ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

University Hills

Austin, TX 78723
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 · 7:47 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
35
single-family resale homes
Median net sale price
$510,000
average $531,194
Median net price per SF
$283
average $293
Median days on market
54 days
average 104 days
1. SummaryResale homes · 35 sales · October 26, 2025 – October 25, 2026

This study looks at the 35 resale homes sold in University Hills in the 365 days to October 25, 2026. A full year is used because only 18 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 sold35 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 $450K · 8 sales$450K–$510K · 10 sales$512K–$595K · 8 salesOver $595K · 9 sales
3. Quartiles by net sale priceResale homes · 35 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.

2026-10-26T07:48:06.434143 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 10 8 9 35 sales Sales per group Under $450K: 8 sales (23%) $450K–$510K: 10 sales (29%) $512K–$595K: 8 sales (23%) Over $595K: 9 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 $450K$450K–$510K$512K–$595KOver $595K
Higher-priced homes Over $595K
  • Are much larger.
  • Sell for more per square foot.
  • Take longer to sell.
Lower-priced homes Under $450K
  • Are much smaller.
  • Sell for less per square foot.
  • Sell faster.

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 $450K$450K–$510K$512K–$595KOver $595KReal difference?
Net price per SF$231 per SFLowest$282 per SF$336 per SFHighest$324 per SF✓ large
Living area1,616 SFSmallest1,696 SF1,782 SF2,302 SFLargest✓ large
Year built1964196319641965unclear
Days on market65 days76 days12 daysFastest102 daysSlowest✓ large
Discount from original list15.9%11.3%2.2%10.9%unclear
Price cut before sale88%70%38%89%unclear
Seller paid closing costs or repairs50%50%88%67%unclear
Has a view0%10%25%33%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 Homes over $595K sell for about $93 per SF (40%) more than homes under $450K

2026-10-26T07:48:06.462583 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $450K 8 sales $450K– $510K 10 sales $512K– $595K 8 sales Over $595K 9 sales $0 $200 $400 $231 $282 $336 $324 Net price per SF, average

The average is $324 per SF for homes over $595K and $231 per SF for homes under $450K. It does not move in a straight line; homes $512K–$595K are highest. Defined in 8.1.2.

✓ Real difference · large

3.4 Homes over $595K are about 690 SF (42%) larger than homes under $450K

2026-10-26T07:48:06.485304 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $450K 8 sales $450K– $510K 10 sales $512K– $595K 8 sales Over $595K 9 sales 0 1,000 2,000 1,616 1,696 1,782 2,302 Living area (SF), average

The average is 2,302 SF for homes over $595K and 1,616 SF for homes under $450K. 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

2026-10-26T07:48:06.509275 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $450K 8 sales $450K– $510K 10 sales $512K– $595K 8 sales Over $595K 9 sales 1962 1964 1966 1968 1964 1963 1964 1965 Year built: median (dot) and middle half (bar)

The median is 1965 for homes over $595K and 1964 for homes under $450K. It does not move in a straight line; homes over $595K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Homes over $595K take 37 days longer to sell than homes under $450K

2026-10-26T07:48:06.531962 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $450K 8 sales $450K– $510K 10 sales $512K– $595K 8 sales Over $595K 9 sales 0 50 100 65 76 12 102 Days on market, median

The median is 102 days for homes over $595K and 65 days for homes under $450K. It does not move in a straight line; homes over $595K are highest. Defined in 8.1.5.

✓ Real difference · large

3.7 Discount from original list: no clear difference

2026-10-26T07:48:06.553827 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $450K 8 sales $450K– $510K 10 sales $512K– $595K 8 sales Over $595K 9 sales 0.0% 5.0% 10.0% 15.0% 15.9% 11.3% 2.2% 10.9% Discount from original list, average

The average is 10.9% for homes over $595K and 15.9% for homes under $450K. It does not move in a straight line; homes under $450K 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

2026-10-26T07:48:06.584119 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 50 0 70 50 10 38 88 25 89 67 33 Under $450K $450K–$510K $512K–$595K Over $595K

