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Contract No. DE-AC36-08GO28308

Rooftop Solar Technical 

Potential for Low-to-Moderate 

Income Households in the 

United States

Benjamin Sigrin and Meghan Mooney

National Renewable Energy Laboratory

Technical Report

NREL/TP-6A20-70901

April 2018
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Rooftop Solar Technical 

Potential for Low-to-Moderate 

Income Households in the 

United States

Benjamin Sigrin and Meghan Mooney

National Renewable Energy Laboratory

Suggested Citation 

Sigrin, Ben, and Mooney, Meghan. 2018. Rooftop Solar Technical 

Potential for Low-to-Moderate Income Households in the United States. 

Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-

70901. https://www.nrel.gov/docs/fy18osti/70901.pdf.

Technical Report

NREL/TP-6A20-70901

April 2018
NOTICE

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Acknowledgements

This work was funded by the Solar Energy Technologies Office of the U.S. Department of 

Energy’s Office of Energy Efficiency and Renewable Energy under contract number DE-AC36-

08GO28308. The authors thank Zach Franklin, Nichole Hanus, Jessica Lin, Seungwook Ma, 

Robert Margolis, Caleb Phillips, Tony Reames, Emma Tome, and Kim Wolske for their reviews 

and input. We thank Evan Rosenleib and Anand Govindarajan (NREL) for their assistance in 

processing data. Lastly, we thank Nichole Hanus (Carnegie Mellon University) for providing us 

with the geocoded school addresses. Opinions represented in this article are the authors’ own and 

do not reflect the view of the U.S. Department of Energy or the U.S. government. Any errors or 

omissions are the sole responsibility of the authors.
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Acronyms

ACS U.S. Census Bureau American Community Survey

ANOVA Analysis of variance

AMI area median income

CARE California Alternate Rates for Energy

CBECS Commercial Buildings Energy Consumption Survey

CDBG Community Development Block Grant

CPUC California Public Utility Commission

DHS U.S. Department of Homeland Security

DOE / U.S. DOE U.S. Department of Energy

EERE U.S. Department of Energy, Office of Energy Efficiency and 

Renewable Energy

EIA Energy Information Administration

FEMA Federal Emergency Management Agency

FMR fair market rent

FPL federal poverty level

GWh gigawatt-hour

GIS geographic information system

HHS U.S. Department of Health and Human Services

HSIP Homeland Security Infrastructure Program

HUD U.S. Department of Housing and Urban Development

KDE Kernel Density Estimation

kWh kilowatt-hour

LEAD Low-Income Energy Affordability Data

LiDAR light detection and ranging

LIHEAP Low Income Home Energy Assistance Program

LMI low-to-moderate income

MFOO multi-family owner-occupied building

MFRO multi-family renter-occupied building

NCES National Center for Education Statistics

NHGIS National Historic Geographic Information System

NHPD National Housing Preservation Database

NLCD National Land Cover Database

NREL National Renewable Energy Laboratory

NSRDB National Solar Radiation Database

OSM OpenStreetMap

PUMS public use microdata sample

PV photovoltaic

RECS Residential Energy Consumption Survey

ReEDS Regional Energy Deployment System

REPLICA Rooftop Energy Potential of Low Income Communities in America

reV Renewable Energy Potential Model

SFOO single-family owner-occupied building

SFRO single-family renter-occupied building

TMY typical meteorological year
3

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TMY3 Typical Meteorological Year Version 3

TWh terawatt hour
4

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Executive Summary

Adoption of rooftop solar in the United States primarily has been concentrated in higher-income 

households (Moezzi et al. 2017; Vaishnav et al. 2017). As technology costs decline and markets 

expand, however, focus is shifting to increasing solar access in underserved market segments—

particularly to low-to-moderate income (LMI) households, or those earning 80% or less of the 

area median income (AMI). A key policy goal is to expand solar access more equitably to ensure 

the benefits of solar, including reduced energy burden, increased resilience, and hedge against 

electricity rate changes are available to all ratepayers. To achieve this goal, a deeper 

understanding of the potential LMI market is needed. Although LMI households represent about 

43% of the U.S. population, it is unknown what proportion live in buildings suitable for PV, how 

this potential is distributed among the buildings they live in, or what fraction of their electricity 

needs could be met with rooftop solar.

This report serves to expand upon previous NREL research investigating the technical potential 

of rooftop solar in the United States, aiming to improve the understanding in the residential 

sector, particularly for low-to-moderate income households. Technical potential is a metric that 

quantifies the maximum generation available from a technology for a given region and does not 

consider the economic or market viability. A unique contribution of this work is to estimate 

rooftop solar technical potential of residential buildings per U.S. Census Tract by income, 

building type, and tenure. The underlying data—as documented in Gagnon et al. 2016—are a 

series of light detection and ranging (LiDAR)–based scans of the building stock in 128 metro 

regions. Using LiDAR scans, as opposed to aerial imagery, allows researchers to infer the 

building footprint and the unshaded roof area, azimuth, and tilt for each distinct roof plane, 

although roof age or other structural concerns are not considered. This data then is intersected 

with U.S. Census Bureau socio-demographic and building stock data at the tract-level to better 

understand how rooftop solar technical potential is allocated among different building types. 

This research also uses statistical techniques to estimate rooftop potential in areas not covered by 

the LiDAR scans. Collectively, these data and methods are used to address three research 

questions.

1. What is the quantity and spatial distribution of rooftop technical potential, 

stratified by income, building type, and tenure? At a gross level, these data enable 

improved estimates of the possible opportunity for deployment, as well as the magnitude 

of various policy interventions.

2. Among low-to-moderate-income households, what is the feasibility of achieving 

parity in solar access across income groups? Compared to high-income households, 

LMI households disproportionately live in rental-occupied, multi-family buildings. 

Because of principal-agent issues in solar adoption, there are significant economic and 

regulatory barriers inhibiting the installation of solar equipment on buildings occupied by 

LMI households. This study investigates the feasibility of offsetting at least 33% of LMI 

household electrical consumption with solar energy and, where infeasible, highlight 

consideration of novel deployment models—such as community solar, virtual net 

metering, and other shared solar models.

