Vibrancy and Property Performance of Major U.S. Employment Centers

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Volume 42, Number 13
November 13, 2018
By Emil E. Malizia, Ph.D., FAICP, and Yan Chen

Photo: MaLija/Shutterstock.com


Since the turn of the century, the spatial pattern of real property development in the United States has changed. Many central cities across the country have attracted impressive amounts of jobs, residents, and investments compared to their weak status in the 1990s. Although suburban development dominated the metropolitan landscape after World War II, central cities and especially their downtowns have grown faster than their suburbs in numerous areas. Recently, the growth of large cities has slowed, and in some downtowns, the urban revival has begun to stall. Investment appears to be migrating from primary “Gateway cities” to secondary cities.

Often, this changing pattern of development is attributed to the “live-work-play” dynamic driven by Gen Y/Millennial preferences for large urban centers, preferences that may shift to the suburbs or to smaller cities as Gen Y ages. Analysts tracking employment growth and other socio-economic indicators have identified places where the demand for development is on the rise as well as appreciation where supply is constrained. Although such analysis is useful, this article applies the multi-dimensional concept of vibrancy to explain recent property performance. Vibrant places have crosscutting, interactive activities such as working, learning, shopping, dining, entertaining, convening, etc. The concept takes us beyond the city-suburb or primary-secondary city distinctions with the finer-grained analysis of different types of employment centers in the U.S.

In the following sections, we analyze inventories, rents, and vacancies in 90 employment centers and draw conclusions relevant to real estate professionals. Based on this analysis and related previous work, the vibrancy of an employment center predicts performance better than traditional metrics of property demand. Other factors found to be important are stronger employment growth, lower crime rates, and location in the West. Yet, the most compelling take-away from this study is that real estate professionals should keep track of the vibrancy of employment centers. Attention to vibrancy metrics should lead to better-informed decisions.

Four Categories of Employment Centers

Levy and Gilchrist defined 231 major employment centers in the 150 largest U.S. cities carefully delineating mutually exclusive primary downtowns and 81 other employment centers that are combinations of census tracts.1 The 90 employment centers selected from this study fall into three major groups and two subgroups. The three major groups are primary downtowns, secondary downtowns, and suburban office parks within city jurisdictions. The secondary downtowns are either 1) centers adjacent to or near the CBD and/or anchored by educational and medical facilities or 2) the downtowns of secondary cities located within a metropolitan region that has a larger central city. The breakdown is 48 CBDs, 18 adjacent/anchored centers, 12 secondary-city downtowns and 12 suburban office centers. Appendix A lists the centers in each category.

These centers share three characteristics. First, public transit is better in these centers than transit in the average U.S. city.2 Second, the centers have relatively high job density. Third, as the largest employment centers in the U.S., they offer advantages attractive to employers. These advantages include 1) sharing infrastructure, 2) matching in labor markets, among suppliers, and to consummate deals, and 3) learning that may increase productivity and innovation. Economists call these positive outcomes associated with size agglomeration economies.3 Real estate investors also prefer larger centers since size associates with stability and mitigates market risk to some extent.

Among the 48 cities, 14 have one adjacent or anchored employment center included in the study in addition to the CBD. LA and New York City have two others: Hollywood and Wilshire/Koreatown in LA and Brooklyn’s CBD and Downtown Manhattan in addition to Midtown. Midtown Manhattan is the largest employment center in the country with more than 1.4 million jobs in 2010.

The 12 secondary cities shown in Appendix A are within the MSA or Consolidated MSA of one of eight larger cities: Detroit, LA (3), Miami, New York, Raleigh, Phoenix (2), Seattle (2), and Washington, D.C.

The 12 suburban office parks (SOPs) under study should perform better than typical SOPs for three reasons. First, they are located in relatively large metropolitan regions, which should result in more market stability. Second, major companies like Microsoft, Cisco Systems, and Sprint anchor one of these SOPs. Third, they are much larger than typical SOPs found in suburban jurisdictions. The Las Vegas strip is the largest of the 12 SOPs and the seventh largest center in the U.S. with over 300,000 jobs in 2010. It contains more commercial square footage than 36 of the 48 CBDs. On average, the 12 SOPs offer more jobs than the 12 downtowns of the secondary cities.

