ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

La Conterra

Georgetown, TX 78626
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 · 5:00 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
29
single-family resale homes
Median net sale price
$390,720
average $383,585
Median net price per SF
$142
average $152
Median days on market
47 days
average 64 days
1. SummaryResale homes · 29 sales · October 26, 2025 – October 25, 2026

This study looks at the 29 resale homes sold in La Conterra 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 sold29 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 $342K · 7 sales$342K–$362K · 7 sales$391K–$428K · 8 salesOver $428K · 7 sales
3. Quartiles by net sale priceResale homes · 29 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.

2026-10-26T05:00:16.905999 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 7 8 7 29 sales Sales per group Under $342K: 7 sales (24%) $342K–$362K: 7 sales (24%) $391K–$428K: 8 sales (28%) Over $428K: 7 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 $342K$342K–$362K$391K–$428KOver $428K
Higher-priced homes Over $428K
  • Are much larger.
Lower-priced homes Under $342K
  • 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 $342K$342K–$362K$391K–$428KOver $428KReal difference?
Net price per SF$175 per SF$147 per SF$150 per SF$136 per SFunclear
Living area1,842 SFSmallest2,471 SF2,741 SF3,388 SFLargest✓ large
Year built2016201520152016unclear
Days on market51 days47 days32 days44 daysunclear
Discount from original list7.1%3.9%5.8%4.1%unclear
Price cut before sale71%14%62%43%unclear
Seller paid closing costs or repairs86%86%75%71%unclear
Has a view0%29%25%43%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

2026-10-26T05:00:16.935784 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $342K 7 sales $342K– $362K 7 sales $391K– $428K 8 sales Over $428K 7 sales $0 $100 $200 $175 $147 $150 $136 Net price per SF, average

The average is $136 per SF for homes over $428K and $175 per SF for homes under $342K. It does not move in a straight line; homes under $342K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Homes over $428K are about 1,550 SF (84%) larger than homes under $342K

2026-10-26T05:00:16.958957 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $342K 7 sales $342K– $362K 7 sales $391K– $428K 8 sales Over $428K 7 sales 0 2,000 4,000 1,842 2,471 2,741 3,388 Living area (SF), average

The average is 3,388 SF for homes over $428K and 1,842 SF for homes under $342K. 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

2026-10-26T05:00:16.982562 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $342K 7 sales $342K– $362K 7 sales $391K– $428K 8 sales Over $428K 7 sales 2014 2016 2018 2016 2015 2015 2016 Year built: median (dot) and middle half (bar)

The median is 2016 for homes over $428K and 2016 for homes under $342K. It does not move in a straight line; homes under $342K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-26T05:00:17.005971 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $342K 7 sales $342K– $362K 7 sales $391K– $428K 8 sales Over $428K 7 sales 0 20 40 60 51 47 32 44 Days on market, median

The median is 44 days for homes over $428K and 51 days for homes under $342K. It does not move in a straight line; homes under $342K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-10-26T05:00:17.029130 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $342K 7 sales $342K– $362K 7 sales $391K– $428K 8 sales Over $428K 7 sales 0.0% 2.5% 5.0% 7.5% 7.1% 3.9% 5.8% 4.1% Discount from original list, average

The average is 4.1% for homes over $428K and 7.1% for homes under $342K. It does not move in a straight line; homes under $342K 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

2026-10-26T05:00:17.060287 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% 71 86 0 14 86 29 62 75 25 43 71 43 Under $342K $342K–$362K $391K–$428K Over $428K

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

  • 43% of homes over $428K had a price cut before sale, against 71% of homes under $342K. Defined in 8.1.7.
  • 71% of homes over $428K had the seller pay closing costs or repairs, against 86% of homes under $342K. Defined in 8.1.8.
  • 43% of homes over $428K have a view, against 0% of homes under $342K. 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

3.9 The numbers: price and size

Under $342K7 sales$342K–$362K7 sales$391K–$428K8 salesOver $428K7 salesAll29 salesReal difference?
Net sale price average$312,250$351,971$406,778$460,029$383,585by design
Net price per SF average$175$147$150$136$152unclear
Living area average1,8422,4712,7413,3882,615✓ large
Year built median20162015201520162016unclear

3.10 The numbers: selling

Under $342K7 sales$342K–$362K7 sales$391K–$428K8 salesOver $428K7 salesAll29 salesReal difference?
Days on market median5147324447unclear
Discount from original list average7.1%3.9%5.8%4.1%5.2%unclear
Price cut before sale71%14%62%43%48%unclear
Seller paid closing costs or repairs86%86%75%71%79%unclear
Has a view0%29%25%43%24%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 · 29 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.