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

  • 89% of homes over $595K had a price cut before sale, against 88% of homes under $450K. Defined in 8.1.7.
  • 67% of homes over $595K had the seller pay closing costs or repairs, against 50% of homes under $450K. Defined in 8.1.8.
  • 33% of homes over $595K have a view, against 0% of homes under $450K. 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 $450K8 sales$450K–$510K10 sales$512K–$595K8 salesOver $595K9 salesAll35 salesReal difference?
Net sale price average$364,938$469,050$545,028$735,728$531,194by design
Net price per SF average$231$282$336$324$293✓ large
Living area average1,6161,6961,7822,3021,854✓ large
Year built median19641963196419651964unclear

3.10 The numbers: selling

Under $450K8 sales$450K–$510K10 sales$512K–$595K8 salesOver $595K9 salesAll35 salesReal difference?
Days on market median65761210254✓ large
Discount from original list average15.9%11.3%2.2%10.9%10.2%unclear
Price cut before sale88%70%38%89%71%unclear
Seller paid closing costs or repairs50%50%88%67%63%unclear
Has a view0%10%25%33%17%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 · 35 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.

2026-10-26T07:48:06.604898 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 9 9 8 9 35 sales Sales per group Under 1,608 SF: 9 sales (26%) 1,608–1,809 SF: 9 sales (26%) 1,812–1,989 SF: 8 sales (23%) Over 1,989 SF: 9 sales (26%)
Bottom line: no measure differs enough between the groups to rule out chance.

4.1 Key differences

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

4.2 At a glance

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

Under 1,608 SF1,608–1,809 SF1,812–1,989 SFOver 1,989 SFReal difference?
Net sale price$436K$479K$536K$675Kunclear
Net price per SF$338 per SF$275 per SF$289 per SF$271 per SFunclear
Year built1963196319641965unclear
Days on market51 days41 days74 days98 daysunclear
Discount from original list10.9%6.6%9.6%13.6%unclear
Price cut before sale67%44%88%89%unclear
Seller paid closing costs or repairs44%78%75%56%unclear
Has a view0%33%12%22%unclear

4.3 Net sale price: no clear difference

2026-10-26T07:48:06.625692 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,608 SF 9 sales 1,608– 1,809 SF 9 sales 1,812– 1,989 SF 8 sales Over 1,989 SF 9 sales $0K $250K $500K $750K $436K $479K $536K $675K Net sale price, average

The average is $675K for homes over 1,989 SF and $436K for homes under 1,608 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 Net price per SF: no clear difference

2026-10-26T07:48:06.648372 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,608 SF 9 sales 1,608– 1,809 SF 9 sales 1,812– 1,989 SF 8 sales Over 1,989 SF 9 sales $0 $200 $400 $338 $275 $289 $271 Net price per SF, average

The average is $271 per SF for homes over 1,989 SF and $338 per SF for homes under 1,608 SF. It does not move in a straight line; homes under 1,608 SF are highest. Defined in 8.1.2.

– No clear difference · could be chance

4.5 Year built: no clear difference

2026-10-26T07:48:06.671676 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,608 SF 9 sales 1,608– 1,809 SF 9 sales 1,812– 1,989 SF 8 sales Over 1,989 SF 9 sales 1962 1964 1966 1968 1963 1963 1964 1965 Year built: median (dot) and middle half (bar)

The median is 1965 for homes over 1,989 SF and 1963 for homes under 1,608 SF. It rises step by step from the lowest range to the highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-26T07:48:06.694794 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,608 SF 9 sales 1,608– 1,809 SF 9 sales 1,812– 1,989 SF 8 sales Over 1,989 SF 9 sales 0 50 100 51 41 74 98 Days on market, median

The median is 98 days for homes over 1,989 SF and 51 days for homes under 1,608 SF. It does not move in a straight line; homes over 1,989 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-26T07:48:06.716719 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,608 SF 9 sales 1,608– 1,809 SF 9 sales 1,812– 1,989 SF 8 sales Over 1,989 SF 9 sales 0.0% 5.0% 10.0% 15.0% 10.9% 6.6% 9.6% 13.6% Discount from original list, average

