San Leanna median real estate price is $605,321, which is more expensive than 90.7% of the neighborhoods in Texas and 80.1% of the neighborhoods in the U.S.
The average rental price in San Leanna is currently $2,502, based on NeighborhoodScout's exclusive analysis. The average rental cost in this neighborhood is higher than 83.1% of the neighborhoods in Texas.
San Leanna is a suburban neighborhood (based on population density) located in Austin, Texas.
San Leanna real estate is primarily made up of medium sized (three or four bedroom) to small (studio to two bedroom) single-family homes and apartment complexes/high-rise apartments. Most of the residential real estate is owner occupied. Many of the residences in the San Leanna neighborhood are newer, built in 2000 or more recently. A number of residences were also built between 1970 and 1999.
San Leanna has a 13.2% vacancy rate, which is well above average compared to other U.S. neighborhoods (higher than 70.9% of American neighborhoods). Most vacant housing here is vacant year round. This could either signal that there is a weak demand for real estate in the neighborhood or that large amount of new housing has been built and not yet occupied. Either way, if you live here, you may find many of the homes or apartments are empty.
When you see a neighborhood for the first time, the most important thing is often the way it looks, like its homes and its setting. Some places look the same, but they only reveal their true character after living in them for a while because they contain a unique mix of occupational or cultural groups. This neighborhood is very unique in some important ways, according to NeighborhoodScout's exclusive exploration and analysis.
Homes built from 2000 through today make up a higher proportion of the San Leanna neighborhood's real estate landscape than 97.1% of the neighborhoods in America. When you are driving around this neighborhood, you'll notice right away that it is one of the newest built of any, with the smell of fresh paint, and the look of young landscaping nearly everywhere you look. In fact, 74.8% of the residential real estate here is classified as newer.
If you're planning where to retire, the San Leanna neighborhood in Austin is a great option to consider. According to NeighborhoodScout's exclusive retirement dream area analysis, it's peaceful and quiet, has above average safety ratings compared to other neighborhoods in TX, offers a wide range of housing options, and has already attracted an enviable mix of college educated seniors. This neighborhood ranks as better for retirement living than 87.9% of the neighborhoods in Texas. If you are considering retiring to Texas, this is a good neighborhood to look at. In addition to being an excellent choice for active retirees, this neighborhood is also a very good choice for highly educated executives.
Did you know that the San Leanna neighborhood has more Lebanese ancestry people living in it than nearly any neighborhood in America? It's true! In fact, 1.0% of this neighborhood's residents have Lebanese ancestry.
There are two complementary measures for understanding the income of a neighborhood's residents: the average and the extremes. While a neighborhood may be relatively wealthy overall, it is equally important to understand the rate of people - particularly children - who are living at or below the federal poverty line, which is extremely low income. Some neighborhoods with a lower average income may actually have a lower childhood poverty rate than another with a higher average income, and this helps us understand the conditions and character of a neighborhood.
The neighbors in the San Leanna neighborhood in Austin are upper-middle income, making it an above average income neighborhood. NeighborhoodScout's exclusive analysis reveals that this neighborhood has a higher income than 68.1% of the neighborhoods in America. In addition, 9.9% of the children seventeen and under living in this neighborhood are living below the federal poverty line, which is a lower rate of childhood poverty than is found in 52.7% of America's neighborhoods.
The old saying "you are what you eat" is true. But it is also true that you are what you do for a living. The types of occupations your neighbors have shape their character, and together as a group, their collective occupations shape the culture of a place.
In the San Leanna neighborhood, 48.7% of the working population is employed in executive, management, and professional occupations. The second most important occupational group in this neighborhood is sales and service jobs, from major sales accounts, to working in fast food restaurants, with 21.9% of the residents employed. Other residents here are employed in manufacturing and laborer occupations (18.0%), and 11.3% in clerical, assistant, and tech support occupations.
The most common language spoken in the San Leanna neighborhood is English, spoken by 75.7% of households. Some people also speak Spanish (21.6%).
Culture is the shared learned behavior of peoples. Undeniably, different ethnicities and ancestries have different cultural traditions, and as a result, neighborhoods with concentrations of residents of one or another ethnicities or ancestries will express those cultures. It is what makes the North End in Boston so fun to visit for the Italian restaurants, bakeries, culture, and charm, and similarly, why people enjoy visiting Chinatown in San Francisco.
In the San Leanna neighborhood in Austin, TX, residents most commonly identify their ethnicity or ancestry as Mexican (24.9%). There are also a number of people of German ancestry (11.5%), and residents who report English roots (6.9%), and some of the residents are also of Irish ancestry (6.7%), along with some Italian ancestry residents (3.2%), among others.
