ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Briarcreek

Manor, TX 78653
Resale homes · closed Nov 5 – Nov 4, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
November 5, 2026 · 3:52 PM 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, 365 days
29
single-family resale homes
Median net sale price
$245,000
average $246,200
Median net price per SF
$149
average $152
Median days on market
49 days
average 93 days
1. SummaryResale homes · 29 sales · November 5, 2025 – November 4, 2026

This study looks at the 29 resale homes sold in Briarcreek in the 365 days to November 4, 2026. A full year is used because only 17 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 $218K · 7 sales$218K–$240K · 7 sales$245K–$260K · 7 salesOver $260K · 8 sales
3. Quartiles by net sale priceResale homes · 29 sales · November 5, 2025 – November 4, 2026

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

2026-11-05T15:52:54.143404 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 7 7 8 29 sales Sales per group Under $218K: 7 sales (24%) $218K–$240K: 7 sales (24%) $245K–$260K: 7 sales (24%) Over $260K: 8 sales (28%)
Bottom line: no measure differs enough between the groups to rule out chance.

3.1 Key differences

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

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 $218K$218K–$240K$245K–$260KOver $260KReal difference?
Net price per SF$139 per SF$165 per SF$147 per SF$156 per SFunclear
Living area1,441 SF1,404 SF1,777 SF1,930 SFunclear
Year built2005200620072011unclear
Days on market43 days32 days95 days40 daysunclear
Discount from original list15.7%4.8%5.9%5.5%unclear
Price cut before sale57%43%71%50%unclear
Seller paid closing costs or repairs71%43%43%62%unclear
Has a view14%29%29%12%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

2026-11-05T15:52:54.171953 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $218K 7 sales $218K– $240K 7 sales $245K– $260K 7 sales Over $260K 8 sales $0 $50 $100 $150 $139 $165 $147 $156 Net price per SF, average

The average is $156 per SF for homes over $260K and $139 per SF for homes under $218K. It does not move in a straight line; homes $218K–$240K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Living area: no clear difference

2026-11-05T15:52:54.194676 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $218K 7 sales $218K– $240K 7 sales $245K– $260K 7 sales Over $260K 8 sales 0 1,000 2,000 1,441 1,404 1,777 1,930 Living area (SF), average

The average is 1,930 SF for homes over $260K and 1,441 SF for homes under $218K. It does not move in a straight line; homes over $260K are highest. Defined in 8.1.3.

– No clear difference · could be chance

3.5 Year built: no clear difference

2026-11-05T15:52:54.218345 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $218K 7 sales $218K– $240K 7 sales $245K– $260K 7 sales Over $260K 8 sales 2005 2008 2011 2014 2005 2006 2007 2011 Year built: median (dot) and middle half (bar)

The median is 2011 for homes over $260K and 2005 for homes under $218K. 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

2026-11-05T15:52:54.241433 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $218K 7 sales $218K– $240K 7 sales $245K– $260K 7 sales Over $260K 8 sales 0 50 100 43 32 95 40 Days on market, median

The median is 40 days for homes over $260K and 43 days for homes under $218K. It does not move in a straight line; homes $245K–$260K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-11-05T15:52:54.262700 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $218K 7 sales $218K– $240K 7 sales $245K– $260K 7 sales Over $260K 8 sales 0.0% 5.0% 10.0% 15.0% 15.7% 4.8% 5.9% 5.5% Discount from original list, average

The average is 5.5% for homes over $260K and 15.7% for homes under $218K. It does not move in a straight line; homes under $218K 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

2026-11-05T15:52:54.292585 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 71 14 43 43 29 71 43 29 50 62 12 Under $218K $218K–$240K $245K–$260K Over $260K

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

  • 50% of homes over $260K had a price cut before sale, against 57% of homes under $218K. Defined in 8.1.7.
  • 62% of homes over $260K had the seller pay closing costs or repairs, against 71% of homes under $218K. Defined in 8.1.8.
  • 12% of homes over $260K have a view, against 14% of homes under $218K. 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 $218K7 sales$218K–$240K7 sales$245K–$260K7 salesOver $260K8 salesAll29 salesReal difference?
Net sale price average$198,360$230,914$255,071$293,672$246,200by design
Net price per SF average$139$165$147$156$152unclear
Living area average1,4411,4041,7771,9301,648unclear
Year built median20052006200720112007unclear

3.10 The numbers: selling

Under $218K7 sales$218K–$240K7 sales$245K–$260K7 salesOver $260K8 salesAll29 salesReal difference?
Days on market median4332954049unclear
Discount from original list average15.7%4.8%5.9%5.5%7.9%unclear
Price cut before sale57%43%71%50%55%unclear
Seller paid closing costs or repairs71%43%43%62%55%unclear
Has a view14%29%29%12%21%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 · November 5, 2025 – November 4, 2026

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

2026-11-05T15:52:54.312914 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 6 7 8 29 sales Sales per group Under 1,347 SF: 8 sales (28%) 1,347–1,567 SF: 6 sales (21%) 1,579–1,858 SF: 7 sales (24%) Over 1,858 SF: 8 sales (28%)
Bottom line: no measure differs enough between the groups to rule out chance.