Performance of Employment Centers

CoStar data was used to examine performance in the 90 employment centers. CoStar’s proprietary database offers very comprehensive information on property markets across the country.4

CoStar data was compiled for the second quarter of 2017 (2Q17) and for the second quarter of 2011 (2Q11). A circle with a radius of one mile around the central point of each center is large enough to capture all or most of the center’s geography without infringing on adjacent employment centers under study. Within this area, the center’s inventory was measured by aggregating the rentable building area (RBA) for the following property types: office, retail, industrial, flex, hospitality, health care, and multi-family. Data on rents and vacancies were also compiled for all classes of office space in the 90 centers (CoStar’s Class A, B, and C categories).

According to CoStar’s National Market Reports, total RBA in the U.S. for these seven property types was about 38 billion SF in 2Q17. From 2Q11 to 2Q17, the six-year national growth rate of this inventory was 3.27%. Within this timeframe national vacancy rates for all classes of office properties declined from 11.8% to 9.6%; average asking rents for office space increased from $21.10 to $24.40.

The 90 employment centers performed better than the national averages. Total inventory grew 8.09% for the six years after 2Q11, about 2½ times faster than the national rate. Whereas the national office vacancy rate declined by 220 bps over these six years, office vacancy declined by 332 bps in the 90 centers. Similarly, average office rents increased by $5.60 in these centers compared to $3.30 nationwide. These outcomes are consistent with the selection of large and relatively strong employment centers. Table 1 compares the results for the 90 employment centers to all markets.

Table 1: Total Inventory, Office Vacancy Changes & Office Rent Changes from 2Q11 to 2Q17: Statistics for 90 Employment Centers compared to All Markets

Employment Centers
All Markets
Difference
Inventory Growth 8.09% 3.27% +4.82%
Office Vacancy
Decline
-3.32% -2.20% +1.12%
Office Rent Increase +$5.60 +$3.30 +$2.30

Table 2 shows the variation in performance among the 90 employment centers by type of center. As expected, the average inventory size in the 48 CBDs is substantially larger than average inventory size in other centers. Inventories in the downtowns of secondary cities and in SOPs are about the same size. The 18 adjacent or anchored employment centers had the lowest office vacancy rates and the highest office asking rents in 2Q17. The suburban office parks had the highest office vacancy rates in both periods.

Table 2: Average Inventory, Office Vacancy Rates and Asking Rents for Employment Centers

Type
Inventory in RBA
Vacancies
Asking Rents
2Q17 2Q11 2Q17 2Q11 2Q17 2Q11
CBDs 70,743,041 65,561,040 9.5% 12.3% $27.85 $22.57
Adjacent DTs 47,817,433 44,110,771 6.8% 10.2% $31.69 $24.41
2nd City DT 22,571,803 20,699,811 7.5% 12.2% $29.19 $24.77
Sub Office 24,081,554 22,190,943 13.2% 17.4% $28.36 $22.86

Table 3 presents the relative performance of the centers from 2Q11 to 2Q17. The first two columns are the percent differences between 2Q11 and 2Q17 values shown in Table 2. These differences therefore represent rates of change for the six-year period. The change in asking rents is shown as the dollar increase and as the percent increase for the six-year period.

Table 3: Six-Year Rates of Change in Average Inventory, Office Vacancy Rates & Asking Rents

Type
Inventory
Vacancies
Asking Rents
% Increase % Decline  $ Increase % Increase
2Q11- 2Q17 2Q11- 2Q17 2Q11- 2Q17
CBDs 7.9% -2.23% $5.28 23.4%
Adjacent DTs 8.4% – 3.31% $7.28 29.8%
2nd City DT 9.0% -3.80% $4.42 17.8%
Sub Office 8.5% -2.44% $5.50 24.1%

Inventory growth rates are similar across the four center types. The percentage change in centers with smaller inventories is higher, which is the expectation. The 48 CBDs in the sample registered the smallest reduction in office vacancy rates and the next to lowest increase in asking rents for office space. This relatively weak performance stems from the fact that the CBD measures are not weighted by inventory size. Adjacent or anchored secondary downtowns had the second largest reduction in office vacancies and had the greatest increase in asking rents. The downtown areas of secondary cities experienced the largest decline in office vacancy rates and the smallest increase in office rents. The suburban office parks had a larger vacancy rate decline and a slightly higher rent increase than the CBDs, evidence that these SOPs are much stronger than typical SOPs.

Vibrancy Considerations

One way to inform the discussion of growth and performance is to introduce the concept of vibrancy. In essence, vibrancy measures the urban form, features and amenities of a place that provide a statistically meaningful expression of the live-work-play dynamic. The vibrancy of an employment center is a better predictor of performance in terms of rent levels, property values, cap rates, investment volumes, etc. than its recent growth, its size, its type (CBD, SOP, etc.) or its geographic location in the country.

