ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Sweetwater Ranch

Austin, TX 78738
Resale homes · closed Oct 27 – Oct 26, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 27, 2026 · 7:34 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
41
single-family resale homes
Median net sale price
$624,400
average $766,819
Median net price per SF
$251
average $261
Median days on market
52 days
average 75 days
1. SummaryResale homes · 41 sales · October 27, 2025 – October 26, 2026

This study looks at the 41 resale homes sold in Sweetwater Ranch in the 365 days to October 26, 2026. A full year is used because only 24 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 sold41 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 $550K · 10 sales$550K–$600K · 10 sales$624K–$745K · 11 salesOver $745K · 10 sales
3. Quartiles by net sale priceResale homes · 41 sales · October 27, 2025 – October 26, 2026

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

2026-10-27T07:34:30.608312 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 10 10 11 10 41 sales Sales per group Under $550K: 10 sales (24%) $550K–$600K: 10 sales (24%) $624K–$745K: 11 sales (27%) Over $745K: 10 sales (24%)
Bottom line

3.1 Key differences

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

Under $550K$550K–$600K$624K–$745KOver $745K
Higher-priced homes Over $745K
  • Are much larger.
Lower-priced homes Under $550K
  • Are much smaller.

3.2 At a glance

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

Under $550K$550K–$600K$624K–$745KOver $745KReal difference?
Net price per SF$240 per SF$248 per SF$241 per SF$317 per SFunclear
Living area2,047 SFSmallest2,372 SF2,791 SF4,152 SFLargest✓ large
Year built2017201820172022unclear
Days on market103 days28 days39 days70 daysunclear
Discount from original list8.6%5.6%6.3%8.3%unclear
Price cut before sale60%50%45%70%unclear
Seller paid closing costs or repairs70%50%55%60%unclear
Has a view50%60%36%90%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

2026-10-27T07:34:30.637039 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $550K 10 sales $550K– $600K 10 sales $624K– $745K 11 sales Over $745K 10 sales $0 $100 $200 $300 $240 $248 $241 $317 Net price per SF, average

The average is $317 per SF for homes over $745K and $240 per SF for homes under $550K. It does not move in a straight line; homes over $745K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Homes over $745K are about 2,110 SF (103%) larger than homes under $550K

2026-10-27T07:34:30.659508 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $550K 10 sales $550K– $600K 10 sales $624K– $745K 11 sales Over $745K 10 sales 0 2,000 4,000 2,047 2,372 2,791 4,152 Living area (SF), average

The average is 4,152 SF for homes over $745K and 2,047 SF for homes under $550K. It rises step by step from the lowest range to the highest. Defined in 8.1.3.

✓ Real difference · large

3.5 Year built: no clear difference

2026-10-27T07:34:30.683458 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $550K 10 sales $550K– $600K 10 sales $624K– $745K 11 sales Over $745K 10 sales 2016 2018 2020 2022 2017 2018 2017 2022 Year built: median (dot) and middle half (bar)

The median is 2022 for homes over $745K and 2017 for homes under $550K. It does not move in a straight line; homes over $745K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-27T07:34:30.705962 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $550K 10 sales $550K– $600K 10 sales $624K– $745K 11 sales Over $745K 10 sales 0 50 100 103 28 39 70 Days on market, median

The median is 70 days for homes over $745K and 103 days for homes under $550K. It does not move in a straight line; homes under $550K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-10-27T07:34:30.727832 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $550K 10 sales $550K– $600K 10 sales $624K– $745K 11 sales Over $745K 10 sales 0.0% 5.0% 10.0% 8.6% 5.6% 6.3% 8.3% Discount from original list, average

The average is 8.3% for homes over $745K and 8.6% for homes under $550K. It does not move in a straight line; homes under $550K 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

2026-10-27T07:34:30.757463 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% 60 70 50 50 50 60 45 55 36 70 60 90 Under $550K $550K–$600K $624K–$745K Over $745K