2026-10-26T05:00:17.082251 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 7 8 7 29 sales Sales per group Under 1,908 SF: 7 sales (24%) 1,908–2,747 SF: 7 sales (24%) 2,770–3,010 SF: 8 sales (28%) Over 3,010 SF: 7 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 1,908 SF1,908–2,747 SF2,770–3,010 SFOver 3,010 SF
Larger homes Over 3,010 SF
  • Sell for much more.
  • Sell for less per square foot.
Smaller homes Under 1,908 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 1,908 SF1,908–2,747 SF2,770–3,010 SFOver 3,010 SFReal difference?
Net sale price$313KLowest$373K$390K$457KHighest✓ large
Net price per SF$185 per SFHighest$155 per SF$135 per SF$135 per SFLowest✓ large
Year built2016201520152015unclear
Days on market49 days24 days46 days95 daysunclear
Discount from original list5.0%4.5%6.7%4.6%unclear
Price cut before sale57%29%62%43%unclear
Seller paid closing costs or repairs86%86%62%86%unclear
Has a view14%14%25%43%unclear

4.3 Homes over 3,010 SF sell for about $143K (46%) more than homes under 1,908 SF

2026-10-26T05:00:17.104347 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,908 SF 7 sales 1,908– 2,747 SF 7 sales 2,770– 3,010 SF 8 sales Over 3,010 SF 7 sales $0K $200K $400K $313K $373K $390K $457K Net sale price, average

The average is $457K for homes over 3,010 SF and $313K for homes under 1,908 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 3,010 SF sell for about $50 per SF (27%) less than homes under 1,908 SF

2026-10-26T05:00:17.127130 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,908 SF 7 sales 1,908– 2,747 SF 7 sales 2,770– 3,010 SF 8 sales Over 3,010 SF 7 sales $0 $100 $200 $185 $155 $135 $135 Net price per SF, average

The average is $135 per SF for homes over 3,010 SF and $185 per SF for homes under 1,908 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

2026-10-26T05:00:17.149964 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,908 SF 7 sales 1,908– 2,747 SF 7 sales 2,770– 3,010 SF 8 sales Over 3,010 SF 7 sales 2014 2016 2018 2016 2015 2015 2015 Year built: median (dot) and middle half (bar)

The median is 2015 for homes over 3,010 SF and 2016 for homes under 1,908 SF. It falls step by step from the lowest range to the highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-26T05:00:17.173431 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,908 SF 7 sales 1,908– 2,747 SF 7 sales 2,770– 3,010 SF 8 sales Over 3,010 SF 7 sales 0 50 100 49 24 46 95 Days on market, median

The median is 95 days for homes over 3,010 SF and 49 days for homes under 1,908 SF. It does not move in a straight line; homes over 3,010 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-26T05:00:17.196401 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,908 SF 7 sales 1,908– 2,747 SF 7 sales 2,770– 3,010 SF 8 sales Over 3,010 SF 7 sales 0.0% 2.5% 5.0% 7.5% 5.0% 4.5% 6.7% 4.6% Discount from original list, average

The average is 4.6% for homes over 3,010 SF and 5.0% for homes under 1,908 SF. It does not move in a straight line; homes 2,770–3,010 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

2026-10-26T05:00:17.224071 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% 57 86 14 29 86 14 62 62 25 43 86 43 Under 1,908 SF 1,908–2,747 SF 2,770–3,010 SF Over 3,010 SF

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

  • 43% of homes over 3,010 SF had a price cut before sale, against 57% of homes under 1,908 SF. Defined in 8.1.7.
  • 86% of homes over 3,010 SF had the seller pay closing costs or repairs, against 86% of homes under 1,908 SF. Defined in 8.1.8.
  • 43% of homes over 3,010 SF have a view, against 14% of homes under 1,908 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 1,908 SF7 sales1,908–2,747 SF7 sales2,770–3,010 SF8 salesOver 3,010 SF7 salesAll29 salesReal difference?
Net sale price average$313,486$373,410$389,938$456,600$383,585✓ large
Net price per SF average$185$155$135$135$152✓ large
Living area average1,7082,4322,8873,3932,615by design
Year built median20162015201520152016unclear

4.10 The numbers: selling

Under 1,908 SF7 sales1,908–2,747 SF7 sales2,770–3,010 SF8 salesOver 3,010 SF7 salesAll29 salesReal difference?
Days on market median4924469547unclear
Discount from original list average5.0%4.5%6.7%4.6%5.2%unclear
Price cut before sale57%29%62%43%48%unclear
Seller paid closing costs or repairs86%86%62%86%79%unclear
Has a view14%14%25%43%24%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 · 29 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.