3. Finally, low- and moderate-income households interact with a vast web of nonprofit 

entities (e.g., places of worship and public schools). What is the quantity of technical 
5 

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potential for these classes of buildings, and to what extent might these buildings 

“oversize” systems on their roofs to export excess generation to LMI households?

Increasing access to solar for such third-parties could reduce these entities’ operational 

costs and indirectly provide roof space to households without access. 

National Residential Rooftop PV Technical Potential

Over all tracts and all residential buildings in the United States, the estimated residential rooftop 

solar technical potential is nearly 1,000 terawatt-hour (TWh) of generation, or about 75% of 

residential consumption. Significant potential is found in every income group (Table ES-1), with 

the greatest overall potential in the non-LMI (>80% AMI) income group, and with 416 TWh for 

low-to-moderate (0% to 80% AMI) households over 25.5 million solar-suitable buildings. The 

LMI potential is approximately 42% of the total U.S. residential potential, a considerable portion 

of the overall rooftop opportunity (Figure ES-1). 

Table ES-1. National Residential PV Rooftop Technical Potential by Income Group 

Income Group Households 

(millions)

Suitable 

Buildings 

(millions)

Suitable 

Module 

Area 

(millions of 

m2) 

Capacity 

Potential 

(GWDC) 

Annual 

Generation 

Potential 

(TWh/year) 

LMI Very Low

(0%–30% AMI)

19.5 9.4 794.4 127.1 160.8

Low

(30%–50% AMI)

11.5 5.7 472.8 75.6 95.3

Moderate

(50%–80% AMI)

18.8 10.4 792.0 126.7 159.8

NonLMI

Middle

(80%–120% 

AMI)

21.1 12.3 900.4 144.1 180.8

High

(> 120% AMI)

46.0 29.4 2,003.3 320.5 403.1

All LMI Buildings 49.8 25.5 2,059.2 329.4 415.9

All Residential Buildings 116.9 67.2 4,962.9 794.0 999.8

A majority of the overall residential potential (683 TWh, 68.4%) is situated on single-family 

buildings, as compared to multi-family dwellings (316 TWh, 31.6%) and single-family potential 

exceeded multi-family potential for each income group. Similar ratios are seen for owneroccupied and renter-occupied buildings as there is a strong correlation between multi-family 

occupancy and rental status. For LMI households specifically, the largest modality of potential is 

for single-family owner-occupied buildings (176.8 TWh), followed by multi-family renteroccupied buildings (140.1 TWh). Though deployment of rooftop solar historically has been 

concentrated on single-family owner-occupied buildings, nearly 60% of potential for LMI 

buildings exists on renter-occupied and multi-family buildings. 
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Figure ES-1. County LMI rooftop technical potential as percent of total residential potential

Feasibility of Parity in Adoption Rates

The U.S. Department of Energy (DOE) Solar Energy Technology Office recently announced 

updated cost targets for solar energy, which could result in 971 GW of solar capacity, providing 

33% of electrical generation by 2050 (Cole et al. 2017). Inspired by this goal we investigate the 

feasibility of rooftop solar offsetting at least 33% of low- and moderate-income household 

electrical consumption in each U.S. county. Note that the data does not enable assessment of the 

feasible offset fraction at the household level, and instead should be interpreted in aggregate over 

the county. The analysis also only considers consumption offsets over an annual basis and does 

not consider hourly mismatches in generation and consumption—which could be substantial 

without energy storage or other load-shifting methods.

Offsetting 33% of LMI household electrical consumption (“offset target”) with rooftop solar is 

technically feasible on a national scale when only considering households in single-family 

owner-occupied (SFOO) buildings, although to do so requires buildout on essentially all SFOO 

buildings—an impractical and unforgiving market challenge. In contrast, on a technical basis,

there is more than sufficient roof space to meet the 33% offset target when including singlefamily rental-occupied (SFRO), multi-family owner-occupied (MFOO), and multi-family renteroccupied (MFRO) buildings (Figure ES-2). Specifically, the average fraction of generation 

potential to consumption nationally is 85% for the Very Low, 92% for Low, 80% for Moderate, 

and 70% for Non-LMI income groups. The lower per-capita levels of consumption for lowincome households makes offset targets more feasible for this group than for non-LMI 

households.
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Figure ES-2. Technical feasibility of matching residential electrical consumption with

rooftop solar (shown by income group)

Not all counties can offset 33% of their LMI consumption with in-county rooftop solar. When 

considering only single-family owner-occupied buildings, 40% of U.S. counties have insufficient 

rooftop solar potential to offset 33% of LMI electrical consumption (Figure ES-3 left), though 

when including the remaining residential building stock this figure decreases to 1% (Figure ES-3 

right). In the latter condition, shortfalls are most significant for mobile homes (38 TWh)—whose

roofs are typically not suitable for solar, followed by single-family (6 TWh) and multi-family 

(2 TWh) buildings. Spatial trends in the potential for solar to offset LMI consumption moststrongly reflected regional variation in per-capita electricity consumed, primarily due to which 

fuels are used for building heating and cooling loads. Other trends include solar irradiance, 

building-stock composition, and the prevalence of solar-suitable rooftops.

Figure ES-3. Percent of LMI electrical consumption that can be offset by rooftop solar 

generation—single-family owner-occupied LMI buildings only (left), all LMI buildings (right)

From a technical basis alone, there is sufficient rooftop space for at least 33% of LMI 

consumption to be met with rooftop solar generation. Reaching this potential, however, requires
8

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the deployment of models other than those commonly found today, particularly, models that can 

address coordination issues inherent to rental-occupied and multi-family buildings. Such models 

must ensure that rental-property owners are incentivized to install solar on their buildings—for 

example, by bundling utility expenses with rent payments as a means of passing solar costs and 

savings through to the renter. These models also would need to address the diverging 

requirements and energy burdens of owners and tenants. For example, California recently 

developed an incentive program dedicated to affordable multi-family housing, with requirements 

that at least half of energy generated onsite be used to serve tenants loads (CPUC 2017b). In 

Colorado, the Denver Housing Authority’s 2 MW low-income community solar garden has 

demonstrated a scalable model for offsite generation through utility partnerships (DOE 2017). 

