ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Gaston-Sheldon

Pflugerville, TX 78660
Resale homes · closed Oct 29 – Oct 28, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 29, 2026 · 7:48 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
$295,500
average $297,458
Median net price per SF
$196
average $193
Median days on market
35 days
average 63 days
1. SummaryResale homes · 31 sales · October 29, 2025 – October 28, 2026

This study looks at the 31 resale homes sold in Gaston-Sheldon in the 365 days to October 28, 2026. A full year is used because only 20 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 $282K · 8 sales$282K–$296K · 8 sales$299K–$325K · 7 salesOver $325K · 8 sales
3. Quartiles by net sale priceResale homes · 31 sales · October 29, 2025 – October 28, 2026

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

2026-10-29T07:48:29.730520 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 7 8 31 sales Sales per group Under $282K: 8 sales (26%) $282K–$296K: 8 sales (26%) $299K–$325K: 7 sales (23%) Over $325K: 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 $282K$282K–$296K$299K–$325KOver $325K
Higher-priced homes Over $325K
  • Sell closer to their first asking price.
Lower-priced homes Under $282K
  • Sell further below their first asking price.

3.2 At a glance

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

Under $282K$282K–$296K$299K–$325KOver $325KReal difference?
Net price per SF$192 per SF$195 per SF$193 per SF$193 per SFunclear
Living area1,369 SF1,513 SF1,634 SF1,745 SFunclear
Year built2003200420032003unclear
Days on market87 days44 days33 days16 daysunclear
Discount from original list13.3%Biggest5.8%5.6%1.6%Smallest✓ large
Price cut before sale88%62%43%12%unclear
Seller paid closing costs or repairs88%100%86%50%unclear
Has a view38%25%14%25%unclear

How to read this section. Each heading gives the finding. The gold box says whether the gap is real (✓, with its size) or could be chance (–). The section ends with its numbers; the tests are in section 8.3.

3.3 Net price per SF: no clear difference

2026-10-29T07:48:29.757873 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $282K 8 sales $282K– $296K 8 sales $299K– $325K 7 sales Over $325K 8 sales $0 $100 $200 $192 $195 $193 $193 Net price per SF, average

The average is $193 per SF for homes over $325K and $192 per SF for homes under $282K. It does not move in a straight line; homes $282K–$296K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Living area: no clear difference

2026-10-29T07:48:29.779408 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $282K 8 sales $282K– $296K 8 sales $299K– $325K 7 sales Over $325K 8 sales 0 1,000 2,000 1,369 1,513 1,634 1,745 Living area (SF), average

The average is 1,745 SF for homes over $325K and 1,369 SF for homes under $282K. It rises step by step from the lowest range to the highest. Defined in 8.1.3.

– No clear difference · could be chance

3.5 Year built: no clear difference

2026-10-29T07:48:29.801513 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $282K 8 sales $282K– $296K 8 sales $299K– $325K 7 sales Over $325K 8 sales 2002 2003 2004 2003 2004 2003 2003 Year built: median (dot) and middle half (bar)

The median is 2003 for homes over $325K and 2003 for homes under $282K. It does not move in a straight line; homes $282K–$296K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-29T07:48:29.823622 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $282K 8 sales $282K– $296K 8 sales $299K– $325K 7 sales Over $325K 8 sales 0 50 100 87 44 33 16 Days on market, median

The median is 16 days for homes over $325K and 87 days for homes under $282K. It falls step by step from the lowest range to the highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Homes over $325K sell about 12 percentage points closer to their first asking price than homes under $282K

2026-10-29T07:48:29.844723 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $282K 8 sales $282K– $296K 8 sales $299K– $325K 7 sales Over $325K 8 sales 0.0% 5.0% 10.0% 15.0% 13.3% 5.8% 5.6% 1.6% Discount from original list, average

The average is 1.6% for homes over $325K and 13.3% for homes under $282K. It falls step by step from the lowest range to the highest. Defined in 8.1.6.