2026-10-26T05:00:17.245209 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 5 9 7 29 sales Sales per group Before 2014: 8 sales (28%) 2014–2015: 5 sales (17%) 2016–2018: 9 sales (31%) After 2018: 7 sales (24%)
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 20142014–20152016–2018After 2018Real difference?
Net sale price$366K$408K$390K$378Kunclear
Net price per SF$137 per SF$135 per SF$163 per SF$167 per SFunclear
Living area2,716 SF3,030 SF2,466 SF2,394 SFunclear
Days on market76 days81 days41 days44 daysunclear
Discount from original list6.2%3.3%3.7%7.6%unclear
Price cut before sale38%20%33%100%unclear
Seller paid closing costs or repairs88%80%78%71%unclear
Has a view12%40%33%14%unclear

5.3 Net sale price: no clear difference

2026-10-26T05:00:17.266553 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2014 8 sales 2014– 2015 5 sales 2016– 2018 9 sales After 2018 7 sales $0K $200K $400K $366K $408K $390K $378K Net sale price, average

The average is $378K for homes built after 2018 and $366K for homes built before 2014. It does not move in a straight line; homes built 2014–2015 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-26T05:00:17.289815 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2014 8 sales 2014– 2015 5 sales 2016– 2018 9 sales After 2018 7 sales $0 $50 $100 $150 $137 $135 $163 $167 Net price per SF, average

The average is $167 per SF for homes built after 2018 and $137 per SF for homes built before 2014. It does not move in a straight line; homes built after 2018 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-26T05:00:17.312181 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2014 8 sales 2014– 2015 5 sales 2016– 2018 9 sales After 2018 7 sales 0 1,000 2,000 3,000 2,716 3,030 2,466 2,394 Living area (SF), average

The average is 2,394 SF for homes built after 2018 and 2,716 SF for homes built before 2014. It does not move in a straight line; homes built 2014–2015 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-26T05:00:17.335507 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2014 8 sales 2014– 2015 5 sales 2016– 2018 9 sales After 2018 7 sales 0 25 50 75 76 81 41 44 Days on market, median

The median is 44 days for homes built after 2018 and 76 days for homes built before 2014. It does not move in a straight line; homes built 2014–2015 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-26T05:00:17.359685 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2014 8 sales 2014– 2015 5 sales 2016– 2018 9 sales After 2018 7 sales 0.0% 2.5% 5.0% 7.5% 6.2% 3.3% 3.7% 7.6% Discount from original list, average

The average is 7.6% for homes built after 2018 and 6.2% for homes built before 2014. It does not move in a straight line; homes built after 2018 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

2026-10-26T05:00:17.386950 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 88 12 20 80 40 33 78 33 100 71 14 Before 2014 2014–2015 2016–2018 After 2018

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

  • 100% of homes built after 2018 had a price cut before sale, against 38% of homes built before 2014. Defined in 8.1.7.
  • 71% of homes built after 2018 had the seller pay closing costs or repairs, against 88% of homes built before 2014. Defined in 8.1.8.
  • 14% of homes built after 2018 have a view, against 12% of homes built before 2014. 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 20148 sales2014–20155 sales2016–20189 salesAfter 20187 salesAll29 salesReal difference?
Net sale price average$366,296$407,680$389,800$378,143$383,585unclear
Net price per SF average$137$135$163$167$152unclear
Living area average2,7163,0302,4662,3942,615unclear
Year built median20122015201620192016by design

5.10 The numbers: selling

Before 20148 sales2014–20155 sales2016–20189 salesAfter 20187 salesAll29 salesReal difference?
Days on market median7681414447unclear
Discount from original list average6.2%3.3%3.7%7.6%5.2%unclear
Price cut before sale38%20%33%100%48%unclear
Seller paid closing costs or repairs88%80%78%71%79%unclear
Has a view12%40%33%14%24%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 · 29 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.