The average is 13.6% for homes over 1,989 SF and 10.9% for homes under 1,608 SF. It does not move in a straight line; homes over 1,989 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

2026-10-26T07:48:06.743790 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% 67 44 0 44 78 33 88 75 12 89 56 22 Under 1,608 SF 1,608–1,809 SF 1,812–1,989 SF Over 1,989 SF

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

  • 89% of homes over 1,989 SF had a price cut before sale, against 67% of homes under 1,608 SF. Defined in 8.1.7.
  • 56% of homes over 1,989 SF had the seller pay closing costs or repairs, against 44% of homes under 1,608 SF. Defined in 8.1.8.
  • 22% of homes over 1,989 SF have a view, against 0% of homes under 1,608 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 1,608 SF9 sales1,608–1,809 SF9 sales1,812–1,989 SF8 salesOver 1,989 SF9 salesAll35 salesReal difference?
Net sale price average$435,778$478,536$536,362$674,672$531,194unclear
Net price per SF average$338$275$289$271$293unclear
Living area average1,3051,7401,8582,5111,854by design
Year built median19631963196419651964unclear

4.10 The numbers: selling

Under 1,608 SF9 sales1,608–1,809 SF9 sales1,812–1,989 SF8 salesOver 1,989 SF9 salesAll35 salesReal difference?
Days on market median5141749854unclear
Discount from original list average10.9%6.6%9.6%13.6%10.2%unclear
Price cut before sale67%44%88%89%71%unclear
Seller paid closing costs or repairs44%78%75%56%63%unclear
Has a view0%33%12%22%17%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 · 35 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.

2026-10-26T07:48:06.764300 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 10 11 7 35 sales Sales per group Before 1963: 7 sales (20%) 1963: 10 sales (29%) 1964–1967: 11 sales (31%) After 1967: 7 sales (20%)
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 196319631964–1967After 1967Real difference?
Net sale price$488K$533K$554K$537Kunclear
Net price per SF$301 per SF$325 per SF$258 per SF$295 per SFunclear
Living area1,592 SF1,705 SF2,174 SF1,823 SFunclear
Days on market64 days44 days66 days62 daysunclear
Discount from original list10.5%7.2%12.7%10.2%unclear
Price cut before sale57%60%82%86%unclear
Seller paid closing costs or repairs57%80%55%57%unclear
Has a view0%20%18%29%unclear

5.3 Net sale price: no clear difference

2026-10-26T07:48:06.784420 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1963 7 sales 1963 10 sales 1964– 1967 11 sales After 1967 7 sales $0K $200K $400K $600K $488K $533K $554K $537K Net sale price, average

The average is $537K for homes built after 1967 and $488K for homes built before 1963. It does not move in a straight line; homes built 1964–1967 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-26T07:48:06.806988 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1963 7 sales 1963 10 sales 1964– 1967 11 sales After 1967 7 sales $0 $100 $200 $300 $301 $325 $258 $295 Net price per SF, average

The average is $295 per SF for homes built after 1967 and $301 per SF for homes built before 1963. It does not move in a straight line; homes built 1963 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-26T07:48:06.828926 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1963 7 sales 1963 10 sales 1964– 1967 11 sales After 1967 7 sales 0 1,000 2,000 1,592 1,705 2,174 1,823 Living area (SF), average

The average is 1,823 SF for homes built after 1967 and 1,592 SF for homes built before 1963. It does not move in a straight line; homes built 1964–1967 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-26T07:48:06.849838 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1963 7 sales 1963 10 sales 1964– 1967 11 sales After 1967 7 sales 0 20 40 60 64 44 66 62 Days on market, median

The median is 62 days for homes built after 1967 and 64 days for homes built before 1963. It does not move in a straight line; homes built 1964–1967 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-26T07:48:06.872131 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1963 7 sales 1963 10 sales 1964– 1967 11 sales After 1967 7 sales 0.0% 5.0% 10.0% 15.0% 10.5% 7.2% 12.7% 10.2% Discount from original list, average

The average is 10.2% for homes built after 1967 and 10.5% for homes built before 1963. It does not move in a straight line; homes built 1964–1967 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

