ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Meridian

Austin, TX 78739
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 · 3:53 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
31
single-family resale homes
Median net sale price
$889,500
average $1,002,655
Median net price per SF
$259
average $292
Median days on market
12 days
average 40 days
1. SummaryResale homes · 31 sales · October 26, 2025 – October 25, 2026

This study looks at the 31 resale homes sold in Meridian in the 365 days to October 25, 2026. A full year is used because only 19 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 sold31 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 $756K · 8 sales$756K–$890K · 8 sales$905K–$970K · 7 salesOver $970K · 8 sales
3. Quartiles by net sale priceResale homes · 31 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.

2026-10-26T03:54:09.532230 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 7 8 31 sales Sales per group Under $756K: 8 sales (26%) $756K–$890K: 8 sales (26%) $905K–$970K: 7 sales (23%) Over $970K: 8 sales (26%)
Bottom line

3.1 Key differences

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

Under $756K$756K–$890K$905K–$970KOver $970K
Higher-priced homes Over $970K
  • Are much larger.
  • Tend to be newer.
Lower-priced homes Under $756K
  • Are much smaller.
  • Tend to be older.

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 $756K$756K–$890K$905K–$970KOver $970KReal difference?
Net price per SF$264 per SF$250 per SF$253 per SF$396 per SFunclear
Living area2,455 SFSmallest3,168 SF3,716 SF4,174 SFLargest✓ large
Year built200820082008Oldest2015Newest✓ large
Days on market56 days12 days12 days8 daysunclear
Discount from original list4.1%0.7%2.1%3.0%unclear
Price cut before sale25%12%14%0%unclear
Seller paid closing costs or repairs50%50%43%50%unclear
Has a view12%38%57%38%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

2026-10-26T03:54:09.562126 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $756K 8 sales $756K– $890K 8 sales $905K– $970K 7 sales Over $970K 8 sales $0 $200 $400 $264 $250 $253 $396 Net price per SF, average

The average is $396 per SF for homes over $970K and $264 per SF for homes under $756K. It does not move in a straight line; homes over $970K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Homes over $970K are about 1,720 SF (70%) larger than homes under $756K

2026-10-26T03:54:09.585629 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $756K 8 sales $756K– $890K 8 sales $905K– $970K 7 sales Over $970K 8 sales 0 2,000 4,000 2,455 3,168 3,716 4,174 Living area (SF), average

The average is 4,174 SF for homes over $970K and 2,455 SF for homes under $756K. It rises step by step from the lowest range to the highest. Defined in 8.1.3.

✓ Real difference · large

3.5 Homes over $970K are about 6 years newer than homes under $756K

2026-10-26T03:54:09.610816 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $756K 8 sales $756K– $890K 8 sales $905K– $970K 7 sales Over $970K 8 sales 2008 2011 2014 2017 2008 2008 2008 2015 Year built: median (dot) and middle half (bar)

The median is 2015 for homes over $970K and 2008 for homes under $756K. It does not move in a straight line; homes over $970K are highest. Defined in 8.1.4.

✓ Real difference · large

3.6 Days on market: no clear difference

2026-10-26T03:54:09.634452 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $756K 8 sales $756K– $890K 8 sales $905K– $970K 7 sales Over $970K 8 sales 0 20 40 60 56 12 12 8 Days on market, median

The median is 8 days for homes over $970K and 56 days for homes under $756K. It falls step by step from the lowest range to the highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-10-26T03:54:09.656888 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $756K 8 sales $756K– $890K 8 sales $905K– $970K 7 sales Over $970K 8 sales 0.0% 2.0% 4.0% 4.1% 0.7% 2.1% 3.0% Discount from original list, average

The average is 3.0% for homes over $970K and 4.1% for homes under $756K. It does not move in a straight line; homes under $756K 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

2026-10-26T03:54:09.687875 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% 25 50 12 12 50 38 14 43 57 0 50 38 Under $756K $756K–$890K $905K–$970K Over $970K

