463K had a price cut before sale, against 55% of homes under $310K. Defined in 8.1.7.
  • 58% of homes over $463K had the seller pay closing costs or repairs, against 66% of homes under $310K. Defined in 8.1.8.
  • 80% of homes over $463K 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–$360K75 sales$362K–$463K75 salesOver $463K76 salesAll303 salesReal difference?
Net sale price average$273,133$334,777$417,134$611,790$408,979by design
Net price per SF average$196$202$215$246$215✓ large
Living area average1,4121,6771,9572,4801,880✓ large
Year built median20052006200720072006unclear

3.10 The numbers: selling

Under $310K77 sales$310K–$360K75 sales$362K–$463K75 salesOver $463K76 salesAll303 salesReal difference?
Days on market median5248282543unclear
Discount from original list average8.1%7.3%3.6%4.3%5.8%✓ medium
Price cut before sale55%55%36%37%46%unclear
Seller paid closing costs or repairs66%61%53%58%60%unclear
Has a view45%52%47%80%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 · 303 sales · May 5, 2026 – October 31, 2026

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

2026-11-01T06:42:18.179060 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 76 75 78 74 303 sales Sales per group Under 1,490 SF: 76 sales (25%) 1,490–1,822 SF: 75 sales (25%) 1,845–2,091 SF: 78 sales (26%) Over 2,091 SF: 74 sales (24%)
Bottom line

4.1 Key differences

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

Under 1,490 SF1,490–1,822 SF1,845–2,091 SFOver 2,091 SF
Larger homes Over 2,091 SF
  • Sell for much more.
  • Sell for more per square foot.
Smaller homes Under 1,490 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 1,490 SF1,490–1,822 SF1,845–2,091 SFOver 2,091 SFReal difference?
Net sale price$283KLowest$338K$420K$599KHighest✓ large
Net price per SF$208 per SF$204 per SFLowest$214 per SF$232 per SFHighest✓ medium
Year built2007200520062006unclear
Days on market48 days35 days28 days47 daysunclear
Discount from original list7.0%5.9%5.4%4.9%unclear
Price cut before sale53%47%40%43%unclear
Seller paid closing costs or repairs67%60%58%54%unclear
Has a view49%53%54%69%unclear

4.3 Homes over 2,091 SF sell for about $316K (112%) more than homes under 1,490 SF

2026-11-01T06:42:18.201687 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,490 SF 76 sales 1,490– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 74 sales $0K $200K $400K $600K $283K $338K $420K $599K Net sale price, average

The average is $599K for homes over 2,091 SF and $283K for homes under 1,490 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 $23 per SF (11%) more than homes under 1,490 SF

2026-11-01T06:42:18.227407 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,490 SF 76 sales 1,490– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 74 sales $0 $100 $200 $208 $204 $214 $232 Net price per SF, average

The average is $232 per SF for homes over 2,091 SF and $208 per SF for homes under 1,490 SF. It does not move in a straight line; homes over 2,091 SF are highest. Defined in 8.1.2.

✓ Real difference · medium

4.5 Year built: no clear difference

2026-11-01T06:42:18.252954 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,490 SF 76 sales 1,490– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 74 sales 2000 2005 2010 2015 2007 2005 2006 2006 Year built: median (dot) and middle half (bar)

The median is 2006 for homes over 2,091 SF and 2007 for homes under 1,490 SF. It does not move in a straight line; homes under 1,490 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-11-01T06:42:18.278130 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,490 SF 76 sales 1,490– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 74 sales 0 20 40 48 35 28 47 Days on market, median

The median is 47 days for homes over 2,091 SF and 48 days for homes under 1,490 SF. It does not move in a straight line; homes under 1,490 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-11-01T06:42:18.302408 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,490 SF 76 sales 1,490– 1,822 SF 75 sales 1,845– 2,091 SF 78 sales Over 2,091 SF 74 sales 0.0% 2.5% 5.0% 7.5% 7.0% 5.9% 5.4% 4.9% Discount from original list, average

The average is 4.9% for homes over 2,091 SF and 7.0% for homes under 1,490 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