4.1 Key differences

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

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 1,347 SF1,347–1,567 SF1,579–1,858 SFOver 1,858 SFReal difference?
Net sale price$226K$223K$243K$287Kunclear
Net price per SF$172 per SF$152 per SF$149 per SF$134 per SFunclear
Year built2005200420082011unclear
Days on market58 days44 days18 days96 daysunclear
Discount from original list6.6%9.1%9.2%7.2%unclear
Price cut before sale62%50%43%62%unclear
Seller paid closing costs or repairs62%67%43%50%unclear
Has a view12%33%14%25%unclear

4.3 Net sale price: no clear difference

2026-11-05T15:52:54.333372 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,347 SF 8 sales 1,347– 1,567 SF 6 sales 1,579– 1,858 SF 7 sales Over 1,858 SF 8 sales $0K $100K $200K $300K $226K $223K $243K $287K Net sale price, average

The average is $287K for homes over 1,858 SF and $226K for homes under 1,347 SF. It does not move in a straight line; homes over 1,858 SF are highest. Defined in 8.1.1.

– No clear difference · could be chance

4.4 Net price per SF: no clear difference

2026-11-05T15:52:54.356408 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,347 SF 8 sales 1,347– 1,567 SF 6 sales 1,579– 1,858 SF 7 sales Over 1,858 SF 8 sales $0 $100 $200 $172 $152 $149 $134 Net price per SF, average

The average is $134 per SF for homes over 1,858 SF and $172 per SF for homes under 1,347 SF. It falls step by step from the lowest range to the highest. Defined in 8.1.2.

– No clear difference · could be chance

4.5 Year built: no clear difference

2026-11-05T15:52:54.381039 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,347 SF 8 sales 1,347– 1,567 SF 6 sales 1,579– 1,858 SF 7 sales Over 1,858 SF 8 sales 2002 2005 2008 2011 2014 2005 2004 2008 2011 Year built: median (dot) and middle half (bar)

The median is 2011 for homes over 1,858 SF and 2005 for homes under 1,347 SF. It does not move in a straight line; homes over 1,858 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-11-05T15:52:54.405078 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,347 SF 8 sales 1,347– 1,567 SF 6 sales 1,579– 1,858 SF 7 sales Over 1,858 SF 8 sales 0 50 100 58 44 18 96 Days on market, median

The median is 96 days for homes over 1,858 SF and 58 days for homes under 1,347 SF. It does not move in a straight line; homes over 1,858 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-11-05T15:52:54.427162 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,347 SF 8 sales 1,347– 1,567 SF 6 sales 1,579– 1,858 SF 7 sales Over 1,858 SF 8 sales 0.0% 5.0% 10.0% 6.6% 9.1% 9.2% 7.2% Discount from original list, average

The average is 7.2% for homes over 1,858 SF and 6.6% for homes under 1,347 SF. It does not move in a straight line; homes 1,579–1,858 SF are highest. Defined in 8.1.6.

– No clear difference · could be chance

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

2026-11-05T15:52:54.452696 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 62 12 50 67 33 43 43 14 62 50 25 Under 1,347 SF 1,347–1,567 SF 1,579–1,858 SF Over 1,858 SF

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

  • 62% of homes over 1,858 SF had a price cut before sale, against 62% of homes under 1,347 SF. Defined in 8.1.7.
  • 50% of homes over 1,858 SF had the seller pay closing costs or repairs, against 62% of homes under 1,347 SF. Defined in 8.1.8.
  • 25% of homes over 1,858 SF have a view, against 12% of homes under 1,347 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 1,347 SF8 sales1,347–1,567 SF6 sales1,579–1,858 SF7 salesOver 1,858 SF8 salesAll29 salesReal difference?
Net sale price average$225,550$223,317$243,270$286,575$246,200unclear
Net price per SF average$172$152$149$134$152unclear
Living area average1,3121,4661,6412,1261,648by design
Year built median20052004200820112007unclear

4.10 The numbers: selling

Under 1,347 SF8 sales1,347–1,567 SF6 sales1,579–1,858 SF7 salesOver 1,858 SF8 salesAll29 salesReal difference?
Days on market median5844189649unclear
Discount from original list average6.6%9.1%9.2%7.2%7.9%unclear
Price cut before sale62%50%43%62%55%unclear
Seller paid closing costs or repairs62%67%43%50%55%unclear
Has a view12%33%14%25%21%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 · November 5, 2025 – November 4, 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.