2026-10-26T05:00:17.407080 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 22 29 sales Sales per group View: 7 sales (24%) No view: 22 sales (76%)
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$418K$373Kunclear
Net price per SF$152 per SF$152 per SFunclear
Living area2,816 SF2,551 SFunclear
Year built20162016unclear
Days on market25 days56 daysunclear
Discount from original list3.9%5.7%unclear
Price cut before sale43%50%unclear
Seller paid closing costs or repairs86%77%unclear

6.3 Net sale price: no clear difference

2026-10-26T05:00:17.423687 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 22 sales $0K $200K $400K $418K $373K Net sale price, average

The average is $418K for homes with a view and $373K 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

2026-10-26T05:00:17.441017 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 22 sales $0 $50 $100 $150 $152 $152 Net price per SF, average

The average is $152 per SF for homes with a view and $152 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

2026-10-26T05:00:17.459763 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 22 sales 0 1,000 2,000 3,000 2,816 2,551 Living area (SF), average

The average is 2,816 SF for homes with a view and 2,551 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-26T05:00:17.555476 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 22 sales 2014 2016 2018 2016 2016 Year built: median (dot) and middle half (bar)

The median is 2016 for homes with a view and 2016 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-26T05:00:17.573565 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 22 sales 0 20 40 60 25 56 Days on market, median

The median is 25 days for homes with a view and 56 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

2026-10-26T05:00:17.592396 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 22 sales 0.0% 2.0% 4.0% 6.0% 3.9% 5.7% Discount from original list, average

The average is 3.9% for homes with a view and 5.7% 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

2026-10-26T05:00:17.611737 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 43 86 50 77 View No view

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

  • 43% of homes with a view had a price cut before sale, against 50% of homes without a view. Defined in 8.1.7.
  • 86% of homes with a view had the seller pay closing costs or repairs, against 77% 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

View7 salesNo view22 salesAll29 salesReal difference?
Net sale price average$418,131$372,593$383,585unclear
Net price per SF average$152$152$152unclear
Living area average2,8162,5512,615unclear
Year built median201620162016unclear

6.11 The numbers: selling

View7 salesNo view22 salesAll29 salesReal difference?
Days on market median255647unclear
Discount from original list average3.9%5.7%5.2%unclear
Price cut before sale43%50%48%unclear
Seller paid closing costs or repairs86%77%79%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 La Conterra.

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
29 resale sales. Average $383,585; median $390,720; standard deviation $59,660; middle half (25th–75th percentile) $342,000–$428,500; lowest $253,000, highest $494,000. 23 sales had a seller payment; their median payment was $7,000.

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
29 resale sales. Average $152; median $142; standard deviation $26; middle half (25th–75th percentile) $134–$173; lowest $118, highest $207.

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
29 resale sales. Average 2,615; median 2,770; standard deviation 649; middle half (25th–75th percentile) 1,908–3,010; lowest 1,381, highest 3,690.

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
29 resale sales. Average 2016; median 2016; standard deviation 3; middle half (25th–75th percentile) 2013–2018; lowest 2008, highest 2021.

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
29 resale sales. Average 64; median 47; standard deviation 59; middle half (25th–75th percentile) 24–81; lowest 4, highest 222. 0 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
29 resale sales. Average 5.2%; median 3.8%; standard deviation 4.5%; middle half (25th–75th percentile) 1.9%–7.3%; lowest −0.7%, highest 18.4%. 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
14 of 29 resale sales (48.3%).

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
23 of 29 resale sales (79.3%).

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
7 sales with a view, 22 without, 0 unclassified. Most common View entries: None 13, Neighborhood 11, Trees/Woods 4, Park/Greenbelt 4, Garden 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 “la conterra”, “la conterra north”, “la conterra sub” (case-insensitive). A record is kept when its subdivision name belongs to the La Conterra family, the same rule comparable sales use (it includes La Conterra North Ph 2, proven adjacent and priced alike).
The data
Pulled October 26, 2026: 29 closed La Conterra records over the prior year, in ZIP 78626. 14 subdivision spellings kept, the most common: La Conterra (6); La Conterra Sub Sec 7 (6); La Conterra Sec 01 (4); La Conterra Sec 2 (2); La Conterra North Ph 2 (2); La Conterra Sub Sec 8 (1); La Conterra Sub Sec 2 (1); La Conterra Sub Sec 5 (1); LA CONTERRA SUB SEC 2 (1); La Conterra Sec 7 (1); La Conterra North (1); La Conterra Sec 6 (1); and 2 more (2 records). No other names were found by the search.

