ALEX · Alexander Tiffany Southwest, LLC – ALEX Companies

Georgetown

Williamson County, TX
Resale homes · closed May 4 – Oct 30, 2026
Prepared for Buyers, sellers and agents
Prepared by ALEX Real Estate
Market Study · Resale Analysis
Version 2.4
October 31, 2026 · 3:37 PM 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, 180 days
1,047
single-family resale homes
Median net sale price
$417,100
average $509,165
Median net price per SF
$203
average $218
Median days on market
46 days
average 77 days
1. SummaryResale homes · 1,047 sales · May 4, 2026 – October 30, 2026

This study looks at the 1,047 resale homes sold in Georgetown in the 180 days to October 30, 2026. 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 sold1,047 resale homes · colored by price range

Each dot is one resale home, colored by its price range. The map is drawn around the middle 99% of sales; 20 sales with map points far outside the area are left off it (they stay in every figure). 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 $334K · 262 sales$334K–$417K · 262 sales$418K–$573K · 261 salesOver $573K · 262 sales
3. Quartiles by net sale priceResale homes · 1,047 sales · May 4, 2026 – October 30, 2026

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

2026-10-31T15:37:49.127003 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 262 262 261 262 1047 sales Sales per group Under $334K: 262 sales (25%) $334K–$417K: 262 sales (25%) $418K–$573K: 261 sales (25%) Over $573K: 262 sales (25%)
Bottom line

3.1 Key differences

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

Under $334K$334K–$417K$418K–$573KOver $573K
Higher-priced homes Over $573K
  • Are much larger.
  • Are more likely to have a view.
  • Sell for more per square foot.
  • Are less likely to need a price cut.
  • Sell closer to their first asking price.
Lower-priced homes Under $334K
  • Are much smaller.
  • Are less likely to have a view.
  • Sell for less per square foot.
  • Are more likely to have had a price cut.
  • Sell further below their first asking price.

3.2 At a glance

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

Under $334K$334K–$417K$418K–$573KOver $573KReal difference?
Net price per SF$190 per SF$189 per SFLowest$208 per SF$285 per SFHighest✓ large
Living area1,567 SFSmallest2,046 SF2,390 SF3,199 SFLargest✓ large
Year built2006201420152012unclear
Days on market48 days51 days43 days41 daysunclear
Discount from original list8.4%Biggest6.0%5.7%Smallest6.2%✓ small
Price cut before sale57%Most52%49%39%Fewest✓ small
Seller paid closing costs or repairs64%63%65%56%unclear
Has a view25%Fewest34%41%71%Most✓ medium

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 $573K sell for about $95 per SF (50%) more than homes under $334K

2026-10-31T15:37:49.157360 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $334K 262 sales $334K– $417K 262 sales $418K– $573K 261 sales Over $573K 262 sales $0 $100 $200 $300 $190 $189 $208 $285 Net price per SF, average

The average is $285 per SF for homes over $573K and $190 per SF for homes under $334K. It does not move in a straight line; homes over $573K are highest. Defined in 8.1.2.

✓ Real difference · large

3.4 Homes over $573K are about 1,630 SF (104%) larger than homes under $334K

2026-10-31T15:37:49.179712 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $334K 262 sales $334K– $417K 262 sales $418K– $573K 261 sales Over $573K 262 sales 0 1,000 2,000 3,000 1,567 2,046 2,390 3,199 Living area (SF), average

The average is 3,199 SF for homes over $573K and 1,567 SF for homes under $334K. 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

2026-10-31T15:37:49.205468 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $334K 262 sales $334K– $417K 262 sales $418K– $573K 261 sales Over $573K 262 sales 2000 2005 2010 2015 2020 2006 2014 2015 2012 Year built: median (dot) and middle half (bar)

The median is 2012 for homes over $573K and 2006 for homes under $334K. It does not move in a straight line; homes $418K–$573K are highest. Defined in 8.1.4.

– No clear difference · could be chance

3.6 Days on market: no clear difference

2026-10-31T15:37:49.228017 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $334K 262 sales $334K– $417K 262 sales $418K– $573K 261 sales Over $573K 262 sales 0 20 40 60 48 51 43 41 Days on market, median

The median is 41 days for homes over $573K and 48 days for homes under $334K. It does not move in a straight line; homes $334K–$417K are highest. Defined in 8.1.5.

