The Updated Neighborhood Atlas: Methodology

By Erin Talkington, Managing Director, RCLCO;  Jake Ross, Principal, RCLCO

Introduction

In 2016, RCLCO unveiled a new framework for thinking about local housing markets. Created in conjunction with the ULI Terwilliger Center for Housing, this framework initially examined the 50 largest metropolitan areas to classify each of their suburbs into one of five categories. Two years later, RCLCO expanded its analysis to cover six different types of urban neighborhoods as well.

Since its creation in December 2016, the resulting Neighborhood Atlas has served as a tool for real estate practitioners, academic figures, policy analysts, and others to examine the communities in which they live, work, and play. Importantly, the classifications are just a single snapshot of these communities. In reality, neighborhoods are constantly evolving, as they experience such changes as urbanization, suburbanization, gentrification, and—in some cases—deterioration. The methodology behind the Neighborhood Atlas is described below.

Initial Classifications

First, RCLCO developed a methodology to broadly categorize geographies in almost 150 of the largest metropolitan statistical areas (MSAs) as urban, suburban, or rural/other at the census-tract level. Using this framework, RCLCO delineated the census tracts into more specific categories. Urban categories included high-density urban, urban, and low-density urban. Suburban categories included high-density suburban, suburban, and low-density suburban. Other categories included low-density commercial/institutional/park and rural.

To account for regional differences in development patterns and densities, RCLCO classified metropolitan areas into one of five categories—Gateway, Sun Belt, New West, Heartland, and Legacy—with a sixth and separate category for New York, which has a unique urban fabric and is nearly twice as dense as the next densest metropolitan area. These categories provide a localized view of urbanity to reflect that certain regions are more likely to follow similar development patterns than are others. In other words, the methodology assumes that a metropolitan area like Columbus, Ohio, is more likely to exhibit patterns similar to Indianapolis than it is to San Francisco. When observing development patterns and densities within these categories, RCLCO relied on those of the 50 largest metropolitan areas as a baseline. This distinction reflects that what is considered “urban” in a smaller metropolitan area like Fayetteville, North Carolina, is unlikely to have an impact on what is considered “urban” in a more densely populated metropolitan area within the same region, like Atlanta, Georgia.

As part of the analysis, RCLCO combined and added MSAs to reflect the practical and geographic boundaries of metropolitan areas, as defined by their regional economies and housing markets. For example, RCLCO analyzed San Francisco and San Jose together, just as it did for Los Angeles and Riverside. In addition, RCLCO considered smaller MSAs alongside larger ones with which they interact. When determining the 50 largest metropolitan areas, RCLCO thus included the following smaller MSAs that would not have otherwise met this threshold: Worcester (overlaps with Boston); Akron (overlaps with Cleveland); Boulder and Greeley (overlap with Denver); Oxnard (overlaps with Los Angeles); Bridgeport (overlaps with New York); The Villages (overlaps with Orlando); Durham (overlaps with Raleigh); Ogden and Provo (overlap with Salt Lake City); Napa, Santa Rosa, and Vallejo (overlap with San Francisco); and Bremerton and Olympia (overlap with Seattle). While RCLCO used these additional MSAs for the purpose of determining the relative densities of census tracts in the 50 largest metropolitan areas, it did not include these other MSAs when calculating the economic and demographic trends seen in the “Top 50 MSAs.”

Please see below for more information on the MSAs used in this analysis:

MSA Categories

MSA	CATEGORY	METROPOLITAN AREA	TYPE	TOP 50 METROPOLITAN AREAS
New York-Newark-Jersey City, NY-NJ-PA	New York	New York, NY	Primary	Yes
Bridgeport-Stamford-Norwalk, CT	New York	New York, NY	Secondary	Yes
Boston-Cambridge-Newton, MA-NH	Gateway	Boston, MA	Primary	Yes
Worcester, MA-CT	Gateway	Boston, MA	Secondary	Yes
Chicago-Naperville-Elgin, IL-IN-WI	Gateway	Chicago, IL	Primary	Yes
Los Angeles-Long Beach-Anaheim, CA	Gateway	Los Angeles, CA	Primary	Yes
Oxnard-Thousand Oaks-Ventura, CA	Gateway	Los Angeles, CA	Secondary	Yes
Riverside-San Bernardino-Ontario, CA	Gateway	Los Angeles, CA	Secondary	Yes
Miami-Fort Lauderdale-Pompano Beach, FL	Gateway	Miami, FL	Primary	Yes
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Gateway	Philadelphia, PA	Primary	Yes
San Francisco-Oakland-Berkeley, CA	Gateway	San Francisco/San Jose, CA	Primary	Yes
Napa, CA	Gateway	San Francisco/San Jose, CA	Secondary	Yes
San Jose-Sunnyvale-Santa Clara, CA	Gateway	San Francisco/San Jose, CA	Secondary	Yes
Santa Rosa-Petaluma, CA	Gateway	San Francisco/San Jose, CA	Secondary	Yes
Vallejo, CA	Gateway	San Francisco/San Jose, CA	Secondary	Yes
Washington-Arlington-Alexandria, DC-VA-MD-WV	Gateway	Washington, D.C.	Primary	Yes
Atlanta-Sandy Springs-Alpharetta, GA	Sun Belt	Atlanta, GA	Primary	Yes
Austin-Round Rock-Georgetown, TX	Sun Belt	Austin, TX	Primary	Yes
Birmingham-Hoover, AL	Sun Belt	Birmingham, AL	Primary	Yes
Charlotte-Concord-Gastonia, NC-SC	Sun Belt	Charlotte, NC	Primary	Yes
Dallas-Fort Worth-Arlington, TX	Sun Belt	Dallas, TX	Primary	Yes
Houston-The Woodlands-Sugar Land, TX	Sun Belt	Houston, TX	Primary	Yes
Jacksonville, FL	Sun Belt	Jacksonville, FL	Primary	Yes
Nashville-Davidson–Murfreesboro–Franklin, TN	Sun Belt	Nashville, TN	Primary	Yes
New Orleans-Metairie, LA	Sun Belt	New Orleans, LA	Primary	Yes
Orlando-Kissimmee-Sanford, FL	Sun Belt	Orlando, FL	Primary	Yes
The Villages, FL	Sun Belt	Orlando, FL	Secondary	Yes
Phoenix-Mesa-Chandler, AZ	Sun Belt	Phoenix, AZ	Primary	Yes
Raleigh-Cary, NC	Sun Belt	Raleigh/Durham, NC	Primary	Yes
Durham-Chapel Hill, NC	Sun Belt	Raleigh/Durham, NC	Secondary	Yes
Richmond, VA	Sun Belt	Richmond, VA	Primary	Yes
San Antonio-New Braunfels, TX	Sun Belt	San Antonio, TX	Primary	Yes
Tampa-St. Petersburg-Clearwater, FL	Sun Belt	Tampa, FL	Primary	Yes
Virginia Beach-Norfolk-Newport News, VA-NC	Sun Belt	Virginia Beach, VA	Primary	Yes
Albuquerque, NM	Sun Belt	Albuquerque, NM	Primary	No
Asheville, NC	Sun Belt	Asheville, NC	Primary	No
Baton Rouge, LA	Sun Belt	Baton Rouge, LA	Primary	No
Charleston-North Charleston, SC	Sun Belt	Charleston, SC	Primary	No
Charlottesville, VA	Sun Belt	Charlottesville, VA	Primary	No
Chattanooga, TN-GA	Sun Belt	Chattanooga, TN	Primary	No
Columbia, SC	Sun Belt	Columbia, SC	Primary	No