Virtual net metering also can be effective in enabling building owners to provide surplus 

generation directly to their occupants (Feldman et al. 2015).

Technical Potential for Buildings that Serve LMI Populations

Low- and moderate-income households interact with a vast web of non-profit entities, and it is 

plausible that these buildings could “oversize” photovoltaic (PV) systems on their buildings to 

share solar generation with their communities. We estimate the generation potential within the 

cities of Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C., for five 

building types: public sites, public housing, K–12 public schools, homeless shelters, and places 

of worship. These cities were selected for congruence with a forthcoming companion analysis of 

the technical, market, regulatory, and social factors affecting LMI solar markets in those three

cities. Based on the building’s size and average electrical consumption per square foot for 

comparable buildings, each building’s electrical consumption was estimated, and thus the 

feasibility of oversizing systems to export excess generation to nearby LMI households. Based 

on these assumption, buildings in Chicago, San Bernardino/Riverside, and Washington, D.C. 

could feasibly oversize PV systems to provide 2.1%, 8.7%, and 1.3% of LMI consumption,

respectively. None of the estimates include on-site ground-mounted systems.

Schools, followed by places of worship had the greatest opportunity to export generation to the 

community of the five building classes considered. Schools typically have large flat roofs and 

decrease consumption in the summer, when solar irradiance is highest. Places of worship are 

favorable because they have low levels of electricity consumption year-round and moderately 

favorable roof characteristics. Unfortunately, public housing, public sites, and homeless shelters 

likely have insufficient rooftop space to offset 100% of their own on-site consumption. These 

results indicate that there is a modest, though not overwhelming, opportunity for buildings that 

serve LMI populations to oversize a PV system—and that projects are best evaluated on a caseby-case basis.

Conclusions

This report presents a first-of-its-kind assessment of the technical potential of rooftop solar for 

low- and moderate-income households, as well as providing insight on the distribution of solar 

potential by tenure, income, and other building characteristics. The findings show that a 

substantial fraction of the national rooftop solar potential is located on LMI buildings and, for all 

incomes, a substantial fraction is located on multi-family and renter-occupied buildings. The 

research also demonstrates that reaching substantial LMI deployment penetration requires 

deployment on multi-family and renter-occupied buildings. Traditional deployment models have 
9

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insufficiently enabled access to solar for these income groups and building types and, without 

additional innovation either in regulatory, market, or policy arenas, a large fraction of the U.S. 

potential is unlikely to be met. Potential electric bill savings from rooftop solar would have the 

greatest material impact on the lives of low-income households as compared to their highincome counterparts and could help mitigate the energy burden faced by these households.

This report ultimately seeks to provide objective data for regulators, policymakers, nonprofits, 

and project developers to make informed decisions that are best for their own communities. To 

this effect, data used in this report are provided freely via NREL’s website in two formats. One 

format—an interactive web application using NREL’s OpenCarto platform—was developed to 

enable user to browse, visualize, and export results (https://maps.nrel.gov/solar-for-all). The 

other format is the Rooftop Energy Potential of Low Income Communities in America

(REPLICA) data set (https:/data.nrel.gov/submissions/81) which contains the technical potential 

data used in this report, accompanied by several additional techno-economic variables (e.g. 

electricity expenditures ($/month), demographics, utility electric rates) which are available for 

download as flat files.
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Table of Contents

1. Introduction ............................................................................................................................................. 1

2. Methods.................................................................................................................................................... 4

2.1. Using LiDAR Data to Estimate PV Rooftop Technical Potential................................................. 4

2.2. Simulating PV Productivity Using reV and PVWatts................................................................... 6

2.3. Estimating National LMI PV Rooftop Technical Potential by Tract............................................ 7

2.3.1. Estimating Number of LMI Households by Building Type and by Tenure..................... 7

2.3.2. Estimating LMI Technical Potential for LiDAR Covered Tracts.................................... 9

2.3.3. Imputing LMI Technical Potential for Tracts Outside of LiDAR Coverage ................. 11

2.4. Model Uncertainty and Caution in Interpreting Results.............................................................. 12

2.4.1 Uncertainty of solar potential for LiDAR-covered and non-covered areas.................... 12

2.4.2 Uncertainty of distribution of solar potential by income-tenure-types........................... 13

3. Results ................................................................................................................................................... 14

3.1. National Residential Rooftop PV Technical Potential ................................................................ 14

3.1.1. Spatial Trends in LMI Technical Potential .................................................................... 15

3.1.2. Technical Potential by Tenure, Building Type, and Income.......................................... 18

3.2. Feasibility of Parity in Solar Adoption Rates.............................................................................. 21

3.2.1. Additional Factors Impacting Building Technical Suitability........................................ 29

3.3. Technical Potential for Select Buildings that Serve LMI Households........................................ 30

4. Conclusion............................................................................................................................................. 33

4.1. Data for Public Use ..................................................................................................................... 33

4.2. Future Work ................................................................................................................................ 34

References................................................................................................................................................. 35

Appendix A. Allocation of Demographic Attributes .............................................................................. 39

A.1. Calculate the Number of Households Belonging to Each $1,000 Income Group, at the Tract 

Level 39

A.2. Disaggregate from County Data to Estimate the Number of Households, per $1,000 Income 

Group, per Tenure, at the Tract Level......................................................................................... 40

A.3. Disaggregate from County Data to Estimate the Number of Households, per $1,000 Income 

Group, per Tenure, per Dwelling Type, per Household Size...................................................... 40

A.4. Aggregate Tract Estimates by AMI Income Group .................................................................... 40

Appendix B. Validation of Monte Carlo Simulations for Building Sampling ...................................... 41

Appendix C. Predictive Modeling Framework for Imputing Solar Suitability ..................................... 46

C.1. Household-to-Building Model .................................................................................................... 46

C.1.1. Household-to-Building-Count Ratios............................................................................... 46