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

  • 70% of homes over $745K had a price cut before sale, against 60% of homes under $550K. Defined in 8.1.7.
  • 60% of homes over $745K had the seller pay closing costs or repairs, against 70% of homes under $550K. Defined in 8.1.8.
  • 90% of homes over $745K have a view, against 50% of homes under $550K. 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 $550K10 sales$550K–$600K10 sales$624K–$745K11 salesOver $745K10 salesAll41 salesReal difference?
Net sale price average$485,522$579,754$665,185$1,346,979$766,819by design
Net price per SF average$240$248$241$317$261unclear
Living area average2,0472,3722,7914,1522,839✓ large
Year built median20172018201720222019unclear

3.10 The numbers: selling

Under $550K10 sales$550K–$600K10 sales$624K–$745K11 salesOver $745K10 salesAll41 salesReal difference?
Days on market median10328397052unclear
Discount from original list average8.6%5.6%6.3%8.3%7.2%unclear
Price cut before sale60%50%45%70%56%unclear
Seller paid closing costs or repairs70%50%55%60%59%unclear
Has a view50%60%36%90%59%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 · 41 sales · October 27, 2025 – October 26, 2026

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

2026-10-27T07:34:30.778264 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 9 11 11 10 41 sales Sales per group Under 2,181 SF: 9 sales (22%) 2,181–2,544 SF: 11 sales (27%) 2,608–3,106 SF: 11 sales (27%) Over 3,106 SF: 10 sales (24%)
Bottom line

4.1 Key differences

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

Under 2,181 SF2,181–2,544 SF2,608–3,106 SFOver 3,106 SF
Larger homes Over 3,106 SF
  • Sell for much more.
  • Are more likely to have a view.
Smaller homes Under 2,181 SF
  • Sell for much less.
  • Are less likely to have a view.

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 2,181 SF2,181–2,544 SF2,608–3,106 SFOver 3,106 SFReal difference?
Net sale price$511KLowest$564K$676K$1.32MHighest✓ large
Net price per SF$264 per SF$243 per SF$239 per SF$303 per SFunclear
Year built2019201820202022unclear
Days on market52 days47 days32 days73 daysunclear
Discount from original list6.5%7.9%4.7%9.7%unclear
Price cut before sale44%64%45%70%unclear
Seller paid closing costs or repairs67%55%55%60%unclear
Has a view56%73%18%Fewest90%Most✓ large

4.3 Homes over 3,106 SF sell for about $810K (159%) more than homes under 2,181 SF

2026-10-27T07:34:30.799332 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,181 SF 9 sales 2,181– 2,544 SF 11 sales 2,608– 3,106 SF 11 sales Over 3,106 SF 10 sales $0K $500K $1.00M $1.50M $511K $564K $676K $1.32M Net sale price, average

The average is $1.32M for homes over 3,106 SF and $511K for homes under 2,181 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

2026-10-27T07:34:30.822320 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,181 SF 9 sales 2,181– 2,544 SF 11 sales 2,608– 3,106 SF 11 sales Over 3,106 SF 10 sales $0 $100 $200 $300 $264 $243 $239 $303 Net price per SF, average

The average is $303 per SF for homes over 3,106 SF and $264 per SF for homes under 2,181 SF. It does not move in a straight line; homes over 3,106 SF are highest. Defined in 8.1.2.

– No clear difference · could be chance

4.5 Year built: no clear difference

2026-10-27T07:34:30.846720 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,181 SF 9 sales 2,181– 2,544 SF 11 sales 2,608– 3,106 SF 11 sales Over 3,106 SF 10 sales 2016 2018 2020 2022 2019 2018 2020 2022 Year built: median (dot) and middle half (bar)

The median is 2022 for homes over 3,106 SF and 2019 for homes under 2,181 SF. It does not move in a straight line; homes over 3,106 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-27T07:34:30.870139 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,181 SF 9 sales 2,181– 2,544 SF 11 sales 2,608– 3,106 SF 11 sales Over 3,106 SF 10 sales 0 25 50 75 52 47 32 73 Days on market, median

