ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Georgetown Village

Georgetown, TX 78628 · 78633
Resale homes · closed Oct 26 – Oct 25, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 26, 2026 · 7:12 AM 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, 365 days
33
single-family resale homes
Median net sale price
$416,900
average $430,106
Median net price per SF
$173
average $172
Median days on market
29 days
average 59 days
1. SummaryResale homes · 33 sales · October 26, 2025 – October 25, 2026

This study looks at the 33 resale homes sold in Georgetown Village in the 365 days to October 25, 2026. A full year is used because only 16 resale homes sold in the last 180 days. 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 sold33 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 $387K · 8 sales$387K–$416K · 8 sales$417K–$467K · 9 salesOver $467K · 8 sales
3. Quartiles by net sale priceResale homes · 33 sales · October 26, 2025 – October 25, 2026

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

2026-10-26T07:12:51.666434 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 9 8 33 sales Sales per group Under $387K: 8 sales (24%) $387K–$416K: 8 sales (24%) $417K–$467K: 9 sales (27%) Over $467K: 8 sales (24%)
Bottom line

3.1 Key differences

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

Under $387K$387K–$416K$417K–$467KOver $467K
Higher-priced homes Over $467K
  • More often have the seller pay buyer costs or repairs.
  • Are much larger.
Lower-priced homes Under $387K
  • Less often have the seller pay buyer costs or repairs.
  • Are much smaller.

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 $387K$387K–$416K$417K–$467KOver $467KReal difference?
Net price per SF$174 per SF$172 per SF$169 per SF$173 per SFunclear
Living area2,079 SFSmallest2,390 SF2,676 SF3,046 SFLargest✓ large
Year built2011201120102013unclear
Days on market76 days10 days46 days25 daysunclear
Discount from original list8.0%4.4%4.6%4.6%unclear
Price cut before sale62%12%33%50%unclear
Seller paid closing costs or repairs25%Fewest100%Most89%50%✓ large
Has a view38%12%33%25%unclear

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 Net price per SF: no clear difference

2026-10-26T07:12:51.695504 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $387K 8 sales $387K– $416K 8 sales $417K– $467K 9 sales Over $467K 8 sales $0 $100 $200 $174 $172 $169 $173 Net price per SF, average

The average is $173 per SF for homes over $467K and $174 per SF for homes under $387K. It does not move in a straight line; homes under $387K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Homes over $467K are about 970 SF (47%) larger than homes under $387K

2026-10-26T07:12:51.717936 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $387K 8 sales $387K– $416K 8 sales $417K– $467K 9 sales Over $467K 8 sales 0 1,000 2,000 3,000 2,079 2,390 2,676 3,046 Living area (SF), average

The average is 3,046 SF for homes over $467K and 2,079 SF for homes under $387K. 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

2026-10-26T07:12:51.743645 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $387K 8 sales $387K– $416K 8 sales $417K– $467K 9 sales Over $467K 8 sales 2000 2004 2008 2012 2016 2011 2011 2010 2013 Year built: median (dot) and middle half (bar)

The median is 2013 for homes over $467K and 2011 for homes under $387K. It does not move in a straight line; homes over $467K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-26T07:12:51.768118 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $387K 8 sales $387K– $416K 8 sales $417K– $467K 9 sales Over $467K 8 sales 0 25 50 75 76 10 46 25 Days on market, median

The median is 25 days for homes over $467K and 76 days for homes under $387K. It does not move in a straight line; homes under $387K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-10-26T07:12:51.791621 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $387K 8 sales $387K– $416K 8 sales $417K– $467K 9 sales Over $467K 8 sales 0.0% 2.5% 5.0% 7.5% 8.0% 4.4% 4.6% 4.6% Discount from original list, average

The average is 4.6% for homes over $467K and 8.0% for homes under $387K. It does not move in a straight line; homes under $387K are highest. Defined in 8.1.6.

