ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Highlands

Austin, TX 78751
Resale homes · closed Oct 28 – Oct 27, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 28, 2026 · 7:41 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
$680,000
average $717,632
Median net price per SF
$449
average $479
Median days on market
35 days
average 63 days
1. SummaryResale homes · 31 sales · October 28, 2025 – October 27, 2026

This study looks at the 31 resale homes sold in Highlands in the 365 days to October 27, 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 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 $573K · 8 sales$573K–$680K · 8 sales$688K–$739K · 7 salesOver $739K · 8 sales
3. Quartiles by net sale priceResale homes · 31 sales · October 28, 2025 – October 27, 2026

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

2026-10-28T07:42:02.066297 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 7 8 31 sales Sales per group Under $573K: 8 sales (26%) $573K–$680K: 8 sales (26%) $688K–$739K: 7 sales (23%) Over $739K: 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 $573K$573K–$680K$688K–$739KOver $739K
Higher-priced homes Over $739K
  • Are much larger.
Lower-priced homes Under $573K
  • 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 $573K$573K–$680K$688K–$739KOver $739KReal difference?
Net price per SF$501 per SF$513 per SF$477 per SF$424 per SFunclear
Living area1,041 SFSmallest1,250 SF1,524 SF2,515 SFLargest✓ large
Year built1965194219501953unclear
Days on market60 days54 days10 days38 daysunclear
Discount from original list9.5%4.3%1.5%7.7%unclear
Price cut before sale50%38%0%38%unclear
Seller paid closing costs or repairs38%50%43%62%unclear
Has a view38%0%14%38%unclear

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

3.3 Net price per SF: no clear difference

2026-10-28T07:42:02.094805 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $573K 8 sales $573K– $680K 8 sales $688K– $739K 7 sales Over $739K 8 sales $0 $200 $400 $600 $501 $513 $477 $424 Net price per SF, average

The average is $424 per SF for homes over $739K and $501 per SF for homes under $573K. It does not move in a straight line; homes $573K–$680K are highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Homes over $739K are about 1,470 SF (141%) larger than homes under $573K

2026-10-28T07:42:02.117587 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $573K 8 sales $573K– $680K 8 sales $688K– $739K 7 sales Over $739K 8 sales 0 1,000 2,000 3,000 1,041 1,250 1,524 2,515 Living area (SF), average

The average is 2,515 SF for homes over $739K and 1,041 SF for homes under $573K. 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

2026-10-28T07:42:02.143077 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $573K 8 sales $573K– $680K 8 sales $688K– $739K 7 sales Over $739K 8 sales 1940 1960 1980 2000 2020 1965 1942 1950 1953 Year built: median (dot) and middle half (bar)

The median is 1953 for homes over $739K and 1965 for homes under $573K. It does not move in a straight line; homes under $573K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-28T07:42:02.166449 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $573K 8 sales $573K– $680K 8 sales $688K– $739K 7 sales Over $739K 8 sales 0 20 40 60 60 54 10 38 Days on market, median

The median is 38 days for homes over $739K and 60 days for homes under $573K. It does not move in a straight line; homes under $573K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-10-28T07:42:02.189150 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $573K 8 sales $573K– $680K 8 sales $688K– $739K 7 sales Over $739K 8 sales 0.0% 5.0% 10.0% 9.5% 4.3% 1.5% 7.7% Discount from original list, average

The average is 7.7% for homes over $739K and 9.5% for homes under $573K. It does not move in a straight line; homes under $573K 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

2026-10-28T07:42:02.218972 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 38 38 38 50 0 0 43 14 38 62 38 Under $573K $573K–$680K $688K–$739K Over $739K

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

  • 38% of homes over $739K had a price cut before sale, against 50% of homes under $573K. Defined in 8.1.7.
  • 62% of homes over $739K had the seller pay closing costs or repairs, against 38% of homes under $573K. Defined in 8.1.8.
  • 38% of homes over $739K have a view, against 38% of homes under $573K. 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 $573K8 sales$573K–$680K8 sales$688K–$739K7 salesOver $739K8 salesAll31 salesReal difference?
Net sale price average$498,943$630,992$709,795$1,029,817$717,632by design
Net price per SF average$501$513$477$424$479unclear
Living area average1,0411,2501,5242,5151,584✓ large
Year built median19651942195019531952unclear

