ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Spicewood Estates Sec 02

Austin, TX 78750
Resale homes · closed Oct 26 – Oct 25, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 26, 2026 · 3:58 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
26
single-family resale homes
Median net sale price
$833,250
average $853,503
Median net price per SF
$300
average $304
Median days on market
22 days
average 59 days
1. SummaryResale homes · 26 sales · October 26, 2025 – October 25, 2026

This study looks at the 26 resale homes sold in Spicewood Estates Sec 02 in the 365 days to October 25, 2026. A full year is used because only 10 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 sold26 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 $730K · 6 sales$730K–$810K · 7 sales$856K–$975K · 7 salesOver $975K · 6 sales
3. Quartiles by net sale priceResale homes · 26 sales · October 26, 2025 – October 25, 2026

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

2026-10-26T03:59:02.526034 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 6 7 7 6 26 sales Sales per group Under $730K: 6 sales (23%) $730K–$810K: 7 sales (27%) $856K–$975K: 7 sales (27%) Over $975K: 6 sales (23%)
Bottom line: no measure differs enough between the groups to rule out chance.

3.1 Key differences

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

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 $730K$730K–$810K$856K–$975KOver $975KReal difference?
Net price per SF$277 per SF$288 per SF$300 per SF$355 per SFunclear
Living area2,378 SF2,706 SF3,125 SF3,125 SFunclear
Year built1985198619911984unclear
Days on market28 days117 days23 days10 daysunclear
Discount from original list7.9%8.2%7.3%2.0%unclear
Price cut before sale50%57%29%0%unclear
Seller paid closing costs or repairs67%57%14%100%unclear
Has a view17%14%57%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 Net price per SF: no clear difference

2026-10-26T03:59:02.554362 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $730K 6 sales $730K– $810K 7 sales $856K– $975K 7 sales Over $975K 6 sales $0 $200 $400 $277 $288 $300 $355 Net price per SF, average

The average is $355 per SF for homes over $975K and $277 per SF for homes under $730K. It rises step by step from the lowest range to the highest. Defined in 8.1.2.

– No clear difference · could be chance

3.4 Living area: no clear difference

2026-10-26T03:59:02.576729 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $730K 6 sales $730K– $810K 7 sales $856K– $975K 7 sales Over $975K 6 sales 0 1,000 2,000 3,000 2,378 2,706 3,125 3,125 Living area (SF), average

The average is 3,125 SF for homes over $975K and 2,378 SF for homes under $730K. It rises step by step from the lowest range to the highest. Defined in 8.1.3.

– No clear difference · could be chance

3.5 Year built: no clear difference

2026-10-26T03:59:02.600227 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $730K 6 sales $730K– $810K 7 sales $856K– $975K 7 sales Over $975K 6 sales 1984 1987 1990 1985 1986 1991 1984 Year built: median (dot) and middle half (bar)

The median is 1984 for homes over $975K and 1985 for homes under $730K. It does not move in a straight line; homes $856K–$975K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-26T03:59:02.623224 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $730K 6 sales $730K– $810K 7 sales $856K– $975K 7 sales Over $975K 6 sales 0 50 100 28 117 23 10 Days on market, median

The median is 10 days for homes over $975K and 28 days for homes under $730K. It does not move in a straight line; homes $730K–$810K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Discount from original list: no clear difference

2026-10-26T03:59:02.645537 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $730K 6 sales $730K– $810K 7 sales $856K– $975K 7 sales Over $975K 6 sales 0.0% 2.5% 5.0% 7.5% 7.9% 8.2% 7.3% 2.0% Discount from original list, average

The average is 2.0% for homes over $975K and 7.9% for homes under $730K. It does not move in a straight line; homes $730K–$810K are highest. Defined in 8.1.6.