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

  • 0% of homes over $970K had a price cut before sale, against 25% of homes under $756K. Defined in 8.1.7.
  • 50% of homes over $970K had the seller pay closing costs or repairs, against 50% of homes under $756K. Defined in 8.1.8.
  • 38% of homes over $970K have a view, against 12% of homes under $756K. 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 $756K8 sales$756K–$890K8 sales$905K–$970K7 salesOver $970K8 salesAll31 salesReal difference?
Net sale price average$634,831$783,644$941,143$1,643,312$1,002,655by design
Net price per SF average$264$250$253$396$292unclear
Living area average2,4553,1683,7164,1743,367✓ large
Year built median20082008200820152009✓ large

3.10 The numbers: selling

Under $756K8 sales$756K–$890K8 sales$905K–$970K7 salesOver $970K8 salesAll31 salesReal difference?
Days on market median561212812unclear
Discount from original list average4.1%0.7%2.1%3.0%2.5%unclear
Price cut before sale25%12%14%0%13%unclear
Seller paid closing costs or repairs50%50%43%50%48%unclear
Has a view12%38%57%38%35%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 · 31 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.

2026-10-26T03:54:09.710493 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 7 8 31 sales Sales per group Under 2,940 SF: 8 sales (26%) 2,940–3,492 SF: 8 sales (26%) 3,585–3,762 SF: 7 sales (23%) Over 3,762 SF: 8 sales (26%)
Bottom line

4.1 Key differences

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

Under 2,940 SF2,940–3,492 SF3,585–3,762 SFOver 3,762 SF
Larger homes Over 3,762 SF
  • Sell for much more.
Smaller homes Under 2,940 SF
  • Sell for much less.

4.2 At a glance

Shaded rows are real differences; the darkest cell is the highest. Gray rows show no clear difference. Prices are averages; days on market and year built are medians.

Under 2,940 SF2,940–3,492 SF3,585–3,762 SFOver 3,762 SFReal difference?
Net sale price$637KLowest$891K$920K$1.55MHighest✓ large
Net price per SF$273 per SF$283 per SF$252 per SF$355 per SFunclear
Year built2008200920082012unclear
Days on market22 days22 days3 days16 daysunclear
Discount from original list3.8%1.0%1.8%3.3%unclear
Price cut before sale25%12%14%0%unclear
Seller paid closing costs or repairs50%50%29%62%unclear
Has a view25%25%29%62%unclear

4.3 Homes over 3,762 SF sell for about $914K (143%) more than homes under 2,940 SF

2026-10-26T03:54:09.731886 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,940 SF 8 sales 2,940– 3,492 SF 8 sales 3,585– 3,762 SF 7 sales Over 3,762 SF 8 sales $0K $500K $1.00M $1.50M $637K $891K $920K $1.55M Net sale price, average

The average is $1.55M for homes over 3,762 SF and $637K for homes under 2,940 SF. It rises step by step from the lowest range to the highest. Defined in 8.1.1.

✓ Real difference · large

4.4 Net price per SF: no clear difference

2026-10-26T03:54:09.755327 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,940 SF 8 sales 2,940– 3,492 SF 8 sales 3,585– 3,762 SF 7 sales Over 3,762 SF 8 sales $0 $200 $400 $273 $283 $252 $355 Net price per SF, average

The average is $355 per SF for homes over 3,762 SF and $273 per SF for homes under 2,940 SF. It does not move in a straight line; homes over 3,762 SF are highest. Defined in 8.1.2.