The median is 73 days for homes over 3,106 SF and 52 days for homes under 2,181 SF. It does not move in a straight line; homes over 3,106 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-27T07:34:30.893014 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,181 SF 9 sales 2,181– 2,544 SF 11 sales 2,608– 3,106 SF 11 sales Over 3,106 SF 10 sales 0.0% 5.0% 10.0% 6.5% 7.9% 4.7% 9.7% Discount from original list, average

The average is 9.7% for homes over 3,106 SF and 6.5% for homes under 2,181 SF. It does not move in a straight line; homes over 3,106 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

2026-10-27T07:34:30.919983 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% 44 67 56 64 55 73 45 55 18 70 60 90 Under 2,181 SF 2,181–2,544 SF 2,608–3,106 SF Over 3,106 SF

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

  • 70% of homes over 3,106 SF had a price cut before sale, against 44% of homes under 2,181 SF. Defined in 8.1.7.
  • 60% of homes over 3,106 SF had the seller pay closing costs or repairs, against 67% of homes under 2,181 SF. Defined in 8.1.8.
  • 90% of homes over 3,106 SF have a view, against 56% of homes under 2,181 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: ✓ Real difference · large

4.9 The numbers: price and size

Under 2,181 SF9 sales2,181–2,544 SF11 sales2,608–3,106 SF11 salesOver 3,106 SF10 salesAll41 salesReal difference?
Net sale price average$510,712$564,378$675,582$1,320,363$766,819✓ large
Net price per SF average$264$243$239$303$261unclear
Living area average1,9332,3312,8264,2292,839by design
Year built median20192018202020222019unclear

4.10 The numbers: selling

Under 2,181 SF9 sales2,181–2,544 SF11 sales2,608–3,106 SF11 salesOver 3,106 SF10 salesAll41 salesReal difference?
Days on market median5247327352unclear
Discount from original list average6.5%7.9%4.7%9.7%7.2%unclear
Price cut before sale44%64%45%70%56%unclear
Seller paid closing costs or repairs67%55%55%60%59%unclear
Has a view56%73%18%90%59%✓ large

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 · 41 sales · October 27, 2025 – October 26, 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.

2026-10-27T07:34:30.940687 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 10 12 11 41 sales Sales per group Before 2016: 8 sales (20%) 2016–2018: 10 sales (24%) 2019–2022: 12 sales (29%) After 2022: 11 sales (27%)
Bottom line: no measure differs enough between the groups to rule out chance.

5.1 Key differences

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

5.2 At a glance

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

Before 20162016–20182019–2022After 2022Real difference?
Net sale price$590K$626K$787K$1.00Munclear
Net price per SF$218 per SF$257 per SF$264 per SF$293 per SFunclear
Living area2,723 SF2,442 SF2,851 SF3,273 SFunclear
Days on market64 days33 days48 days52 daysunclear
Discount from original list5.0%7.4%6.9%8.7%unclear
Price cut before sale50%50%58%64%unclear
Seller paid closing costs or repairs75%70%58%36%unclear
Has a view25%70%42%91%unclear

5.3 Net sale price: no clear difference

2026-10-27T07:34:30.961461 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2016 8 sales 2016– 2018 10 sales 2019– 2022 12 sales After 2022 11 sales $0K $500K $1.00M $590K $626K $787K $1.00M Net sale price, average

The average is $1.00M for homes built after 2022 and $590K for homes built before 2016. It rises step by step from the lowest range to the highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-27T07:34:30.984154 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2016 8 sales 2016– 2018 10 sales 2019– 2022 12 sales After 2022 11 sales $0 $100 $200 $300 $218 $257 $264 $293 Net price per SF, average

The average is $293 per SF for homes built after 2022 and $218 per SF for homes built before 2016. It rises step by step from the lowest range to the highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-27T07:34:31.006054 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2016 8 sales 2016– 2018 10 sales 2019– 2022 12 sales After 2022 11 sales 0 1,000 2,000 3,000 2,723 2,442 2,851 3,273 Living area (SF), average