C.1.2. Building-Type-to-Building-Size Ratios............................................................................ 47

C.2. Small Building Suitability Model................................................................................................. 47

C.3. Rooftop Plane Area Model.......................................................................................................... 48

C.4. Rooftop Tilt and Azimuth Model................................................................................................ 50

Appendix D. Quantifying the Technical Potential and On-Site Consumption of LMI-Serving 

Buildings in Three Case Study Cities............................................................................................... 52

D.1. Quantifying the Technical Potential of Third-Party Buildings ................................................... 52

K–12 Public Schools................................................................................................................... 54

Homeless Shelters....................................................................................................................... 54

Places of Worship........................................................................................................................ 54

Public Housing ............................................................................................................................ 54

Public Sites.................................................................................................................................. 55

D.2. Estimating the On-Site Consumption of Third-Party Buildings ................................................. 55
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List of Figures

Figure 1. LiDAR data coverage .................................................................................................................... 5

Figure 2. County residential technical potential (GWh)............................................................................. 16

Figure 3. County LMI rooftop technical potential as percent of total residential potential........................ 17

Figure 4. Percentage of tract-level rooftop solar potential (MW) on LMI buildings.................................. 18

Figure 5. Generation potential by income group and building type............................................................ 19

Figure 6. Generation potential by income group and building tenure ........................................................ 19

Figure 7. Generation potential (TWh) by building type and tenure............................................................ 20

Figure 8. Comparison of fraction of consumption met by generation potential for single-family owneroccupied buildings alone, by income group........................................................................... 25

Figure 9. Technical feasibility of matching residential electrical consumption with rooftop solar, by 

income group.......................................................................................................................... 26

Figure 10. Percent of LMI electrical consumption that can be offset by rooftop solar generation 

(county)—all LMI buildings.................................................................................................. 27

Figure 11. Percent of LMI electrical consumption that can be offset by rooftop solar generation 

(county)—single-family owner-occupied LMI buildings only.............................................. 28

List of Tables

Table ES-1. National Residential PV Rooftop Technical Potential by Income Group................................. 5

Table 1. Requirements for Solar Suitable Surfaces ...................................................................................... 6

Table 2. Assumptions for PV Performance Simulations .............................................................................. 7

Table 3. 2015 Five-Year American Community Survey Published Tables Used......................................... 8

Table 4. Building Type to Building Size Schema....................................................................................... 10

Table 5. National Residential PV Rooftop Technical Potential by Income Group..................................... 15

Table 6. National residential PV rooftop technical potential by income group.......................................... 21

Table 7. Annual Residential Electricity Consumption by Income, Building Tenure, and Type................. 23

Table 8. Estimates of National Shortfall in Rooftop Solar Generation Needed to Nationally 

Offset 17% and 33% of LMI Consumption ......................................................................................... 26

Table 9. Annual Generation Potential for Select Third-Party Buildings in Chicago, Washington

D.C., and San Bernardino/Riverside (GWh)........................................................................................ 31

Table 10. Net Excess Rooftop Potential Accounting for Building Self-Consumption by Building 

Class and City....................................................................................................................................... 32

Table B-1. Assumptions for PV Performance Simulations Summary Statistics for City-Level 

Technical Potential............................................................................................................................... 42

Table C-1. National Mean Household-to-Building Ratio........................................................................... 46

Table C-2. National Mean Building Type to Building Size Ratio for Census Tracts................................. 47

Table C-3. ANOVA of Small Building Suitability OLS Model................................................................. 48

Table C-4. National Distributions of Building Plane Count Probabilities by Building Size Class ............ 49

Table C-5. Fitted Areas for Each ith Plane, per Building Size Class.......................................................... 50

Table C-6. National Average Tilt/Azimuth Distribution for Building Planes, by Building Size Class...... 51

Table D-1. Core GIS Data Sources for LMI-serving Buildings in Case Study Cities................................ 53

Table D-2. Parcel Data Used for Appropriating Buildings to Public Housing Properties.......................... 55

Table D-3. Representative Building Types Used for Third-Party Building Energy Consumption 

Estimates .............................................................................................................................................. 56
1

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1. Introduction

Residential solar offers a compelling opportunity for reducing the energy burdens faced by lowincome households, whose utility bills comprise a larger fraction of expenses than for highincome households. Moreover, because residential solar has disproportionately been adopted by 

high-income households (Moezzi et al. 2017; Vaishnav et al. 2017), often benefiting from 

public-funded incentives, it is important to ensure that all utility ratepayers have access to solar 

benefits including reduced energy burden, increased resilience, and as a hedge against electricity 

rate changes. 

This report seeks to improve understanding of the technical potential of rooftop solar in the 

residential sector, particularly for low- to moderate- (LMI) income households. Technical 

potential is a metric that quantifies the maximum generation available from a technology for a 

given region and does not consider the economic or market viability. This report expands upon 

previous NREL research investigating the rooftop solar technical potential using light detecting 

and ranging (LiDAR) scans of individual rooftops in 128 metro regions (Gagnon et al. 2018; 

Margolis et al. 2017; Gagnon et al. 2016; Phillips and Melius 2016). Using LiDAR scans, as 

opposed to aerial imagery, allows us to infer the building footprint and the unshaded roof area, 

azimuth, and tilt for each distinct roof plane. A unique contribution of this work is to estimate 

rooftop solar technical potential of residential buildings per U.S. Census tract by income, 

building type, and tenure. To do so, the LiDAR data set is intersected with U.S. Census Bureau 

socio-demographic and building stock data at the tract-level to better understand how rooftop 

solar technical potential is allocated among different building types. Statistical techniques also 

are used to estimate rooftop potential in areas not covered by the LiDAR scans. Collectively, 

these data and methods enable the study to address three research questions:

1. What is the quantity and spatial distribution of rooftop technical potential, 

stratified by income, building type, and tenure? At a gross level, these data allow 

improved estimates of the possible opportunity for deployment, as well as the magnitude 

of various policy interventions.

2. Among low and moderate-income households, what is the feasibility of achieving 

parity in solar access across income groups? Compared to high-income households, 

LMI households disproportionately live in renter-occupied, multi-family buildings. 