Corpus Christi, TX	Sun Belt	Corpus Christi, TX	Primary	No
Deltona-Daytona Beach-Ormond Beach, FL	Sun Belt	Daytona Beach, FL	Primary	No
El Paso, TX	Sun Belt	El Paso, TX	Primary	No
Fayetteville-Springdale-Rogers, AR	Sun Belt	Fayetteville, AR	Primary	No
Fayetteville, NC	Sun Belt	Fayetteville, NC	Primary	No
Cape Coral-Fort Myers, FL	Sun Belt	Fort Myers, FL	Primary	No
Punta Gorda, FL	Sun Belt	Fort Myers, FL	Secondary	No
Gainesville, FL	Sun Belt	Gainesville, FL	Primary	No
Greensboro-High Point, NC	Sun Belt	Greensboro, NC	Primary	No
Greenville-Anderson, SC	Sun Belt	Greenville, SC	Primary	No
Huntsville, AL	Sun Belt	Huntsville, AL	Primary	No
Jackson, MS	Sun Belt	Jackson, MS	Primary	No
Knoxville, TN	Sun Belt	Knoxville, TN	Primary	No
Lakeland-Winter Haven, FL	Sun Belt	Lakeland, FL	Primary	No
Myrtle Beach-Conway-North Myrtle Beach, SC-NC	Sun Belt	Myrtle Beach, FL	Primary	No
Naples-Marco Island, FL	Sun Belt	Naples, FL	Primary	No
Ocala, FL	Sun Belt	Ocala, FL	Primary	No
Palm Bay-Melbourne-Titusville, FL	Sun Belt	Palm Bay/Melbourne, FL	Primary	No
Sebastian-Vero Beach, FL	Sun Belt	Palm Bay/Melbourne, FL	Secondary	No
Port St. Lucie, FL	Sun Belt	Port St. Lucie, FL	Primary	No
Santa Fe, NM	Sun Belt	Santa Fe, NM	Primary	No
North Port-Sarasota-Bradenton, FL	Sun Belt	Sarasota, FL	Primary	No
Savannah, GA	Sun Belt	Savannah, GA	Primary	No
Hilton Head Island-Bluffton, SC	Sun Belt	Savannah, GA	Secondary	No
Tallahassee, FL	Sun Belt	Tallahassee, FL	Primary	No
Tucson, AZ	Sun Belt	Tucson, AZ	Primary	No
Waco, TX	Sun Belt	Waco, TX	Primary	No
Wilmington, NC	Sun Belt	Wilmington, NC	Primary	No
Winston-Salem, NC	Sun Belt	Winston-Salem, NC	Primary	No
Denver-Aurora-Lakewood, CO	New West	Denver, CO	Primary	Yes
Boulder, CO	New West	Denver, CO	Secondary	Yes
Greeley, CO	New West	Denver, CO	Secondary	Yes
Las Vegas-Henderson-Paradise, NV	New West	Las Vegas, NV	Primary	Yes
Portland-Vancouver-Hillsboro, OR-WA	New West	Portland, OR	Primary	Yes
Sacramento-Roseville-Folsom, CA	New West	Sacramento, CA	Primary	Yes
Salt Lake City, UT	New West	Salt Lake City, UT	Primary	Yes
Ogden-Clearfield, UT	New West	Salt Lake City, UT	Secondary	Yes
Provo-Orem, UT	New West	Salt Lake City, UT	Secondary	Yes
San Diego-Chula Vista-Carlsbad, CA	New West	San Diego, CA	Primary	Yes
Seattle-Tacoma-Bellevue, WA	New West	Seattle, WA	Primary	Yes
Bremerton-Silverdale-Port Orchard, WA	New West	Seattle, WA	Secondary	Yes
Olympia-Lacey-Tumwater, WA	New West	Seattle, WA	Secondary	Yes
Bakersfield, CA	New West	Bakersfield, CA	Primary	No
Bend, OR	New West	Bend, OR	Primary	No
Boise City, ID	New West	Boise, ID	Primary	No
Colorado Springs, CO	New West	Colorado Springs, CO	Primary	No
Eugene-Springfield, OR	New West	Eugene, OR	Primary	No
Fresno, CA	New West	Fresno, CA	Primary	No
Urban Honolulu, HI	New West	Honolulu, HI	Primary	No