– No clear difference · could be chance

3.7 Homes over $573K sell about 2 percentage points closer to their first asking price than homes under $334K

2026-10-31T15:37:49.251199 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under $334K 262 sales $334K– $417K 262 sales $418K– $573K 261 sales Over $573K 262 sales 0.0% 5.0% 10.0% 8.4% 6.0% 5.7% 6.2% Discount from original list, average

The average is 6.2% for homes over $573K and 8.4% for homes under $334K. It does not move in a straight line; homes under $334K are highest. Defined in 8.1.6.

✓ Real difference · small

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

2026-10-31T15:37:49.280796 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 64 25 52 63 34 49 65 41 39 56 71 Under $334K $334K–$417K $418K–$573K Over $573K

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

  • 39% of homes over $573K had a price cut before sale, against 57% of homes under $334K. Defined in 8.1.7.
  • 56% of homes over $573K had the seller pay closing costs or repairs, against 64% of homes under $334K. Defined in 8.1.8.
  • 71% of homes over $573K have a view, against 25% of homes under $334K. Defined in 8.1.9.
Price cut before sale: ✓ Real difference · small
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: ✓ Real difference · medium

3.9 The numbers: price and size

Under $334K262 sales$334K–$417K262 sales$418K–$573K261 salesOver $573K262 salesAll1,047 salesReal difference?
Net sale price average$291,023$374,807$484,715$886,021$509,165by design
Net price per SF average$190$189$208$285$218✓ large
Living area average1,5672,0462,3903,1992,300✓ large
Year built median20062014201520122012unclear

3.10 The numbers: selling

Under $334K262 sales$334K–$417K262 sales$418K–$573K261 salesOver $573K262 salesAll1,047 salesReal difference?
Days on market median4851434146unclear
Discount from original list average8.4%6.0%5.7%6.2%6.6%✓ small
Price cut before sale57%52%49%39%49%✓ small
Seller paid closing costs or repairs64%63%65%56%62%unclear
Has a view25%34%41%71%43%✓ medium

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 · 1,047 sales · May 4, 2026 – October 30, 2026

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

2026-10-31T15:37:49.304504 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 261 263 260 263 1047 sales Sales per group Under 1,712 SF: 261 sales (25%) 1,712–2,145 SF: 263 sales (25%) 2,146–2,689 SF: 260 sales (25%) Over 2,689 SF: 263 sales (25%)
Bottom line

4.1 Key differences

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

Under 1,712 SF1,712–2,145 SF2,146–2,689 SFOver 2,689 SF
Larger homes Over 2,689 SF
  • Sell for much more.
  • Are more likely to have a view.
  • Tend to be newer.
  • Sell for more per square foot.
Smaller homes Under 1,712 SF
  • Sell for much less.
  • Are less likely to have a view.
  • Tend to be older.
  • Sell for less 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 1,712 SF1,712–2,145 SF2,146–2,689 SFOver 2,689 SFReal difference?
Net sale price$316KLowest$406K$499K$814KHighest✓ large
Net price per SF$221 per SF$210 per SF$205 per SFLowest$235 per SFHighest✓ small
Year built2005Oldest20082016Newest2014✓ small
Days on market45 days46 days44 days52 daysunclear
Discount from original list7.2%5.7%6.4%6.9%unclear
Price cut before sale52%49%50%45%unclear
Seller paid closing costs or repairs61%64%67%57%unclear
Has a view33%Fewest44%40%55%Most✓ small

4.3 Homes over 2,689 SF sell for about $498K (158%) more than homes under 1,712 SF

2026-10-31T15:37:49.327188 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,712 SF 261 sales 1,712– 2,145 SF 263 sales 2,146– 2,689 SF 260 sales Over 2,689 SF 263 sales $0K $250K $500K $750K $316K $406K $499K $814K Net sale price, average

The average is $814K for homes over 2,689 SF and $316K for homes under 1,712 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 2,689 SF sell for about $14 per SF (6%) more than homes under 1,712 SF

2026-10-31T15:37:49.351326 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,712 SF 261 sales 1,712– 2,145 SF 263 sales 2,146– 2,689 SF 260 sales Over 2,689 SF 263 sales $0 $100 $200 $221 $210 $205 $235 Net price per SF, average

The average is $235 per SF for homes over 2,689 SF and $221 per SF for homes under 1,712 SF. It does not move in a straight line; homes over 2,689 SF are highest. Defined in 8.1.2.