Because of principal-agent issues in solar adoption, there are significant economic and 

regulatory barriers inhibiting LMI households from benefiting from the installation of 

solar on buildings they occupy. Recent U.S. Department of Energy (DOE) solar cost 

goals could result in solar penetration rates of 17% by 2030 and 33% by 2050 as a 

fraction of national electricity consumption. This report investigates the feasibility of 

reaching at least 33% solar penetration for LMI households and, where infeasible, 

highlights consideration of novel deployment models, such as community solar, virtual 

net metering, and other shared solar models.

3. Finally, low- and moderate-income households interact with a vast web of nonprofit 

entities (e.g., churches, schools, public sites, homeless shelters, subsidized housing). 

What is the quantity of technical potential for these classes of buildings, and to what 

extent might these buildings “oversize” systems on their roofs to export generation 

to LMI households? Increasing access to solar for such third-parties could either 
2

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

increase their operational efficiency by reducing operational costs, or indirectly provide 

roof space to households without access. 

On a technical basis, the potential for rooftop solar to serve a large fraction of the nation’s energy 

needs is well-recognized (Gagnon et al. 2016; Lopez et al. 2012; Denholm and Margolis 2008; 

Paidipati et al. 2008; Chaudhari et al. 2004). Rooftops provide a large and sparsely used space to 

install panels; onsite solar generation can reduce the building’s net demand for grid-sourced

energy and can reduce losses associated with the distribution and transmission of electricity. 

Recent analysis has estimated that buildout of all suitable roof space could serve approximately 

38.6% of U.S. load (Gagnon et al. 2016). This potential is disproportionately located within the 

residential sector, with small buildings (<5,000 ft2) contributing nearly 65% of the nation’s 

potential (Gagnon et al. 2016). Previous work, however, has not explicitly considered the 

intersection of rooftop solar potential with income, building characteristics (e.g., rental versus

owner-occupied), and other demographic factors. Though building tenure is not relevant on a 

technical basis alone, it is very significant from a market perspective. 

Adoption of distributed solar in the U.S. residential sector to date primarily has been 

concentrated among high-income households (Moezzi et al. 2017; Vaishnav et al. 2017). 

Unlocking technology access and demand from all households could substantially increase 

market potential—in 2015 43% of all households could be considered to have low to moderate 

income (<80% area median income(AMI)) and 18% to be middle income (80% to 120% AMI) 

(U.S. Census 2015). Further, it is not equitable for solar adoption to occur only among highincome households. Recent studies have explored the transfer of wealth implied by technology 

incentives and net metering policies, finding that majority of public subsidies have been passed

through to high-income households (Vaishnav et al. 2017; Borenstein 2017). Without recourse, 

rooftop solar risks a reputational backlash—in time, it could be perceived as solely the purview 

of the wealthy, and its deployment not seen as a public benefit.

Ensuring access to affordable energy for all households is a policy mandate for federal, state, and 

local governments. Costs of energy for low-income households can comprise a substantial 

fraction of household income and, as a result, many programs exist to ensure its affordability. A 

notable example is the Low-Income Home Energy Assistance Program (LIHEAP) run by the 

U.S. Department of Health and Human Services. The program provides financial assistance for 

home energy bills, energy crises, and weatherization and energy-related minor home repairs

(HHS 2017). The U.S. Department of Energy Weatherization Assistance Program also provides 

assistance for LMI households by increasing the energy efficiency of their homes (DOE 2018). 

A state-level model is the California Alternate Rates for Energy (CPUC 2017a) program, which 

gives low-income customers discounts on their electricity and natural gas rates.

As the cost of solar energy continues to decline, many have begun to question what role solar can 

play in increasing energy affordability or as an efficient alternative or supplemental use of public 

funds for energy-assistance programs. For instance, use of funds to directly or indirectly 

incentivize solar deployment might be more cost-efficient than rate subsidization, because it 

creates a durable asset for low-income households and in the long term could lead to greater 

program effectiveness. The exact cost-benefit of diverting rate-assistance funds depends on 

program details and is beyond the scope of this study. 
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Incentivizing solar deployment in LMI communities requires a reimagination of traditional 

deployment models. These new models should address coordination issues inherent to rentaloccupied and multi-family buildings as well as LMI financing and affordability barriers (Cook 

and Bird 2018). Such models also should ensure that rental-property owners are incentivized to 

install solar on their buildings, for example, by bundling utility expenses with rent payments as a 

means of passing savings through to the renter. Alternatively, various shared solar models 

(Feldman et al. 2015), including virtual net metering, could be effective in allowing building 

owners to sell rooftop generation directly to the building occupants. Similar issues exist for 

multi-family buildings, where shared solar or community solar models could help to address 

tenant-owner coordination issues. 

This report ultimately seeks to provide objective data for regulators, policymakers, nonprofits, 

and project developers to make informed decisions that are best for their own communities; for 

example, to assess the potential for rooftop solar in their jurisdictions and perform policy costbenefit analyses. To that end, this report is accompanied by two supporting data products. One is 

an interactive web application that uses NREL’s OpenCarto platform; it was developed to enable 

users to browse, visualize, and export results (https://maps.nrel.gov/solar-for-all). Another is the 

technical potential data used in this report—accompanied by several additional techno-economic 

variables and aggregated at the tract-level—available for download at 

(https://data.nrel.gov/submissions/81). The Rooftop Energy Potential of Low Income 

Communities in America (REPLICA) data set includes measures of electricity expenditures 

($/month), demographics, utility electric rates, state-level photovoltaic (PV) incentives, measures 

of environmental quality, and locations of public housing, compiled from a variety of sources 

and tagged to each Census Tract. 