3.10 The numbers: selling

Under $573K8 sales$573K–$680K8 sales$688K–$739K7 salesOver $739K8 salesAll31 salesReal difference?
Days on market median6054103835unclear
Discount from original list average9.5%4.3%1.5%7.7%5.9%unclear
Price cut before sale50%38%0%38%32%unclear
Seller paid closing costs or repairs38%50%43%62%48%unclear
Has a view38%0%14%38%23%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 28, 2025 – October 27, 2026

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

2026-10-28T07:42:02.240089 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 8 7 8 31 sales Sales per group Under 1,100 SF: 8 sales (26%) 1,100–1,362 SF: 8 sales (26%) 1,512–1,704 SF: 7 sales (23%) Over 1,704 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 1,100 SF1,100–1,362 SF1,512–1,704 SFOver 1,704 SF
Larger homes Over 1,704 SF
  • Sell for much more.
  • Sell for less per square foot.
Smaller homes Under 1,100 SF
  • Sell for much less.
  • 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 1,100 SF1,100–1,362 SF1,512–1,704 SFOver 1,704 SFReal difference?
Net sale price$540KLowest$639K$681K$1.01MHighest✓ large
Net price per SF$557 per SFHighest$522 per SF$430 per SF$401 per SFLowest✓ large
Year built1952193719521953unclear
Days on market79 days44 days10 days38 daysunclear
Discount from original list3.8%4.8%7.6%7.6%unclear
Price cut before sale38%38%14%38%unclear
Seller paid closing costs or repairs25%50%71%50%unclear
Has a view25%12%29%25%unclear

4.3 Homes over 1,704 SF sell for about $467K (86%) more than homes under 1,100 SF

2026-10-28T07:42:02.260766 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,100 SF 8 sales 1,100– 1,362 SF 8 sales 1,512– 1,704 SF 7 sales Over 1,704 SF 8 sales $0K $500K $1.00M $540K $639K $681K $1.01M Net sale price, average

The average is $1.01M for homes over 1,704 SF and $540K for homes under 1,100 SF. It rises step by step from the lowest range to the highest. Defined in 8.1.1.

✓ Real difference · large

4.4 Homes over 1,704 SF sell for about $156 per SF (28%) less than homes under 1,100 SF

2026-10-28T07:42:02.283542 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,100 SF 8 sales 1,100– 1,362 SF 8 sales 1,512– 1,704 SF 7 sales Over 1,704 SF 8 sales $0 $200 $400 $600 $557 $522 $430 $401 Net price per SF, average

The average is $401 per SF for homes over 1,704 SF and $557 per SF for homes under 1,100 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

2026-10-28T07:42:02.307688 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,100 SF 8 sales 1,100– 1,362 SF 8 sales 1,512– 1,704 SF 7 sales Over 1,704 SF 8 sales 1940 1960 1980 2000 1952 1937 1952 1953 Year built: median (dot) and middle half (bar)

The median is 1953 for homes over 1,704 SF and 1952 for homes under 1,100 SF. It does not move in a straight line; homes over 1,704 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-28T07:42:02.331184 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,100 SF 8 sales 1,100– 1,362 SF 8 sales 1,512– 1,704 SF 7 sales Over 1,704 SF 8 sales 0 25 50 75 79 44 10 38 Days on market, median

The median is 38 days for homes over 1,704 SF and 79 days for homes under 1,100 SF. It does not move in a straight line; homes under 1,100 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-28T07:42:02.354048 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,100 SF 8 sales 1,100– 1,362 SF 8 sales 1,512– 1,704 SF 7 sales Over 1,704 SF 8 sales 0.0% 2.5% 5.0% 7.5% 3.8% 4.8% 7.6% 7.6% Discount from original list, average

The average is 7.6% for homes over 1,704 SF and 3.8% for homes under 1,100 SF. It rises step by step from the lowest range to the 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

2026-10-28T07:42:02.380888 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% 38 25 25 38 50 12 14 71 29 38 50 25 Under 1,100 SF 1,100–1,362 SF 1,512–1,704 SF Over 1,704 SF