– No clear difference · could be chance

4.5 Year built: no clear difference

2026-10-26T03:54:09.779845 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,940 SF 8 sales 2,940– 3,492 SF 8 sales 3,585– 3,762 SF 7 sales Over 3,762 SF 8 sales 2008 2011 2014 2017 2008 2009 2008 2012 Year built: median (dot) and middle half (bar)

The median is 2012 for homes over 3,762 SF and 2008 for homes under 2,940 SF. It does not move in a straight line; homes over 3,762 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-26T03:54:09.803621 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,940 SF 8 sales 2,940– 3,492 SF 8 sales 3,585– 3,762 SF 7 sales Over 3,762 SF 8 sales 0 10 20 22 22 3 16 Days on market, median

The median is 16 days for homes over 3,762 SF and 22 days for homes under 2,940 SF. It does not move in a straight line; homes under 2,940 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-26T03:54:09.826091 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,940 SF 8 sales 2,940– 3,492 SF 8 sales 3,585– 3,762 SF 7 sales Over 3,762 SF 8 sales 0.0% 2.0% 4.0% 3.8% 1.0% 1.8% 3.3% Discount from original list, average

The average is 3.3% for homes over 3,762 SF and 3.8% for homes under 2,940 SF. It does not move in a straight line; homes under 2,940 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

2026-10-26T03:54:09.853727 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% 25 50 25 12 50 25 14 29 29 0 62 62 Under 2,940 SF 2,940–3,492 SF 3,585–3,762 SF Over 3,762 SF

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

  • 0% of homes over 3,762 SF had a price cut before sale, against 25% of homes under 2,940 SF. Defined in 8.1.7.
  • 62% of homes over 3,762 SF had the seller pay closing costs or repairs, against 50% of homes under 2,940 SF. Defined in 8.1.8.
  • 62% of homes over 3,762 SF have a view, against 25% of homes under 2,940 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 2,940 SF8 sales2,940–3,492 SF8 sales3,585–3,762 SF7 salesOver 3,762 SF8 salesAll31 salesReal difference?
Net sale price average$637,225$891,162$920,386$1,551,562$1,002,655✓ large
Net price per SF average$273$283$252$355$292unclear
Living area average2,3673,1773,6594,3033,367by design
Year built median20082009200820122009unclear

4.10 The numbers: selling

Under 2,940 SF8 sales2,940–3,492 SF8 sales3,585–3,762 SF7 salesOver 3,762 SF8 salesAll31 salesReal difference?
Days on market median222231612unclear
Discount from original list average3.8%1.0%1.8%3.3%2.5%unclear
Price cut before sale25%12%14%0%13%unclear
Seller paid closing costs or repairs50%50%29%62%48%unclear
Has a view25%25%29%62%35%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 · 31 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.

2026-10-26T03:54:09.875206 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 10 6 8 31 sales Sales per group Before 2008: 7 sales (23%) 2008–2009: 10 sales (32%) 2010: 6 sales (19%) After 2010: 8 sales (26%)
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 20082008–20092010After 2010Real difference?
Net sale price$841K$781K$850K$1.54Munclear
Net price per SF$257 per SF$258 per SF$246 per SF$400 per SFunclear
Living area3,310 SF3,071 SF3,469 SF3,712 SFunclear
Days on market23 days8 days32 days14 daysunclear
Discount from original list2.0%2.7%2.5%2.7%unclear
Price cut before sale14%20%17%0%unclear
Seller paid closing costs or repairs43%60%50%38%unclear
Has a view29%40%50%25%unclear

5.3 Net sale price: no clear difference

2026-10-26T03:54:09.896279 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2008 7 sales 2008– 2009 10 sales 2010 6 sales After 2010 8 sales $0K $500K $1.00M $1.50M $841K $781K $850K $1.54M Net sale price, average

The average is $1.54M for homes built after 2010 and $841K for homes built before 2008. It does not move in a straight line; homes built after 2010 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-26T03:54:09.919870 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2008 7 sales 2008– 2009 10 sales 2010 6 sales After 2010 8 sales $0 $200 $400 $257 $258 $246 $400 Net price per SF, average