✓ Real difference · small

4.5 Homes over 2,689 SF are about 9 years newer than homes under 1,712 SF

2026-10-31T15:37:49.375763 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,712 SF 261 sales 1,712– 2,145 SF 263 sales 2,146– 2,689 SF 260 sales Over 2,689 SF 263 sales 1998 2004 2010 2016 2005 2008 2016 2014 Year built: median (dot) and middle half (bar)

The median is 2014 for homes over 2,689 SF and 2005 for homes under 1,712 SF. It does not move in a straight line; homes 2,146–2,689 SF are highest. Defined in 8.1.4.

✓ Real difference · small

4.6 Days on market: no clear difference

2026-10-31T15:37:49.398284 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,712 SF 261 sales 1,712– 2,145 SF 263 sales 2,146– 2,689 SF 260 sales Over 2,689 SF 263 sales 0 20 40 60 45 46 44 52 Days on market, median

The median is 52 days for homes over 2,689 SF and 45 days for homes under 1,712 SF. It does not move in a straight line; homes over 2,689 SF are highest. Defined in 8.1.5.

– No clear difference · could be chance

4.7 Discount from original list: no clear difference

2026-10-31T15:37:49.422272 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Under 1,712 SF 261 sales 1,712– 2,145 SF 263 sales 2,146– 2,689 SF 260 sales Over 2,689 SF 263 sales 0.0% 2.5% 5.0% 7.5% 7.2% 5.7% 6.4% 6.9% Discount from original list, average

The average is 6.9% for homes over 2,689 SF and 7.2% for homes under 1,712 SF. It does not move in a straight line; homes under 1,712 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

2026-10-31T15:37:49.449156 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% 52 61 33 49 64 44 50 67 40 45 57 55 Under 1,712 SF 1,712–2,145 SF 2,146–2,689 SF Over 2,689 SF

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

  • 45% of homes over 2,689 SF had a price cut before sale, against 52% of homes under 1,712 SF. Defined in 8.1.7.
  • 57% of homes over 2,689 SF had the seller pay closing costs or repairs, against 61% of homes under 1,712 SF. Defined in 8.1.8.
  • 55% of homes over 2,689 SF have a view, against 33% of homes under 1,712 SF. Defined in 8.1.9.
Price cut before sale: – No clear difference · could be chance
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: ✓ Real difference · small

4.9 The numbers: price and size

Under 1,712 SF261 sales1,712–2,145 SF263 sales2,146–2,689 SF260 salesOver 2,689 SF263 salesAll1,047 salesReal difference?
Net sale price average$315,971$406,118$498,939$814,045$509,165✓ large
Net price per SF average$221$210$205$235$218✓ small
Living area average1,4571,9292,4293,3822,300by design
Year built median20052008201620142012✓ small

4.10 The numbers: selling

Under 1,712 SF261 sales1,712–2,145 SF263 sales2,146–2,689 SF260 salesOver 2,689 SF263 salesAll1,047 salesReal difference?
Days on market median4546445246unclear
Discount from original list average7.2%5.7%6.4%6.9%6.6%unclear
Price cut before sale52%49%50%45%49%unclear
Seller paid closing costs or repairs61%64%67%57%62%unclear
Has a view33%44%40%55%43%✓ small

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 · 1,047 sales · May 4, 2026 – October 30, 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.

2026-10-31T15:37:49.473023 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 265 258 279 245 1047 sales Sales per group Before 2000: 265 sales (25%) 2000–2011: 258 sales (25%) 2012–2019: 279 sales (27%) After 2019: 245 sales (23%)
Bottom line

5.1 Key differences

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

Before 20002000–20112012–2019After 2019
Newer homes After 2019
  • Are more likely to have had a price cut.
  • Are less likely to have a view.
  • Are larger.
  • Sell for less per square foot.
  • Take longer to sell.
Older homes Before 2000
  • Are less likely to need a price cut.
  • Are more likely to have a view.
  • Are smaller.
  • Sell for more per square foot.
  • Sell faster.