– No clear difference · could be chance

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

2026-10-26T07:12:51.822738 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% 62 25 38 12 100 12 33 89 33 50 50 25 Under $387K $387K–$416K $417K–$467K Over $467K

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

  • 50% of homes over $467K had a price cut before sale, against 62% of homes under $387K. Defined in 8.1.7.
  • 50% of homes over $467K had the seller pay closing costs or repairs, against 25% of homes under $387K. Defined in 8.1.8.
  • 25% of homes over $467K have a view, against 38% of homes under $387K. Defined in 8.1.9.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: ✓ Real difference · large
Has a view: – No clear difference · could be chance

3.9 The numbers: price and size

Under $387K8 sales$387K–$416K8 sales$417K–$467K9 salesOver $467K8 salesAll33 salesReal difference?
Net sale price average$352,925$399,682$444,961$521,000$430,106by design
Net price per SF average$174$172$169$173$172unclear
Living area average2,0792,3902,6763,0462,552✓ large
Year built median20112011201020132011unclear

3.10 The numbers: selling

Under $387K8 sales$387K–$416K8 sales$417K–$467K9 salesOver $467K8 salesAll33 salesReal difference?
Days on market median7610462529unclear
Discount from original list average8.0%4.4%4.6%4.6%5.4%unclear
Price cut before sale62%12%33%50%39%unclear
Seller paid closing costs or repairs25%100%89%50%67%✓ large
Has a view38%12%33%25%27%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 8.3.1.

4. Quartiles by living areaResale homes · 33 sales · October 26, 2025 – October 25, 2026

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

2026-10-26T07:12:51.844222 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 9 8 33 sales Sales per group Under 2,267 SF: 8 sales (24%) 2,267–2,442 SF: 8 sales (24%) 2,499–2,919 SF: 9 sales (27%) Over 2,919 SF: 8 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 2,267 SF2,267–2,442 SF2,499–2,919 SFOver 2,919 SF
Larger homes Over 2,919 SF
  • Sell for much more.
  • Sell for less per square foot.
Smaller homes Under 2,267 SF
  • Sell for much less.
  • Sell for more 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 2,267 SF2,267–2,442 SF2,499–2,919 SFOver 2,919 SFReal difference?
Net sale price$366KLowest$425K$442K$486KHighest✓ large
Net price per SF$195 per SFHighest$179 per SF$162 per SF$152 per SFLowest✓ large
Year built2013200620122010unclear
Days on market33 days18 days56 days84 daysunclear
Discount from original list5.7%4.0%4.8%7.1%unclear
Price cut before sale38%25%33%62%unclear
Seller paid closing costs or repairs38%75%78%75%unclear
Has a view50%25%11%25%unclear

4.3 Homes over 2,919 SF sell for about $120K (33%) more than homes under 2,267 SF

2026-10-26T07:12:51.865725 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,267 SF 8 sales 2,267– 2,442 SF 8 sales 2,499– 2,919 SF 9 sales Over 2,919 SF 8 sales $0K $200K $400K $366K $425K $442K $486K Net sale price, average

The average is $486K for homes over 2,919 SF and $366K for homes under 2,267 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 2,919 SF sell for about $43 per SF (22%) less than homes under 2,267 SF

2026-10-26T07:12:51.888982 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,267 SF 8 sales 2,267– 2,442 SF 8 sales 2,499– 2,919 SF 9 sales Over 2,919 SF 8 sales $0 $100 $200 $195 $179 $162 $152 Net price per SF, average

The average is $152 per SF for homes over 2,919 SF and $195 per SF for homes under 2,267 SF. It falls step by step from the lowest range to the highest. Defined in 8.1.2.

✓ Real difference · large

4.5 Year built: no clear difference

2026-10-26T07:12:51.913090 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,267 SF 8 sales 2,267– 2,442 SF 8 sales 2,499– 2,919 SF 9 sales Over 2,919 SF 8 sales 2004 2008 2012 2016 2013 2006 2012 2010 Year built: median (dot) and middle half (bar)

The median is 2010 for homes over 2,919 SF and 2013 for homes under 2,267 SF. It does not move in a straight line; homes under 2,267 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-26T07:12:51.937495 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,267 SF 8 sales 2,267– 2,442 SF 8 sales 2,499– 2,919 SF 9 sales Over 2,919 SF 8 sales 0 50 100 33 18 56 84 Days on market, median

The median is 84 days for homes over 2,919 SF and 33 days for homes under 2,267 SF. It does not move in a straight line; homes over 2,919 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-26T07:12:51.960380 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,267 SF 8 sales 2,267– 2,442 SF 8 sales 2,499– 2,919 SF 9 sales Over 2,919 SF 8 sales 0.0% 2.5% 5.0% 7.5% 5.7% 4.0% 4.8% 7.1% Discount from original list, average