✓ Real difference · large

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

2026-10-29T07:48:29.874337 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% 88 88 38 62 100 25 43 86 14 12 50 25 Under $282K $282K–$296K $299K–$325K Over $325K

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

  • 12% of homes over $325K had a price cut before sale, against 88% of homes under $282K. Defined in 8.1.7.
  • 50% of homes over $325K had the seller pay closing costs or repairs, against 88% of homes under $282K. Defined in 8.1.8.
  • 25% of homes over $325K have a view, against 38% of homes under $282K. 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 $282K8 sales$282K–$296K8 sales$299K–$325K7 salesOver $325K8 salesAll31 salesReal difference?
Net sale price average$256,804$290,218$307,418$336,638$297,458by design
Net price per SF average$192$195$193$193$193unclear
Living area average1,3691,5131,6341,7451,563unclear
Year built median20032004200320032003unclear

3.10 The numbers: selling

Under $282K8 sales$282K–$296K8 sales$299K–$325K7 salesOver $325K8 salesAll31 salesReal difference?
Days on market median8744331635unclear
Discount from original list average13.3%5.8%5.6%1.6%6.6%✓ large
Price cut before sale88%62%43%12%52%unclear
Seller paid closing costs or repairs88%100%86%50%81%unclear
Has a view38%25%14%25%26%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 29, 2025 – October 28, 2026

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

2026-10-29T07:48:29.894860 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 6 9 8 8 31 sales Sales per group Under 1,355 SF: 6 sales (19%) 1,355–1,590 SF: 9 sales (29%) 1,685 SF: 8 sales (26%) Over 1,685 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 1,355 SF1,355–1,590 SF1,685 SFOver 1,685 SF
Larger homes Over 1,685 SF
  • Sell for less per square foot.
Smaller homes Under 1,355 SF
  • 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 1,355 SF1,355–1,590 SF1,685 SFOver 1,685 SFReal difference?
Net sale price$278K$285K$301K$321Kunclear
Net price per SF$228 per SFHighest$199 per SF$179 per SF$176 per SFLowest✓ large
Year built2004200320032004unclear
Days on market66 days35 days29 days22 daysunclear
Discount from original list6.1%7.7%8.4%4.0%unclear
Price cut before sale83%44%50%38%unclear
Seller paid closing costs or repairs67%100%88%62%unclear
Has a view50%22%25%12%unclear

4.3 Net sale price: no clear difference

2026-10-29T07:48:29.915039 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,355 SF 6 sales 1,355– 1,590 SF 9 sales 1,685 SF 8 sales Over 1,685 SF 8 sales $0K $100K $200K $300K $278K $285K $301K $321K Net sale price, average

The average is $321K for homes over 1,685 SF and $278K for homes under 1,355 SF. It rises step by step from the lowest range to the highest. Defined in 8.1.1.

– No clear difference · could be chance

4.4 Homes over 1,685 SF sell for about $52 per SF (23%) less than homes under 1,355 SF

2026-10-29T07:48:29.937870 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,355 SF 6 sales 1,355– 1,590 SF 9 sales 1,685 SF 8 sales Over 1,685 SF 8 sales $0 $100 $200 $228 $199 $179 $176 Net price per SF, average

The average is $176 per SF for homes over 1,685 SF and $228 per SF for homes under 1,355 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

2026-10-29T07:48:29.960777 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,355 SF 6 sales 1,355– 1,590 SF 9 sales 1,685 SF 8 sales Over 1,685 SF 8 sales 2002 2003 2004 2005 2004 2003 2003 2004 Year built: median (dot) and middle half (bar)

The median is 2004 for homes over 1,685 SF and 2004 for homes under 1,355 SF. It does not move in a straight line; homes under 1,355 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-29T07:48:29.983048 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,355 SF 6 sales 1,355– 1,590 SF 9 sales 1,685 SF 8 sales Over 1,685 SF 8 sales 0 20 40 60 66 35 29 22 Days on market, median