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 20002000–20112012–2019After 2019Real difference?
Net sale price$491K$488K$530K$526Kunclear
Net price per SF$237 per SFHighest$211 per SFLowest$211 per SF$213 per SF✓ small
Living area2,121 SFSmallest2,245 SF2,429 SFLargest2,406 SF✓ small
Days on market40 days37 daysFastest46 days62 daysSlowest✓ small
Discount from original list7.4%6.3%6.3%6.2%unclear
Price cut before sale49%41%Fewest49%58%Most✓ small
Seller paid closing costs or repairs60%62%66%60%unclear
Has a view42%52%Most37%Fewest41%✓ small

5.3 Net sale price: no clear difference

2026-10-31T15:37:49.495247 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 265 sales 2000– 2011 258 sales 2012– 2019 279 sales After 2019 245 sales $0K $200K $400K $600K $491K $488K $530K $526K Net sale price, average

The average is $526K for homes built after 2019 and $491K for homes built before 2000. It does not move in a straight line; homes built 2012–2019 are highest. Defined in 8.1.1.

– No clear difference · could be chance

5.4 Homes built after 2019 sell for about $24 per SF (10%) less than homes built before 2000

2026-10-31T15:37:49.518540 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 265 sales 2000– 2011 258 sales 2012– 2019 279 sales After 2019 245 sales $0 $100 $200 $237 $211 $211 $213 Net price per SF, average

The average is $213 per SF for homes built after 2019 and $237 per SF for homes built before 2000. It does not move in a straight line; homes built before 2000 are highest. Defined in 8.1.2.

✓ Real difference · small

5.5 Homes built after 2019 are about 280 SF (13%) larger than homes built before 2000

2026-10-31T15:37:49.541934 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 265 sales 2000– 2011 258 sales 2012– 2019 279 sales After 2019 245 sales 0 1,000 2,000 2,121 2,245 2,429 2,406 Living area (SF), average

The average is 2,406 SF for homes built after 2019 and 2,121 SF for homes built before 2000. It does not move in a straight line; homes built 2012–2019 are highest. Defined in 8.1.3.

✓ Real difference · small

5.6 Homes built after 2019 take 22 days longer to sell than homes built before 2000

2026-10-31T15:37:49.563734 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 265 sales 2000– 2011 258 sales 2012– 2019 279 sales After 2019 245 sales 0 20 40 60 40 37 46 62 Days on market, median

The median is 62 days for homes built after 2019 and 40 days for homes built before 2000. It does not move in a straight line; homes built after 2019 are highest. Defined in 8.1.5.

✓ Real difference · small

5.7 Discount from original list: no clear difference

2026-10-31T15:37:49.586761 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Before 2000 265 sales 2000– 2011 258 sales 2012– 2019 279 sales After 2019 245 sales 0.0% 2.5% 5.0% 7.5% 7.4% 6.3% 6.3% 6.2% Discount from original list, average

The average is 6.2% for homes built after 2019 and 7.4% for homes built before 2000. It falls step by step from the lowest range to the 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

2026-10-31T15:37:49.613565 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% 49 60 42 41 62 52 49 66 37 58 60 41 Before 2000 2000–2011 2012–2019 After 2019

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

  • 58% of homes built after 2019 had a price cut before sale, against 49% of homes built before 2000. Defined in 8.1.7.
  • 60% of homes built after 2019 had the seller pay closing costs or repairs, against 60% of homes built before 2000. Defined in 8.1.8.
  • 41% of homes built after 2019 have a view, against 42% of homes built before 2000. Defined in 8.1.9.
Price cut before sale: ✓ Real difference · small
Seller paid closing costs or repairs: – No clear difference · could be chance
Has a view: ✓ Real difference · small

5.9 The numbers: price and size

Before 2000265 sales2000–2011258 sales2012–2019279 salesAfter 2019245 salesAll1,047 salesReal difference?
Net sale price average$491,263$488,453$530,394$526,163$509,165unclear
Net price per SF average$237$211$211$213$218✓ small
Living area average2,1212,2452,4292,4062,300✓ small
Year built median19952005201720212012by design

5.10 The numbers: selling

Before 2000265 sales2000–2011258 sales2012–2019279 salesAfter 2019245 salesAll1,047 salesReal difference?
Days on market median4037466246✓ small
Discount from original list average7.4%6.3%6.3%6.2%6.6%unclear
Price cut before sale49%41%49%58%49%✓ small
Seller paid closing costs or repairs60%62%66%60%62%unclear
Has a view42%52%37%41%43%✓ small

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 · 1,047 sales · May 4, 2026 – October 30, 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. 24 sales whose MLS view entry is only “See Remarks” are left out.