The average is 7.1% for homes over 2,919 SF and 5.7% for homes under 2,267 SF. It does not move in a straight line; homes over 2,919 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

2026-10-26T07:12:51.988210 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% 38 38 50 25 75 25 33 78 11 62 75 25 Under 2,267 SF 2,267–2,442 SF 2,499–2,919 SF Over 2,919 SF

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

  • 62% of homes over 2,919 SF had a price cut before sale, against 38% of homes under 2,267 SF. Defined in 8.1.7.
  • 75% of homes over 2,919 SF had the seller pay closing costs or repairs, against 38% of homes under 2,267 SF. Defined in 8.1.8.
  • 25% of homes over 2,919 SF have a view, against 50% of homes under 2,267 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 2,267 SF8 sales2,267–2,442 SF8 sales2,499–2,919 SF9 salesOver 2,919 SF8 salesAll33 salesReal difference?
Net sale price average$366,392$424,562$441,915$486,079$430,106✓ large
Net price per SF average$195$179$162$152$172✓ large
Living area average1,8862,3742,7233,2032,552by design
Year built median20132006201220102011unclear

4.10 The numbers: selling

Under 2,267 SF8 sales2,267–2,442 SF8 sales2,499–2,919 SF9 salesOver 2,919 SF8 salesAll33 salesReal difference?
Days on market median3318568429unclear
Discount from original list average5.7%4.0%4.8%7.1%5.4%unclear
Price cut before sale38%25%33%62%39%unclear
Seller paid closing costs or repairs38%75%78%75%67%unclear
Has a view50%25%11%25%27%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 · 33 sales · October 26, 2025 – October 25, 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.

2026-10-26T07:12:52.009424 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 7 9 9 33 sales Sales per group Before 2006: 8 sales (24%) 2006–2010: 7 sales (21%) 2011–2014: 9 sales (27%) After 2014: 9 sales (27%)
Bottom line: no measure differs enough between the groups to rule out chance.

5.1 Key differences

The top group against the bottom group: no measure differs enough to rule out chance.

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 20062006–20102011–2014After 2014Real difference?
Net sale price$412K$443K$442K$424Kunclear
Net price per SF$180 per SF$168 per SF$160 per SF$179 per SFunclear
Living area2,366 SF2,658 SF2,763 SF2,423 SFunclear
Days on market8 days82 days63 days56 daysunclear
Discount from original list1.7%5.2%6.7%7.4%unclear
Price cut before sale12%57%44%44%unclear
Seller paid closing costs or repairs75%57%67%67%unclear
Has a view38%43%11%22%unclear

5.3 Net sale price: no clear difference

2026-10-26T07:12:52.030928 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2006 8 sales 2006– 2010 7 sales 2011– 2014 9 sales After 2014 9 sales $0K $200K $400K $412K $443K $442K $424K Net sale price, average

The average is $424K for homes built after 2014 and $412K for homes built before 2006. It does not move in a straight line; homes built 2006–2010 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-26T07:12:52.053994 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2006 8 sales 2006– 2010 7 sales 2011– 2014 9 sales After 2014 9 sales $0 $100 $200 $180 $168 $160 $179 Net price per SF, average

The average is $179 per SF for homes built after 2014 and $180 per SF for homes built before 2006. It does not move in a straight line; homes built before 2006 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-26T07:12:52.075617 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2006 8 sales 2006– 2010 7 sales 2011– 2014 9 sales After 2014 9 sales 0 1,000 2,000 3,000 2,366 2,658 2,763 2,423 Living area (SF), average

The average is 2,423 SF for homes built after 2014 and 2,366 SF for homes built before 2006. It does not move in a straight line; homes built 2011–2014 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-26T07:12:52.097973 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2006 8 sales 2006– 2010 7 sales 2011– 2014 9 sales After 2014 9 sales 0 25 50 75 8 82 63 56 Days on market, median

The median is 56 days for homes built after 2014 and 8 days for homes built before 2006. It does not move in a straight line; homes built 2006–2010 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-26T07:12:52.121326 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2006 8 sales 2006– 2010 7 sales 2011– 2014 9 sales After 2014 9 sales 0.0% 2.5% 5.0% 7.5% 1.7% 5.2% 6.7% 7.4% Discount from original list, average