2026-11-05T15:52:54.473644 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 6 8 7 29 sales Sales per group Before 2005: 8 sales (28%) 2005–2006: 6 sales (21%) 2007–2014: 8 sales (28%) After 2014: 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 20052005–20062007–2014After 2014Real difference?
Net sale price$248K$229K$256K$248Kunclear
Net price per SF$161 per SF$152 per SF$156 per SF$136 per SFunclear
Living area1,561 SF1,524 SF1,675 SF1,822 SFunclear
Days on market74 days38 days75 days35 daysunclear
Discount from original list10.1%8.2%2.3%11.6%unclear
Price cut before sale62%67%38%57%unclear
Seller paid closing costs or repairs62%50%62%43%unclear
Has a view38%17%25%0%unclear

5.3 Net sale price: no clear difference

2026-11-05T15:52:54.493465 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2005 8 sales 2005– 2006 6 sales 2007– 2014 8 sales After 2014 7 sales $0K $100K $200K $300K $248K $229K $256K $248K Net sale price, average

The average is $248K for homes built after 2014 and $248K for homes built before 2005. It does not move in a straight line; homes built 2007–2014 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-11-05T15:52:54.515998 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2005 8 sales 2005– 2006 6 sales 2007– 2014 8 sales After 2014 7 sales $0 $50 $100 $150 $161 $152 $156 $136 Net price per SF, average

The average is $136 per SF for homes built after 2014 and $161 per SF for homes built before 2005. It does not move in a straight line; homes built before 2005 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-11-05T15:52:54.537511 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2005 8 sales 2005– 2006 6 sales 2007– 2014 8 sales After 2014 7 sales 0 1,000 2,000 1,561 1,524 1,675 1,822 Living area (SF), average

The average is 1,822 SF for homes built after 2014 and 1,561 SF for homes built before 2005. It does not move in a straight line; homes built after 2014 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-11-05T15:52:54.558944 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2005 8 sales 2005– 2006 6 sales 2007– 2014 8 sales After 2014 7 sales 0 25 50 75 74 38 75 35 Days on market, median

The median is 35 days for homes built after 2014 and 74 days for homes built before 2005. It does not move in a straight line; homes built 2007–2014 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-11-05T15:52:54.582057 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2005 8 sales 2005– 2006 6 sales 2007– 2014 8 sales After 2014 7 sales 0.0% 5.0% 10.0% 10.1% 8.2% 2.3% 11.6% Discount from original list, average

The average is 11.6% for homes built after 2014 and 10.1% for homes built before 2005. It does not move in a straight line; homes built after 2014 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

2026-11-05T15:52:54.607728 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 62 38 67 50 17 38 62 25 57 43 0 Before 2005 2005–2006 2007–2014 After 2014

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

  • 57% of homes built after 2014 had a price cut before sale, against 62% of homes built before 2005. Defined in 8.1.7.
  • 43% of homes built after 2014 had the seller pay closing costs or repairs, against 62% of homes built before 2005. Defined in 8.1.8.
  • 0% of homes built after 2014 have a view, against 38% of homes built before 2005. 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 20058 sales2005–20066 sales2007–20148 salesAfter 20147 salesAll29 salesReal difference?
Net sale price average$247,925$228,836$256,209$247,671$246,200unclear
Net price per SF average$161$152$156$136$152unclear
Living area average1,5611,5241,6751,8221,648unclear
Year built median20032005200820172007by design

5.10 The numbers: selling

Before 20058 sales2005–20066 sales2007–20148 salesAfter 20147 salesAll29 salesReal difference?
Days on market median7438753549unclear
Discount from original list average10.1%8.2%2.3%11.6%7.9%unclear
Price cut before sale62%67%38%57%55%unclear
Seller paid closing costs or repairs62%50%62%43%55%unclear
Has a view38%17%25%0%21%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 · November 5, 2025 – November 4, 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.