The average is $400 per SF for homes built after 2010 and $257 per SF for homes built before 2008. It does not move in a straight line; homes built after 2010 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-26T03:54:09.941679 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2008 7 sales 2008– 2009 10 sales 2010 6 sales After 2010 8 sales 0 2,000 4,000 3,310 3,071 3,469 3,712 Living area (SF), average

The average is 3,712 SF for homes built after 2010 and 3,310 SF for homes built before 2008. It does not move in a straight line; homes built after 2010 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-26T03:54:09.963559 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2008 7 sales 2008– 2009 10 sales 2010 6 sales After 2010 8 sales 0 10 20 30 23 8 32 14 Days on market, median

The median is 14 days for homes built after 2010 and 23 days for homes built before 2008. It does not move in a straight line; homes built 2010 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-26T03:54:09.987113 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2008 7 sales 2008– 2009 10 sales 2010 6 sales After 2010 8 sales 0.0% 1.0% 2.0% 3.0% 2.0% 2.7% 2.5% 2.7% Discount from original list, average

The average is 2.7% for homes built after 2010 and 2.0% for homes built before 2008. It does not move in a straight line; homes built after 2010 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

2026-10-26T03:54:10.014132 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% 14 43 29 20 60 40 17 50 50 0 38 25 Before 2008 2008–2009 2010 After 2010

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

  • 0% of homes built after 2010 had a price cut before sale, against 14% of homes built before 2008. Defined in 8.1.7.
  • 38% of homes built after 2010 had the seller pay closing costs or repairs, against 43% of homes built before 2008. Defined in 8.1.8.
  • 25% of homes built after 2010 have a view, against 29% of homes built before 2008. 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 20087 sales2008–200910 sales20106 salesAfter 20108 salesAll31 salesReal difference?
Net sale price average$841,000$780,990$849,792$1,535,831$1,002,655unclear
Net price per SF average$257$258$246$400$292unclear
Living area average3,3103,0713,4693,7123,367unclear
Year built median20062008201020152009by design

5.10 The numbers: selling

Before 20087 sales2008–200910 sales20106 salesAfter 20108 salesAll31 salesReal difference?
Days on market median238321412unclear
Discount from original list average2.0%2.7%2.5%2.7%2.5%unclear
Price cut before sale14%20%17%0%13%unclear
Seller paid closing costs or repairs43%60%50%38%48%unclear
Has a view29%40%50%25%35%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 · 31 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.

2026-10-26T03:54:10.033492 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 11 20 31 sales Sales per group View: 11 sales (35%) No view: 20 sales (65%)
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$938K$1.04Munclear
Net price per SF$261 per SF$309 per SFunclear
Living area3,610 SF3,234 SFunclear
Year built20092009unclear
Days on market8 days22 daysunclear
Discount from original list1.1%3.3%unclear
Price cut before sale9%15%unclear
Seller paid closing costs or repairs73%35%unclear

6.3 Net sale price: no clear difference

2026-10-26T03:54:10.050703 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 11 sales No view 20 sales $0K $250K $500K $750K $1.00M $938K $1.04M Net sale price, average

The average is $938K for homes with a view and $1.04M 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

2026-10-26T03:54:10.069046 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 11 sales No view 20 sales $0 $100 $200 $300 $261 $309 Net price per SF, average

The average is $261 per SF for homes with a view and $309 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

2026-10-26T03:54:10.089075 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 11 sales No view 20 sales 0 1,000 2,000 3,000 4,000 3,610 3,234 Living area (SF), average

The average is 3,610 SF for homes with a view and 3,234 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-26T03:54:10.208047 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 11 sales No view 20 sales 2008 2009 2010 2011 2009 2009 Year built: median (dot) and middle half (bar)

The median is 2009 for homes with a view and 2009 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-26T03:54:10.227609 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 11 sales No view 20 sales 0 10 20 8 22 Days on market, median

The median is 8 days for homes with a view and 22 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