The average is 7.4% for homes built after 2014 and 1.7% for homes built before 2006. It rises step by step from the lowest range to the 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

2026-10-26T07:12:52.147612 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% 12 75 38 57 57 43 44 67 11 44 67 22 Before 2006 2006–2010 2011–2014 After 2014

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

  • 44% of homes built after 2014 had a price cut before sale, against 12% of homes built before 2006. Defined in 8.1.7.
  • 67% of homes built after 2014 had the seller pay closing costs or repairs, against 75% of homes built before 2006. Defined in 8.1.8.
  • 22% of homes built after 2014 have a view, against 38% of homes built before 2006. 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

5.9 The numbers: price and size

Before 20068 sales2006–20107 sales2011–20149 salesAfter 20149 salesAll33 salesReal difference?
Net sale price average$412,025$442,671$442,362$424,150$430,106unclear
Net price per SF average$180$168$160$179$172unclear
Living area average2,3662,6582,7632,4232,552unclear
Year built median20002010201220162011by design

5.10 The numbers: selling

Before 20068 sales2006–20107 sales2011–20149 salesAfter 20149 salesAll33 salesReal difference?
Days on market median882635629unclear
Discount from original list average1.7%5.2%6.7%7.4%5.4%unclear
Price cut before sale12%57%44%44%39%unclear
Seller paid closing costs or repairs75%57%67%67%67%unclear
Has a view38%43%11%22%27%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 8.3.3.

6. View and no viewResale homes · 33 sales · October 26, 2025 – October 25, 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. 1 sales whose MLS view entry is only “See Remarks” are left out.

2026-10-26T07:12:52.166379 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 9 23 32 sales Sales per group View: 9 sales (28%) No view: 23 sales (72%)
Bottom line: no measure differs enough between the groups to rule out chance.

6.1 Key differences

The top group against the bottom group: no measure differs enough to rule out chance.

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$426K$433Kunclear
Net price per SF$179 per SF$169 per SFunclear
Living area2,456 SF2,599 SFunclear
Year built20082012unclear
Days on market63 days29 daysunclear
Discount from original list4.9%5.8%unclear
Price cut before sale56%35%unclear
Seller paid closing costs or repairs56%70%unclear

6.3 Net sale price: no clear difference

2026-10-26T07:12:52.182818 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 9 sales No view 23 sales $0K $200K $400K $426K $433K Net sale price, average

The average is $426K for homes with a view and $433K for homes without a view. Defined in 8.1.1.

– No clear difference · could be chance

6.4 Net price per SF: no clear difference

2026-10-26T07:12:52.201007 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 9 sales No view 23 sales $0 $50 $100 $150 $200 $179 $169 Net price per SF, average

The average is $179 per SF for homes with a view and $169 per SF for homes without a view. Defined in 8.1.2.

– No clear difference · could be chance

6.5 Living area: no clear difference

2026-10-26T07:12:52.219687 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 9 sales No view 23 sales 0 1,000 2,000 3,000 2,456 2,599 Living area (SF), average

The average is 2,456 SF for homes with a view and 2,599 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-26T07:12:52.296941 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 9 sales No view 23 sales 2000 2004 2008 2012 2008 2012 Year built: median (dot) and middle half (bar)

The median is 2008 for homes with a view and 2012 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-26T07:12:52.315080 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 9 sales No view 23 sales 0 20 40 60 63 29 Days on market, median

The median is 63 days for homes with a view and 29 days for homes without a view. Defined in 8.1.5.

– No clear difference · could be chance

6.8 Discount from original list: no clear difference

2026-10-26T07:12:52.334533 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 9 sales No view 23 sales 0.0% 2.0% 4.0% 6.0% 4.9% 5.8% Discount from original list, average

The average is 4.9% for homes with a view and 5.8% 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

2026-10-26T07:12:52.353665 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 56 56 35 70 View No view

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

  • 56% of homes with a view had a price cut before sale, against 35% of homes without a view. Defined in 8.1.7.
  • 56% of homes with a view had the seller pay closing costs or repairs, against 70% 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

View9 salesNo view23 salesAll32 salesReal difference?
Net sale price average$426,144$432,966$431,047unclear
Net price per SF average$179$169$172unclear
Living area average2,4562,5992,559unclear
Year built median200820122011unclear

6.11 The numbers: selling

View9 salesNo view23 salesAll32 salesReal difference?
Days on market median632938unclear
Discount from original list average4.9%5.8%5.5%unclear
Price cut before sale56%35%41%unclear
Seller paid closing costs or repairs56%70%66%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 · 0 sales · October 26, 2025 – October 25, 2026

Homes sold new by the builder in the same 365 days. They are left out of every other section and shown here as basic figures only: 0 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 Georgetown Village.