2026-11-05T15:52:54.625775 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 6 23 29 sales Sales per group View: 6 sales (21%) No view: 23 sales (79%)
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$266K$241Kunclear
Net price per SF$150 per SF$152 per SFunclear
Living area1,797 SF1,609 SFunclear
Year built20042008unclear
Days on market120 days38 daysunclear
Discount from original list6.5%8.3%unclear
Price cut before sale50%57%unclear
Seller paid closing costs or repairs33%61%unclear

6.3 Net sale price: no clear difference

2026-11-05T15:52:54.642357 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 23 sales $0K $100K $200K $300K $266K $241K Net sale price, average

The average is $266K for homes with a view and $241K 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

2026-11-05T15:52:54.659527 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 23 sales $0 $50 $100 $150 $150 $152 Net price per SF, average

The average is $150 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

2026-11-05T15:52:54.677562 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 23 sales 0 500 1,000 1,500 2,000 1,797 1,609 Living area (SF), average

The average is 1,797 SF for homes with a view and 1,609 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-11-05T15:52:54.721902 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 23 sales 2005 2008 2011 2014 2004 2008 Year built: median (dot) and middle half (bar)

The median is 2004 for homes with a view and 2008 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-11-05T15:52:54.739947 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 23 sales 0 50 100 120 38 Days on market, median

The median is 120 days for homes with a view and 38 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

2026-11-05T15:52:54.757951 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 23 sales 0.0% 2.0% 4.0% 6.0% 8.0% 6.5% 8.3% Discount from original list, average

The average is 6.5% for homes with a view and 8.3% 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

2026-11-05T15:52:54.777560 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 50 33 57 61 View No view

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

  • 50% of homes with a view had a price cut before sale, against 57% of homes without a view. Defined in 8.1.7.
  • 33% of homes with a view had the seller pay closing costs or repairs, against 61% 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

View6 salesNo view23 salesAll29 salesReal difference?
Net sale price average$265,900$241,060$246,200unclear
Net price per SF average$150$152$152unclear
Living area average1,7971,6091,648unclear
Year built median200420082007unclear

6.11 The numbers: selling

View6 salesNo view23 salesAll29 salesReal difference?
Days on market median1203849unclear
Discount from original list average6.5%8.3%7.9%unclear
Price cut before sale50%57%55%unclear
Seller paid closing costs or repairs33%61%55%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 · November 5, 2025 – November 4, 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 Briarcreek.

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 $246,200; median $245,000; standard deviation $45,964; middle half (25th–75th percentile) $217,900–$265,000; lowest $158,000, highest $420,000. 16 sales had a seller payment; their median payment was $8,000.

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 $149; standard deviation $23; middle half (25th–75th percentile) $133–$168; lowest $105, highest $199.

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 1,648; median 1,579; standard deviation 367; middle half (25th–75th percentile) 1,332–1,962; lowest 1,300, highest 2,918.

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 2008; median 2007; standard deviation 5; middle half (25th–75th percentile) 2004–2014; lowest 2002, highest 2017.

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 93; median 49; standard deviation 128; middle half (25th–75th percentile) 18–119; lowest 3, highest 667. 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 7.9%; median 6.4%; standard deviation 9.7%; middle half (25th–75th percentile) 2.9%–9.1%; lowest −6.0%, highest 42.2%. 3 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
16 of 29 resale sales (55.2%).

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
16 of 29 resale sales (55.2%).

