Factors influencing commercial buildingsto obtain green certificates in New York:

Building characteristics and opposite-principal-agent problem

Yueming Qiu · Xin Su · Yi David Wang

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Contents

Conclusions

Discussions

Model and results

Research design and data

Introduction

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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.

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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.

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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 CorrelationSignificant CorrelationOpposite of

Agency ProblemOpposite of

Agency Problem ImplicationsImplications

Unique Contributions:

Use richer and larger dataset; Provide new evidence against Principal-Agent Problem.

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Contents

Conclusions

Discussions

Model and results

Research design and data

Introduction

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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

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Dependent variables

Z: Decisions to obtain green certificatesZ: Decisions to obtain green certificates

Value 1: green-certified;Value 0: non-green.

E L EL N

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Economic Financial Physical

1.AP: Total annual payroll2.EST:Number ofestablishments(0.795)

1.Market value2.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 type2.Property type3.Property use4.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.

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Use of PropertyProperty Use Freq.

COM-Commercial (General)COM-Commercial Office (General)COM-Department Store (apparel- household goods- furniture)COM-Financial Bldg (Bank- SandL Mtge Loan Credit)COM-Food Store MarketCOM-Funeral Home- Mortuary (Commercial)COM-Hospitals Convalescent HomesCOM-Hotel/MotelCOM-Medical BldgCOM-Mobile Home Parks Trailer ParksCOM-Nursery- Greenhouse- Florist (retail wholesale)COM-Parking Lot Parking StructureCOM-RestaurantCOM-Service Station Gas StationCOM-Shopping CenterCOM-Store/Office (mixed use)COM-Vehicle Rentals- Vehicle Sales (auto/truck/RV/boat/etc.)COM-VeterinaryCOM-Wholesale Outlet- Discount Store (Franchise)

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Descriptive statisticsVariable Obs Mean SD Min MaxPer square footage market value 98,454 176.27 1.44 0.001 84635.21Number of buildings 98,454 17.70 0.68 1 3629Sqfootage 98,454 17456.16 262.64 500 7616756Lotsize 98,454 41937.41 816.13 137 31488534Years have been built 98,454 70.49 0.10 3 315Dummy variable: occupied by owner 98,454 0.337 0.473 0.00 1.00Number of establishments 98,454 1479.43 4.42 8 7241Dummy variable: owned by company 98,454 0.618 0.486 0.00 1.00Dummy variable: green certified 98,454 0.004 0.058 0.00 1.00Dummy variable: non-green building 98,454 0.996 0.058 0.00 1.00Dummy variable: Energy Star certified 98,454 0.001 0.032 0.00 1.00Dummy variable: LEED certified 98,454 0.002 0.044 0.00 1.00Dummy variable: Both E and L certified

98,454 0.0003 0.020 0.00 1.00

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Contents

Conclusions

Discussions

Model and results

Research design and data

Introduction

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Logit model results

Base case (Z=0) Coefficient Standard ErrorUnit 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 -0.0098 (0.0021) ***

Owner Occupied -0.1514 (0.1421)EST 8.E-05 (5.E-05)Owner type: Company owned 1.0352 (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/Motel 0.5327 (0.3290) *

COM-Restaurant -0.8382 (0.4514) *

COM-Service Station Gas Station -1.1516 (0.4863) **

COM-Shopping Center 0.8409 (0.2924) ***

County fixed effects YesConstant -5.1546 (0.0000) ***

LR = 715.28

Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

Pr (Z=1|X )= 1

1+𝑒−𝛽 𝑋

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Multinomial logit model

Equations

Pr (E=1|X )= 𝑒𝛽𝐸 𝑋

1+𝑒𝛽 𝐸𝑋+𝑒𝛽 𝐿𝑋+𝑒𝛽𝐸𝐿𝑋

Pr (L=1|X )= 𝑒𝛽𝐿 𝑋

1+𝑒𝛽𝐸 𝑋+𝑒𝛽𝐿 𝑋+𝑒𝛽 𝐸𝐿𝑋

Pr (EL=1|X )= 𝑒𝛽𝐸𝐿𝑋

1+𝑒𝛽𝐸𝑋+𝑒𝛽𝐿 𝑋+𝑒𝛽𝐸𝐿𝑋

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Multinomial logit model results

Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

Base case (N) E L ELUnit market value -0.0004 (0.0008) 3.E-05 (8.E-05) -0.0001 (0.0007)Number of Bldgs -0.0005 (0.0016) 9.E-05 (0.0003) -0.0053 (0.0093)

Sqfootage 1.E-06 (3.E-07) *** 2.E-06 (2.E-07) *** 2.E-06 (3.E-07) ***

Lot Size 5.2-07 (1.E-07) *** 3.E-07 (1.E-07) *** 7.E-07 (2.E-07) ***

Years Built -0.0095 (0.0041) ** -0.0088 (0.0027) *** -0.0157 (0.0064) **

Owner Occupied -1.2571 (0.3768) *** 0.1454 (0.1711) 0.2289 (0.4123)EST 0.0002 (0.0001) 5.E-05 (6.E-05) 5.E-05 (0.0001)

Owner type: Company owned 1.2115 (0.4331) *** 1.0809 (0.2492) *** 0.3244 (0.5578)Property Use (Base case: COM-Wholesale Outlet)

COM-Commercial (General) -1.8591 (0.5256) *** -0.2447 (0.3058) -2.9673 (1.1205) ***

COM-Commercial Office 0.7946 (0.3527) ** 1.1374 (0.2832) *** 0.7109 (0.5211)COM-Department Store 3.1329 (0.7086) *** -16.6873 (9239.566) 2.5702 (1.1897) **

COM-Financial Bldg -0.5494 (0.7783) 0.9680 (0.4593) ** -17.2550 (4859.843)COM-Food Store Market 0.2031 (1.0583) 1.2685 (0.7659) * -17.5203 (12133.03)COM-Hospitals -0.0392 (0.6651) 0.8658 (0.4131) ** -0.9603 (1.1705)COM-Hotel/Motel 0.1911 (0.5970) 0.8442 (0.4329) ** 0.1629 (0.8559)COM-Restaurant -0.7990 (0.6633) -0.6254 (0.6361) -17.2605 (3118.644)COM-Service Station Gas Station -2.0392 (1.0529) * -0.4605 (0.5647) -17.3069 (2844.695)COM-Shopping Center 0.6908 (0.4416) 0.9340 (0.4438) ** -0.7792 (0.9967)COM-Store/Office (mixed use) 0.0035 (0.6866) 0.6310 (0.3702) * -0.9516 (1.1297)

County fixed effects Yes Yes YesConstant -22.661 (13970.94) -22.095 (10907.25) -22.157 (20057.9)LR chi2= 909.76

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Nested logit

Commercial buildings

No certificate Green certificate

Energy Star LEED E & L

No Yes

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Nested logit

𝑃 𝑖𝑚=𝑃𝑚× 𝑃 𝑖∨𝑚

¿𝑃𝑚= 𝑒

(𝜆𝑚𝑉𝑚+𝜆𝑚

𝑢𝑚𝐼𝑉𝑚)

∑𝑛

𝑒(𝜆𝑛𝑉𝑛+

𝜆𝑛

𝑢𝑛

𝐼𝑉 𝑛)

𝑃 𝑖∨𝑚= 𝑒𝑢𝑚𝑉 𝑖∨𝑚

∑𝑗∈𝐶𝑚

𝑒𝑢𝑚𝑉 𝑗∨𝑚

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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 -0.0150 (0.0018) ***

Owner Occupied -0.2889 (0.1336) **

Owner type: Company owned 1.4919 (0.1911) ***

Choice of alternative

Green certificate -6.8477 (628.17)

Government-issued certificate -1.5106 (1114.7)

Wald chi2=1470.57

Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

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Contents

Conclusions

Discussions

Model and results

Research design and data

Introduction

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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.

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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

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Contents

Conclusions

Discussions

Model and results

Research design and data

Introduction

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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?

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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. 1509077.

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