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

  • 38% of homes over 1,704 SF had a price cut before sale, against 38% of homes under 1,100 SF. Defined in 8.1.7.
  • 50% of homes over 1,704 SF had the seller pay closing costs or repairs, against 25% of homes under 1,100 SF. Defined in 8.1.8.
  • 25% of homes over 1,704 SF have a view, against 25% of homes under 1,100 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 1,100 SF8 sales1,100–1,362 SF8 sales1,512–1,704 SF7 salesOver 1,704 SF8 salesAll31 salesReal difference?
Net sale price average$539,756$638,531$681,407$1,006,304$717,632✓ large
Net price per SF average$557$522$430$401$479✓ large
Living area average9761,2331,5832,5451,584by design
Year built median19521937195219531952unclear

4.10 The numbers: selling

Under 1,100 SF8 sales1,100–1,362 SF8 sales1,512–1,704 SF7 salesOver 1,704 SF8 salesAll31 salesReal difference?
Days on market median7944103835unclear
Discount from original list average3.8%4.8%7.6%7.6%5.9%unclear
Price cut before sale38%38%14%38%32%unclear
Seller paid closing costs or repairs25%50%71%50%48%unclear
Has a view25%12%29%25%23%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 28, 2025 – October 27, 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.

2026-10-28T07:42:02.401454 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 8 7 9 31 sales Sales per group Before 1939: 7 sales (23%) 1939–1951: 8 sales (26%) 1952–1977: 7 sales (23%) After 1977: 9 sales (29%)
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 19391939–19511952–1977After 1977Real difference?
Net sale price$748K$691K$640K$778Kunclear
Net price per SF$500 per SF$480 per SF$450 per SF$484 per SFunclear
Living area1,613 SF1,541 SF1,530 SF1,643 SFunclear
Days on market6 days32 days41 days46 daysunclear
Discount from original list5.1%4.8%9.7%4.5%unclear
Price cut before sale14%25%57%33%unclear
Seller paid closing costs or repairs29%50%43%67%unclear
Has a view29%12%14%33%unclear

5.3 Net sale price: no clear difference

2026-10-28T07:42:02.422534 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1939 7 sales 1939– 1951 8 sales 1952– 1977 7 sales After 1977 9 sales $0K $250K $500K $750K $748K $691K $640K $778K Net sale price, average

The average is $778K for homes built after 1977 and $748K for homes built before 1939. It does not move in a straight line; homes built after 1977 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-28T07:42:02.445707 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1939 7 sales 1939– 1951 8 sales 1952– 1977 7 sales After 1977 9 sales $0 $200 $400 $600 $500 $480 $450 $484 Net price per SF, average

The average is $484 per SF for homes built after 1977 and $500 per SF for homes built before 1939. It does not move in a straight line; homes built before 1939 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-28T07:42:02.467798 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1939 7 sales 1939– 1951 8 sales 1952– 1977 7 sales After 1977 9 sales 0 500 1,000 1,500 1,613 1,541 1,530 1,643 Living area (SF), average

The average is 1,643 SF for homes built after 1977 and 1,613 SF for homes built before 1939. It does not move in a straight line; homes built after 1977 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-28T07:42:02.490005 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1939 7 sales 1939– 1951 8 sales 1952– 1977 7 sales After 1977 9 sales 0 20 40 6 32 41 46 Days on market, median

The median is 46 days for homes built after 1977 and 6 days for homes built before 1939. It rises step by step from the lowest range to the highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-28T07:42:02.512779 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1939 7 sales 1939– 1951 8 sales 1952– 1977 7 sales After 1977 9 sales 0.0% 5.0% 10.0% 5.1% 4.8% 9.7% 4.5% Discount from original list, average

The average is 4.5% for homes built after 1977 and 5.1% for homes built before 1939. It does not move in a straight line; homes built 1952–1977 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

2026-10-28T07:42:02.538200 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs Has a view 0% 25% 50% 75% 100% 14 29 29 25 50 12 57 43 14 33 67 33 Before 1939 1939–1951 1952–1977 After 1977