The average is 3,273 SF for homes built after 2022 and 2,723 SF for homes built before 2016. It does not move in a straight line; homes built after 2022 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-27T07:34:31.027965 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2016 8 sales 2016– 2018 10 sales 2019– 2022 12 sales After 2022 11 sales 0 20 40 60 64 33 48 52 Days on market, median

The median is 52 days for homes built after 2022 and 64 days for homes built before 2016. It does not move in a straight line; homes built before 2016 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-27T07:34:31.051079 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2016 8 sales 2016– 2018 10 sales 2019– 2022 12 sales After 2022 11 sales 0.0% 5.0% 10.0% 5.0% 7.4% 6.9% 8.7% Discount from original list, average

The average is 8.7% for homes built after 2022 and 5.0% for homes built before 2016. It does not move in a straight line; homes built after 2022 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

2026-10-27T07:34:31.076575 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% 50 75 25 50 70 70 58 58 42 64 36 91 Before 2016 2016–2018 2019–2022 After 2022

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

  • 64% of homes built after 2022 had a price cut before sale, against 50% of homes built before 2016. Defined in 8.1.7.
  • 36% of homes built after 2022 had the seller pay closing costs or repairs, against 75% of homes built before 2016. Defined in 8.1.8.
  • 91% of homes built after 2022 have a view, against 25% of homes built before 2016. 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 20168 sales2016–201810 sales2019–202212 salesAfter 202211 salesAll41 salesReal difference?
Net sale price average$589,639$626,176$786,636$1,001,917$766,819unclear
Net price per SF average$218$257$264$293$261unclear
Living area average2,7232,4422,8513,2732,839unclear
Year built median20152016202020232019by design

5.10 The numbers: selling

Before 20168 sales2016–201810 sales2019–202212 salesAfter 202211 salesAll41 salesReal difference?
Days on market median6433485252unclear
Discount from original list average5.0%7.4%6.9%8.7%7.2%unclear
Price cut before sale50%50%58%64%56%unclear
Seller paid closing costs or repairs75%70%58%36%59%unclear
Has a view25%70%42%91%59%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 · 41 sales · October 27, 2025 – October 26, 2026

Sales where the MLS lists a view (greenbelt or park, trees, golf course, hill country, water and similar) against those listing none or only the neighborhood. 1 sales whose MLS view entry is only “See Remarks” are left out.

2026-10-27T07:34:31.094555 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 24 16 40 sales Sales per group View: 24 sales (60%) No view: 16 sales (40%)
Bottom line

6.1 Key differences

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

ViewNo view
Homes with a view MLS lists a view
  • Sell for more per square foot.
Homes without a view no view listed
  • Sell for less per square foot.

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$888K$577Kunclear
Net price per SF$279 per SFHighest$232 per SFLowest✓ large
Living area3,044 SF2,516 SFunclear
Year built20202016unclear
Days on market58 days36 daysunclear
Discount from original list8.0%6.2%unclear
Price cut before sale58%56%unclear
Seller paid closing costs or repairs62%50%unclear

6.3 Net sale price: no clear difference

2026-10-27T07:34:31.111781 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 24 sales No view 16 sales $0K $250K $500K $750K $1.00M $888K $577K Net sale price, average

The average is $888K for homes with a view and $577K for homes without a view. Defined in 8.1.1.

– No clear difference · could be chance

6.4 Homes with a view sell for about $47 per SF (20%) more than homes without a view

2026-10-27T07:34:31.129797 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 24 sales No view 16 sales $0 $100 $200 $300 $279 $232 Net price per SF, average

The average is $279 per SF for homes with a view and $232 per SF for homes without a view. Defined in 8.1.2.