The median is 22 days for homes over 1,685 SF and 66 days for homes under 1,355 SF. It falls step by step from the lowest range to the highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-29T07:48:30.005096 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,355 SF 6 sales 1,355– 1,590 SF 9 sales 1,685 SF 8 sales Over 1,685 SF 8 sales 0.0% 5.0% 10.0% 6.1% 7.7% 8.4% 4.0% Discount from original list, average

The average is 4.0% for homes over 1,685 SF and 6.1% for homes under 1,355 SF. It does not move in a straight line; homes 1,685 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

2026-10-29T07:48:30.031161 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% 83 67 50 44 100 22 50 88 25 38 62 12 Under 1,355 SF 1,355–1,590 SF 1,685 SF Over 1,685 SF

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

  • 38% of homes over 1,685 SF had a price cut before sale, against 83% of homes under 1,355 SF. Defined in 8.1.7.
  • 62% of homes over 1,685 SF had the seller pay closing costs or repairs, against 67% of homes under 1,355 SF. Defined in 8.1.8.
  • 12% of homes over 1,685 SF have a view, against 50% of homes under 1,355 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 1,355 SF6 sales1,355–1,590 SF9 sales1,685 SF8 salesOver 1,685 SF8 salesAll31 salesReal difference?
Net sale price average$278,498$285,291$301,440$321,385$297,458unclear
Net price per SF average$228$199$179$176$193✓ large
Living area average1,2261,4371,6851,8361,563by design
Year built median20042003200320042003unclear

4.10 The numbers: selling

Under 1,355 SF6 sales1,355–1,590 SF9 sales1,685 SF8 salesOver 1,685 SF8 salesAll31 salesReal difference?
Days on market median6635292235unclear
Discount from original list average6.1%7.7%8.4%4.0%6.6%unclear
Price cut before sale83%44%50%38%52%unclear
Seller paid closing costs or repairs67%100%88%62%81%unclear
Has a view50%22%25%12%26%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 29, 2025 – October 28, 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.

2026-10-29T07:48:30.050946 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 11 7 6 31 sales Sales per group Before 2003: 7 sales (23%) 2003: 11 sales (35%) 2004: 7 sales (23%) After 2004: 6 sales (19%)
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 200320032004After 2004Real difference?
Net sale price$297K$301K$300K$288Kunclear
Net price per SF$194 per SF$192 per SF$205 per SF$182 per SFunclear
Living area1,543 SF1,602 SF1,494 SF1,596 SFunclear
Days on market35 days33 days29 days66 daysunclear
Discount from original list5.6%7.1%5.2%8.6%unclear
Price cut before sale43%45%57%67%unclear
Seller paid closing costs or repairs86%73%71%100%unclear
Has a view43%27%29%0%unclear

5.3 Net sale price: no clear difference

2026-10-29T07:48:30.070548 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2003 7 sales 2003 11 sales 2004 7 sales After 2004 6 sales $0K $100K $200K $300K $297K $301K $300K $288K Net sale price, average

The average is $288K for homes built after 2004 and $297K for homes built before 2003. It does not move in a straight line; homes built 2003 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-29T07:48:30.093420 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2003 7 sales 2003 11 sales 2004 7 sales After 2004 6 sales $0 $50 $100 $150 $200 $194 $192 $205 $182 Net price per SF, average

The average is $182 per SF for homes built after 2004 and $194 per SF for homes built before 2003. It does not move in a straight line; homes built 2004 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-29T07:48:30.114583 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2003 7 sales 2003 11 sales 2004 7 sales After 2004 6 sales 0 500 1,000 1,500 1,543 1,602 1,494 1,596 Living area (SF), average