The four dimensions of vibrancy are a) density, which includes compactness, b) land-use diversity, c) connectivity and d) walkable urban form. Together, these physical features capture the benefits of agglomerating people and economic activity in space. Density is so fundamental that it is part of the definition of urban areas. Compactness is closely related focusing on the clustering of development in and near the urban core. Diverse land uses support different economic and social activities that occur at different times during the day and night. Density and diversity are necessary conditions for productive economic and social exchange. Connectivity provides the “glue” since high levels of interaction cannot occur without ease of access. Urban form can either promote or retard connectivity. Walkable places served by public transit provide the highest level of connectivity in dense urban space. Previous studies have examined these vibrancy dimensions and their implications for urban areas. 5,6,7,8,9,10,11,12,13,14,15

Various indicators can be used to measure these four dimensions. In a previous study,16 EPA’s Smart Location Database (SLD) was used exclusively for all vibrancy measures. The SLD provides over 60 measures of density, diversity, destination accessibility, distance to transit, and design at the census block-group level for all urban areas in the U.S.17 Two connectivity measures in this analysis are drawn from the SLD: workforce accessible to the center within 45 minutes by auto or by transit. As noted, greater connectivity should lead to more interaction and exchange.

The Levy-Gilchrist study offered two attractive measures: job density and the live-work quotient. They compiled these measures for core census tracts and for census tracts in zones within one-half mile and one mile from the core. Job density accounts for the jobs per acre counts in all three zones.

In a study of 24-hour and 18-hour cities,18 the live-work quotient served as a good indicator of diversity. This measure is the percentage of people living in the core, in the half-mile zone, and in the mile zone who also work within this geography. The assumption is that land-use diversity increases as more people both live and work in the center because they spend more time and money there.

Floor-area ratio (FAR) within the core area measures compactness. Using the CoStar radius function, we drew a circle with a radius of one-half mile from the center of the employment node. This circle contains an area of almost 22 million square feet (pi times 0.5 miles squared). Then, we aggregated all existing vertical development within this circle. FAR is the square footage of vertical development divided by about 22 million square feet Most CBDs have FARs greater than 1.0. By comparison, FARs for commercial development in suburban areas rarely exceed 0.30 primarily because of the acreage required for surface parking.

The second diversity measure is the Walk score for the employment center. To compute the walk score, which ranges from zero to 100, each of ten common destinations like banks, grocery stores, other businesses, theatres, parks, etc. within a five-minute walk or one-quarter mile receives the maximum score of 10; if these destinations require a walk of more than 30 minutes, the score is zero. The score also addresses pedestrian friendliness by including measures of population density, intersection density, and block length.19 Walk score can be considered an indicator of internal connectivity, but higher scores also indicate ease of access to common urban amenities that are present when land uses are diverse. The average Walk score for all census tracts in each center was the measure used.

Walkable urban form is primarily a function of the street pattern, how closely the pattern reflects a perfect grid20 together with block size and prominence of the public realm. We used 2010 census data at the census tract level to calculate the measures. Unlike all other measures that positively associate with vibrancy, smaller blocks are better because pathways for connectivity increase as blocks get smaller. Jane Jacobs argued that these urban-form features enhance economic development in city districts.21

The final walkability measure combines qualitative assessments of three factors. Open space can be devoted to the public realm including parks. Water features and mountains provide boundaries and edges and add physical beauty. Water bodies also provide vistas whereas mountains offer orientation. Boundaries, aesthetics, and orientation are features that tend to associate with more walking.22 Centers receiving low scores have less open space and no bodies of water or mountains in close proximity. Centers with higher scores have more open space and at least one large body of water or several rivers near the center. Visible mountain ranges further increase the score.

The nine measures shown in Table 4 have face-validity and use publicly available data.

Table 4: Vibrant Center Dimensions, Measures, and Data Sources

Measure
Source
Density & Compactness
Jobs per Acre Levy & Gilchrist (L-G) 2014
Floor Area Ratio CoStar Database
Diversity
Live-Work Quotient L-G 2014
Walk Score Walk score website
Connectivity
Workforce accessible w/in 45 minutes
By auto Smart Location Database
By transit Measures
Walkable Urban Form
Perfect Grid Measure Smart Location Database
Average Block Size Census data
Open space, Water & Mountains Google Earth

The vibrancy index combines these measures giving each of the four dimensions equal weight. To calculate the index, the vibrancy measures are standardized, weighted and aggregated. The resulting standard scores are transformed such that the average score is 100 instead of zero. One standard deviation unit is assigned the value of 25. Rather than having a set of small positive and negative numbers, vibrancy scores are above or below the overall average value of 100.

