What do we mean by a neighborhood?
NeighborhoodScout® uses the official government designation for neighborhoods - the census tract.
Census tracts are small, relatively permanent subdivisions of a county that are defined by the U.S. Census Bureau in conjunction with local authorities all across the country to define real neighborhoods that are bounded to contain areas with homogeneous population characteristics (including economic status, lifestyle, and living conditions). Census tracts usually have 4,000 persons, but can range between 1,500 and 8,000 persons. This is the most fine-grained area for which detailed information is made available from the government, to protect the individual privacy of each of us. Because census tracts are based on population, they vary in size depending on the density of settlement. In urban areas, they are small, and in rural areas they can cover an entire small town or even a few small towns in very rural areas.
Since census tracts are subdivisions of a county, we did a spatial overlay of the census tracts onto city and town boundaries using a geographic information system to properly assign each census tract to its appropriate city or town. Then we named each census tract to the local colloquially recognized neighborhood name for that spot (e.g., Boston, MA (Dorchester). If there was not a name available, we named the census tract by the largest street intersection in the census tract (e.g., Worcester, MA (Lincoln St/Plantation St)
Why we don't use Zip Codes as neighborhoods
Zip codes were developed by the Postal Service for the purpose of delivering mail, but were never intended to define coherent neighborhoods. In addition, Zip Codes can have as many as 10 distinctly different census tracts contained within them. When distinct census tracts are blended together in a single Zip Code, the data reflects their average conditions and often gives a false sense of the area. The result is that potentially great matching neighborhoods can be missed, and erroneous matches can also be produced and delivered to you.
Using census tracts makes great matches possible, and helps people find the areas that are best for them and their families. Because we are committed to providing the highest possible quality and finding the neighborhoods that best meet your criteria, we use census tracts. And, as always, we show you the actual street map of the neighborhood so you know exactly where your best matching neighborhood is located.
What is a Condition Alert?
A condition alert is a condition in the neighborhood that triggers an alert. NeighborhoodScout contains condition alerts to highlight conditions that are extreme, whether good or bad. This helps answer crucial questions about the state of the neighborhood being evaluated, such as:
What are the risks of violent crime here?
Is the risk of home break-ins or other property crimes increasing?
Does real estate here hold its value?
Are the trends in local vacancies or unemployment concerning?
...and dozens more.
Information at a glance that you need to know to invest, appraise or finance a property.
University Mound median real estate price is $1,207,812, which is more expensive than 72.6% of the neighborhoods in California and 94.0% of the neighborhoods in the U.S.
The average rental price in University Mound is currently $4,707, based on NeighborhoodScout's exclusive analysis. The average rental cost in this neighborhood is higher than 85.9% of the neighborhoods in California.
University Mound is a densely urban neighborhood (based on population density) located in San Francisco, California.
University Mound real estate is primarily made up of medium sized (three or four bedroom) to small (studio to two bedroom) townhomes and single-family homes. Most of the residential real estate is owner occupied. Many of the residences in the University Mound neighborhood are older, well-established, built between 1940 and 1969. A number of residences were also built between 1970 and 1999.
Real estate vacancies in University Mound are 5.3%, which is lower than one will find in 64.6% of American neighborhoods. Demand for real estate in University Mound is above average for the U.S., and may signal some demand for either price increases or new construction of residential product for this neighborhood.
Notable & Unique Neighborhood Characteristics
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.
Real Estate
Many people dream of living along a street lined with row houses or other attached homes. Such places do often have an abundance of charm. If you are one of these people, the University Mound neighborhood could be your paradise. With 44.6% of the homes and real estate here classified as rowhouses or other attached homes, this neighborhood brims with opportunity to find the right place for you. Only 1.5% of U.S. neighborhoods have more row houses than this neighborhood, making it one of the most interesting things about this special neighborhood.
Diversity
Did you know that the University Mound neighborhood has more Asian and Swiss ancestry people living in it than nearly any neighborhood in America? It's true! In fact, 43.8% of this neighborhood's residents have Asian ancestry and 1.5% have Swiss ancestry.
University Mound is also pretty special linguistically. Significantly, 23.7% of its residents five years old and above primarily speak Chinese at home. While this may seem like a small percentage, it is higher than 99.4% of the neighborhoods in America.
The Neighbors
How wealthy a neighborhood is, from very wealthy, to middle income, to low income is very formative with regard to the personality and character of a neighborhood. Equally important is the rate of people, particularly children, who live below the federal poverty line. In some wealthy gated communities, the areas immediately surrounding can have high rates of childhood poverty, which indicates other social issues. NeighborhoodScout's analysis reveals both aspects of income and poverty for this neighborhood.
The neighbors in the University Mound neighborhood in San Francisco are wealthy, making it among the 15% highest income neighborhoods in America. NeighborhoodScout's exclusive analysis reveals that this neighborhood has a higher income than 93.4% of the neighborhoods in America. In addition, 6.5% 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 60.1% of America's neighborhoods.
A neighborhood is far different if it is dominated by enlisted military personnel rather than people who earn their living by farming. It is also different if most of the neighbors are clerical support or managers. What is wonderful is the sheer diversity of neighborhoods, allowing you to find the type that fits your lifestyle and aspirations.
In the University Mound neighborhood, 54.0% 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 17.1% of the residents employed. Other residents here are employed in clerical, assistant, and tech support occupations (16.6%), and 12.4% in manufacturing and laborer occupations.
Languages
The languages spoken by people in this neighborhood are diverse. These are tabulated as the languages people preferentially speak when they are at home with their families. The most common language spoken in the University Mound neighborhood is English, spoken by 39.2% of households. Other important languages spoken here include Chinese, Spanish, Tagalog (the first language of the Philippine region) and Italian.
Ethnicity / Ancestry
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 University Mound neighborhood in San Francisco, CA, residents most commonly identify their ethnicity or ancestry as Asian (43.8%). There are also a number of people of Mexican ancestry (16.6%), and residents who report Italian roots (5.2%), and some of the residents are also of Irish ancestry (4.8%), along with some German ancestry residents (4.6%), among others. In addition, 36.9% of the residents of this neighborhood were born in another country.
Getting to Work
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 University Mound neighborhood spend between 15 and 30 minutes commuting one-way to work (35.2% of working residents), which is shorter than the time spent commuting to work for most Americans.
Here most residents (62.9%) 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 (14.5%) and 9.5% of residents also ride the bus for their daily commute. In a neighborhood like this, as in most of the nation, many residents find owning a car useful for getting to work.
SCOUT’s patented match algorithm identifies the most similar neighborhoods to a subject location, and quantifies the similarity to the subject location. This allows analysts to identify the most similar neighborhoods in a local market area for appraisals, investments, and research.
The algorithm uses 273 neighborhood conditions, identifies the dominant dimensions underlying the conditions and calculates the similarity. This captures the true culture and character of locations, economic conditions, and trends.
The closer to 100% means the locations are nearly identical. 80% is a very good, but not excellent match, and so forth.