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

  • 33% of homes built after 1977 had a price cut before sale, against 14% of homes built before 1939. Defined in 8.1.7.
  • 67% of homes built after 1977 had the seller pay closing costs or repairs, against 29% of homes built before 1939. Defined in 8.1.8.
  • 33% of homes built after 1977 have a view, against 29% of homes built before 1939. 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 19397 sales1939–19518 sales1952–19777 salesAfter 19779 salesAll31 salesReal difference?
Net sale price average$747,993$691,201$639,961$777,922$717,632unclear
Net price per SF average$500$480$450$484$479unclear
Living area average1,6131,5411,5301,6431,584unclear
Year built median19321948195520181952by design

5.10 The numbers: selling

Before 19397 sales1939–19518 sales1952–19777 salesAfter 19779 salesAll31 salesReal difference?
Days on market median632414635unclear
Discount from original list average5.1%4.8%9.7%4.5%5.9%unclear
Price cut before sale14%25%57%33%32%unclear
Seller paid closing costs or repairs29%50%43%67%48%unclear
Has a view29%12%14%33%23%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 28, 2025 – October 27, 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.

2026-10-28T07:42:02.557491 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 7 24 31 sales Sales per group View: 7 sales (23%) No view: 24 sales (77%)
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$740K$711Kunclear
Net price per SF$456 per SF$486 per SFunclear
Living area1,728 SF1,542 SFunclear
Year built19551952unclear
Days on market37 days34 daysunclear
Discount from original list7.6%5.4%unclear
Price cut before sale29%33%unclear
Seller paid closing costs or repairs29%54%unclear

6.3 Net sale price: no clear difference

2026-10-28T07:42:02.574435 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 24 sales $0K $200K $400K $600K $800K $740K $711K Net sale price, average

The average is $740K for homes with a view and $711K 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

2026-10-28T07:42:02.592891 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 24 sales $0 $200 $400 $456 $486 Net price per SF, average

The average is $456 per SF for homes with a view and $486 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

2026-10-28T07:42:02.611906 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 24 sales 0 500 1,000 1,500 2,000 1,728 1,542 Living area (SF), average

The average is 1,728 SF for homes with a view and 1,542 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-28T07:42:02.657088 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 24 sales 1940 1960 1980 2000 1955 1952 Year built: median (dot) and middle half (bar)

The median is 1955 for homes with a view and 1952 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-28T07:42:02.675730 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 24 sales 0 10 20 30 40 37 34 Days on market, median

The median is 37 days for homes with a view and 34 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

2026-10-28T07:42:02.695567 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 7 sales No view 24 sales 0.0% 2.0% 4.0% 6.0% 8.0% 7.6% 5.4% Discount from original list, average

The average is 7.6% for homes with a view and 5.4% 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

2026-10-28T07:42:02.714693 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 29 29 33 54 View No view

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

  • 29% of homes with a view had a price cut before sale, against 33% of homes without a view. Defined in 8.1.7.
  • 29% of homes with a view had the seller pay closing costs or repairs, against 54% 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

View7 salesNo view24 salesAll31 salesReal difference?
Net sale price average$740,055$711,092$717,632unclear
Net price per SF average$456$486$479unclear
Living area average1,7281,5421,584unclear
Year built median195519521952unclear

6.11 The numbers: selling

View7 salesNo view24 salesAll31 salesReal difference?
Days on market median373435unclear
Discount from original list average7.6%5.4%5.9%unclear
Price cut before sale29%33%32%unclear
Seller paid closing costs or repairs29%54%48%unclear

Prices, price per SF, living area and discount are averages; days on market and year built are medians. The test behind each verdict is in 8.3.4.

7. New constructionBasic statistics · 7 sales · October 28, 2025 – October 27, 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: 7 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 Highlands.
MeasureAverageMedian
Sales7
Net sale price$1,235,143$1,025,000
Net price per SF$555$601
Living area (SF)2,2282,053
Days on market3612
Discount from original list5.8%4.0%
Price cut before sale43% of sales
Seller paid closing costs or repairs14% of sales
Year built2025–2026

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 $717,632; median $680,000; standard deviation $216,899; middle half (25th–75th percentile) $572,650–$808,524; lowest $360,000, highest $1,249,500. 15 sales had a seller payment; their median payment was $6,951.

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 $479; median $449; standard deviation $102; middle half (25th–75th percentile) $422–$552; lowest $238, highest $697.

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 1,584; median 1,362; standard deviation 660; middle half (25th–75th percentile) 1,091–1,752; lowest 796, highest 3,296.

