Factors influencing commercial buildings to obtain green certificates in New York: Building characteristics and opposite-principal-agent problem Yueming Qiu · Xin Su · Yi David Wang

L o g o Contents Conclusions Discussions Model and results Research design and data Introduction

L o g o Introduction World’s total: 529 quadrillion Btu; US: 18%. Commercial buildings: 20%, HVAC, lighting, etc. Energy efficiency: $200 billion, save 20%. LEED: Leadership in Energy & Environment Design. Energy star: 25,000 buildings and 1.5 million homes.  The LEED and Energy Star programs are complimentary to each other, Energy Star is the basic foundation of energy benchmarking, while LEED has more requirements to meet.

L o g o Literature Review Economic Returns Incentives & Factors Firms’ Initiatives Principal-Agent Problem Being green certified contribute significantly to increases in market rents and asset values. Demographics and regional economic activities; Municipal level policies; Spatial clustering. Interests and willingness; Limited knowledge. Owners cannot get paid back from reduced energy bills that accrue to the tenants.

L o g o Contributions Characteristics of commercial buildings & Choices to get certificates. Owner-occupied buildings are less likely to go green. Energy policy makers & Real estate businesses Significant Correlation Opposite of Agency Problem Opposite of Agency Problem Implications Unique Contributions: Use richer and larger dataset; Provide new evidence against Principal-Agent Problem.

L o g o Contents Conclusions Discussions Model and results Research design and data Introduction

L o g o Data Collection Individual building in New York State (NY): Square footage over 500 square feet; Lot size over 100 square feet. Unit of Analysis LEED: U.S. Green Building Council; Energy Star: The Energy Star program; General database: ProspectNow.com. Data Sources Name, lot size, sq ft, property value; Owner occupancy information; Green certification status. Information Gathered Green office buildings; Non-office: markets, medical buildings, hospitals, hotels, parking structures, etc. Building Types

L o g o Dependent variables Z: Decisions to obtain green certificates Value 1: green-certified; Value 0: non-green. E L EL N

L o g o EconomicFinancialPhysical 1.AP: Total annual payroll 2.EST: Number of establishments (0.795) 1.Market value 2.Improved value (0.978) 3.land market value (0.877) 4. Improvement value (0.969) 5.Total assessed value (0.989) Independent variables 1.Owner type 2.Property type 3.Property use 4.Owner occupancy 5.Number of buildings 6.Square footage 7.Lot size Main types of variables hypothesized to have an influence on decisions to go green.

L o g o Use of Property

L o g o Descriptive statistics VariableObsMeanSDMinMax Per square footage market value98, Number of buildings98, Sqfootage98, Lotsize98, Years have been built98, Dummy variable: occupied by owner98, Number of establishments98, Dummy variable: owned by company98, Dummy variable: green certified98, Dummy variable: non-green building98, Dummy variable: Energy Star certified98, Dummy variable: LEED certified98, Dummy variable: Both E and L certified 98,

L o g o Contents Conclusions Discussions Model and results Research design and data Introduction

L o g o Logit model results Base case (Z=0)Coefficient Standard Error Unit market value -(2.E-05)(0.0001) Number of Bldgs 2.E-05(0.0003) Sqfootage 2.E-06(2.E-07) *** Lot Size 4.E-07(6.E-08) *** Years Built (0.0021) *** Owner Occupied (0.1421) EST 8.E-05(5.E-05) Owner type: Company owned1.0352(0.0001) *** Property Use (Base case: COM-Wholesale Outlet- Discount Store) COM-Commercial (General)-8.340(0.2418) *** COM-Commercial Office (General)1.0143(0.2041) *** COM-Department Store (apparel-household goods-furniture)2.2242(0.5730) *** COM-Hotel/Motel0.5327(0.3290) * COM-Restaurant (0.4514) * COM-Service Station Gas Station (0.4863) ** COM-Shopping Center0.8409(0.2924) *** County fixed effectsYes Constant (0.0000) *** LR = Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

L o g o Multinomial logit model Equations

L o g o Multinomial logit model results Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1 Base case (N)ELEL Unit market value (0.0008)3.E-05(8.E-05) (0.0007) Number of Bldgs (0.0016)9.E-05(0.0003) (0.0093) Sqfootage1.E-06(3.E-07) *** 2.E-06(2.E-07) *** 2.E-06(3.E-07) *** Lot Size5.2-07(1.E-07) *** 3.E-07(1.E-07) *** 7.E-07(2.E-07) *** Years Built (0.0041) ** (0.0027) *** (0.0064) ** Owner Occupied (0.3768) *** (0.1711)0.2289(0.4123) EST0.0002(0.0001)5.E-05(6.E-05)5.E-05(0.0001) Owner type: Company owned1.2115(0.4331) *** (0.2492) *** (0.5578) Property Use (Base case: COM-Wholesale Outlet) COM-Commercial (General) (0.5256) *** (0.3058) (1.1205) *** COM-Commercial Office0.7946(0.3527) ** (0.2832) *** (0.5211) COM-Department Store3.1329(0.7086) *** ( )2.5702(1.1897) ** COM-Financial Bldg (0.7783)0.9680(0.4593) ** ( ) COM-Food Store Market0.2031(1.0583)1.2685(0.7659) * ( ) COM-Hospitals (0.6651)0.8658(0.4131) ** (1.1705) COM-Hotel/Motel0.1911(0.5970)0.8442(0.4329) ** (0.8559) COM-Restaurant (0.6633) (0.6361) ( ) COM-Service Station Gas Station (1.0529) * (0.5647) ( ) COM-Shopping Center0.6908(0.4416)0.9340(0.4438) ** (0.9967) COM-Store/Office (mixed use)0.0035(0.6866)0.6310(0.3702) * (1.1297) County fixed effectsYes Constant ( ) ( ) ( ) LR chi2=

L o g o Nested logit Commercial buildings No certificateGreen certificate Energy StarLEEDE & L NoYes

L o g o Nested logit

L o g o Nested logit results Choice of nest ： Base case (No certificate) Green certificate Sqfootage 2.E-06(1.E-07) *** Lot Size 4.E-07(5.E-08) *** Years Built (0.0018) *** Owner Occupied (0.1336) ** Owner type: Company owned1.4919(0.1911) *** Choice of alternative Green certificate (628.17) Government-issued certificate (1114.7) Wald chi2= Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

L o g o Contents Conclusions Discussions Model and results Research design and data Introduction

L o g o Building characteristics NegativePositiveNo-Impact In both binary and multinomial logit models Unit market value Number of buildings EST Square footage Lot size Company-owned Years built ● Implications: buildings with larger square footage and lot size, buildings owned by companies, and younger buildings.

L o g o Opposite of principal-agent problem Energy efficiency gap Marketing motivation to go green More prominent for Energy Star Implications - Premium in rental price - Motivated by marketing effects - Underestimate saved energy bills - Energy Star: energy savings - LEED: many other aspects - Owners need more education - Property for rent are more likely to go green

L o g o Contents Conclusions Discussions Model and results Research design and data Introduction

L o g o Conclusions Contributions & Restrictions Certain characteristics Associated with higher likelihood to obtain green certificates Opposite of principal-agent problem Marketing motivation Confirm hypothesis Actual energy savings; Economic gains A wider scope study Availability of dataset · What types of buildings are more likely to become green-certified? · Does principal-agent problem exists when attaining certifications?

L o g o Acknowledgements  This research was funded by the SSE program in College of Technology and Innovation at the Arizona State University and the National Science Foundation under grant NO

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