Many opportunities exist to improve the methods used in this report, but there are three 

important restrictions. One is that the geographic span of LiDAR aerial rooftop imagery is 

restricted to 128 metro regions, spanning approximately 40% of the U.S. population. A statistical 

model was developed and validated to estimate potential for unsampled areas, particularly for 

rural areas. Section 2.2.3 discusses efforts to train and validate this model. Future work could 

improve the geographic coverage of LiDAR scans in these areas. The second, is that the LiDAR 

data do not enable the direct observation of the true income, tenure, number of housing units, or 

income of the building occupants. Instead, these attributes are assigned from tract-level crosstabulation tables from the 2011–2015 U.S. Census American Community Survey using statistical 

techniques, including sampling from probability distributions. The final tract-level estimates are 

based on the median value of a 100-run Monte Carlo process (see Appendix B). This sampling 

process could be greatly improved with access to a national zoning or parcel data set, which 

would contain a richer set of property-level attributes. Finally, this analysis solely considers the 

technical potential of rooftop solar—or the total solar resource that could be captured given 

physically-available area and technology performance. Notably, this analysis does not consider 

the economic or market feasibly of solar installations, nor the feasibly of on-site use of the solar 

generation. 
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2. Methods

This section describes the methods used to estimate PV rooftop technical potential by Census 

tract, stratified by income, building type, and tenure. It includes methods to process LiDAR 

rooftop scans in the 128 metro regions with coverage (Section 2.1) and estimate their generation 

productivity (Section 2.1.2). Next, it discusses the intersection of tract-level technical potential 

and demographic cross-tabulations (Section 2.2.1 and Section 2.2.2). Finally, a statistical model 

of rooftop potential for the remaining regions of the United States (Section 2.2) is estimated by 

training it on sampled areas (i.e., those with LiDAR rooftop observations). Additional details on 

methods are described in the appendices: Allocation of Demographic Attributes (Appendix A); 

Validation of Monte Carlo Simulations for Building Sampling (Appendix B); Predictive 

Modeling Framework for Imputing Solar Suitability (C1–C4). 

2.1. Using LiDAR Data to Estimate PV Rooftop Technical Potential

This analysis builds upon previous work pioneered by NREL (Gagnon et al. 2016) using LiDAR 

data to model rooftop suitability for solar photovoltaics. Light Detection and Ranging (LiDAR) 

is a remote-sensing method that uses pulsed laser beams to measure distances to the ground. The 

LiDAR sensing algorithms can be used to infer (1) the presence of individual buildings and their 

footprint within a city, and (2) the area, tilt, azimuth, and shading of each distinct geometric roof 

plane on a building’s roof. Based on these roof characteristics, the NREL PVWatts (NREL 2017) 

tool is used to estimate the technical performance for the individual building and thus the 

collective tract-level building stock.

This work relies on LiDAR data sets provided by the U.S. Department of Homeland Security 

(DHS) Homeland Security Infrastructure program for 2006–2014. This data set consists of firstreturn rasters of 1 m2 resolution and building footprint vectors for 128 metropolitan areas across 

the nation. Figure 1 shows the metro regions with LiDAR coverage, where the blue polygons are 

the areas with observed data. In total, this data includes 26.9 million buildings, or about 23% of 

U.S. buildings (EIA 2013) and covers an area that representing about 40% of the U.S. 

population.
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Figure 1. LiDAR data coverage

Using these LiDAR data, Gagnon et al. (2016) developed a geospatial model to identify rooftop 

planes suitable for rooftop-mounted solar PV given the roof’s orientation (tilt and azimuth) and 

shading characteristics. To account for potential shading from adjacent buildings, trees, or other 

obstacles, NREL researchers generated hourly hillshades that identify roof square-pixel areas 

with sufficient sunlight illumination. To identify developable surfaces for PV installment, a zonal 

mean neighborhood function was used to identify and remove data noise and complex features 

on roofs (e.g., peaks, edges, chimneys, steeples, facades). Finally, roofs were filtered for rooftop 

developable surfaces that met basic PV suitability requirements, such as being south-facing and 

having a minimum area of 10 m2 (Table 1). The end result is a database of rooftop plane-level 

data with detailed attribution regarding the slope, tilt, azimuth, installed capacity, and annual 

generation potential (kWh/year) (discussed in more detail in Section 3.1.2) of all suitable 

surfaces on all rooftops in the 128 LiDAR–covered metropolitan areas. This data set was an 

important milestone in the distributed PV field and has paved the way for many subsequent

analyses, including the work presented in this report.
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Table 1. Requirements for Solar Suitable Surfaces

Requirement Description

Shading Seasonal requirements1: March requires 60% illumination, June requires 70% 

illumination, September requires 60% illumination, and December requires 50% 

illumination.

Azimuth East, Southeast, South, Southwest, or West-facing

Tilt Average surface tilt <= 60 degree.

Minimum Area2 >= 10 m2

Source: Gagnon et al. (2016).

2.2. Simulating PV Productivity Using reV and PVWatts

Simulations of solar generation were conducted at the tract-level and for 21 tilt-azimuth 

combinations using the NREL reV tool.

3 The Renewable Energy Potential (reV) model’s 

generation module is a complex wrapper that enables distributed generator performance 

modeling using NREL’s PVWatts model with large renewable resource databases in a parallel 

computing environment. To simulate PV productivity, reV uses multiple historic solar irradiance 

time series data from the National Solar Radiation Database (NSRDB), producing generation and 

capacity factor profiles for a typical meteorological year (TMY). Generation profiles based on 

TMY data capture hourly variability of an average year and are estimates of the long-term 

performance of a solar system, though they do not capture significant annual variation in 

generation due to weather/storm events.

The solar resource and meteorological data used by reV/PVWatts for this analysis are from the 

National Solar Radiation Database TMY3 data (Wilcox and Marion 2008). These data include 

hourly representative profiles for 1,001 stations throughout the United States. Each Census Tract

is assigned the TMY3 irradiance profile for the weather station closest to the tract’s centroid. We 

also relied on the set of standard technical assumptions used by Gagnon et al. (2016) 

representing the 2015 average performance of PV systems (Table 2).

 

1 Seasonal shading requirements, as defined by Gagnon et al. (2016), are used to identify suitable rooftops with 

adequate percent sunlight illumination throughout the year. Refer to Gagnon et al. (2016) for further details.