The average is 1,596 SF for homes built after 2004 and 1,543 SF for homes built before 2003. It does not move in a straight line; homes built 2003 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-29T07:48:30.135164 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2003 7 sales 2003 11 sales 2004 7 sales After 2004 6 sales 0 20 40 60 35 33 29 66 Days on market, median

The median is 66 days for homes built after 2004 and 35 days for homes built before 2003. It does not move in a straight line; homes built after 2004 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-29T07:48:30.157949 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2003 7 sales 2003 11 sales 2004 7 sales After 2004 6 sales 0.0% 2.5% 5.0% 7.5% 10.0% 5.6% 7.1% 5.2% 8.6% Discount from original list, average

The average is 8.6% for homes built after 2004 and 5.6% for homes built before 2003. It does not move in a straight line; homes built after 2004 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

2026-10-29T07:48:30.183345 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% 43 86 43 45 73 27 57 71 29 67 100 0 Before 2003 2003 2004 After 2004

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

  • 67% of homes built after 2004 had a price cut before sale, against 43% of homes built before 2003. Defined in 8.1.7.
  • 100% of homes built after 2004 had the seller pay closing costs or repairs, against 86% of homes built before 2003. Defined in 8.1.8.
  • 0% of homes built after 2004 have a view, against 43% of homes built before 2003. 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 20037 sales200311 sales20047 salesAfter 20046 salesAll31 salesReal difference?
Net sale price average$297,270$301,423$299,854$287,615$297,458unclear
Net price per SF average$194$192$205$182$193unclear
Living area average1,5431,6021,4941,5961,563unclear
Year built median20022003200420052003by design

5.10 The numbers: selling

Before 20037 sales200311 sales20047 salesAfter 20046 salesAll31 salesReal difference?
Days on market median3533296635unclear
Discount from original list average5.6%7.1%5.2%8.6%6.6%unclear
Price cut before sale43%45%57%67%52%unclear
Seller paid closing costs or repairs86%73%71%100%81%unclear
Has a view43%27%29%0%26%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 29, 2025 – October 28, 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.

2026-10-29T07:48:30.200963 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 23 31 sales Sales per group View: 8 sales (26%) No view: 23 sales (74%)
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$293K$299Kunclear
Net price per SF$204 per SF$190 per SFunclear
Living area1,446 SF1,604 SFunclear
Year built20032003unclear
Days on market76 days33 daysunclear
Discount from original list6.3%6.7%unclear
Price cut before sale50%52%unclear
Seller paid closing costs or repairs100%74%unclear

6.3 Net sale price: no clear difference

2026-10-29T07:48:30.216239 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 23 sales $0K $100K $200K $300K $293K $299K Net sale price, average

The average is $293K for homes with a view and $299K 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

2026-10-29T07:48:30.234306 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 23 sales $0 $50 $100 $150 $200 $204 $190 Net price per SF, average

The average is $204 per SF for homes with a view and $190 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

2026-10-29T07:48:30.252551 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 23 sales 0 500 1,000 1,500 1,446 1,604 Living area (SF), average

The average is 1,446 SF for homes with a view and 1,604 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-29T07:48:30.291298 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 23 sales 2002 2003 2004 2003 2003 Year built: median (dot) and middle half (bar)

The median is 2003 for homes with a view and 2003 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-29T07:48:30.309091 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 23 sales 0 20 40 60 80 76 33 Days on market, median

The median is 76 days for homes with a view and 33 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

2026-10-29T07:48:30.328193 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 23 sales 0.0% 2.0% 4.0% 6.0% 8.0% 6.3% 6.7% Discount from original list, average

The average is 6.3% for homes with a view and 6.7% for homes without a view. Defined in 8.1.6.