2026-10-26T07:48:06.897759 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs Has a view 0% 25% 50% 75% 100% 57 57 0 60 80 20 82 55 18 86 57 29 Before 1963 1963 1964–1967 After 1967

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

  • 86% of homes built after 1967 had a price cut before sale, against 57% of homes built before 1963. Defined in 8.1.7.
  • 57% of homes built after 1967 had the seller pay closing costs or repairs, against 57% of homes built before 1963. Defined in 8.1.8.
  • 29% of homes built after 1967 have a view, against 0% of homes built before 1963. 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 19637 sales196310 sales1964–196711 salesAfter 19677 salesAll35 salesReal difference?
Net sale price average$487,921$532,930$553,745$536,546$531,194unclear
Net price per SF average$301$325$258$295$293unclear
Living area average1,5921,7052,1741,8231,854unclear
Year built median19611963196719701964by design

5.10 The numbers: selling

Before 19637 sales196310 sales1964–196711 salesAfter 19677 salesAll35 salesReal difference?
Days on market median6444666254unclear
Discount from original list average10.5%7.2%12.7%10.2%10.2%unclear
Price cut before sale57%60%82%86%71%unclear
Seller paid closing costs or repairs57%80%55%57%63%unclear
Has a view0%20%18%29%17%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 · 35 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.

2026-10-26T07:48:06.916812 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 6 29 35 sales Sales per group View: 6 sales (17%) No view: 29 sales (83%)
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$604K$516Kunclear
Net price per SF$286 per SF$295 per SFunclear
Living area2,134 SF1,796 SFunclear
Year built19661963unclear
Days on market30 days62 daysunclear
Discount from original list4.7%11.3%unclear
Price cut before sale50%76%unclear
Seller paid closing costs or repairs50%66%unclear

6.3 Net sale price: no clear difference

2026-10-26T07:48:06.932513 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 29 sales $0K $200K $400K $600K $604K $516K Net sale price, average

The average is $604K for homes with a view and $516K 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

2026-10-26T07:48:06.950011 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 29 sales $0 $100 $200 $300 $286 $295 Net price per SF, average

The average is $286 per SF for homes with a view and $295 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

2026-10-26T07:48:06.968651 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 29 sales 0 1,000 2,000 2,134 1,796 Living area (SF), average

The average is 2,134 SF for homes with a view and 1,796 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-26T07:48:07.011268 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 29 sales 1964 1966 1968 1966 1963 Year built: median (dot) and middle half (bar)

The median is 1966 for homes with a view and 1963 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-26T07:48:07.028519 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 29 sales 0 20 40 60 30 62 Days on market, median

The median is 30 days for homes with a view and 62 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

2026-10-26T07:48:07.046879 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 6 sales No view 29 sales 0.0% 5.0% 10.0% 4.7% 11.3% Discount from original list, average

The average is 4.7% for homes with a view and 11.3% 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

2026-10-26T07:48:07.064993 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 50 50 76 66 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 76% of homes without a view. Defined in 8.1.7.
  • 50% of homes with a view had the seller pay closing costs or repairs, against 66% of homes without a view. Defined in 8.1.8.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance

6.10 The numbers: price and size

View6 salesNo view29 salesAll35 salesReal difference?
Net sale price average$604,371$516,053$531,194unclear
Net price per SF average$286$295$293unclear
Living area average2,1341,7961,854unclear
Year built median196619631964unclear

6.11 The numbers: selling

View6 salesNo view29 salesAll35 salesReal difference?
Days on market median306254unclear
Discount from original list average4.7%11.3%10.2%unclear
Price cut before sale50%76%71%unclear
Seller paid closing costs or repairs50%66%63%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 University Hills.

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
35 resale sales. Average $531,194; median $510,000; standard deviation $156,428; middle half (25th–75th percentile) $450,000–$597,500; lowest $225,000, highest $977,450. 22 sales had a seller payment; their median payment was $10,000.

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
35 resale sales. Average $293; median $283; standard deviation $71; middle half (25th–75th percentile) $259–$336; lowest $167, highest $492.