2026-10-26T03:54:10.246296 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 11 sales No view 20 sales 0.0% 1.0% 2.0% 3.0% 1.1% 3.3% Discount from original list, average

The average is 1.1% for homes with a view and 3.3% 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

2026-10-26T03:54:10.265808 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 9 73 15 35 View No view

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

  • 9% of homes with a view had a price cut before sale, against 15% of homes without a view. Defined in 8.1.7.
  • 73% of homes with a view had the seller pay closing costs or repairs, against 35% 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

View11 salesNo view20 salesAll31 salesReal difference?
Net sale price average$937,813$1,038,318$1,002,655unclear
Net price per SF average$261$309$292unclear
Living area average3,6103,2343,367unclear
Year built median200920092009unclear

6.11 The numbers: selling

View11 salesNo view20 salesAll31 salesReal difference?
Days on market median82212unclear
Discount from original list average1.1%3.3%2.5%unclear
Price cut before sale9%15%13%unclear
Seller paid closing costs or repairs73%35%48%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 Meridian.

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
31 resale sales. Average $1,002,655; median $889,500; standard deviation $500,376; middle half (25th–75th percentile) $747,825–$976,250; lowest $575,000, highest $2,535,000. 15 sales had a seller payment; their median payment was $6,000.

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
31 resale sales. Average $292; median $259; standard deviation $95; middle half (25th–75th percentile) $241–$290; lowest $209, highest $562.

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
31 resale sales. Average 3,367; median 3,492; standard deviation 783; middle half (25th–75th percentile) 2,937–3,781; lowest 1,946, highest 4,944.

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
31 resale sales. Average 2010; median 2009; standard deviation 3; middle half (25th–75th percentile) 2008–2010; lowest 2006, 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
31 resale sales. Average 40; median 12; standard deviation 55; middle half (25th–75th percentile) 4–58; lowest 0, highest 216. 1 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
31 resale sales. Average 2.5%; median 1.3%; standard deviation 3.2%; middle half (25th–75th percentile) 0.3%–3.9%; lowest −2.1%, highest 10.9%. 4 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
4 of 31 resale sales (12.9%).

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
15 of 31 resale sales (48.4%).