2026-11-01T06:42:18.331242 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 67 49 47 60 53 40 58 54 43 54 69 Under 1,490 SF 1,490–1,822 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 53% of homes under 1,490 SF. Defined in 8.1.7.
  • 54% of homes over 2,091 SF had the seller pay closing costs or repairs, against 67% of homes under 1,490 SF. Defined in 8.1.8.
  • 69% of homes over 2,091 SF have a view, against 49% of homes under 1,490 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 1,490 SF76 sales1,490–1,822 SF75 sales1,845–2,091 SF78 salesOver 2,091 SF74 salesAll303 salesReal difference?
Net sale price average$282,870$338,302$419,978$598,534$408,979✓ large
Net price per SF average$208$204$214$232$215✓ medium
Living area average1,3631,6561,9612,5531,880by design
Year built median20072005200620062006unclear

4.10 The numbers: selling

Under 1,490 SF76 sales1,490–1,822 SF75 sales1,845–2,091 SF78 salesOver 2,091 SF74 salesAll303 salesReal difference?
Days on market median4835284743unclear
Discount from original list average7.0%5.9%5.4%4.9%5.8%unclear
Price cut before sale53%47%40%43%46%unclear
Seller paid closing costs or repairs67%60%58%54%60%unclear
Has a view49%53%54%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 · 303 sales · May 5, 2026 – October 31, 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.

2026-11-01T06:42:18.353895 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 79 67 80 77 303 sales Sales per group Before 2000: 79 sales (26%) 2000–2005: 67 sales (22%) 2006–2014: 80 sales (26%) After 2014: 77 sales (25%)
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$388K$418K$397K$435Kunclear
Net price per SF$210 per SF$208 per SFLowest$214 per SF$226 per SFHighest✓ medium
Living area1,842 SF1,938 SF1,841 SF1,909 SFunclear
Days on market41 days43 days25 days59 daysunclear
Discount from original list6.3%6.1%5.1%5.8%unclear
Price cut before sale48%43%36%55%unclear
Seller paid closing costs or repairs56%66%61%57%unclear
Has a view46%45%Fewest78%Most55%✓ small

5.3 Net sale price: no clear difference

2026-11-01T06:42:18.376847 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 79 sales 2000– 2005 67 sales 2006– 2014 80 sales After 2014 77 sales $0K $200K $400K $388K $418K $397K $435K Net sale price, average

The average is $435K for homes built after 2014 and $388K 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 $16 per SF (8%) more than homes built before 2000

2026-11-01T06:42:18.401955 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 79 sales 2000– 2005 67 sales 2006– 2014 80 sales After 2014 77 sales $0 $100 $200 $210 $208 $214 $226 Net price per SF, average

The average is $226 per SF for homes built after 2014 and $210 per SF for homes built before 2000. It does not move in a straight line; homes built after 2014 are highest. Defined in 8.1.2.

✓ Real difference · medium

5.5 Living area: no clear difference

2026-11-01T06:42:18.425101 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 79 sales 2000– 2005 67 sales 2006– 2014 80 sales After 2014 77 sales 0 1,000 2,000 1,842 1,938 1,841 1,909 Living area (SF), average

The average is 1,909 SF for homes built after 2014 and 1,842 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

2026-11-01T06:42:18.448016 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 79 sales 2000– 2005 67 sales 2006– 2014 80 sales After 2014 77 sales 0 20 40 60 41 43 25 59 Days on market, median

The median is 59 days for homes built after 2014 and 41 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

2026-11-01T06:42:18.472575 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 79 sales 2000– 2005 67 sales 2006– 2014 80 sales After 2014 77 sales 0.0% 2.0% 4.0% 6.0% 6.3% 6.1% 5.1% 5.8% Discount from original list, average

The average is 5.8% for homes built after 2014 and 6.3% 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

2026-11-01T06:42:18.500538 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 46 43 66 45 36 61 78 55 57 55 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 48% of homes built before 2000. 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 2000. Defined in 8.1.8.
  • 55% of homes built after 2014 have a view, against 46% 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 200079 sales2000–200567 sales2006–201480 salesAfter 201477 salesAll303 salesReal difference?
Net sale price average$388,406$418,148$396,862$434,696$408,979unclear
Net price per SF average$210$208$214$226$215✓ medium
Living area average1,8421,9381,8411,9091,880unclear
Year built median19972003200820192006by design

5.10 The numbers: selling

Before 200079 sales2000–200567 sales2006–201480 salesAfter 201477 salesAll303 salesReal difference?
Days on market median4143255943unclear
Discount from original list average6.3%6.1%5.1%5.8%5.8%unclear
Price cut before sale48%43%36%55%46%unclear
Seller paid closing costs or repairs56%66%61%57%60%unclear
Has a view46%45%78%55%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 · 303 sales · May 5, 2026 – October 31, 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.

