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.
Green Haven median real estate price is $443,578, which is more expensive than 45.2% of the neighborhoods in Maryland and 59.2% of the neighborhoods in the U.S.
The average rental price in Green Haven is currently $4,010, based on NeighborhoodScout's exclusive analysis. The average rental cost in this neighborhood is higher than 92.5% of the neighborhoods in Maryland.
Green Haven is an urban neighborhood (based on population density) located in Pasadena, Maryland. This is a coastal neighborhood (i.e., is on the ocean, a bay, or inlet).
Green Haven real estate is primarily made up of medium sized (three or four bedroom) to large (four, five or more bedroom) single-family homes and townhomes. Most of the residential real estate is owner occupied. Many of the residences in the Green Haven neighborhood are established but not old, having been built between 1970 and 1999. A number of residences were also built between 1940 and 1969.
In Green Haven, the current vacancy rate is 1.3%, which is a lower rate of vacancies than 90.5% of all neighborhoods in the U.S. This means that the housing supply in Green Haven is very tight compared to the demand for property here.
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
Green Haven is a neighborhood that is on the ocean, a bay, or inlet. Many times, such places have amenities that bring locals and visitors to the waterfront for recreational activities or to check out the scenery. In some densely populated areas that are less financially well-off, the neighborhood waterfront can be relatively industrial and less open to recreation. In addition to being coastal, Green Haven is a very nautical neighborhood, meaning that it is somewhat historic, walkable, densely populated and on the water. This gives the neighborhood a very nautical feel, with some seaside and shipping feel, which some may really enjoy the sights and sounds of.
Occupations
The government often provides some of the more stable jobs in the economy. From local, to state, to federal government workers, the government can also be a major employer. What NeighborhoodScout's analysis revealed, is that the Green Haven neighborhood in particular stands out when compared nationally for the proportion of its working residents who are employed by the government. At 15.9% of its workforce, this neighborhood has a greater concentration of government workers than 97.6% of U.S. neighborhoods.
The Neighbors
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 Green Haven neighborhood in Pasadena are upper-middle income, making it an above average income neighborhood. NeighborhoodScout's exclusive analysis reveals that this neighborhood has a higher income than 80.3% of the neighborhoods in America. In addition, 10.0% 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 50.7% of America's neighborhoods.
What we choose to do for a living reflects who we are. Each neighborhood has a different mix of occupations represented, and together these tell you about the neighborhood and help you understand if this neighborhood may fit your lifestyle.
In the Green Haven neighborhood, 30.5% of the working population is employed in executive, management, and professional occupations. The second most important occupational group in this neighborhood is clerical, assistant, and tech support occupations, with 25.1% of the residents employed. Other residents here are employed in manufacturing and laborer occupations (24.4%), and 20.0% in sales and service jobs, from major sales accounts, to working in fast food restaurants.
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 Green Haven neighborhood is English, spoken by 91.5% of households. Other important languages spoken here include Polish and Spanish.
Ethnicity / Ancestry
Culture is shared learned behavior. We learn it from our parents, their parents, our houses of worship, and much of our culture – our learned behavior – comes from our ancestors. That is why ancestry and ethnicity can be so interesting and important to understand: places with concentrations of people of one or more ancestries often express those shared learned behaviors and this gives each neighborhood its own culture. Even different neighborhoods in the same city can have drastically different cultures.
In the Green Haven neighborhood in Pasadena, MD, residents most commonly identify their ethnicity or ancestry as German (14.7%). There are also a number of people of Irish ancestry (14.6%), and residents who report English roots (12.1%), and some of the residents are also of Mexican ancestry (6.1%), along with some Asian ancestry residents (4.0%), among others.
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 Green Haven neighborhood spend between 45 minutes and one hour commuting one-way to work (27.5% of working residents), longer and tougher than most commutes in America.
Here most residents (67.5%) 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 (12.6%) . 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.