✓ Real difference · large

6.5 Living area: no clear difference

2026-10-27T07:34:31.147961 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 24 sales No view 16 sales 0 1,000 2,000 3,000 3,044 2,516 Living area (SF), average

The average is 3,044 SF for homes with a view and 2,516 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-27T07:34:31.233316 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 24 sales No view 16 sales 2016 2018 2020 2022 2020 2016 Year built: median (dot) and middle half (bar)

The median is 2020 for homes with a view and 2016 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-27T07:34:31.251526 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 24 sales No view 16 sales 0 20 40 60 58 36 Days on market, median

The median is 58 days for homes with a view and 36 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

2026-10-27T07:34:31.271780 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 24 sales No view 16 sales 0.0% 2.0% 4.0% 6.0% 8.0% 8.0% 6.2% Discount from original list, average

The average is 8.0% for homes with a view and 6.2% 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

2026-10-27T07:34:31.291484 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 58 62 56 50 View No view

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

  • 58% of homes with a view had a price cut before sale, against 56% of homes without a view. Defined in 8.1.7.
  • 62% of homes with a view had the seller pay closing costs or repairs, against 50% 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

View24 salesNo view16 salesAll40 salesReal difference?
Net sale price average$888,348$577,202$763,890unclear
Net price per SF average$279$232$260✓ large
Living area average3,0442,5162,833unclear
Year built median202020162019unclear

6.11 The numbers: selling

View24 salesNo view16 salesAll40 salesReal difference?
Days on market median583654unclear
Discount from original list average8.0%6.2%7.3%unclear
Price cut before sale58%56%58%unclear
Seller paid closing costs or repairs62%50%58%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 · 1 sales · October 27, 2025 – October 26, 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: 1 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 Sweetwater Ranch.
MeasureAverageMedian
Sales1
Net sale price$920,000$920,000
Net price per SF$309$309
Living area (SF)2,9732,973
Days on market159159
Discount from original list16.3%16.3%
Price cut before sale100% of sales
Seller paid closing costs or repairs0% of sales
Year built2022–2022

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
41 resale sales. Average $766,819; median $624,400; standard deviation $435,712; middle half (25th–75th percentile) $549,935–$744,839; lowest $441,250, highest $2,600,000. 24 sales had a seller payment; their median payment was $3,750.

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
41 resale sales. Average $261; median $251; standard deviation $56; middle half (25th–75th percentile) $225–$280; lowest $192, highest $524.

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
41 resale sales. Average 2,839; median 2,608; standard deviation 950; middle half (25th–75th percentile) 2,181–3,106; lowest 1,707, highest 5,416.

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
41 resale sales. Average 2019; median 2019; standard deviation 3; middle half (25th–75th percentile) 2016–2023; lowest 2014, highest 2024.

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
41 resale sales. Average 75; median 52; standard deviation 68; middle half (25th–75th percentile) 23–107; lowest 0, highest 265. 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
41 resale sales. Average 7.2%; median 6.1%; standard deviation 5.3%; middle half (25th–75th percentile) 3.3%–10.3%; lowest −0.8%, highest 20.0%. 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
23 of 41 resale sales (56.1%).

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
24 of 41 resale sales (58.5%).

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
24 sales with a view, 16 without, 1 unclassified. Most common View entries: Hill Country 21, Neighborhood 15, Park/Greenbelt 10, Trees/Woods 8, Panoramic 7, None 7, See Remarks 2, Canyon 2.

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 “sweetwater ranch” (case-insensitive). A record is kept when its subdivision name belongs to the Sweetwater Ranch family, the same rule comparable sales use.
The data
Pulled October 27, 2026: 42 closed Sweetwater Ranch records over the prior year, in ZIP 78738. 17 subdivision spellings kept, the most common: Sweetwater Ranch Sec 1 Village (11); Sweetwater Ranch (7); Sweetwater Ranch Sec 2 Vlgs C (4); Sweetwater Ranch Sec 1 Village L P (3); Sweetwater Ranch Sec 2 Village N (2); Sweetwater Ranch Sec 2 Village P3A (2); Sweetwater Ranch Sec 2 Village (2); Sweetwater Ranch Sec 2 Vlgs M (2); Sweetwater Ranch Sec 2 Village E1 (1); Sweetwater Ranch Sec 2 Village O (1); Sweetwater Ranch Sec 2 Village P2 (1); SWEETWATER RANCH SEC 2 (1); and 5 more (5 records). No other names were found by the search.