Reno, NV	New West	Reno, NV	Secondary	No
Carson City, NV	New West	Reno, NV	Secondary	No
Salem, OR	New West	Salem, OR	Primary	No
Salinas, CA	New West	Salinas, CA	Primary	No
Santa Cruz-Watsonville, CA	New West	Santa Cruz, CA	Primary	No
Santa Maria-Santa Barbara, CA	New West	Santa Maria/Santa Barbara, CA	Primary	No
San Luis Obispo-Paso Robles, CA	New West	Santa Maria/Santa Barbara, CA	Secondary	No
Spokane-Spokane Valley, WA	New West	Spokane, WA	Primary	No
Stockton, CA	New West	Stockton, CA	Primary	No
Cincinnati, OH-KY-IN	Heartland	Cincinnati, OH	Primary	Yes
Columbus, OH	Heartland	Columbus, OH	Primary	Yes
Indianapolis-Carmel-Anderson, IN	Heartland	Indianapolis, IN	Primary	Yes
Kansas City, MO-KS	Heartland	Kansas City, MO	Primary	Yes
Louisville/Jefferson County, KY-IN	Heartland	Louisville, KY	Primary	Yes
Memphis, TN-MS-AR	Heartland	Memphis, TN	Primary	Yes
Minneapolis-St. Paul-Bloomington, MN-WI	Heartland	Minneapolis, MN	Primary	Yes
Oklahoma City, OK	Heartland	Oklahoma City, OK	Primary	Yes
St. Louis, MO-IL	Heartland	St. Louis, MO	Primary	Yes
Ann Arbor, MI	Heartland	Ann Arbor, MI	Primary	No
Des Moines-West Des Moines, IA	Heartland	Des Moines, IA	Primary	No
Lafayette-West Lafayette, IN	Heartland	Lafayette, IN	Primary	No
Lincoln, NE	Heartland	Lincoln, NE	Primary	No
Little Rock-North Little Rock-Conway, AR	Heartland	Little Rock, AR	Primary	No
Madison, WI	Heartland	Madison, WI	Primary	No
Omaha-Council Bluffs, NE-IA	Heartland	Omaha, NE	Primary	No
Tulsa, OK	Heartland	Tulsa, OK	Primary	No
Wichita, KS	Heartland	Wichita, KS	Primary	No
Baltimore-Columbia-Towson, MD	Legacy	Baltimore, MD	Primary	Yes
Buffalo-Cheektowaga, NY	Legacy	Buffalo, NY	Primary	Yes
Cleveland-Elyria, OH	Legacy	Cleveland, OH	Primary	Yes
Akron, OH	Legacy	Cleveland, OH	Secondary	Yes
Detroit-Warren-Dearborn, MI	Legacy	Detroit, MI	Primary	Yes
Grand Rapids-Kentwood, MI	Legacy	Grand Rapids, MI	Primary	Yes
Hartford-East Hartford-Middletown, CT	Legacy	Hartford, CT	Primary	Yes
Milwaukee-Waukesha, WI	Legacy	Milwaukee, WI	Primary	Yes
Pittsburgh, PA	Legacy	Pittsburgh, PA	Primary	Yes
Providence-Warwick, RI-MA	Legacy	Providence, RI	Primary	Yes
Rochester, NY	Legacy	Rochester, NY	Primary	Yes
Albany-Schenectady-Troy, NY	Legacy	Albany, NY	Primary	No
Dayton-Kettering, OH	Legacy	Dayton, OH	Primary	No
Fort Wayne, IN	Legacy	Fort Wayne, IN	Primary	No
Green Bay, WI	Legacy	Green Bay, WI	Primary	No
Lansing-East Lansing, MI	Legacy	Lansing, MI	Primary	No
New Haven-Milford, CT	Legacy	New Haven, CT	Primary	No
Norwich-New London, CT	Legacy	Norwich, CT	Primary	No
Portland-South Portland, ME	Legacy	Portland, ME	Primary	No
South Bend-Mishawaka, IN-MI	Legacy	South Bend, IN	Primary	No
Syracuse, NY	Legacy	Syracuse, NY	Primary	No
Toledo, OH	Legacy	Toledo, OH	Primary	No
Trenton-Princeton, NJ	Legacy	Trenton, NJ	Primary	No