– No clear difference · could be chance

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

2026-10-26T03:59:02.675718 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 67 17 57 57 14 29 14 57 0 100 33 Under $730K $730K–$810K $856K–$975K Over $975K

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

  • 0% of homes over $975K had a price cut before sale, against 50% of homes under $730K. Defined in 8.1.7.
  • 100% of homes over $975K had the seller pay closing costs or repairs, against 67% of homes under $730K. Defined in 8.1.8.
  • 33% of homes over $975K have a view, against 17% of homes under $730K. 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 $730K6 sales$730K–$810K7 sales$856K–$975K7 salesOver $975K6 salesAll26 salesReal difference?
Net sale price average$635,284$762,200$926,214$1,093,415$853,503by design
Net price per SF average$277$288$300$355$304unclear
Living area average2,3782,7063,1253,1252,840unclear
Year built median19851986199119841985unclear

3.10 The numbers: selling

Under $730K6 sales$730K–$810K7 sales$856K–$975K7 salesOver $975K6 salesAll26 salesReal difference?
Days on market median28117231022unclear
Discount from original list average7.9%8.2%7.3%2.0%6.4%unclear
Price cut before sale50%57%29%0%35%unclear
Seller paid closing costs or repairs67%57%14%100%58%unclear
Has a view17%14%57%33%31%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 · 26 sales · October 26, 2025 – October 25, 2026

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

2026-10-26T03:59:02.696645 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 6 7 7 6 26 sales Sales per group Under 2,357 SF: 6 sales (23%) 2,357–2,843 SF: 7 sales (27%) 3,028–3,268 SF: 7 sales (27%) Over 3,268 SF: 6 sales (23%)
Bottom line

4.1 Key differences

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

Under 2,357 SF2,357–2,843 SF3,028–3,268 SFOver 3,268 SF
Larger homes Over 3,268 SF
  • Sell for much more.
Smaller homes Under 2,357 SF
  • Sell for much less.

4.2 At a glance

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

Under 2,357 SF2,357–2,843 SF3,028–3,268 SFOver 3,268 SFReal difference?
Net sale price$643KLowest$853K$910K$1.00MHighest✓ large
Net price per SF$305 per SF$336 per SF$292 per SF$280 per SFunclear
Year built1985198419841990unclear
Days on market28 days27 days21 days20 daysunclear
Discount from original list4.2%1.4%11.4%8.8%unclear
Price cut before sale50%29%43%17%unclear
Seller paid closing costs or repairs67%71%29%67%unclear
Has a view33%29%29%33%unclear

4.3 Homes over 3,268 SF sell for about $356K (55%) more than homes under 2,357 SF

2026-10-26T03:59:02.717765 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,357 SF 6 sales 2,357– 2,843 SF 7 sales 3,028– 3,268 SF 7 sales Over 3,268 SF 6 sales $0K $500K $1.00M $643K $853K $910K $1.00M Net sale price, average

The average is $1.00M for homes over 3,268 SF and $643K for homes under 2,357 SF. It rises step by step from the lowest range to the highest. Defined in 8.1.1.

✓ Real difference · large

4.4 Net price per SF: no clear difference

2026-10-26T03:59:02.740323 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,357 SF 6 sales 2,357– 2,843 SF 7 sales 3,028– 3,268 SF 7 sales Over 3,268 SF 6 sales $0 $200 $400 $305 $336 $292 $280 Net price per SF, average

The average is $280 per SF for homes over 3,268 SF and $305 per SF for homes under 2,357 SF. It does not move in a straight line; homes 2,357–2,843 SF are highest. Defined in 8.1.2.

– No clear difference · could be chance

4.5 Year built: no clear difference

2026-10-26T03:59:02.763668 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,357 SF 6 sales 2,357– 2,843 SF 7 sales 3,028– 3,268 SF 7 sales Over 3,268 SF 6 sales 1984 1987 1990 1993 1985 1984 1984 1990 Year built: median (dot) and middle half (bar)

The median is 1990 for homes over 3,268 SF and 1985 for homes under 2,357 SF. It does not move in a straight line; homes over 3,268 SF are highest. Defined in 8.1.4.