2026-10-31T15:37:49.633670 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ 450 573 1023 sales Sales per group View: 450 sales (44%) No view: 573 sales (56%)
Bottom line

6.1 Key differences

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

ViewNo view
Homes with a view MLS lists a view
  • Sell for more per square foot.
  • Sell for more.
  • Are larger.
  • Are less likely to need a price cut.
  • Sell faster.
  • Sell closer to their first asking price.
Homes without a view no view listed
  • Sell for less per square foot.
  • Sell for less.
  • Are smaller.
  • Are more likely to have had a price cut.
  • Take longer to sell.
  • Sell further below their first asking price.

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$621KHighest$422KLowest✓ medium
Net price per SF$245 per SFHighest$197 per SFLowest✓ medium
Living area2,479 SFLargest2,161 SFSmallest✓ small
Year built20092014unclear
Days on market39 daysFastest51 daysSlowest✓ small
Discount from original list5.9%Smallest7.1%Biggest✓ small
Price cut before sale43%Fewest54%Most✓ small
Seller paid closing costs or repairs60%63%unclear

6.3 Homes with a view sell for about $199K (47%) more than homes without a view

2026-10-31T15:37:49.652401 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 450 sales No view 573 sales $0K $200K $400K $600K $621K $422K Net sale price, average

The average is $621K for homes with a view and $422K for homes without a view. Defined in 8.1.1.

✓ Real difference · medium

6.4 Homes with a view sell for about $49 per SF (25%) more than homes without a view

2026-10-31T15:37:49.693242 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 450 sales No view 573 sales $0 $100 $200 $245 $197 Net price per SF, average

The average is $245 per SF for homes with a view and $197 per SF for homes without a view. Defined in 8.1.2.

✓ Real difference · medium

6.5 Homes with a view are about 320 SF (15%) larger than homes without a view

2026-10-31T15:37:49.711428 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 450 sales No view 573 sales 0 1,000 2,000 2,479 2,161 Living area (SF), average

The average is 2,479 SF for homes with a view and 2,161 SF for homes without a view. Defined in 8.1.3.

✓ Real difference · small

6.6 Year built: no clear difference

2026-10-31T15:37:49.731452 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 450 sales No view 573 sales 2000 2005 2010 2015 2020 2009 2014 Year built: median (dot) and middle half (bar)

The median is 2009 for homes with a view and 2014 for homes without a view. Defined in 8.1.4.

– No clear difference · could be chance

6.7 Homes with a view sell 12 days faster than homes without a view

2026-10-31T15:37:49.749601 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 450 sales No view 573 sales 0 20 40 60 39 51 Days on market, median

The median is 39 days for homes with a view and 51 days for homes without a view. Defined in 8.1.5.

✓ Real difference · small

6.8 Homes with a view sell about 1 percentage point closer to their first asking price than homes without a view

2026-10-31T15:37:49.768820 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ View 450 sales No view 573 sales 0.0% 2.0% 4.0% 6.0% 8.0% 5.9% 7.1% Discount from original list, average

The average is 5.9% for homes with a view and 7.1% for homes without a view. Defined in 8.1.6.

✓ Real difference · small

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

2026-10-31T15:37:49.789076 image/svg+xml Matplotlib v3.11.2, https://matplotlib.org/ Price cut Seller paid costs or repairs 0% 25% 50% 75% 100% 43 60 54 63 View No view

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

  • 43% of homes with a view had a price cut before sale, against 54% of homes without a view. Defined in 8.1.7.
  • 60% of homes with a view had the seller pay closing costs or repairs, against 63% of homes without a view. Defined in 8.1.8.
Price cut before sale: ✓ Real difference · small
Seller paid closing costs or repairs: – No clear difference · could be chance

6.10 The numbers: price and size

View450 salesNo view573 salesAll1,023 salesReal difference?
Net sale price average$621,099$422,025$509,594✓ medium
Net price per SF average$245$197$218✓ medium
Living area average2,4792,1612,301✓ small
Year built median200920142011unclear