The vibrancy scores for the 90 centers under study range from 9.3 for Tysons Corner to 448.6 for Midtown Manhattan. The average vibrancy score for the four types of center are CDBs 123.7, Adjacent/anchored downtowns 117.6, Secondary downtowns 85.3, and SOPs 41.0. Appendix A lists the scores for each center.

Results of the Analysis

The regression models analyzing inventory growth, rent change, and rent level are in Appendix B. The results turned out to be consistent with previous work, which found that more vibrant employment centers perform better than less vibrant ones ceteris paribus.23

Vibrancy is not the only factor to influence property performance in this study. However, vibrancy is the most important factor. The most consistent and strongest result is that the vibrancy of employment centers in 2010 has significant positive relationships with inventory growth from 2011 to 2017, 2017 rent levels, and rent increases during this six-year period. Although the models are not causal, the influence of vibrancy on subsequent performance is very impressive.

Implications for Practice

This final section explains the live-work-play dynamic more fully, offers applications of the vibrancy concept, and relates the findings of the study to real property valuation.

The live-work-play dynamic connects the downtown resurgence in U.S. central cities to Gen Y/Millennials (born circa 1980-1998) who have preferences that are strikingly different from the previous generation (Gen X born circa 1965-1979). Gen Y wants seamless transitions from one activity to another and fuller integration of working, learning, convening, dining, recreating, etc. These preferences can be realized best in dense urban places like downtowns. Employers increasingly concerned about talent recruitment and retention have invested in core urban areas to capture this Gen Y talent. Thus, the location of talent is assumed to be behind the urban revival.

This explanation is partial at best. First, Gen Y/Millennials are not the dominant segment of downtown households. Many cannot afford to live downtown either because their service-industry jobs pay low wages or because they are burdened with substantial student-loan debt. More importantly, companies locating downtown or in major employment centers are not random but concentrated in specific industries related to the city’s economic base. They sort and cluster to realize industry-specific agglomeration economies related to sharing, matching, and learning. As a consequence, finance types go to New York City, techies head for San Jose, engineers seek jobs with energy firms in Houston or with robotics firms near Detroit. Thus, companies moving to large employment centers to exploit agglomeration economies are largely responsible for the urban revival. Prospective workers follow them. Therefore, real estate professionals assessing employment centers should first determine how well they function as workshops and then consider their amenities and residential opportunities.

Real estate professionals are constantly assessing markets of interest trying to decipher emerging trends. They can use the quantitative measures of vibrancy, which are an improvement over qualitative and anecdotal descriptions of live-work-play. Unlike cyclical factors that can change valuations quickly, vibrancy factors tend to change very slowly over time. Some locational factors like street patterns, the public realm, water features, or mountains are rather permanent. Cities tend to improve public transit incrementally over time and at considerable expense. Substantial redevelopment is required to increase density or compactness. Although land-use diversity can change more rapidly as leases expire and uses evolve, value premiums associated with vibrancy are not likely to be arbitraged away in the near term. Among real estate professionals, developers play a special role because the projects they execute directly influence vibrancy for better or worse. The “container” they create is as important as its leasable contents. New projects generally add density, but the influence on other vibrancy factors is less straightforward. Compactness, land-use diversity, or connectivity may improve or suffer. Mixed-use projects can increase diversity. The creation of new links, transit stops, or pathways usually increase connectivity. Projects that improve vibrancy around them should perform well over time.

Finally, real estate professionals are very interested in valuations. Cap rate data provided by Integra Realty Resources are instructive in this regard. Before the Great Recession, cap rates for CDB office were higher than cap rates for suburban office. Similarly, urban multifamily cap rates were above suburban multifamily cap rates. In the post-recession period, the reverse is true: both CBD office cap rates and urban multifamily cap rates are lower than their suburban counterparts.

Several years ago, Real Capital Analytics used walk scores to produce its Commercial Property Price Indices (CPPI) that captured the value premium associated with walkable places. Counselor Hugh Kelly has demonstrated the lasting value premiums in 24-hour cities compared to 9-to-5 cities. Kelly’s updated six 24-hour cities have downtowns that also have the highest vibrancy scores with New York City leading the way.24 (The others are Boston, Chicago, San Francisco, Washington, DC, and Philadelphia.)