– No clear difference · could be chance

4.6 Days on market: no clear difference

2026-10-26T03:59:02.786603 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,357 SF 6 sales 2,357– 2,843 SF 7 sales 3,028– 3,268 SF 7 sales Over 3,268 SF 6 sales 0 10 20 30 28 27 21 20 Days on market, median

The median is 20 days for homes over 3,268 SF and 28 days for homes under 2,357 SF. It falls step by step from the lowest range to the highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-26T03:59:02.809190 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 2,357 SF 6 sales 2,357– 2,843 SF 7 sales 3,028– 3,268 SF 7 sales Over 3,268 SF 6 sales 0.0% 5.0% 10.0% 4.2% 1.4% 11.4% 8.8% Discount from original list, average

The average is 8.8% for homes over 3,268 SF and 4.2% for homes under 2,357 SF. It does not move in a straight line; homes 3,028–3,268 SF are highest. Defined in 8.1.6.

– No clear difference · could be chance

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

2026-10-26T03:59:02.835896 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 67 33 29 71 29 43 29 29 17 67 33 Under 2,357 SF 2,357–2,843 SF 3,028–3,268 SF Over 3,268 SF

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

  • 17% of homes over 3,268 SF had a price cut before sale, against 50% of homes under 2,357 SF. Defined in 8.1.7.
  • 67% of homes over 3,268 SF had the seller pay closing costs or repairs, against 67% of homes under 2,357 SF. Defined in 8.1.8.
  • 33% of homes over 3,268 SF have a view, against 33% of homes under 2,357 SF. Defined in 8.1.9.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: – No clear difference · could be chance

4.9 The numbers: price and size

Under 2,357 SF6 sales2,357–2,843 SF7 sales3,028–3,268 SF7 salesOver 3,268 SF6 salesAll26 salesReal difference?
Net sale price average$642,763$853,429$910,057$998,351$853,503✓ large
Net price per SF average$305$336$292$280$304unclear
Living area average2,1142,5563,1233,5672,840by design
Year built median19851984198419901985unclear

4.10 The numbers: selling

Under 2,357 SF6 sales2,357–2,843 SF7 sales3,028–3,268 SF7 salesOver 3,268 SF6 salesAll26 salesReal difference?
Days on market median2827212022unclear
Discount from original list average4.2%1.4%11.4%8.8%6.4%unclear
Price cut before sale50%29%43%17%35%unclear
Seller paid closing costs or repairs67%71%29%67%58%unclear
Has a view33%29%29%33%31%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 · 26 sales · October 26, 2025 – October 25, 2026

Sales ranked by year built and cut into four groups, from the oldest to the newest. Homes built in the same year always stay in the same group, so group sizes can differ a little.

2026-10-26T03:59:02.856299 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 6 7 7 6 26 sales Sales per group Before 1984: 6 sales (23%) 1984: 7 sales (27%) 1986–1990: 7 sales (27%) After 1990: 6 sales (23%)
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 198419841986–1990After 1990Real difference?
Net sale price$829K$891K$779K$922Kunclear
Net price per SF$308 per SF$339 per SF$285 per SF$282 per SFunclear
Living area2,710 SF2,634 SF2,781 SF3,280 SFunclear
Days on market20 days84 days61 days20 daysunclear
Discount from original list6.3%1.9%12.5%4.8%unclear
Price cut before sale33%29%57%17%unclear
Seller paid closing costs or repairs50%71%71%33%unclear
Has a view17%29%14%67%unclear

5.3 Net sale price: no clear difference

2026-10-26T03:59:02.876885 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1984 6 sales 1984 7 sales 1986– 1990 7 sales After 1990 6 sales $0K $500K $1.00M $829K $891K $779K $922K Net sale price, average

The average is $922K for homes built after 1990 and $829K for homes built before 1984. It does not move in a straight line; homes built after 1990 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Net price per SF: no clear difference

2026-10-26T03:59:02.899069 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1984 6 sales 1984 7 sales 1986– 1990 7 sales After 1990 6 sales $0 $200 $400 $308 $339 $285 $282 Net price per SF, average

The average is $282 per SF for homes built after 1990 and $308 per SF for homes built before 1984. It does not move in a straight line; homes built 1984 are highest. Defined in 8.1.2.