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 1966; median 1952; standard deviation 34; middle half (25th–75th percentile) 1939–2001; lowest 1930, highest 2022.

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 85; middle half (25th–75th percentile) 10–86; lowest 3, highest 429. 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 5.9%; median 3.5%; standard deviation 9.4%; middle half (25th–75th percentile) 1.8%–7.7%; lowest −13.2%, highest 34.5%. 4 sales closed above their first asking price.

8.1.7 Price cut before sale

In simple terms
The asking price was lowered at least once before the home went under contract.
Statistically
Indicator: ListPrice at close < OriginalListPrice. Reported as the share of sales (%).
The data
10 of 31 resale sales (32.3%).

8.1.8 Seller paid closing costs or repairs

In simple terms
The seller paid some of the buyer's closing costs or repairs.
Statistically
Indicator: ACTRIS_BuyersClsgCostPdbySell + ACTRIS_RepairsAmount > 0. Reported as the share of sales (%).
The data
15 of 31 resale sales (48.4%).

8.1.9 Has a view

In simple terms
The MLS says the home looks over something: a greenbelt or park, trees, golf course, hill country, water and similar.
Statistically
View = the View field holds any entry other than “None”, “Neighborhood” or “See Remarks”. No view = only “None” and/or “Neighborhood”. A record whose only entry is “See Remarks” cannot be classified and is left out of the view slice.
The data
7 sales with a view, 24 without, 0 unclassified. Most common View entries: None 17, Neighborhood 11, Trees/Woods 5, City 3, Panoramic 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 “highlands”, “north loop”, “hegman”, “hegman sub” (case-insensitive). A record is kept when its subdivision name belongs to the Highlands family, the same rule comparable sales use (it includes North Loop, Hegman, proven adjacent and priced alike).
The data
Pulled October 28, 2026: 38 closed Highlands records over the prior year, in ZIP 78751. 5 subdivision spellings kept: Highlands (27); Hegman (4); The Highlands (3); North Loop (3); Hegman Sub (1). Names found by the search but not part of Highlands, left out: Highlands/University Hills Sec (6); Highlands At Oak Forest (5); Lakeway Highlands Ph 1 Sec 7B (3); Austin Highlands Sec 03 (3); Westlake Highlands Sec 02 (2); Lakeway Highlands Ph 01 Sec 02 (2); HIGHLANDS THE (2); Lakeway Highlands Ph 01 Sec 01 (2); South Highlands Amd (2); Lakeway Highlands Ph 1 Sec 5 (2); Westlake Highlands Sec 06 (2); Lakeway Highlands Ph 1 Sec 8A (2); Westlake Highlands (1); Westlake Highlands Sec 07 (1); Austin Highlands Sec 01 (1); Rough Hollow- Lakeway Highlands Ph 1 Sec 6 (1); Rough Hollow - Lakeway Highlands Ph 1 Sec 5 (1); Westlake Highlands Sec 7 (1); Windsor Highlands (1); Highlands at Oak Forest (1); Lakeway Highlands Ph 1 Sec 3 (1); North Loop Terrace (1); Lakeway Highlands Ph 1 Sec 2 (1); Highlands/University Hills (1); Lakeway Highlands Ph 2 Sec 4 (1); Lakeway Highlands Ph 2 Sec 1A (1); Lakeway Highlands Ph 2 Sec 3 (1); Lakeway Highlands Ph 2 Sec 2A (1); Highlands / University Hills in East Austin (1); Rough Hollow, Lakeway Highlands Ph 2 Sec 4 (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 24 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 28, 2025 to October 27, 2026 inclusive.
The data
38 single-family sales in the window: 31 resale, 7 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; 7 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 $573K”.
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 $573K: $360,000–$572,500 (8 sales); $573K–$680K: $572,800–$680,000 (8 sales); $688K–$739K: $688,000–$739,000 (7 sales); Over $739K: $878,049–$1,249,500 (8 sales)