6.11 The numbers: selling

View450 salesNo view573 salesAll1,023 salesReal difference?
Days on market median395147✓ small
Discount from original list average5.9%7.1%6.6%✓ small
Price cut before sale43%54%49%✓ small
Seller paid closing costs or repairs60%63%62%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 · 550 sales · May 4, 2026 – October 30, 2026

Homes sold new by the builder in the same 180 days. They are left out of every other section and shown here as basic figures only: 550 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 Georgetown.
MeasureAverageMedian
Sales550
Net sale price$524,026$477,250
Net price per SF$200$197
Living area (SF)2,6022,539
Days on market10673
Discount from original list10.4%9.4%
Price cut before sale68% of sales
Seller paid closing costs or repairs75% of sales
Year built2021–2026

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
1,047 resale sales. Average $509,165; median $417,100; standard deviation $310,181; middle half (25th–75th percentile) $333,856–$573,300; lowest $120,000, highest $4,365,000. 649 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
1,047 resale sales. Average $218; median $203; standard deviation $79; middle half (25th–75th percentile) $182–$234; lowest $89, highest $1,530.

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
1,047 resale sales. Average 2,300; median 2,145; standard deviation 833; middle half (25th–75th percentile) 1,712–2,691; lowest 768, highest 11,231.

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 parts of the area.
Statistically
RESO YearBuilt; summarized by the median, with the 25th–75th percentile (middle half) on the charts.
The data
1,047 resale sales. Average 2007; median 2012; standard deviation 16; middle half (25th–75th percentile) 1999–2019; lowest 1875, highest 2026.

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
1,047 resale sales. Average 77; median 46; standard deviation 93; middle half (25th–75th percentile) 15–105; lowest 0, highest 722. 28 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
1,047 resale sales. Average 6.6%; median 5.2%; standard deviation 8.2%; middle half (25th–75th percentile) 2.0%–9.6%; lowest −136.7%, highest 52.0%. 52 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
514 of 1,047 resale sales (49.1%).

8.1.8 Seller paid closing costs or repairs

In simple terms
The seller paid some of the buyer's closing costs or repairs.
Statistically
Indicator: ACTRIS_BuyersClsgCostPdbySell + ACTRIS_RepairsAmount > 0. Reported as the share of sales (%).
The data
649 of 1,047 resale sales (62.0%).

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
450 sales with a view, 573 without, 24 unclassified. Most common View entries: None 357, Neighborhood 302, Park/Greenbelt 223, Trees/Woods 213, Hill Country 61, Golf Course 52, See Remarks 42, Panoramic 31.

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
ABOR MLS via secure APIs, read from ALEX's copy of the MLS (refreshed every 15 minutes; this pull as of 2026-10-31 20:30 UTC), every field. Filter: StandardStatus = Closed, PropertyType = Residential, City = Georgetown.
The data
Pulled October 31, 2026: 3,150 closed Georgetown records over the prior year. ZIP codes: 7, the most sales in 78628, 78633, 78626, 78674, 78641, 78642.

8.2.2 Study window

In simple terms
The most recent 180 days of closed sales: recent enough to describe today's market, long enough to give each group plenty of sales.
Statistically
CloseDate from May 4, 2026 to October 30, 2026 inclusive.
The data
1,597 single-family sales in the window: 1,047 resale, 550 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
1,047 resale; 550 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
41 sale(s) other than single-family homes (condo, townhome and similar); 1 sale(s) with concessions above 10% of price; 3 sale(s) with no original list 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 $334K”.
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 $334K: $120,000–$333,713 (262 sales); $334K–$417K: $334,000–$417,100 (262 sales); $418K–$573K: $418,000–$573,000 (261 sales); Over $573K: $573,601–$4,365,000 (262 sales)
Quartiles by living area: Under 1,712 SF: 768–1,705 SF (261 sales); 1,712–2,145 SF: 1,712–2,145 SF (263 sales); 2,146–2,689 SF: 2,146–2,689 SF (260 sales); Over 2,689 SF: 2,691–11,231 SF (263 sales)
Quartiles by year built: Before 2000: 1875–1999 (265 sales); 2000–2011: 2000–2011 (258 sales); 2012–2019: 2012–2019 (279 sales); After 2019: 2020–2026 (245 sales)
View and no view: View: MLS lists a view (450 sales); No view: None or Neighborhood (573 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 $509,165, median $417,100. Days on market: average 77, median 46: 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
20 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 = 329.353< 0.0001p < 0.0063Yesε² = 0.315large
Living areaKruskal–WallisH = 650.563< 0.0001p < 0.0063Yesε² = 0.622large
Year builtKruskal–WallisH = 7.8830.0486p < 0.0063Unclearε² = 0.008negligible
Days on marketKruskal–WallisH = 3.2430.3557p < 0.0063Unclearε² = 0.003negligible
Discount from original listKruskal–WallisH = 24.643< 0.0001p < 0.0063Yesε² = 0.024small
Price cut before saleChi-squareχ² = 17.0130.0007p < 0.0063YesCramér’s V = 0.127small
Seller paid closing costs or repairsChi-squareχ² = 5.9230.1155p < 0.0063UnclearCramér’s V = 0.075negligible
Has a viewChi-squareχ² = 129.723< 0.0001p < 0.0063YesCramér’s V = 0.352medium