– No clear difference · could be chance

5.5 Living area: no clear difference

2026-10-26T03:59:02.920180 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1984 6 sales 1984 7 sales 1986– 1990 7 sales After 1990 6 sales 0 1,000 2,000 3,000 2,710 2,634 2,781 3,280 Living area (SF), average

The average is 3,280 SF for homes built after 1990 and 2,710 SF for homes built before 1984. It does not move in a straight line; homes built after 1990 are highest. Defined in 8.1.3.

– No clear difference · could be chance

5.6 Days on market: no clear difference

2026-10-26T03:59:02.941765 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1984 6 sales 1984 7 sales 1986– 1990 7 sales After 1990 6 sales 0 50 100 20 84 61 20 Days on market, median

The median is 20 days for homes built after 1990 and 20 days for homes built before 1984. It does not move in a straight line; homes built 1984 are highest. Defined in 8.1.5.

– No clear difference · could be chance

5.7 Discount from original list: no clear difference

2026-10-26T03:59:02.964222 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 1984 6 sales 1984 7 sales 1986– 1990 7 sales After 1990 6 sales 0.0% 5.0% 10.0% 15.0% 6.3% 1.9% 12.5% 4.8% Discount from original list, average

The average is 4.8% for homes built after 1990 and 6.3% for homes built before 1984. It does not move in a straight line; homes built 1986–1990 are highest. Defined in 8.1.6.

– No clear difference · could be chance

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

2026-10-26T03:59:02.989817 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% 33 50 17 29 71 29 57 71 14 17 33 67 Before 1984 1984 1986–1990 After 1990

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

  • 17% of homes built after 1990 had a price cut before sale, against 33% of homes built before 1984. Defined in 8.1.7.
  • 33% of homes built after 1990 had the seller pay closing costs or repairs, against 50% of homes built before 1984. Defined in 8.1.8.
  • 67% of homes built after 1990 have a view, against 17% of homes built before 1984. 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 19846 sales19847 sales1986–19907 salesAfter 19906 salesAll26 salesReal difference?
Net sale price average$829,150$890,740$778,715$921,667$853,503unclear
Net price per SF average$308$339$285$282$304unclear
Living area average2,7102,6342,7813,2802,840unclear
Year built median19831984199019921985by design

5.10 The numbers: selling

Before 19846 sales19847 sales1986–19907 salesAfter 19906 salesAll26 salesReal difference?
Days on market median2084612022unclear
Discount from original list average6.3%1.9%12.5%4.8%6.4%unclear
Price cut before sale33%29%57%17%35%unclear
Seller paid closing costs or repairs50%71%71%33%58%unclear
Has a view17%29%14%67%31%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 · 26 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. 1 sales whose MLS view entry is only “See Remarks” are left out.

2026-10-26T03:59:03.008052 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 8 17 25 sales Sales per group View: 8 sales (32%) No view: 17 sales (68%)
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$892K$833Kunclear
Net price per SF$329 per SF$295 per SFunclear
Living area2,773 SF2,834 SFunclear
Year built19891984unclear
Days on market12 days61 daysunclear
Discount from original list2.8%8.5%unclear
Price cut before sale12%47%unclear
Seller paid closing costs or repairs62%59%unclear

6.3 Net sale price: no clear difference

2026-10-26T03:59:03.025392 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 17 sales $0K $250K $500K $750K $1.00M $892K $833K Net sale price, average

The average is $892K for homes with a view and $833K for homes without a view. Defined in 8.1.1.