8.2.2 Study window

In simple terms
The most recent 365 days of closed sales: recent enough to describe today's market, long enough to give each group plenty of sales. The usual window is 180 days; it holds only 24 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 27, 2025 to October 26, 2026 inclusive.
The data
42 single-family sales in the window: 41 resale, 1 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
41 resale; 1 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 $550K”.
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 $550K: $441,250–$535,000 (10 sales); $550K–$600K: $549,935–$600,000 (10 sales); $624K–$745K: $624,400–$744,839 (11 sales); Over $745K: $767,000–$2,600,000 (10 sales)
Quartiles by living area: Under 2,181 SF: 1,707–2,144 SF (9 sales); 2,181–2,544 SF: 2,181–2,544 SF (11 sales); 2,608–3,106 SF: 2,608–3,106 SF (11 sales); Over 3,106 SF: 3,304–5,416 SF (10 sales)
Quartiles by year built: Before 2016: 2014–2015 (8 sales); 2016–2018: 2016–2018 (10 sales); 2019–2022: 2019–2022 (12 sales); After 2022: 2023–2024 (11 sales)
View and no view: View: MLS lists a view (24 sales); No view: None or Neighborhood (16 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 $766,819, median $624,400. Days on market: average 75, median 52: the average is pulled up by a few long listings.

8.2.7 Kruskal–Wallis test

In simple terms
Checks whether a measure really differs across the four groups of a slice. It uses each sale's rank, not its value, so a few extreme sales cannot drive the answer.
Statistically
H = [12/(n(n+1))] Σ Rⱼ²/nⱼ − 3(n+1), compared with χ² on k − 1 degrees of freedom (Kruskal & Wallis, 1952). Effect size ε² = H/(n − 1): ≥ .01 small, ≥ .06 medium, ≥ .14 large (Tomczak & Tomczak, 2014). A slice is tested only when every group has at least 5 sales; smaller groups are described, not tested.
The data
Run on every measure in sections 3–5; each result is in 8.3.

8.2.8 Mann–Whitney U test

In simple terms
The same check when there are only two groups: view against no view.
Statistically
Two-sided U test (Mann & Whitney, 1947). Effect size = rank-biserial correlation |1 − 2U/(n₁n₂)|: ≥ .10 small, ≥ .30 medium, ≥ .50 large (Kerby, 2014; Cohen, 1988).
The data
Run on every measure in section 6; results in 8.3.

8.2.9 Chi-square test

In simple terms
Checks whether a yes/no share (price cut, seller payment, view) differs between groups more than chance would.
Statistically
Pearson χ² on the 2 × k table of counts, no continuity correction (Pearson, 1900). Effect size Cramér's V = √(χ²/n): ≥ .10 small, ≥ .30 medium, ≥ .50 large (Cramér, 1946).
The data
Run on the three shares in every slice; results in 8.3.

8.2.10 Real difference (significance)

In simple terms
“Real difference” means a gap this large would be very unlikely if the groups were really alike. Because many measures are checked in each slice, the bar is set higher than usual.
Statistically
Significant when p < 0.05 ÷ m, where m is the number of measures tested in that slice (Bonferroni; Dunn, 1961). The slicing variable itself differs by design and is not tested.
The data
Quartiles by net sale price: m = 8, threshold p < 0.0063; Quartiles by living area: m = 8, threshold p < 0.0063; Quartiles by year built: m = 8, threshold p < 0.0063; View and no view: m = 8, threshold p < 0.0063

8.2.11 Size of a difference

In simple terms
A difference can be real but small. The size word (small, medium, large) says how much the groups pull apart.
Statistically
ε² for Kruskal–Wallis, rank-biserial r for Mann–Whitney, Cramér's V for chi-square, with the thresholds above; below the small threshold the size is “negligible”.
The data
4 real differences across the four slices; sizes are listed in 8.3.