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
35 resale sales. Average 1,854; median 1,809; standard deviation 519; middle half (25th–75th percentile) 1,601–2,000; lowest 936, highest 3,335.

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
35 resale sales. Average 1966; median 1964; standard deviation 11; middle half (25th–75th percentile) 1963–1967; lowest 1953, highest 2025.

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
35 resale sales. Average 104; median 54; standard deviation 150; middle half (25th–75th percentile) 27–112; lowest 4, highest 756. 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
35 resale sales. Average 10.2%; median 11.4%; standard deviation 8.6%; middle half (25th–75th percentile) 5.1%–16.3%; lowest −14.2%, highest 23.8%. 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
25 of 35 resale sales (71.4%).

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
22 of 35 resale sales (62.9%).

8.1.9 Has a view

In simple terms
The MLS says the home looks over something: a greenbelt or park, trees, golf course, hill country, water and similar.
Statistically
View = the View field holds any entry other than “None”, “Neighborhood” or “See Remarks”. No view = only “None” and/or “Neighborhood”. A record whose only entry is “See Remarks” cannot be classified and is left out of the view slice.
The data
6 sales with a view, 29 without, 0 unclassified. Most common View entries: None 21, Neighborhood 9, Trees/Woods 5, Park/Greenbelt 3, Creek/Stream 1, Garden 1.

8.2 Methods

8.2.1 Data source

In simple terms
Every figure comes from the Austin Board of REALTORS® MLS, read live by ALEX. No figure is estimated or typed in.
Statistically
Bridge Interactive RESO Web API (OData), ABOR dataset, every field returned. Filter: StandardStatus = Closed, PropertyType = Residential, City = Austin, SubdivisionName contains any of “university hills” (case-insensitive). A record is kept when its subdivision name belongs to the University Hills family, the same rule comparable sales use.
The data
Pulled October 26, 2026: 36 closed University Hills records over the prior year, in ZIP 78723. 16 subdivision spellings kept, the most common: University Hills Sec 01 (6); University Hills (5); University Hills Sec 03 Ph 02 (5); University Hills Sec 02 Ph 03 (4); University Hills Sec 04 Ph 03 (3); University Hills Sec 04 Ph 04 (2); University Hills Sec 02 Ph 05 (2); University Hills Sec 04 Ph 01 (1); University Hills Sec 2 Phs 3 (1); University Hills Sec 2 Phs 2 (1); University Hills Sec 03 Ph 01 (1); University Hills Sec 03 Ph 03 (1); and 4 more (4 records). Names found by the search but not part of University Hills, left out: Highlands/University Hills Sec (6); University Hills West (4); Bluffs University Hills Sec 01 (3); Bluffs University Hills Sec 03 (2); Lakeside University Hills (1); Bluffs University Hills Sec 02 (1); University Hills Auburn Circle (1); Cherrylawn (University Hills) (1); Riverbend Sec 1 At University Hills (1); Summit At University Hills (1); Bluffs Of University Hills Sec 3 (1); Highlands/University Hills (1); Riverbend Sec 03a At University Hills (1); Bluffs University Hills (1); Highlands / University Hills in East Austin (1).

8.2.2 Study window

In simple terms
The most recent 365 days of closed sales: recent enough to describe today's market, long enough to give each group plenty of sales. The usual window is 180 days; it holds only 18 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 26, 2025 to October 25, 2026 inclusive.
The data
35 single-family sales in the window: 35 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
35 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
1 sale(s) with concessions above 10% of price

