# The Office Market, and the Labor Market

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## The Office Market, and the Labor Market

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Employment Decentralization, “edge” cities.

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## Nội dung Text: The Office Market, and the Labor Market

1. MIT Center for Real Estate Week 5: Employment Decentralization, “edge” cities. • Measuring Decentralization, space versus jobs. • Wages, the urban labor market and the incentive for decentralization. • Local agglomeration, clustering, transportation infrastructure, planning and other “limits to sprawl”.
2. MIT Center for Real Estate National % of office space in CBD as opposed to Suburbs (source: CBRE)
3. MIT Center for Real Estate Washington D.C.: City and Suburban Office Space (source: CBRE) 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 District of Columbia Suburban Maryland Northern Virginia
4. MIT Center for Real Estate Decentralization “flattens” the cumulative W.D.C. spatial distribution of office space. [Source: geo-coded building data, CBRE] Pe r ce nt of Stock 120% 100% 80% 60% 40% 20% 0% 0 2 4 6 8 14 16 22 24 30 10 12 18 20 26 28 32 M ile s out fr om Ce nte r 1980 2002
5. MIT Center for Real Estate The Distribution of Office Using Jobs Across The NY CMSA [Source: Employment Zip file, 1999] 120% 100% 80% 60% 40% 20% 0% 102 108 0 6 12 18 24 30 36 42 48 54 66 72 78 84 90 96 60 Inf ormation, Real Es tate, Prof es s ional Serv ic es Financ e Management of Companies , A dminis trativ e Serv ic es
6. MIT Center for Real Estate Figure 7: Los Angeles Spatial Distributions Employment Population 1 Cummulative Employment and Population (%) .75 .5 .25 0 0 10 20 30 40 50 60 70 Distance from Center of the CBD (miles)
7. MIT Center for Real Estate Figure 6: New York Spatial Distributions Employment Population 1 Cummulative Employment and Population (%) .75 .5 .25 0 0 5 10 15 20 25 30 35 40 45 50 55 Distance from Center of the CBD (miles)
8. MIT Center for Real Estate b Concentration = ∫ e(t) dt 0 b Where: e(t): cumulative fraction of jobs (population) at distance t b: distance at which 98% of population live. Figure 8: Employment and Population Centralization in a Sample of 120 Cities Honolulu Wichita DesMoin Rochest .8 Birming Erie Oklahom Jacksonv Syracu Jacksonv Pensac Tulsa Sarasot Lansing Tucson Fresno Anchor Austin NewOrleSacram SanAnton Charlest Employment Centralization Montgom Portland Toledo AtlCity Chatta ColSpr Biloxi Columb PeoriaMinneap FortWay Allent Houston SaltLake Wash Huntingt Lincoln CorpChr Columb Shrevep Louisv Pittsbu Waco Modesto McAllen Flint Albany Spokane Waterb Reno ElPasoIndianap Norfolk NewYork .7 Charlot Kansas LexFay RichmondFtMyers Harrisb GrRapids LosAng SpringfReading Omaha BatonRMadison Cincinn ProvidenStockton August Greenv Mobile Boston SantaB Seattle Provo Savannah Denver Dayton Lrock Roanoke StLouis Buffalo Milwauk Phoenix Worcest SthBend Evansv SanDieg Baltim Daytona LasVegas Rockf Tampa Lubbock Atlanta Dallas Canton Salem Memphis Detroit Kalamaz Knoxv Lancast Raleigh WPalmB Chicago .6 Greensb Orlando Scranton Philad Columb Clevel SanFran Miami Youngst Hartford .5 .5 .6 .7 Population Centralization
9. MIT Center for Real Estate Employment Dispersal and commuting • If people can commute only inward (not true but a useful assumption!). Then the number of people traveling inward at any point is the difference between the cumulative number of jobs located up to that point and the cumulative number of workers living up to that point. • Proof: if the number of inward travelers at distance (t) is less than this difference then not all jobs up to t are being filled. If the reverse, then there are more commuters than jobs up to t and jobs beyond t are not being filled. • Implication: jobs must be more centralized than residences for positive traffic flow in the allowed direction. • With complete job-residence dispersal: no commuting! • With centralized employment traffic worst at the edge of the business district