With respect to property values, this study finds that vibrancy positively associates with inventory growth, rent levels, and rental growth. More inventory growth demonstrates that investors have favored places with greater vibrancy. Higher office rents lead to higher NOI and higher property values that reflect the greater vibrancy of the employment center in which they are located. Finally, higher rent growth in more vibrant centers portends property value increases in the future. These findings suggest that real estate professionals should add multi-dimensional vibrancy metrics to the performance-related indicators they already track. •

The author greatly appreciates the helpful suggestions from T.J. Barringer, Hugh Kelly, Chris Leinberger, John McDermott, Robert Simons, Jim Spaeth, Ernie Sternberg, and especially John Hentschel. Yan Chen provided research assistance.

Endnotes

1. Levy, P., and Gilchrist, L. (2014). Downtown Rebirth: Documenting the Live-Work Dynamic in the 21st Century U.S. Cities. Center City District Report.

2. All cities with commuter rail except Boston are among the 48 cities. Data limitations prevented Levy and Gilchrist from studying large cities in Massachusetts.

3. See Glaeser, E. ed. (2010). Agglomeration Economies. National Bureau of Economic Research. Chicago: The University of Chicago Press.

4. More information on the database is available at www.costar.com.

5. Crankshaw, N. (2009). Creating Vibrant Public Spaces. Washington, DC: Island Press.

6. Ewing, R., and Bartholomew, K. (2013). Pedestrian- & Transit-Oriented Design. Washington, DC: ULI-the Urban Land Institute and APA Press.

7. Jacobs, J. (1961). The Death and Life of Great American Cities. New York: Vintage Books.

8. Kapp, P. and Malizia, E. (2015). “Vibrant Centers: Character and Context.” International Journal of Architecture, Engineering and Construction 4(1), 10-18.

9. Leinberger, C. (2007). The Option of Urbanism. Washington, DC: Island Press.

10. Malizia, E. (2013). Vibrant Places: Clarifying the Terminology of Urbanism in the U.S. Context, BDC Journal (Universita degli Studi di Napoli ), 13, 175-180.

11. Malizia, E. and Song, Y. (2015). “Does Downtown Office Property Perform Better in Live-Work-Play Centers?” Journal of Urbanism: International Research on Placemaking & Urban Sustainability, 9(4), 372-387.

12. Malizia, E. (2015). “Vibrant Centers: Character, Types, Performance and Importance in Larger U.S. Metro Markets.” Real Estate Review, 44(3), 49-58.

13. Malizia, E, and Mauer, L. (2015). “Downtown Vibrancy Influences Public Health and Safety Outcomes in Urban Counties”. Journal of Transport & Health. Published online September 2015. 14. Talen, E. (2012). City Rules: How Rules Affect Urban Form. Washington, DC: Island Press.

15. Zyscovich, B. (2008). Getting Real about Urbanism: Contextual Design for Cities. Washington, DC: ULI – the Urban Land Institute.

16. Malizia, E. and Motoyama, Y. (2016). The Economic Development-Vibrant Center Connection: Tracking High-Growth Firms in the D.C. Region, The Professional Geographer, Vol.68, No. 3, August, 349-355.

17. For more information, see https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

18. Kelly, H. and Malizia, E. (2016). The Influence of 24-Hour Cities and Vibrant Centers on the Value of Office Properties and Apartments in Large U.S. Markets, Real Estate Finance, 32, Winter, 129-139.

19. For additional information, see www.walkscore.com

20. This measure is highly correlated with intersection density but is more accurate than intersection density measures when analyzing dense urban areas.

21. Jacobs, J. (1961) op. cit., 150-151.

22. For an informed discussion of the features that promote walking, see Speck, J. (2012). Walkable City. New York, NY: North Point Press.

23. Closely related previous work includes NAIOP (2014). Preferred Office Locations: Comparing Location Preferences and Performance of Office Space in CBDs, Suburban “Vibrant Centers,” and Suburban Areas. NAIOP Research Foundation, October; and Malizia, E. (2016). Vibrant Center Performance for the Past Ten Years, Real Estate Review, 45(1), 83-94.

24. Kelly, H. (2016). 24-Hour Cities. London: Routledge. Kelly, H. and Malizia, E. (2016) op cit.

Appendices – Real Estate Issues – Vibrancy and Property Performance of Major U.S. Employment Centers