2 Based on current panel power density, 10 m2 provides sufficient area to install a 1.5 kW system. This minimum 

area threshold was chosen to represent a conservative lower-end estimate of viable PV system sizes based on current 

PV performance and historic patterns in reported PV sizing.

3 Documentation for reV did not exist at the time of writing, but additional information is available at 

https://www.nrel.gov/gis/modeling.html.
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Table 2. Assumptions for PV Performance Simulations

PV System Characteristics Value for Flat Roofs Value for Tilted Roofs

Tilt 15 degrees Midpoint of tilt class

Ratio of module area to roof area4 0.70 0.98

Azimuth 180 degrees (south facing) Midpoint of azimuth class

Module power density 160 W/m2

Total system losses 14.08%

Inverter efficiency 96%

DC-to-AC ratio5 1.2

Source: Gagnon et al. (2016).

2.3. Estimating National LMI PV Rooftop Technical Potential by Tract

From the LiDAR PV rooftop data set described in Section 2.2, we extend the data set to estimate 

tract-level solar technical potential by building income, tenure, and building size. This consists of 

three broad steps: (1) Intersecting individual building technical potential estimates with 

demographic factors from the Census Bureau 2011–2015 American Community Survey; (2) 

Aggregating the building-level estimates to the tract, county, and state level and conducting 

Monte Carlo runs to determine the median estimate; and (3) Training a statistical model on tracts 

with LiDAR coverage and imputing technical potential for tracts outside of LiDAR coverage.

2.3.1. Estimating Number of LMI Households by Building Type and by Tenure

For the 128 metro regions with LiDAR-derived PV rooftop technical potential, we overlaid 

demographic data to estimate the total technical potential for residential buildings, per income group, 

per building type, and per tenure. This work relies on demographic data from the American 

Community Survey’s (ACS) 2011–2015 5-year estimates; see Table 3 for a detailed account on 

tables used in this analysis. The ACS publishes these data as smaller crosstabs, with much of the 

detail available only at the larger (i.e., county) geographic levels.

6 To account for this, we use 

random weighted sampling and proportional allocation methods to disaggregate demographic data 

into a tract-level cross-tabulation of household counts by AMI income group, per building type and 

tenure. Additional details on the demographic cross-tabulation process are provided in Appendix A.

 

4 For flat roofs, the ratio of module area to roof area was assumed to 0.7 to reflect the row spacing necessary to incur 

only approximately 2.5% losses from self-shading for south-facing modules at a 15-degree tilt. For tilted roofs, the 

value was assumed to be 0.98 to reflect the 1.27 cm spacing between each module for racking clamps.

5 A system’s direct current to alternating current (DC-to-AC) ratio is the ratio of the nameplate capacity of the PV 

modules to the AC-rated capacity of the inverters. 6 Though much of the data is available at the county level, Census Tracts were chosen as the unit of analysis in this 

report for a few reasons. First, Census Tracts are more homogenous than the larger county and therefore are better 

areas for delineating between income groupings within a city or region. Second, the underlying LiDAR data is 

available at the sub-tract level at a 1-meter resolution. Finally, Census Tracts provide a better unit of analysis from a 

policy and planning perspective, which is the target audience of this report and data.
8

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Table 3. 2015 Five-Year American Community Survey Published Tables Used

Table Source Code Universe Geography

Household Income in the Past 12 Months 

(in 2015 Inflation-Adjusted Dollars)

B19001 Households Tract

Tenure by Household Income in the Past 12 

Months (in 2015 Inflation-Adjusted Dollars)

B25118 Occupied housing units County

Tenure by Units in Structure B25032 Occupied housing units County

Tenure by Household Size by Units in 

Structure

B25124 Occupied housing units County

Source: U.S. Census 2015a, 2015b, 2015c.7

This work defines LMI based on the Department of Housing and Urban Development’s (HUD) 

2016 Area Median Income (AMI) income limits (HUD 2016). These AMI income limits are 

used to determine the eligibility of applicants for federal assistance programs. They are based off 

the median income for Fair Market Rent (FMR) areas (i.e., metropolitan areas, parts of some 

metropolitan areas, and non-metropolitan counties) and are adjusted based on the family size8

(HUD PD&R). Using these AMI income limits and the generated ACS crosstab described above, 

we categorize households into the following low- to moderate-income groups, as defined by the 

Community Development Block Grant (CBDG),

9 based on household income and the number of 

people in the household:

• Very Low Income: 0% to 30% of AMI10

• Low Income: 30% to 50% of AMI

• Moderate Income: 50% to 80% of AMI

• Middle Income: 80% to 120% of AMI

• High Income: >120% of AMI

 

7 Data extracted using NHGIS (Manson et al. 2017). 8 The HUD baseline numbers for each income limit are based off the 4-person family size. For households with 

larger or smaller sizes, percentage adjustments are made to the income break limits based off the number of people 

in the household (HUD PD&R 2016).

9 The income categories used in this report are derived from the Community Development Block Grant (CDBG). 

Though there are many ways in which low- and moderate-income thresholds could be defined, we use the CDBG 

definition because (1) it includes a class for moderate income, whereas the standard Section-8 definition does not, 

and (2) it estimates income based on the local geography and the relative cost of living in a particular location. It is 

important to note that LMI can best be described as a gradient and that delineating between groups does not capture 

the whole story. Indeed, many assistance programs define “moderate” as a range that extends into what we classify 

as middle income (up to 120% of AMI) in this report.

10 The Very-Low Income break limit is based on the greater of the two: (1) 30% AMI or (2) the Federal Poverty 

Level (FPL), defined by the Department of Health and Human Services (HHS), capped at the low-income limit 

(50% of AMI) (HUD PD&R 2016). As a result, this leads to fewer households reported in the low-income bin as 

compared to the very-low income bin. For example, in tracts where the 30% AMI is less than the FPL, the FPL 

(capped at the low income 50% AMI level) is used as the break limit.
9

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To allocate from ACS income bins to the above income categories we discretize the standard 

ACS income bins into $1,000 increments and apply the HUD income breakpoints. This allows us 

to align different ACS crosstabs with different income bins and classify households into the five 

AMI income groups described above using the AMI breakpoints associated with each household 

size class. Using a random weighted sampling and proportional allocation approach, we 

disaggregate county-level tenure and building-type estimates by income group. Further details on 

these data manipulations and key sampling assumptions are outlined in Appendix A. The result

is a data set with total number of households per income group, per building type, and per tenure 

for all 72,760 tracts in the United States. The next section describes how this data set is joined 

with solar-generation estimates.