No new-construction sales were reported in ABOR MLS in this window.

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
33 resale sales. Average $430,106; median $416,900; standard deviation $66,995; middle half (25th–75th percentile) $387,360–$467,000; lowest $318,000, highest $599,999. 22 sales had a seller payment; their median payment was $6,400.

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
33 resale sales. Average $172; median $173; standard deviation $25; middle half (25th–75th percentile) $150–$189; lowest $130, highest $229.

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
33 resale sales. Average 2,552; median 2,499; standard deviation 512; middle half (25th–75th percentile) 2,267–2,919; lowest 1,683, highest 3,617.

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
33 resale sales. Average 2010; median 2011; standard deviation 6; middle half (25th–75th percentile) 2006–2015; lowest 1999, highest 2018.

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
33 resale sales. Average 59; median 29; standard deviation 65; middle half (25th–75th percentile) 9–85; lowest 0, highest 263. 2 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
33 resale sales. Average 5.4%; median 3.5%; standard deviation 5.2%; middle half (25th–75th percentile) 1.9%–7.0%; lowest 0.0%, highest 20.9%. 1 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
13 of 33 resale sales (39.4%).

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
22 of 33 resale sales (66.7%).

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
9 sales with a view, 23 without, 1 unclassified. Most common View entries: None 19, Park/Greenbelt 6, Neighborhood 5, Trees/Woods 2, Hill Country 1, Pool 1, See Remarks 1.

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 “georgetown village”, “shell ranch”, “georgetown vlg” (case-insensitive). A record is kept when its subdivision name belongs to the Georgetown Village family, the same rule comparable sales use (it includes Shell Ranch Sec 02-A, proven adjacent and priced alike).
The data
Pulled October 26, 2026: 33 closed Georgetown Village records over the prior year, in ZIP 78628, 78633. 17 subdivision spellings kept, the most common: Georgetown Village (10); Georgetown Village Sec 09 Ph 01 Pud (4); Georgetown Village Sec 06 (3); Georgetown Village Sec 06 Pud (2); Georgetown Village Sec 09 Ph 01 (2); Georgetown Village Sec 9 Ph 9 & 10 (1); Georgetown Village Sec 07 (1); Georgetown Village Sec 9 Ph 5 (1); Georgetown Village Sec 9 Ph 8 (1); Georgetown Village Sec 07 Pud (1); Shell Ranch Sec 03 (1); Georgetown Village Sec 06 PUD (1); and 5 more (5 records). Names found by the search but not part of Georgetown Village, left out: Creekside/Georgetown Village Ph 1 (2); Creekside @ Georgetown Village (1); Creekside/Georgetown Village Ph 2 (1); City Only Georgetown Village Sec 05 PUD (1); City Only Georgetown Village Sec 05 Pud (1); Creekside/Georgetown Village PUD P (1); Georgetown Village-Shell Ranch Sec 03 (1); Creekside/Georgetown Village Pudph (1); Creekside/Georgetown Village Pud P (1).

8.2.2 Study window

In simple terms
The most recent 365 days of closed sales: recent enough to describe today's market, long enough to give each group plenty of sales. The usual window is 180 days; it holds only 16 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 26, 2025 to October 25, 2026 inclusive.
The data
33 single-family sales in the window: 33 resale, 0 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
33 resale; 0 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
None in this window.