8.2.12 Rounding in the text

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

8.3 Test results

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

8.3.1 Quartiles by net sale price

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net price per SFKruskal–WallisH = 11.1830.0108p < 0.0063Unclearε² = 0.280large
Living areaKruskal–WallisH = 29.273< 0.0001p < 0.0063Yesε² = 0.732large
Year builtKruskal–WallisH = 6.8430.0772p < 0.0063Unclearε² = 0.171large
Days on marketKruskal–WallisH = 6.2230.1016p < 0.0063Unclearε² = 0.155large
Discount from original listKruskal–WallisH = 1.9630.5801p < 0.0063Unclearε² = 0.049small
Price cut before saleChi-squareχ² = 1.5030.6815p < 0.0063UnclearCramér’s V = 0.191small
Seller paid closing costs or repairsChi-squareχ² = 0.9230.8200p < 0.0063UnclearCramér’s V = 0.150small
Has a viewChi-squareχ² = 6.6230.0852p < 0.0063UnclearCramér’s V = 0.402medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 29.093< 0.0001p < 0.0063Yesε² = 0.727large
Net price per SFKruskal–WallisH = 7.1730.0666p < 0.0063Unclearε² = 0.179large
Year builtKruskal–WallisH = 3.1730.3655p < 0.0063Unclearε² = 0.079medium
Days on marketKruskal–WallisH = 4.4630.2156p < 0.0063Unclearε² = 0.112medium
Discount from original listKruskal–WallisH = 4.9230.1777p < 0.0063Unclearε² = 0.123medium
Price cut before saleChi-squareχ² = 2.0430.5640p < 0.0063UnclearCramér’s V = 0.223small
Seller paid closing costs or repairsChi-squareχ² = 0.4030.9406p < 0.0063UnclearCramér’s V = 0.099negligible
Has a viewChi-squareχ² = 12.4030.0061p < 0.0063YesCramér’s V = 0.550large

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 6.8330.0776p < 0.0063Unclearε² = 0.171large
Net price per SFKruskal–WallisH = 12.2530.0066p < 0.0063Unclearε² = 0.306large
Living areaKruskal–WallisH = 3.3630.3394p < 0.0063Unclearε² = 0.084medium
Days on marketKruskal–WallisH = 0.4830.9228p < 0.0063Unclearε² = 0.012small
Discount from original listKruskal–WallisH = 2.2930.5142p < 0.0063Unclearε² = 0.057small
Price cut before saleChi-squareχ² = 0.5530.9078p < 0.0063UnclearCramér’s V = 0.116small
Seller paid closing costs or repairsChi-squareχ² = 3.6630.3002p < 0.0063UnclearCramér’s V = 0.299small
Has a viewChi-squareχ² = 10.4130.0154p < 0.0063UnclearCramér’s V = 0.504large

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 264.00—0.0484p < 0.0063Unclearrank-biserial r = 0.375medium
Net price per SFMann–Whitney UU = 302.00—0.0025p < 0.0063Yesrank-biserial r = 0.573large
Living areaMann–Whitney UU = 225.00—0.3695p < 0.0063Unclearrank-biserial r = 0.172small
Year builtMann–Whitney UU = 276.00—0.0201p < 0.0063Unclearrank-biserial r = 0.438medium
Days on marketMann–Whitney UU = 226.00—0.3549p < 0.0063Unclearrank-biserial r = 0.177small
Discount from original listMann–Whitney UU = 227.00—0.3409p < 0.0063Unclearrank-biserial r = 0.182small
Price cut before saleChi-squareχ² = 0.0210.8961p < 0.0063UnclearCramér’s V = 0.021negligible
Seller paid closing costs or repairsChi-squareχ² = 0.6110.4334p < 0.0063UnclearCramér’s V = 0.124small

8.4 Data notes

Left out of the analysis: 1 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 41 closed MLS resale sales; the 1 new-construction sales appear only in their own section, as basic statistics. Sale and listing data: ABOR MLS via secure APIs, pulled October 27, 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.