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 $450K”.
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 $450K: $225,000–$427,500 (8 sales); $450K–$510K: $450,000–$510,000 (10 sales); $512K–$595K: $512,000–$595,000 (8 sales); Over $595K: $600,000–$977,450 (9 sales)
Quartiles by living area: Under 1,608 SF: 936–1,594 SF (9 sales); 1,608–1,809 SF: 1,608–1,809 SF (9 sales); 1,812–1,989 SF: 1,812–1,989 SF (8 sales); Over 1,989 SF: 2,010–3,335 SF (9 sales)
Quartiles by year built: Before 1963: 1953–1962 (7 sales); 1963: 1963–1963 (10 sales); 1964–1967: 1964–1967 (11 sales); After 1967: 1968–2025 (7 sales)
View and no view: View: MLS lists a view (6 sales); No view: None or Neighborhood (29 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 $531,194, median $510,000. Days on market: average 104, median 54: 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 = 12.5830.0056p < 0.0063Yesε² = 0.370large
Living areaKruskal–WallisH = 12.5830.0056p < 0.0063Yesε² = 0.370large
Year builtKruskal–WallisH = 1.4330.6984p < 0.0063Unclearε² = 0.042small
Days on marketKruskal–WallisH = 12.5530.0057p < 0.0063Yesε² = 0.369large
Discount from original listKruskal–WallisH = 9.6130.0222p < 0.0063Unclearε² = 0.283large
Price cut before saleChi-squareχ² = 6.8830.0758p < 0.0063UnclearCramér’s V = 0.443medium
Seller paid closing costs or repairsChi-squareχ² = 3.4130.3325p < 0.0063UnclearCramér’s V = 0.312medium
Has a viewChi-squareχ² = 4.0230.2590p < 0.0063UnclearCramér’s V = 0.339medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 8.1830.0424p < 0.0063Unclearε² = 0.241large
Net price per SFKruskal–WallisH = 3.9530.2672p < 0.0063Unclearε² = 0.116medium
Year builtKruskal–WallisH = 3.3830.3369p < 0.0063Unclearε² = 0.099medium
Days on marketKruskal–WallisH = 1.9330.5880p < 0.0063Unclearε² = 0.057small
Discount from original listKruskal–WallisH = 3.2330.3577p < 0.0063Unclearε² = 0.095medium
Price cut before saleChi-squareχ² = 5.6730.1289p < 0.0063UnclearCramér’s V = 0.402medium
Seller paid closing costs or repairsChi-squareχ² = 2.8830.4112p < 0.0063UnclearCramér’s V = 0.287small
Has a viewChi-squareχ² = 3.8130.2830p < 0.0063UnclearCramér’s V = 0.330medium

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 2.7430.4331p < 0.0063Unclearε² = 0.081medium
Net price per SFKruskal–WallisH = 3.4030.3334p < 0.0063Unclearε² = 0.100medium
Living areaKruskal–WallisH = 6.2430.1007p < 0.0063Unclearε² = 0.183large
Days on marketKruskal–WallisH = 1.7130.6342p < 0.0063Unclearε² = 0.050small
Discount from original listKruskal–WallisH = 2.1730.5376p < 0.0063Unclearε² = 0.064medium
Price cut before saleChi-squareχ² = 2.6230.4537p < 0.0063UnclearCramér’s V = 0.274small
Seller paid closing costs or repairsChi-squareχ² = 1.7830.6193p < 0.0063UnclearCramér’s V = 0.226small
Has a viewChi-squareχ² = 2.1630.5403p < 0.0063UnclearCramér’s V = 0.248small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 121.00—0.1425p < 0.0063Unclearrank-biserial r = 0.391medium
Net price per SFMann–Whitney UU = 83.00—0.8814p < 0.0063Unclearrank-biserial r = 0.046negligible
Living areaMann–Whitney UU = 115.00—0.2354p < 0.0063Unclearrank-biserial r = 0.322medium
Year builtMann–Whitney UU = 117.00—0.1898p < 0.0063Unclearrank-biserial r = 0.345medium
Days on marketMann–Whitney UU = 67.00—0.3931p < 0.0063Unclearrank-biserial r = 0.230small
Discount from original listMann–Whitney UU = 53.00—0.1458p < 0.0063Unclearrank-biserial r = 0.391medium
Price cut before saleChi-squareχ² = 1.6310.2018p < 0.0063UnclearCramér’s V = 0.216small
Seller paid closing costs or repairsChi-squareχ² = 0.5110.4740p < 0.0063UnclearCramér’s V = 0.121small

8.4 Data notes

Left out of the analysis: 0 new-construction sales (basic statistics only, section 7); 1 sale(s) with concessions above 10% of price. 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 35 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.