– No clear difference · could be chance

6.9 How often: price cut before sale, seller paid closing costs or repairs

2026-10-29T07:48:30.346792 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 50 100 52 74 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 52% of homes without a view. Defined in 8.1.7.
  • 100% of homes with a view had the seller pay closing costs or repairs, against 74% 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

View8 salesNo view23 salesAll31 salesReal difference?
Net sale price average$292,674$299,122$297,458unclear
Net price per SF average$204$190$193unclear
Living area average1,4461,6041,563unclear
Year built median200320032003unclear

6.11 The numbers: selling

View8 salesNo view23 salesAll31 salesReal difference?
Days on market median763335unclear
Discount from original list average6.3%6.7%6.6%unclear
Price cut before sale50%52%52%unclear
Seller paid closing costs or repairs100%74%81%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 29, 2025 – October 28, 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 Gaston-Sheldon.

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 $297,458; median $295,500; standard deviation $33,169; middle half (25th–75th percentile) $277,450–$327,225; lowest $188,700, highest $345,400. 25 sales had a seller payment; their median payment was $6,525.

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 $193; median $196; standard deviation $27; middle half (25th–75th percentile) $176–$209; lowest $112, highest $251.

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 1,563; median 1,685; standard deviation 244; middle half (25th–75th percentile) 1,355–1,696; lowest 1,167, highest 2,116.

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 2003; median 2003; standard deviation 1; middle half (25th–75th percentile) 2003–2004; lowest 2002, highest 2005.

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 63; median 35; standard deviation 73; middle half (25th–75th percentile) 16–87; lowest 3, highest 317. 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
31 resale sales. Average 6.6%; median 5.1%; standard deviation 6.7%; middle half (25th–75th percentile) 2.4%–9.2%; lowest −1.5%, highest 34.7%. 2 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 31 resale sales (51.6%).

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
25 of 31 resale sales (80.6%).

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
8 sales with a view, 23 without, 0 unclassified. Most common View entries: None 20, Neighborhood 7, Park/Greenbelt 4, Trees/Woods 2, Pool 1, See Remarks 1, Pond 1, City Lights 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 = Pflugerville, SubdivisionName contains any of “gaston sheldon”, “gaston-sheldon” (case-insensitive). A record is kept when its subdivision name belongs to the Gaston-Sheldon family, the same rule comparable sales use.
The data
Pulled October 29, 2026: 31 closed Gaston-Sheldon records over the prior year, in ZIP 78660. 7 subdivision spellings kept: Gaston-Sheldon Sec 03 (12); Gaston-Sheldon Sec 05 (6); Gaston-Sheldon Sec 02 (4); Gaston-Sheldon (3); Gaston-Sheldon Sec 04 (3); Gaston-Sheldon Sec 01 (2); Gaston-sheldon Subd Sec 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 20 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 29, 2025 to October 28, 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 $282K”.
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 $282K: $188,700–$273,400 (8 sales); $282K–$296K: $281,500–$295,500 (8 sales); $299K–$325K: $299,000–$324,950 (7 sales); Over $325K: $329,500–$345,400 (8 sales)
Quartiles by living area: Under 1,355 SF: 1,167–1,346 SF (6 sales); 1,355–1,590 SF: 1,355–1,590 SF (9 sales); 1,685 SF: 1,685–1,685 SF (8 sales); Over 1,685 SF: 1,708–2,116 SF (8 sales)
Quartiles by year built: Before 2003: 2002–2002 (7 sales); 2003: 2003–2003 (11 sales); 2004: 2004–2004 (7 sales); After 2004: 2005–2005 (6 sales)
View and no view: View: MLS lists a view (8 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 $297,458, median $295,500. Days on market: average 63, median 35: 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
2 real differences across the four slices; sizes are listed in 8.3.

8.2.12 Rounding in the text

In simple terms
Headings and sentences round figures so they read easily; the tables at the end of each section give them in full.
Statistically
Prices in headings round to the nearest $1K; price per SF and days on market to the nearest whole number; living area to the nearest 10 SF; shares and percentage gaps to whole percents.
The data
Days on market is always shown in days.