10. MIT Center for Real Estate F ig u r e 4 : L a n d U s e a n d T r a v e l C o s t s , 2 m illio n in h a b it a n t s , m ix e d u s e c it y , h ig h a g g lo m e r a t io n 1 .2 500 450 1 400 350 0 .8 % of Total 300 0 .6 250 200 0 .4 150 100 0 .2 50 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 D i sta n c e ( 1 / 1 0 M i l e ) C u m u l a t i ve E m p l o y m e n t C u m u l a t i ve P o p u l a t i o n T r a ve l C o s t s / m i l e F ig u re 6 : L a n d U s e a n d T ra v e l C o s ts , 2 m illio n in h a b ita n ts , m ix e d u s e c ity , lo w a g g lo m e ra tio n 1 .2 350 1 300 250 0 .8 % of Total 200 0 .6 150 0 .4 100 0 .2 50 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 D i sta n c e (1 / 1 0 M i l e ) C u m u la t ive E m p lo y m e n t c u m u la t ive P o p u la t io n Tra ve l C o s t s / m ile
11. MIT Center for Real Estate Wage as well as Rent Gradients • In a location equilibrium, no one wants to change location of either home or work. • For workers at a particular plant – what insures that they are indifferent to different residential locations? Housing Rent (Lecture 2). • For residents at a particular home location what insures that they are indifferent to switching jobs? Different Wages. Jobs closer to the center must pay for the incremental additional cost of commuting: hence a “Wage Gradient”. • But: cities do not have inward-only commuting!
12. MIT Center for Real Estate Commuting times in the greater NY CMSA [internal = Origin and destination in same area] Destination Internal Origin Downtown Midtown CT 56.5 56.2 20 NJ 53.2 52.9 22.1 NY 40.6 39.8 40.9 Weighted Avg 42.1 41.3
13. MIT Center for Real Estate Land Rent and Commuting in a city with both a CBD and a suburban Sub Center Sub Center CBD Land rent rf r(d) r(d) ra commute d1 d6 d1 d5 d2 d3 d4
14. MIT Center for Real Estate Why firms leave the CBD for a Subcenter. • Subcenter workers at d5 pay the same for land as CBD workers living there, but have a shorter commute. Hence their wage must be less by the difference in commute: (d5 – d1 ) versus (d2 – d5 ). • Note that land rents still make workers that are employed at each center indifferent about living at different locations around that center. • Firms at the CBD now must not only pay higher land rent (equal here to residential), but must also pay higher wages for labor. - Wages: 15% more [e.g. $13,500] - Rent (per worker): 250 x$15-20 [e.g. \$4250]
15. MIT Center for Real Estate MIT study of wages and average commuting time by location of employment [POWPUMA]
16. MIT Center for Real Estate Why not a Fully Dispersed Polycentric City? An MSA grows Horizontally with additional sub centers and no increase in commuting at each sub center [See Gordon, et al.] Land Rent rf r(d) ra
17. MIT Center for Real Estate The Degree of Decentralization/Dispersal: Many small –vs- Few large Centers • Clusters (nodularity) versus “sprawl”. • Economic Agglomeration • Realities of Transportation networks. • Heterogeneous workers, housing mix. • Planning limits. - Forced sprawl through height limits - NIMBY - limited commercial land zoning
18. MIT Center for Real Estate Boston Office Market: Nodularity and the distribution of subcenters Office Area, Buildings, and Asking Rents, Boston-Area Towns, 1993, CBRE. Town (Cluster Square Feet (thousands) Number of Buildings Rent Boston Back Bay 10,675 66 25.19 Financial District 26,754 141 26.73 South Station 3,053 21 23.50 Andover 1,438 10 16.25 Burlington 3,498 43 18.90 Cambridge 11,103 116 18.64 Framingham 3,196 39 14.06 Lexington 2,320 38 19.41 Natick 1,518 19 15.50 Newton 1,973 38 18.32 Quincy 4,797 44 15.90 Waltham 5,843 60 19.60 Wellesley 1,774 36 19.45 Westborough 1,664 15 12.50 Residual 26,793 548 15.21 MSA 106,399 1,234 20.74 adapted from DiPasquale and Wheaton (1996)
19. MIT Center for Real Estate Urban “Agglomeration” • Firms of the same type share information and ideas if they are in proximity to each other. [non competes?] • Firms of different types that do business with each other find it more convenient if in proximity. [transportation costs are trivial and the Internet?] • Workers can switch jobs more easily (not have to move residence) when there are many similar jobs in proximity. • Firms find it easier to fill vacancies when there are many workers in other (similar) companies nearby. • Fun, Entertainment, nice lunch spots emerge when lots of firms locate together [implication is that workers accept lower wages!]. • Do Headquarters enjoy agglomeration? [Shilton].
20. MIT Center for Real Estate Firm Production costs are lower in larger subcenters (Agglomeration), but wages are higher Information technology (----) erodes agglomeration? Output Costs Maximum center size Total Costs wages Productio n costs (ag glomeratio n) Sub Center Size