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 $387K”.
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 $387K: $318,000–$380,000 (8 sales); $387K–$416K: $387,360–$415,500 (8 sales); $417K–$467K: $416,900–$467,000 (9 sales); Over $467K: $471,500–$599,999 (8 sales)
Quartiles by living area: Under 2,267 SF: 1,683–2,213 SF (8 sales); 2,267–2,442 SF: 2,267–2,442 SF (8 sales); 2,499–2,919 SF: 2,499–2,919 SF (9 sales); Over 2,919 SF: 2,921–3,617 SF (8 sales)
Quartiles by year built: Before 2006: 1999–2005 (8 sales); 2006–2010: 2006–2010 (7 sales); 2011–2014: 2011–2014 (9 sales); After 2014: 2015–2018 (9 sales)
View and no view: View: MLS lists a view (9 sales); No view: None or Neighborhood (23 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 $430,106, median $416,900. Days on market: average 59, median 29: 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
4 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 = 0.1930.9798p < 0.0063Unclearε² = 0.006negligible
Living areaKruskal–WallisH = 15.4330.0015p < 0.0063Yesε² = 0.482large
Year builtKruskal–WallisH = 1.8030.6146p < 0.0063Unclearε² = 0.056small
Days on marketKruskal–WallisH = 1.8030.6151p < 0.0063Unclearε² = 0.056small
Discount from original listKruskal–WallisH = 2.9830.3942p < 0.0063Unclearε² = 0.093medium
Price cut before saleChi-squareχ² = 4.7330.1928p < 0.0063UnclearCramér’s V = 0.379medium
Seller paid closing costs or repairsChi-squareχ² = 13.2530.0041p < 0.0063YesCramér’s V = 0.634large
Has a viewChi-squareχ² = 1.4930.6847p < 0.0063UnclearCramér’s V = 0.212small

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 15.9430.0012p < 0.0063Yesε² = 0.498large
Net price per SFKruskal–WallisH = 15.6130.0014p < 0.0063Yesε² = 0.488large
Year builtKruskal–WallisH = 5.4530.1419p < 0.0063Unclearε² = 0.170large
Days on marketKruskal–WallisH = 1.2530.7406p < 0.0063Unclearε² = 0.039small
Discount from original listKruskal–WallisH = 1.4630.6916p < 0.0063Unclearε² = 0.046small
Price cut before saleChi-squareχ² = 2.6330.4516p < 0.0063UnclearCramér’s V = 0.283small
Seller paid closing costs or repairsChi-squareχ² = 4.0630.2548p < 0.0063UnclearCramér’s V = 0.351medium
Has a viewChi-squareχ² = 3.3130.3462p < 0.0063UnclearCramér’s V = 0.317medium

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 0.9930.8043p < 0.0063Unclearε² = 0.031small
Net price per SFKruskal–WallisH = 4.5730.2065p < 0.0063Unclearε² = 0.143large
Living areaKruskal–WallisH = 4.4830.2141p < 0.0063Unclearε² = 0.140large
Days on marketKruskal–WallisH = 5.4530.1414p < 0.0063Unclearε² = 0.170large
Discount from original listKruskal–WallisH = 7.3430.0618p < 0.0063Unclearε² = 0.229large
Price cut before saleChi-squareχ² = 3.5430.3157p < 0.0063UnclearCramér’s V = 0.328medium
Seller paid closing costs or repairsChi-squareχ² = 0.5430.9110p < 0.0063UnclearCramér’s V = 0.127small
Has a viewChi-squareχ² = 2.5830.4610p < 0.0063UnclearCramér’s V = 0.280small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 98.00—0.8340p < 0.0063Unclearrank-biserial r = 0.053negligible
Net price per SFMann–Whitney UU = 130.00—0.2758p < 0.0063Unclearrank-biserial r = 0.256small
Living areaMann–Whitney UU = 84.00—0.4258p < 0.0063Unclearrank-biserial r = 0.188small
Year builtMann–Whitney UU = 69.00—0.1521p < 0.0063Unclearrank-biserial r = 0.333medium
Days on marketMann–Whitney UU = 121.50—0.4626p < 0.0063Unclearrank-biserial r = 0.174small
Discount from original listMann–Whitney UU = 94.00—0.7059p < 0.0063Unclearrank-biserial r = 0.092negligible
Price cut before saleChi-squareχ² = 1.1610.2820p < 0.0063UnclearCramér’s V = 0.190small
Seller paid closing costs or repairsChi-squareχ² = 0.5610.4531p < 0.0063UnclearCramér’s V = 0.133small

8.4 Data notes

Left out of the analysis: 0 new-construction sales (basic statistics only, section 7); nothing else. 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 33 closed MLS resale sales; the 0 new-construction sales appear only in their own section, as basic statistics. Sale and listing data: ABOR MLS via secure APIs, pulled October 26, 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.