8.3 Test results

Every test behind every gold box. Statistic, degrees of freedom (df), p-value, the threshold it must beat, and the size of the difference.

8.3.1 Quartiles by net sale price

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net price per SFKruskal–WallisH = 0.1730.9825p < 0.0063Unclearε² = 0.006negligible
Living areaKruskal–WallisH = 10.7930.0129p < 0.0063Unclearε² = 0.360large
Year builtKruskal–WallisH = 3.0330.3874p < 0.0063Unclearε² = 0.101medium
Days on marketKruskal–WallisH = 10.6730.0136p < 0.0063Unclearε² = 0.356large
Discount from original listKruskal–WallisH = 16.2530.0010p < 0.0063Yesε² = 0.542large
Price cut before saleChi-squareχ² = 9.6230.0221p < 0.0063UnclearCramér’s V = 0.557large
Seller paid closing costs or repairsChi-squareχ² = 7.0930.0691p < 0.0063UnclearCramér’s V = 0.478medium
Has a viewChi-squareχ² = 1.0630.7863p < 0.0063UnclearCramér’s V = 0.185small

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 10.9430.0121p < 0.0063Unclearε² = 0.365large
Net price per SFKruskal–WallisH = 17.8330.0005p < 0.0063Yesε² = 0.594large
Year builtKruskal–WallisH = 0.0130.9997p < 0.0063Unclearε² = 0.000negligible
Days on marketKruskal–WallisH = 4.5230.2104p < 0.0063Unclearε² = 0.151large
Discount from original listKruskal–WallisH = 2.4930.4776p < 0.0063Unclearε² = 0.083medium
Price cut before saleChi-squareχ² = 3.2530.3548p < 0.0063UnclearCramér’s V = 0.324medium
Seller paid closing costs or repairsChi-squareχ² = 4.8430.1839p < 0.0063UnclearCramér’s V = 0.395medium
Has a viewChi-squareχ² = 2.6430.4510p < 0.0063UnclearCramér’s V = 0.292small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 1.7430.6290p < 0.0063Unclearε² = 0.058small
Net price per SFKruskal–WallisH = 2.8130.4219p < 0.0063Unclearε² = 0.094medium
Living areaKruskal–WallisH = 0.2730.9647p < 0.0063Unclearε² = 0.009negligible
Days on marketKruskal–WallisH = 1.2830.7338p < 0.0063Unclearε² = 0.043small
Discount from original listKruskal–WallisH = 2.7730.4277p < 0.0063Unclearε² = 0.092medium
Price cut before saleChi-squareχ² = 1.0130.7983p < 0.0063UnclearCramér’s V = 0.181small
Seller paid closing costs or repairsChi-squareχ² = 2.3830.4977p < 0.0063UnclearCramér’s V = 0.277small
Has a viewChi-squareχ² = 3.1930.3632p < 0.0063UnclearCramér’s V = 0.321medium

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 71.00—0.3620p < 0.0063Unclearrank-biserial r = 0.228small
Net price per SFMann–Whitney UU = 125.00—0.1448p < 0.0063Unclearrank-biserial r = 0.359medium
Living areaMann–Whitney UU = 60.00—0.1507p < 0.0063Unclearrank-biserial r = 0.348medium
Year builtMann–Whitney UU = 59.00—0.1273p < 0.0063Unclearrank-biserial r = 0.359medium
Days on marketMann–Whitney UU = 133.00—0.0673p < 0.0063Unclearrank-biserial r = 0.446medium
Discount from original listMann–Whitney UU = 94.00—0.9471p < 0.0063Unclearrank-biserial r = 0.022negligible
Price cut before saleChi-squareχ² = 0.0110.9156p < 0.0063UnclearCramér’s V = 0.019negligible
Seller paid closing costs or repairsChi-squareχ² = 2.5910.1077p < 0.0063UnclearCramér’s V = 0.289small

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 29, 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.