Source: RCLCO

Before distinguishing between metropolitan area categories, RCLCO first identified census tracts without any housing units as nonresidential, classifying them as Low-Density Commercial/Institutional/Park based on the areas in which they appeared. Next, RCLCO examined population and employment densities, using whichever metric was higher, to identify and separate the geographies that function as downtowns or rural areas. RCLCO labeled tracts with more than 25,000 jobs or residents per square mile as high-density urban and tracts with fewer than 100 jobs or residents per square mile as Rural. After these initial cuts, RCLCO then classified any tracts with more than 100 jobs or residents per square mile, but fewer than 250 housing units overall, as Low-Density Commercial/Institutional/Park in order to account for universities, ports, and other institutions that have high population or employment densities but minimal housing. Again, RCLCO applied these categories consistently across all metropolitan area categories.

For the remaining tracts, RCLCO used a standard deviation methodology to determine relative densities and value dynamics within the MSAs. Using the above metropolitan area categories, RCLCO examined the remaining tracts based on:

1. Population or employment densities, again considering whichever density was higher.
   1. High-density
      1. 0+ standard deviations from the mean for residential tracts
      2. 0.5+ standard deviations from the mean for employment tracts
   2. Medium-density
      1. −0.5 to 0 standard deviations from the mean for residential tracts
      2. −0.5 to 0.5 standard deviations from the mean for employment tracts
   3. Low-density
      1. Less than −0.5 standard deviations from the mean for both residential and employment tracts
2. Percentage of housing units that are in single-family detached homes
   1. Within high-density
      1. Tracts with less than 10 percent single-family detached were classified as urban.
   2. Within medium-density
      1. Tracts with more than 30 percent single-family detached were classified as suburban.
   3. Within low-density
      1. Tracts with more than 30 percent single-family detached were classified as low-density suburban.
      2. Tracts with less than 30 percent single-family detached were classified as Low-Density Commercial/Institutional/Park, after RCLCO observed where this classification was occurring (airports, military institutions, regional parks, etc.).
   4. For high-density tracts with more than 10 percent single-family detached and medium-density tracts with less than 30 percent single-family detached, density and housing type alone did not provide enough differentiation to determine whether tracts were more urban or more suburban. RCLCO therefore analyzed these tracts based on distance from the city center and whether they were primarily residential or commercial.
      1. For these areas, all tracts less than five miles from the city center were classified as low-density urban;
      2. Employment-driven tracts between five and ten miles from the city center were classified as low-density urban, and residential-driven ones were classified as high-density suburban; and
      3. All tracts more than ten miles from the city center were classified as high-density suburban.
   5. For metropolitan areas that did not fall within the 50 largest MSAs, RCLCO then re-classified a handful of low-density urban tracts as high-density suburban, given that the larger sizes of census tracts in smaller metropolitan areas made it difficult to reflect their density.
      1. In these MSAs, tracts with population densities that were 1.0+ standard deviations above the mean remained classified as low-density urban;
      2. Tracts with population densities that were 0.25+ standard deviations above the mean and employment densities that were also 0+ standard deviations above the mean remained classified as low-density urban; and
      3. Other census tracts were re-classified as high-density suburban.

This classification system resulted in six key categories of residential places within regions:

• High-Density Urban: Downtowns and outer employment cores
• Urban: Dense in-town neighborhoods and outer employment corridors
• Low-Density Urban: Relatively dense, in-town residential neighborhoods
• High-Density Suburban: Relatively dense outer neighborhoods and commercial corridors
• Suburban: Well-populated neighborhoods where most of the housing stock consists of single-family detached homes
• Low-Density Suburban: Neighborhoods where most of the housing stock consists of single-family detached homes, and some land is undeveloped.