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
29 single-family sales in the window: 29 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
29 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 $342K”.
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 $342K: $253,000–$340,500 (7 sales); $342K–$362K: $342,000–$361,500 (7 sales); $391K–$428K: $390,720–$428,500 (8 sales); Over $428K: $432,000–$494,000 (7 sales)
Quartiles by living area: Under 1,908 SF: 1,381–1,829 SF (7 sales); 1,908–2,747 SF: 1,908–2,747 SF (7 sales); 2,770–3,010 SF: 2,770–3,010 SF (8 sales); Over 3,010 SF: 3,050–3,690 SF (7 sales)
Quartiles by year built: Before 2014: 2008–2013 (8 sales); 2014–2015: 2014–2015 (5 sales); 2016–2018: 2016–2018 (9 sales); After 2018: 2019–2021 (7 sales)
View and no view: View: MLS lists a view (7 sales); No view: None or Neighborhood (22 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 $383,585, median $390,720. Days on market: average 64, median 47: 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
3 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 = 5.4330.1430p < 0.0063Unclearε² = 0.194large
Living areaKruskal–WallisH = 21.1130.0001p < 0.0063Yesε² = 0.754large
Year builtKruskal–WallisH = 1.1330.7698p < 0.0063Unclearε² = 0.040small
Days on marketKruskal–WallisH = 1.2130.7514p < 0.0063Unclearε² = 0.043small
Discount from original listKruskal–WallisH = 4.0430.2573p < 0.0063Unclearε² = 0.144large
Price cut before saleChi-squareχ² = 5.4730.1403p < 0.0063UnclearCramér’s V = 0.434medium
Seller paid closing costs or repairsChi-squareχ² = 0.7130.8719p < 0.0063UnclearCramér’s V = 0.156small
Has a viewChi-squareχ² = 3.6530.3024p < 0.0063UnclearCramér’s V = 0.355medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 21.663< 0.0001p < 0.0063Yesε² = 0.774large
Net price per SFKruskal–WallisH = 14.0830.0028p < 0.0063Yesε² = 0.503large
Year builtKruskal–WallisH = 1.9230.5890p < 0.0063Unclearε² = 0.069medium
Days on marketKruskal–WallisH = 2.9330.4030p < 0.0063Unclearε² = 0.105medium
Discount from original listKruskal–WallisH = 4.6630.1984p < 0.0063Unclearε² = 0.166large
Price cut before saleChi-squareχ² = 2.0430.5643p < 0.0063UnclearCramér’s V = 0.265small
Seller paid closing costs or repairsChi-squareχ² = 1.9030.5929p < 0.0063UnclearCramér’s V = 0.256small
Has a viewChi-squareχ² = 2.0830.5550p < 0.0063UnclearCramér’s V = 0.268small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 1.1730.7609p < 0.0063Unclearε² = 0.042small
Net price per SFKruskal–WallisH = 8.6830.0338p < 0.0063Unclearε² = 0.310large
Living areaKruskal–WallisH = 3.7630.2886p < 0.0063Unclearε² = 0.134medium
Days on marketKruskal–WallisH = 3.1930.3635p < 0.0063Unclearε² = 0.114medium
Discount from original listKruskal–WallisH = 4.8330.1849p < 0.0063Unclearε² = 0.172large
Price cut before saleChi-squareχ² = 10.2830.0163p < 0.0063UnclearCramér’s V = 0.595large
Seller paid closing costs or repairsChi-squareχ² = 0.6130.8950p < 0.0063UnclearCramér’s V = 0.145small
Has a viewChi-squareχ² = 2.0730.5589p < 0.0063UnclearCramér’s V = 0.267small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 108.50—0.1141p < 0.0063Unclearrank-biserial r = 0.409medium
Net price per SFMann–Whitney UU = 84.00—0.7461p < 0.0063Unclearrank-biserial r = 0.091negligible
Living areaMann–Whitney UU = 98.00—0.3036p < 0.0063Unclearrank-biserial r = 0.273small
Year builtMann–Whitney UU = 75.50—0.9591p < 0.0063Unclearrank-biserial r = 0.019negligible
Days on marketMann–Whitney UU = 50.00—0.1768p < 0.0063Unclearrank-biserial r = 0.351medium
Discount from original listMann–Whitney UU = 61.00—0.4383p < 0.0063Unclearrank-biserial r = 0.208small
Price cut before saleChi-squareχ² = 0.1110.7419p < 0.0063UnclearCramér’s V = 0.061negligible
Seller paid closing costs or repairsChi-squareχ² = 0.2310.6311p < 0.0063UnclearCramér’s V = 0.089negligible

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 29 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.