– No clear difference · could be chance

6.4 Net price per SF: no clear difference

2026-10-26T03:59:03.043648 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 17 sales $0 $100 $200 $300 $329 $295 Net price per SF, average

The average is $329 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

2026-10-26T03:59:03.062162 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 17 sales 0 1,000 2,000 3,000 2,773 2,834 Living area (SF), average

The average is 2,773 SF for homes with a view and 2,834 SF for homes without a view. Defined in 8.1.3.

– No clear difference · could be chance

6.6 Year built: no clear difference

2026-10-26T03:59:03.108204 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 17 sales 1984 1987 1990 1993 1989 1984 Year built: median (dot) and middle half (bar)

The median is 1989 for homes with a view and 1984 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Days on market: no clear difference

2026-10-26T03:59:03.126091 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 17 sales 0 20 40 60 12 61 Days on market, median

The median is 12 days for homes with a view and 61 days for homes without a view. Defined in 8.1.5.

– No clear difference · could be chance

6.8 Discount from original list: no clear difference

2026-10-26T03:59:03.145817 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 8 sales No view 17 sales 0.0% 2.5% 5.0% 7.5% 10.0% 2.8% 8.5% Discount from original list, average

The average is 2.8% for homes with a view and 8.5% for homes without a view. Defined in 8.1.6.

– No clear difference · could be chance

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

2026-10-26T03:59:03.165068 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 12 62 47 59 View No view

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

  • 12% of homes with a view had a price cut before sale, against 47% 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 59% of homes without a view. Defined in 8.1.8.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance

6.10 The numbers: price and size

View8 salesNo view17 salesAll25 salesReal difference?
Net sale price average$892,072$833,206$852,044unclear
Net price per SF average$329$295$306unclear
Living area average2,7732,8342,815unclear
Year built median198919841984unclear

6.11 The numbers: selling

View8 salesNo view17 salesAll25 salesReal difference?
Days on market median126121unclear
Discount from original list average2.8%8.5%6.7%unclear
Price cut before sale12%47%36%unclear
Seller paid closing costs or repairs62%59%60%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 Spicewood Estates Sec 02.

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
26 resale sales. Average $853,503; median $833,250; standard deviation $181,446; middle half (25th–75th percentile) $731,724–$973,750; lowest $550,000, highest $1,289,088. 15 sales had a seller payment; their median payment was $5,000.

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
26 resale sales. Average $304; median $300; standard deviation $50; middle half (25th–75th percentile) $271–$330; lowest $191, highest $428.

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
26 resale sales. Average 2,840; median 2,936; standard deviation 574; middle half (25th–75th percentile) 2,362–3,260; lowest 1,851, highest 3,934.

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
26 resale sales. Average 1987; median 1985; standard deviation 4; middle half (25th–75th percentile) 1984–1990; lowest 1982, highest 1996.

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
26 resale sales. Average 59; median 22; standard deviation 67; middle half (25th–75th percentile) 7–102; lowest 0, highest 233. 2 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
26 resale sales. Average 6.4%; median 2.8%; standard deviation 10.8%; middle half (25th–75th percentile) 0.2%–10.7%; lowest −10.1%, highest 38.9%. 6 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
9 of 26 resale sales (34.6%).

8.1.8 Seller paid closing costs or repairs

In simple terms
The seller paid some of the buyer's closing costs or repairs.
Statistically
Indicator: ACTRIS_BuyersClsgCostPdbySell + ACTRIS_RepairsAmount > 0. Reported as the share of sales (%).
The data
15 of 26 resale sales (57.7%).