Quartiles by living area: Under 1,100 SF: 796–1,082 SF (8 sales); 1,100–1,362 SF: 1,100–1,362 SF (8 sales); 1,512–1,704 SF: 1,512–1,704 SF (7 sales); Over 1,704 SF: 1,800–3,296 SF (8 sales)
Quartiles by year built: Before 1939: 1930–1938 (7 sales); 1939–1951: 1939–1951 (8 sales); 1952–1977: 1952–1977 (7 sales); After 1977: 2001–2022 (9 sales)
View and no view: View: MLS lists a view (7 sales); No view: None or Neighborhood (24 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 $717,632, median $680,000. 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
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 = 5.5830.1338p < 0.0063Unclearε² = 0.186large
Living areaKruskal–WallisH = 22.963< 0.0001p < 0.0063Yesε² = 0.765large
Year builtKruskal–WallisH = 4.0730.2535p < 0.0063Unclearε² = 0.136medium
Days on marketKruskal–WallisH = 6.4830.0905p < 0.0063Unclearε² = 0.216large
Discount from original listKruskal–WallisH = 3.1630.3669p < 0.0063Unclearε² = 0.105medium
Price cut before saleChi-squareχ² = 4.6930.1962p < 0.0063UnclearCramér’s V = 0.389medium
Seller paid closing costs or repairsChi-squareχ² = 1.1130.7742p < 0.0063UnclearCramér’s V = 0.189small
Has a viewChi-squareχ² = 4.6530.1996p < 0.0063UnclearCramér’s V = 0.387medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 21.243< 0.0001p < 0.0063Yesε² = 0.708large
Net price per SFKruskal–WallisH = 14.7730.0020p < 0.0063Yesε² = 0.492large
Year builtKruskal–WallisH = 2.0930.5535p < 0.0063Unclearε² = 0.070medium
Days on marketKruskal–WallisH = 6.5530.0877p < 0.0063Unclearε² = 0.218large
Discount from original listKruskal–WallisH = 0.1530.9846p < 0.0063Unclearε² = 0.005negligible
Price cut before saleChi-squareχ² = 1.3430.7205p < 0.0063UnclearCramér’s V = 0.208small
Seller paid closing costs or repairsChi-squareχ² = 3.2630.3537p < 0.0063UnclearCramér’s V = 0.324medium
Has a viewChi-squareχ² = 0.6630.8820p < 0.0063UnclearCramér’s V = 0.146small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 1.9430.5852p < 0.0063Unclearε² = 0.065medium
Net price per SFKruskal–WallisH = 1.0530.7893p < 0.0063Unclearε² = 0.035small
Living areaKruskal–WallisH = 0.5230.9152p < 0.0063Unclearε² = 0.017small
Days on marketKruskal–WallisH = 1.6330.6535p < 0.0063Unclearε² = 0.054small
Discount from original listKruskal–WallisH = 3.0030.3909p < 0.0063Unclearε² = 0.100medium
Price cut before saleChi-squareχ² = 3.2230.3595p < 0.0063UnclearCramér’s V = 0.322medium
Seller paid closing costs or repairsChi-squareχ² = 2.4030.4939p < 0.0063UnclearCramér’s V = 0.278small
Has a viewChi-squareχ² = 1.4830.6870p < 0.0063UnclearCramér’s V = 0.218small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 87.00—0.9081p < 0.0063Unclearrank-biserial r = 0.036negligible
Net price per SFMann–Whitney UU = 78.00—0.7995p < 0.0063Unclearrank-biserial r = 0.071negligible
Living areaMann–Whitney UU = 91.00—0.7640p < 0.0063Unclearrank-biserial r = 0.083negligible
Year builtMann–Whitney UU = 96.50—0.5703p < 0.0063Unclearrank-biserial r = 0.149small
Days on marketMann–Whitney UU = 94.00—0.6534p < 0.0063Unclearrank-biserial r = 0.119small
Discount from original listMann–Whitney UU = 86.00—0.9448p < 0.0063Unclearrank-biserial r = 0.024negligible
Price cut before saleChi-squareχ² = 0.0610.8125p < 0.0063UnclearCramér’s V = 0.043negligible
Seller paid closing costs or repairsChi-squareχ² = 1.4210.2331p < 0.0063UnclearCramér’s V = 0.214small

8.4 Data notes

Left out of the analysis: 7 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 7 new-construction sales appear only in their own section, as basic statistics. Sale and listing data: ABOR MLS via secure APIs, pulled October 28, 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.