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
11 sales with a view, 20 without, 0 unclassified. Most common View entries: None 16, Park/Greenbelt 10, Neighborhood 5, Trees/Woods 5, Pool 1, Golf Course 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 = Austin, SubdivisionName contains any of “meridian” (case-insensitive). A record is kept when its subdivision name belongs to the Meridian family, the same rule comparable sales use.
The data
Pulled October 26, 2026: 31 closed Meridian records over the prior year, in ZIP 78739. 11 subdivision spellings kept: Meridian Sec C Ph 03 (7); Meridian Sec F (4); Meridian Sec D Ph 02 (4); Meridian Sec A2 & B2 (3); Meridian Sec D Ph 03 (3); Meridian (3); Meridian Sec C Ph 02 (2); Meridian Sec C Ph 01 (2); MERIDIAN SEC C PHS 3 (1); Meridian Sec D Ph 01 Amd (1); Meridian Sec C Phs 1 (1). 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 19 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 26, 2025 to October 25, 2026 inclusive.
The data
31 single-family sales in the window: 31 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
31 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 $756K”.
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 $756K: $575,000–$739,250 (8 sales); $756K–$890K: $756,400–$889,500 (8 sales); $905K–$970K: $905,000–$970,000 (7 sales); Over $970K: $982,500–$2,535,000 (8 sales)
Quartiles by living area: Under 2,940 SF: 1,946–2,934 SF (8 sales); 2,940–3,492 SF: 2,940–3,492 SF (8 sales); 3,585–3,762 SF: 3,585–3,762 SF (7 sales); Over 3,762 SF: 3,800–4,944 SF (8 sales)
Quartiles by year built: Before 2008: 2006–2007 (7 sales); 2008–2009: 2008–2009 (10 sales); 2010: 2010–2010 (6 sales); After 2010: 2011–2017 (8 sales)
View and no view: View: MLS lists a view (11 sales); No view: None or Neighborhood (20 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 $1,002,655, median $889,500. Days on market: average 40, median 12: 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 = 8.1930.0423p < 0.0063Unclearε² = 0.273large
Living areaKruskal–WallisH = 22.603< 0.0001p < 0.0063Yesε² = 0.753large
Year builtKruskal–WallisH = 13.7530.0033p < 0.0063Yesε² = 0.458large
Days on marketKruskal–WallisH = 1.9330.5880p < 0.0063Unclearε² = 0.064medium
Discount from original listKruskal–WallisH = 4.4430.2173p < 0.0063Unclearε² = 0.148large
Price cut before saleChi-squareχ² = 2.2430.5241p < 0.0063UnclearCramér’s V = 0.269small
Seller paid closing costs or repairsChi-squareχ² = 0.1130.9905p < 0.0063UnclearCramér’s V = 0.060negligible
Has a viewChi-squareχ² = 3.3130.3464p < 0.0063UnclearCramér’s V = 0.327medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 23.503< 0.0001p < 0.0063Yesε² = 0.783large
Net price per SFKruskal–WallisH = 3.9930.2625p < 0.0063Unclearε² = 0.133medium
Year builtKruskal–WallisH = 6.2430.1006p < 0.0063Unclearε² = 0.208large
Days on marketKruskal–WallisH = 3.3030.3476p < 0.0063Unclearε² = 0.110medium
Discount from original listKruskal–WallisH = 3.1430.3706p < 0.0063Unclearε² = 0.105medium
Price cut before saleChi-squareχ² = 2.2430.5241p < 0.0063UnclearCramér’s V = 0.269small
Seller paid closing costs or repairsChi-squareχ² = 1.7630.6247p < 0.0063UnclearCramér’s V = 0.238small
Has a viewChi-squareχ² = 3.4630.3253p < 0.0063UnclearCramér’s V = 0.334medium

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 7.3230.0624p < 0.0063Unclearε² = 0.244large
Net price per SFKruskal–WallisH = 11.6730.0086p < 0.0063Unclearε² = 0.389large
Living areaKruskal–WallisH = 2.7430.4341p < 0.0063Unclearε² = 0.091medium
Days on marketKruskal–WallisH = 1.2130.7516p < 0.0063Unclearε² = 0.040small
Discount from original listKruskal–WallisH = 0.0330.9985p < 0.0063Unclearε² = 0.001negligible
Price cut before saleChi-squareχ² = 1.7230.6323p < 0.0063UnclearCramér’s V = 0.236small
Seller paid closing costs or repairsChi-squareχ² = 1.0130.7984p < 0.0063UnclearCramér’s V = 0.181small
Has a viewChi-squareχ² = 1.1730.7598p < 0.0063UnclearCramér’s V = 0.194small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 135.00—0.3118p < 0.0063Unclearrank-biserial r = 0.227small
Net price per SFMann–Whitney UU = 93.00—0.4957p < 0.0063Unclearrank-biserial r = 0.155small
Living areaMann–Whitney UU = 147.00—0.1318p < 0.0063Unclearrank-biserial r = 0.336medium
Year builtMann–Whitney UU = 107.50—0.9335p < 0.0063Unclearrank-biserial r = 0.023negligible
Days on marketMann–Whitney UU = 86.50—0.3418p < 0.0063Unclearrank-biserial r = 0.214small
Discount from original listMann–Whitney UU = 61.00—0.0452p < 0.0063Unclearrank-biserial r = 0.445medium
Price cut before saleChi-squareχ² = 0.2210.6387p < 0.0063UnclearCramér’s V = 0.084negligible
Seller paid closing costs or repairsChi-squareχ² = 4.0410.0443p < 0.0063UnclearCramér’s V = 0.361medium

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 31 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.