8.1.9 Has a view

In simple terms
The MLS says the home looks over something: a greenbelt or park, trees, golf course, hill country, water and similar.
Statistically
View = the View field holds any entry other than “None”, “Neighborhood” or “See Remarks”. No view = only “None” and/or “Neighborhood”. A record whose only entry is “See Remarks” cannot be classified and is left out of the view slice.
The data
8 sales with a view, 17 without, 1 unclassified. Most common View entries: None 14, Trees/Woods 6, Neighborhood 4, Park/Greenbelt 4, Panoramic 3, Canyon 2, Hill Country 2, See Remarks 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 “spicewood estates”, “park at spicewood springs”, “park at spicewood spgs” (case-insensitive). A record is kept when its subdivision name belongs to the Spicewood Estates Sec 02 family, the same rule comparable sales use (it includes Park At Spicewood Spgs Ph, proven adjacent and priced alike).
The data
Pulled October 26, 2026: 26 closed Spicewood Estates Sec 02 records over the prior year, in ZIP 78750. 8 subdivision spellings kept: Park At Spicewood Spgs Ph (8); Spicewood Estates Sec 02 (6); Spicewood Estates Sec 01 (4); Spicewood Estates Sec 03 (3); Park At Spicewood Springs (2); Park At Spicewood Spgs Ph 01 Sec 02 (1); Park At Spicewood Spgs (1); Park At Spicewood Spgs Sec 01 (1). No other names were found by the search.

8.2.2 Study window

In simple terms
The most recent 365 days of closed sales: recent enough to describe today's market, long enough to give each group plenty of sales. The usual window is 180 days; it holds only 10 resale sales here (under 80), so the full year is used.
Statistically
CloseDate from October 26, 2025 to October 25, 2026 inclusive.
The data
26 single-family sales in the window: 26 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
26 resale; 0 new construction.

8.2.4 Sales left out

In simple terms
A few records cannot be used as entered and are left out before any analysis.
Statistically
Excluded: PropertySubType other than Single Family Residence; no OriginalListPrice; LivingArea under 400 SF; seller payments above 10% of ClosePrice (treated as entry errors).
The data
None in this window.

8.2.5 Quartiles and groups

In simple terms
Sales lined up from lowest to highest and cut into four groups of about equal size. Each group is named by its range, for example “Under $730K”.
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 $730K: $550,000–$692,020 (6 sales); $730K–$810K: $730,000–$810,000 (7 sales); $856K–$975K: $856,500–$975,000 (7 sales); Over $975K: $984,000–$1,289,088 (6 sales)
Quartiles by living area: Under 2,357 SF: 1,851–2,304 SF (6 sales); 2,357–2,843 SF: 2,357–2,843 SF (7 sales); 3,028–3,268 SF: 3,028–3,268 SF (7 sales); Over 3,268 SF: 3,310–3,934 SF (6 sales)
Quartiles by year built: Before 1984: 1982–1983 (6 sales); 1984: 1984–1984 (7 sales); 1986–1990: 1986–1990 (7 sales); After 1990: 1991–1996 (6 sales)
View and no view: View: MLS lists a view (8 sales); No view: None or Neighborhood (17 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 $853,503, median $833,250. Days on market: average 59, median 22: 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
1 real differences across the four slices; sizes are listed in 8.3.