How you get to work – car, bus, train or other means – and how much of your day it takes to do so is a large quality of life and financial issue. Especially with gasoline prices rising and expected to continue doing so, the length and means of one's commute can be a financial burden. Some neighborhoods are physically located so that many residents have to drive in their own car, others are set up so many walk to work, or can take a train, bus, or bike. The greatest number of commuters in San Leanna neighborhood spend between 45 minutes and one hour commuting one-way to work (35.1% of working residents), longer and tougher than most commutes in America.
Here most residents (83.7%) drive alone in a private automobile to get to work. In addition, quite a number also carpool with coworkers, friends, or neighbors to get to work (5.9%) . In a neighborhood like this, as in most of the nation, many residents find owning a car useful for getting to work.
Analytics built by: Location, Inc.
Raw data sources: National Agriculture Statistics Service, U.S. Department of Agriculture, Federal Housing Finance Agency, U.S. Department of Housing and Urban Development, U.S. Bureau of the Census, U.S. Geological Service, American Community Survey.
Methodology: NeighborhoodScout uses over 600 characteristics to build a neighborhood profile… Read more
Analytics built by: Location, Inc.
Raw data sources: American Community Survey, U.S. Bureau of the Census, U.S. Department of Education, 50 state departments of education, U.S. Bureau of Labor Statistics, Federal Bureau of Investigation, 18,000+ local law enforcement agencies, Federal Housing Finance Agency, U.S. Geological Service, National Agricultural Statistics Service.
Date(s) & Update Frequency: 2020 (latest available). Updated annually. Please note: Unemployment data updated November 2022.
Methodology: Unlike standardly available Census demographics, NeighborhoodScout uses dozens of custom models to transform 8.5 million raw demographic data elements from government sources into proprietary indices and insights…. Read more about Scout's Demographic Data
Analytics built by: Location, Inc.
Raw data sources: 18,000 local law enforcement agencies in the U.S.
Date(s) & Update Frequency: Reflects 2021 calendar year; released from FBI in Oct. 2022 (latest available). Updated annually. Where is 2022 data?
Methodology: Our nationwide meta-analysis overcomes the issues inherent in any crime database, including non-reporting and reporting errors. This is possible by associating the 9.4 million reported crimes in the U.S, including over 2 million geocoded point locations…. Read more about Scout's Crime Data
Analytics built by: Location, Inc.
Methodology: Only NeighborhoodScout gives you nationally comparable school ranks based on test scores, so you can directly compare the quality of schools in any location. Read more about Scout's School Data
School Details | Grades | Quality Rating Compared to TX* | Quality Rating Compared to Nation* |
---|---|---|---|
Jack C Hays H S School
4800 Jack C Hays Trl Buda, TX 78610 |
09-12 | ||
R C Barton Middle School
4950 Jack C Hays Trl Buda, TX 78610 |
06-08 | ||
Tom Green Elementary School
1301 Old Goforth Rd Buda, TX 78610 |
PK-05 | ||
Menchaca Elementary School
12120 Manchaca Rd Austin, TX 78748 |
PK-05 | ||
Casey Elementary School
9400 Texas Oaks Dr Austin, TX 78748 |
PK-05 | ||
Akins H S School
10701 S 1 St St Austin, TX 78748 |
09-12 | ||
Paredes M S School
10100 S Mary Moore Searight Dr Austin, TX 78748 |
06-08 | ||
Blazier Elementary School
8601 Nuckols Crossing Rd Austin, TX 78744 |
KG-06 | ||
* 10 is highest |
GET FULL REPORTS FOR ANY SCHOOL IN THIS DISTRICT
SEE ALL SCHOOLSAnalytics built by: Location, Inc.
Raw data sources: U.S. Department of Education, 50 state departments of education, U.S. Bureau of Labor Statistics, Dow Jones S&P, Federal Bureau of Investigation, 18,000+ local law enforcement agencies, Federal Housing Finance Agency, U.S. Bureau of the Census, American Community Survey, U.S. Department of Housing and Urban Development, U.S. Geological Service, U.S. Department of Transportation, LEHD Origin-Destination Employment Statistics, Federal Highway Administration, National Agricultural Statistics.
Methodology: Scout Vision uniquely solves for investment risk by generating Home Price Appreciation projections with unprecedented geographic granularity and predictive accuracy, for every micro-neighborhood (block group) in the U.S. Read more
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