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
6 sales with a view, 23 without, 0 unclassified. Most common View entries: None 18, Neighborhood 7, Park/Greenbelt 3, Trees/Woods 2, Rural 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
ABOR MLS via secure APIs, read from ALEX's copy of the MLS (refreshed every 15 minutes; this pull as of 2026-11-05 21:45 UTC), every field. Filter: StandardStatus = Closed, PropertyType = Residential, City = Manor, SubdivisionName contains any of “briarcreek” (case-insensitive). A record is kept when its subdivision name belongs to the Briarcreek family, the same rule comparable sales use.
The data
Pulled November 5, 2026: 29 closed Briarcreek records over the prior year, in ZIP 78653. 11 subdivision spellings kept: Briarcreek Sec 04 (8); Briarcreek Sec 05 (6); Briarcreek Sub Sec 6-C (4); Briarcreek Sec 02 (2); Briarcreek Sec 01 (2); Briarcreek Sub Sec 6-B (2); Briarcreek Sec 03 (1); Briarcreek Subd Sec 1 (1); Briarcreek (1); Briarcreek Sub (1); Briarcreek Sub Sec 6-A (1). Names found by the search but not part of Briarcreek, left out: Briarcreek Sund sec 2 (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 17 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from November 5, 2025 to November 4, 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 $218K”.
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 $218K: $158,000–$215,500 (7 sales); $218K–$240K: $217,900–$240,000 (7 sales); $245K–$260K: $245,000–$260,000 (7 sales); Over $260K: $265,000–$420,000 (8 sales)
Quartiles by living area: Under 1,347 SF: 1,300–1,332 SF (8 sales); 1,347–1,567 SF: 1,347–1,567 SF (6 sales); 1,579–1,858 SF: 1,579–1,858 SF (7 sales); Over 1,858 SF: 1,962–2,918 SF (8 sales)
Quartiles by year built: Before 2005: 2002–2004 (8 sales); 2005–2006: 2005–2006 (6 sales); 2007–2014: 2007–2014 (8 sales); After 2014: 2015–2017 (7 sales)
View and no view: View: MLS lists a view (6 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 $246,200, median $245,000. Days on market: average 93, median 49: 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
0 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 = 6.7030.0820p < 0.0063Unclearε² = 0.239large
Living areaKruskal–WallisH = 11.7230.0084p < 0.0063Unclearε² = 0.419large
Year builtKruskal–WallisH = 2.5330.4706p < 0.0063Unclearε² = 0.090medium
Days on marketKruskal–WallisH = 2.0930.5542p < 0.0063Unclearε² = 0.075medium
Discount from original listKruskal–WallisH = 3.7930.2855p < 0.0063Unclearε² = 0.135medium
Price cut before saleChi-squareχ² = 1.2730.7351p < 0.0063UnclearCramér’s V = 0.210small
Seller paid closing costs or repairsChi-squareχ² = 1.7830.6193p < 0.0063UnclearCramér’s V = 0.248small
Has a viewChi-squareχ² = 1.0330.7935p < 0.0063UnclearCramér’s V = 0.189small

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 12.1630.0068p < 0.0063Unclearε² = 0.434large
Net price per SFKruskal–WallisH = 11.4830.0094p < 0.0063Unclearε² = 0.410large
Year builtKruskal–WallisH = 7.8430.0495p < 0.0063Unclearε² = 0.280large
Days on marketKruskal–WallisH = 3.6830.2982p < 0.0063Unclearε² = 0.131medium
Discount from original listKruskal–WallisH = 1.2930.7304p < 0.0063Unclearε² = 0.046small
Price cut before saleChi-squareχ² = 0.8430.8395p < 0.0063UnclearCramér’s V = 0.170small
Seller paid closing costs or repairsChi-squareχ² = 1.0130.7988p < 0.0063UnclearCramér’s V = 0.187small
Has a viewChi-squareχ² = 1.1830.7585p < 0.0063UnclearCramér’s V = 0.201small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 4.3130.2294p < 0.0063Unclearε² = 0.154large
Net price per SFKruskal–WallisH = 4.1430.2470p < 0.0063Unclearε² = 0.148large
Living areaKruskal–WallisH = 8.0730.0446p < 0.0063Unclearε² = 0.288large
Days on marketKruskal–WallisH = 0.5430.9089p < 0.0063Unclearε² = 0.019small
Discount from original listKruskal–WallisH = 5.5030.1386p < 0.0063Unclearε² = 0.196large
Price cut before saleChi-squareχ² = 1.5230.6787p < 0.0063UnclearCramér’s V = 0.229small
Seller paid closing costs or repairsChi-squareχ² = 0.8430.8395p < 0.0063UnclearCramér’s V = 0.170small
Has a viewChi-squareχ² = 3.3530.3403p < 0.0063UnclearCramér’s V = 0.340medium

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 68.00—0.9785p < 0.0063Unclearrank-biserial r = 0.014negligible
Net price per SFMann–Whitney UU = 65.00—0.8544p < 0.0063Unclearrank-biserial r = 0.058negligible
Living areaMann–Whitney UU = 80.00—0.5703p < 0.0063Unclearrank-biserial r = 0.159small
Year builtMann–Whitney UU = 37.50—0.0934p < 0.0063Unclearrank-biserial r = 0.457medium
Days on marketMann–Whitney UU = 105.00—0.0559p < 0.0063Unclearrank-biserial r = 0.522large
Discount from original listMann–Whitney UU = 64.00—0.8135p < 0.0063Unclearrank-biserial r = 0.072negligible
Price cut before saleChi-squareχ² = 0.0810.7748p < 0.0063UnclearCramér’s V = 0.053negligible
Seller paid closing costs or repairsChi-squareχ² = 1.4610.2271p < 0.0063UnclearCramér’s V = 0.224small

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 November 5, 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.