Please see the below infographic for a summary of this methodology:

Initial Classification Methodology

Source: RCLCO

Application to Urban Neighborhoods

Using these categories, RCLCO then selected the high-density urban, urban, and low-density urban tracts as areas to consider “urban” for the purpose of this analysis. To further characterize urban areas relative to their likely current and future development potential, RCLCO outlined six paradigms to incorporate the impact of land value and availability on development trends:

• Economic Center: These locations offer significant concentrations of employment and are often the historic urban cores of the cities in which they are located. While office buildings typically outnumber residential buildings in these areas, new multifamily and mixed-use development is starting to bring a mix of uses to many of the nine-to-five neighborhoods they comprise.
• Emerging Economic Center: Once characterized by single-family residential or low-density commercial land uses, these locations are rapidly emerging as new urban cores. These places are generally well-located but underutilized, and they tend to offer more opportunities for ground-up development than other, more established urban locations.
• Mixed-Use District: Often situated near major employment cores, these locations tend to be more residentially focused, typically with high-density housing and upscale retail. While these places once attracted a large share of new development, construction has moderated in recent years as land availability has declined.
• High-End Neighborhood: These areas are comprised of in-town residential neighborhoods with high home values and apartment rents and convenient access to shops and restaurants. Typically characterized by single-family development, these locations are often lower density and more historic than other, more conventionally urban neighborhoods.
• Middle-Income Neighborhood: These historically working-class neighborhoods feature diverse housing types that are attainable to a broad range of households, making them attractive to households looking for a price alternative to more expensive or established urban locations, given their older and often more affordable housing inventories. In many regions, these areas are therefore beginning to confront issues of gentrification.
• Challenged Neighborhood: These locations have significantly lower home values and apartment rents than other urban neighborhoods, along with aging infrastructure and minimal new development. As a result, these areas tend to be less attractive to households that can afford to live elsewhere, resulting in very high vacancy and unemployment rates.

Please see the below infographic for more information on the methodology used to distinguish between these urban neighborhoods:

Detailed Urban Classification Methodology

Source: RCLCO

Application to Suburban Neighborhoods

RCLCO also selected the high-density suburban, suburban, and low-density suburban tracts as areas to consider “suburban” for the purpose of this analysis. To further characterize suburban areas relative to their likely current and future development potential, RCLCO outlined five suburban paradigms to incorporate the impact of land value and availability on development trends:

• Established High-End Suburb: These locations have high home values and established development patterns that likely offer the best opportunities for market-based development, but also tend to have strident community objection to new growth. When new homes or communities are built in these areas, they are often at higher densities or price points than surrounding neighborhoods.
• Middle-Income Suburb: These locations have a wide range of home values attainable to a broad range of households in the region, often located in close-in areas where most of the housing was built decades ago. Some evidence indicates that these areas are becoming increasingly scarce because these suburbs are either gentrifying into higher-end suburbs or deteriorating into economically challenged areas.
• Economically Challenged Suburbs: These locations have lower home values and little to no population growth in recent years. In many cases, these areas have aging infrastructure or underperforming city services that make them less attractive for new market-rate development.
• Greenfield Lifestyle Suburbs: These locations are at or close to the suburban fringe of established high-end suburbs, where the bulk of new community development is occurring. They have mostly developed over the past 10–15 years and likely have some land still available for new development.
• Greenfield Value Suburbs: These locations are at or close to the suburban fringe of stable or economically challenged areas; they have attracted new value-oriented communities that offer attractive home prices for many households. These areas have been developing over the past 10 to 15 years and sometimes reflect a “drive until you qualify” pattern.

Please see the below infographic for more information on the methodology used to distinguish between these suburban neighborhoods:

 

Detailed Suburban Classification Methodology

Source: RCLCO

 

RCLCO then defined Greenfield neighborhoods with home values 0.25 standard deviations above metropolitan area averages as “Greenfield Lifestyle,” and ones with home values below this threshold as “Greenfield Value.”

 

---

Article and research prepared by Erin Talkington, Managing Director and Jake Ross, Principal.

Disclaimer: Reasonable efforts have been made to ensure that the data contained in this Advisory reflect accurate and timely information, and the data is believed to be reliable and comprehensive. The Advisory is based on estimates, assumptions, and other information developed by RCLCO from its independent research effort and general knowledge of the industry. This Advisory contains opinions that represent our view of reasonable expectations at this particular time, but our opinions are not offered as predictions or assurances that particular events will occur.