2026-11-01T06:42:18.521631 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 170 127 297 sales Sales per group View: 170 sales (57%) No view: 127 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$445KHighest$361KLowest✓ small
Net price per SF$223 per SFHighest$204 per SFLowest✓ medium
Living area1,952 SF1,781 SFunclear
Year built20072003unclear
Days on market26 daysFastest51 daysSlowest✓ small
Discount from original list5.1%6.7%unclear
Price cut before sale40%53%unclear
Seller paid closing costs or repairs61%58%unclear

6.3 Homes with a view sell for about $84K (23%) more than homes without a view

2026-11-01T06:42:18.540167 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 127 sales $0K $200K $400K $445K $361K Net sale price, average

The average is $445K for homes with a view and $361K 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

2026-11-01T06:42:18.559649 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 127 sales $0 $100 $200 $223 $204 Net price per SF, average

The average is $223 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

2026-11-01T06:42:18.609297 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 127 sales 0 500 1,000 1,500 2,000 1,952 1,781 Living area (SF), average

The average is 1,952 SF for homes with a view and 1,781 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-11-01T06:42:18.630950 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 127 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 25 days faster than homes without a view

2026-11-01T06:42:18.650347 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 127 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

2026-11-01T06:42:18.670140 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 170 sales No view 127 sales 0.0% 2.0% 4.0% 6.0% 5.1% 6.7% Discount from original list, average

The average is 5.1% for homes with a view and 6.7% 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

2026-11-01T06:42:18.690379 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 40 61 53 58 View No view

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

  • 40% of homes with a view had a price cut before sale, against 53% 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 view127 salesAll297 salesReal difference?
Net sale price average$445,006$361,338$409,229✓ small
Net price per SF average$223$204$215✓ medium
Living area average1,9521,7811,879unclear
Year built median200720032006unclear

6.11 The numbers: selling

View170 salesNo view127 salesAll297 salesReal difference?
Days on market median265143✓ small
Discount from original list average5.1%6.7%5.8%unclear
Price cut before sale40%53%45%unclear
Seller paid closing costs or repairs61%58%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 5, 2026 – October 31, 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 Neighborhood 04.
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
303 resale sales. Average $408,979; median $359,800; standard deviation $156,960; middle half (25th–75th percentile) $309,000–$463,780; 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
303 resale sales. Average $215; median $210; standard deviation $33; middle half (25th–75th percentile) $194–$232; 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
303 resale sales. Average 1,880; median 1,845; standard deviation 488; middle half (25th–75th percentile) 1,489–2,091; 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
303 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
303 resale sales. Average 73; median 43; standard deviation 97; middle half (25th–75th percentile) 9–92; 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
303 resale sales. Average 5.8%; median 4.4%; standard deviation 6.0%; middle half (25th–75th percentile) 1.5%–8.6%; lowest −6.9%, highest 33.1%. 13 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
138 of 303 resale sales (45.5%).

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
181 of 303 resale sales (59.7%).

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, 127 without, 6 unclassified. Most common View entries: Park/Greenbelt 131, Neighborhood 99, Trees/Woods 63, None 61, Golf Course 34, Hill Country 14, See Remarks 13, Panoramic 8.