8.2.12 Rounding in the text

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

8.3 Test results

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

8.3.1 Quartiles by net sale price

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net price per SFKruskal–WallisH = 8.2730.0408p < 0.0063Unclearε² = 0.331large
Living areaKruskal–WallisH = 8.4130.0382p < 0.0063Unclearε² = 0.337large
Year builtKruskal–WallisH = 1.1030.7770p < 0.0063Unclearε² = 0.044small
Days on marketKruskal–WallisH = 4.5030.2120p < 0.0063Unclearε² = 0.180large
Discount from original listKruskal–WallisH = 1.4830.6863p < 0.0063Unclearε² = 0.059small
Price cut before saleChi-squareχ² = 5.4930.1395p < 0.0063UnclearCramér’s V = 0.459medium
Seller paid closing costs or repairsChi-squareχ² = 10.0030.0185p < 0.0063UnclearCramér’s V = 0.620large
Has a viewChi-squareχ² = 3.7630.2889p < 0.0063UnclearCramér’s V = 0.380medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 13.9330.0030p < 0.0063Yesε² = 0.557large
Net price per SFKruskal–WallisH = 2.5430.4674p < 0.0063Unclearε² = 0.102medium
Year builtKruskal–WallisH = 5.5030.1386p < 0.0063Unclearε² = 0.220large
Days on marketKruskal–WallisH = 0.4730.9250p < 0.0063Unclearε² = 0.019small
Discount from original listKruskal–WallisH = 3.4930.3223p < 0.0063Unclearε² = 0.140medium
Price cut before saleChi-squareχ² = 1.8030.6139p < 0.0063UnclearCramér’s V = 0.263small
Seller paid closing costs or repairsChi-squareχ² = 3.3730.3381p < 0.0063UnclearCramér’s V = 0.360medium
Has a viewChi-squareχ² = 0.0730.9953p < 0.0063UnclearCramér’s V = 0.051negligible

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 3.8230.2820p < 0.0063Unclearε² = 0.153large
Net price per SFKruskal–WallisH = 5.5130.1378p < 0.0063Unclearε² = 0.221large
Living areaKruskal–WallisH = 5.6330.1311p < 0.0063Unclearε² = 0.225large
Days on marketKruskal–WallisH = 1.0830.7811p < 0.0063Unclearε² = 0.043small
Discount from original listKruskal–WallisH = 2.7430.4340p < 0.0063Unclearε² = 0.109medium
Price cut before saleChi-squareχ² = 2.5430.4679p < 0.0063UnclearCramér’s V = 0.313medium
Seller paid closing costs or repairsChi-squareχ² = 2.6930.4426p < 0.0063UnclearCramér’s V = 0.321medium
Has a viewChi-squareχ² = 5.1030.1647p < 0.0063UnclearCramér’s V = 0.443medium

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 85.00—0.3440p < 0.0063Unclearrank-biserial r = 0.250small
Net price per SFMann–Whitney UU = 91.00—0.1943p < 0.0063Unclearrank-biserial r = 0.338medium
Living areaMann–Whitney UU = 69.50—0.9535p < 0.0063Unclearrank-biserial r = 0.022negligible
Year builtMann–Whitney UU = 96.50—0.0976p < 0.0063Unclearrank-biserial r = 0.419medium
Days on marketMann–Whitney UU = 40.00—0.1088p < 0.0063Unclearrank-biserial r = 0.412medium
Discount from original listMann–Whitney UU = 53.00—0.4063p < 0.0063Unclearrank-biserial r = 0.221small
Price cut before saleChi-squareχ² = 2.8210.0931p < 0.0063UnclearCramér’s V = 0.336medium
Seller paid closing costs or repairsChi-squareχ² = 0.0310.8611p < 0.0063UnclearCramér’s V = 0.035negligible

8.4 Data notes

Left out of the analysis: 0 new-construction sales (basic statistics only, section 7); nothing else. Statistics: Python, NumPy 2.5.3, SciPy 1.18.1.

8.5 References

  1. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Erlbaum.
  2. Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press.
  3. Dunn, O. J. (1961). Multiple comparisons among means. JASA, 56(293), 52–64.
  4. Kerby, D. S. (2014). The simple difference formula: an approach to teaching nonparametric correlation. Comprehensive Psychology, 3, 11.IT.3.1.
  5. Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. JASA, 47(260), 583–621.
  6. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.
  7. Pearson, K. (1900). On the criterion that a given system of deviations… Philosophical Magazine, 50(302), 157–175.
  8. Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited. Trends in Sport Sciences, 21(1), 19–25.

This is a market study of closed sales, not an appraisal or an opinion of value for any home. The analysis covers 26 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.