8.3.2 Quartiles by living area

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 651.393< 0.0001p < 0.0063Yesε² = 0.623large
Net price per SFKruskal–WallisH = 19.5430.0002p < 0.0063Yesε² = 0.019small
Year builtKruskal–WallisH = 28.963< 0.0001p < 0.0063Yesε² = 0.028small
Days on marketKruskal–WallisH = 4.1030.2513p < 0.0063Unclearε² = 0.004negligible
Discount from original listKruskal–WallisH = 2.1830.5363p < 0.0063Unclearε² = 0.002negligible
Price cut before saleChi-squareχ² = 3.0030.3913p < 0.0063UnclearCramér’s V = 0.054negligible
Seller paid closing costs or repairsChi-squareχ² = 5.6630.1296p < 0.0063UnclearCramér’s V = 0.073negligible
Has a viewChi-squareχ² = 27.613< 0.0001p < 0.0063YesCramér’s V = 0.162small

8.3.3 Quartiles by year built

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceKruskal–WallisH = 9.4230.0242p < 0.0063Unclearε² = 0.009negligible
Net price per SFKruskal–WallisH = 16.9830.0007p < 0.0063Yesε² = 0.016small
Living areaKruskal–WallisH = 39.203< 0.0001p < 0.0063Yesε² = 0.037small
Days on marketKruskal–WallisH = 15.8030.0012p < 0.0063Yesε² = 0.015small
Discount from original listKruskal–WallisH = 3.4930.3226p < 0.0063Unclearε² = 0.003negligible
Price cut before saleChi-squareχ² = 14.3430.0025p < 0.0063YesCramér’s V = 0.117small
Seller paid closing costs or repairsChi-squareχ² = 2.6030.4581p < 0.0063UnclearCramér’s V = 0.050negligible
Has a viewChi-squareχ² = 13.4430.0038p < 0.0063YesCramér’s V = 0.113small

8.3.4 View and no view

MeasureTestStatisticdfp-valueThresholdReal difference?Effect sizeSize
Net sale priceMann–Whitney UU = 179,864.50—< 0.0001p < 0.0063Yesrank-biserial r = 0.395medium
Net price per SFMann–Whitney UU = 183,206.00—< 0.0001p < 0.0063Yesrank-biserial r = 0.421medium
Living areaMann–Whitney UU = 153,425.00—< 0.0001p < 0.0063Yesrank-biserial r = 0.190small
Year builtMann–Whitney UU = 123,440.00—0.2419p < 0.0063Unclearrank-biserial r = 0.043negligible
Days on marketMann–Whitney UU = 114,583.50—0.0022p < 0.0063Yesrank-biserial r = 0.111small
Discount from original listMann–Whitney UU = 114,999.50—0.0030p < 0.0063Yesrank-biserial r = 0.108small
Price cut before saleChi-squareχ² = 13.5910.0002p < 0.0063YesCramér’s V = 0.115small
Seller paid closing costs or repairsChi-squareχ² = 1.5410.2152p < 0.0063UnclearCramér’s V = 0.039negligible

8.4 Data notes

Left out of the analysis: 550 new-construction sales (basic statistics only, section 7); 41 sale(s) other than single-family homes (condo, townhome and similar); 1 sale(s) with concessions above 10% of price; 3 sale(s) with no original list 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 1,047 closed MLS resale sales; the 550 new-construction sales appear only in their own section, as basic statistics. Sale and listing data: ABOR MLS via secure APIs, pulled October 31, 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.