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 2026-11-01 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 georgetown nbhrd”, “sun city georgetown nbhd”, “sun city nbrhds”, “sun city neighborhoods”, “sun city neighborhoods eighty one e”, “sun city neighborhoods eighty”, “sun city nbrhds eighty one e” (case-insensitive). A record is kept when its subdivision name belongs to the Sun City Georgetown Neighborhood 04 family, the same rule comparable sales use (it includes Sun City, Sun City Texas, proven adjacent and priced alike).
The data
Pulled November 1, 2026: 590 closed Sun City Georgetown Neighborhood 04 records over the prior year, in ZIP 78633. 171 subdivision spellings kept, the most common: Sun City (93); Sun City Texas (65); Sun City Georgetown Neighborhood 04 (19); 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 Ph 02 Neighborhood 10 Pud (9); Sun City Georgetown Neighborhood 07 (9); Sun City Georgetown Neighborhood 40 Pud (8); Sun City Georgetown Neighborhood 02 Amd B (8); Sun City Nbrhd 83 (8); and 159 more (321 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 5, 2026 to October 31, 2026 inclusive.
The data
315 single-family sales in the window: 303 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
303 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 (77 sales); $310K–$360K: $309,500–$359,800 (75 sales); $362K–$463K: $361,500–$462,560 (75 sales); Over $463K: $465,000–$1,588,000 (76 sales)
Quartiles by living area: Under 1,490 SF: 915–1,488 SF (76 sales); 1,490–1,822 SF: 1,490–1,822 SF (75 sales); 1,845–2,091 SF: 1,845–2,091 SF (78 sales); Over 2,091 SF: 2,100–4,866 SF (74 sales)
Quartiles by year built: Before 2000: 1995–1999 (79 sales); 2000–2005: 2000–2005 (67 sales); 2006–2014: 2006–2014 (80 sales); After 2014: 2015–2026 (77 sales)
View and no view: View: MLS lists a view (170 sales); No view: None or Neighborhood (127 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 $408,979, median $359,800. Days on market: average 73, 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
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 = 90.553< 0.0001p < 0.0063Yesε² = 0.300large
Living areaKruskal–WallisH = 228.293< 0.0001p < 0.0063Yesε² = 0.756large
Year builtKruskal–WallisH = 2.3230.5085p < 0.0063Unclearε² = 0.008negligible
Days on marketKruskal–WallisH = 10.4330.0153p < 0.0063Unclearε² = 0.035small
Discount from original listKruskal–WallisH = 35.663< 0.0001p < 0.0063Yesε² = 0.118medium
Price cut before saleChi-squareχ² = 10.1130.0177p < 0.0063UnclearCramér’s V = 0.183small
Seller paid closing costs or repairsChi-squareχ² = 2.8230.4208p < 0.0063UnclearCramér’s V = 0.096negligible
Has a viewChi-squareχ² = 24.783< 0.0001p < 0.0063YesCramér’s V = 0.286small

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 230.743< 0.0001p < 0.0063Yesε² = 0.764large
Net price per SFKruskal–WallisH = 18.5930.0003p < 0.0063Yesε² = 0.062medium
Year builtKruskal–WallisH = 5.5730.1345p < 0.0063Unclearε² = 0.018small
Days on marketKruskal–WallisH = 4.7530.1906p < 0.0063Unclearε² = 0.016small
Discount from original listKruskal–WallisH = 8.1930.0423p < 0.0063Unclearε² = 0.027small
Price cut before saleChi-squareχ² = 2.7930.4246p < 0.0063UnclearCramér’s V = 0.096negligible
Seller paid closing costs or repairsChi-squareχ² = 2.8530.4158p < 0.0063UnclearCramér’s V = 0.097negligible
Has a viewChi-squareχ² = 7.0330.0710p < 0.0063UnclearCramér’s V = 0.152small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 5.6530.1300p < 0.0063Unclearε² = 0.019small
Net price per SFKruskal–WallisH = 23.463< 0.0001p < 0.0063Yesε² = 0.078medium
Living areaKruskal–WallisH = 0.4830.9233p < 0.0063Unclearε² = 0.002negligible
Days on marketKruskal–WallisH = 9.8630.0198p < 0.0063Unclearε² = 0.033small
Discount from original listKruskal–WallisH = 1.3130.7276p < 0.0063Unclearε² = 0.004negligible
Price cut before saleChi-squareχ² = 5.6530.1300p < 0.0063UnclearCramér’s V = 0.137small
Seller paid closing costs or repairsChi-squareχ² = 1.8130.6130p < 0.0063UnclearCramér’s V = 0.077negligible
Has a viewChi-squareχ² = 22.003< 0.0001p < 0.0063YesCramér’s V = 0.269small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 14,005.00—< 0.0001p < 0.0063Yesrank-biserial r = 0.297small
Net price per SFMann–Whitney UU = 14,487.50—< 0.0001p < 0.0063Yesrank-biserial r = 0.342medium
Living areaMann–Whitney UU = 12,761.00—0.0073p < 0.0063Unclearrank-biserial r = 0.182small
Year builtMann–Whitney UU = 12,541.00—0.0170p < 0.0063Unclearrank-biserial r = 0.162small
Days on marketMann–Whitney UU = 8,539.00—0.0021p < 0.0063Yesrank-biserial r = 0.209small
Discount from original listMann–Whitney UU = 9,328.50—0.0453p < 0.0063Unclearrank-biserial r = 0.136small
Price cut before saleChi-squareχ² = 4.7710.0289p < 0.0063UnclearCramér’s V = 0.127small
Seller paid closing costs or repairsChi-squareχ² = 0.1610.6868p < 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 303 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 1, 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.