2.3.2. Estimating LMI Technical Potential for LiDAR Covered Tracts

We developed a model to estimate the number of occupied residential buildings and gross rooftop 

technical potential for tracts with LiDAR coverage.

11 In total, LiDAR-covered tracts comprise 

approximately 32% of the total U.S. Census Tracts. Starting with the demographic crosstab 

described above, we rely on a series of methods tailored toward translating the counts of 

households per building type (e.g., single-family detached, 2–4 units, 5+ units) into estimates of 

building counts by building size class (i.e., small, medium, large). This crosstab ultimately is used 

to assign building attributes to the individual building IDs from the LiDAR database; LiDAR data 

alone cannot identify characteristics of building occupants for individual buildings (e.g., residential 

or not, multi-family or not, low-income or not). To address this challenge, we rely on a few core 

assumptions and leverage a variety of data to improve the precision of the sampling and allocation 

process. Note that parcel data—such as property tax assessment—would be a more accurate 

alternative although no such public data set exists with detailed attribution at a national level.

First, we define a schema of building type to building size (Table 4) to map from building types 

(e.g., 2- to 4-unit buildings, single family) as reported in the American Community Survey to 

building sizes in the LiDAR data (e.g., small buildings; 0 to 5,000 ft2

). This schema was derived 

from the 2009 EIA Residential Energy Consumption Survey (RECS) microdata (EIA 2013), 

which reports frequencies of total floor space, number of stories, and building types. To estimate 

the building footprint area, the amount of floor space is divided by the number of building 

stories. From these calculations, we identified that 99.8% of residential buildings in the United 

States belong to either the small (< 5,000 ft2 footprint) or medium (5,000 to 25,000 ft2 footprint) 

building classes, 99% of single family buildings are small, 87% of multi-family buildings are 

small and, 13% are medium. Technical potential for mobile homes is assumed to be null—as the 

roofs are typically not suitable for solar. These national probabilities are used as weighting 

factors for sampling residential buildings by building type.

 

11 Tracts were determined to be covered if there was at least a 90% spatial intersect requirement with the LiDAR 

extent.
10

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Table 4. Building Type to Building Size Schema

Building Type Building Size Class12 Rooftop Area (ft2)

Single-Family Detached Small 0 to 5,000 ft2

Single-Family Attached Small or Medium 0 to 25,000 ft2

Multi-Family 2–4 Units Small or Medium 0 to 25,000 ft2

Multi-Family 5–19 Units Small or Medium 0 to 25,000 ft2

Multi-Family 19+ Units Medium or Large > 5,001 ft2

Next, we generate a cross-tabulation of possible building count combinations from the ACS 

household counts by building type (e.g., four housing units in 2- to 4-unit buildings could either 

be two 2-unit buildings, or one 4-unit building). This array of combinations is narrowed down by 

removing Census Blocks reported by the HAZUS model13 (FEMA 2016) to have no residential 

buildings. We then generate an array of actual building IDs that meet the size criteria of the 

building-type combinations.

14 In cases where building IDs do not satisfy the criteria of the 

building-type combinations,

15 we downgrade the building type requirements by the smallest 

percentage possible. We then bootstrap actual buildings for each building type using our 

randomly sampled household-to-building count combination as the sampling rate. The sums of 

the sampled building technical potentials are generated per tract and the income and tenure 

estimates are stratified using a proportional allocation approach. 

The process described above is a stochastic process; the final tract-level and national-level 

results are determined based on the median of a 100-sample Monte Carlo simulation. The 

coefficient of variance between samples for each tract and each city were computed for 

validation purposes, and the tract averages of the 100 runs were calculated. Overall, we found the 

sampling error to be low, with a mean coefficient of variance of 6.8% and 1.1% for tracts and 

cities. Further details on the Monte Carlo simulation are covered in Appendix B. The result is a 

data set of tract-level estimates of suitable buildings and their technical potentials per income 

group, building type, and tenure for the 37% of U.S. tracts with LiDAR coverage.

These methods rely on a few key assumptions with notable limitations. One is that the 

proportional allocation approach used to estimate the technical potential by income and tenure 

assumes that LMI and non-LMI groups (or renter and owner groups) residing in the same 

building type are equally as likely to be suitable. Suitability in this report is based only on the 

size, shading, and orientation the roof planes. Other factors such as roof age or roof material,

however, are likely to disproportionately affect low-income households and could affect building 

 

12 Small, medium, and large building sizes area convention defined by Gagnon et al. (2016). 13 HAZUS is a GIS-based natural hazard analysis tool developed by the Federal Emergency Management Agency. 14 For cases in which the sampling pool of buildings does not satisfy the criteria of the building-type combinations, 

we downgrade the building-type requirements by the smallest percentage possible. 15 For any given tract, there are potentially three reasons why the pool of potential residential buildings does not 

satisfy the building type combinations: (1) the LiDAR data might be outdated (years vary for each city and range 

from 2006–2014) and new builds could have occurred since the LiDAR scan was collected, (2) the HAZUS General 

Building Stock reports of residential blocks could be inaccurate or outdated (i.e., residential buildings could exist in 

blocks reported to only contain non-residential buildings), and (3) the random weighted sampling methodology used 

in the ACS county-to-tract building type disaggregation might not accurately reflect the building stock in the tract. 
11

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solar suitability. Additionally, due to data limitations, the model is unable to identify building 

occupants’ demographics, and this biases intra-tract estimates. That is, although the algorithm 

does consider inter-tract variance (i.e., smaller homes in low-income tracts), it cannot distinguish 

intra-tract variance of building size. Notably, we would expect a positive correlation between 

income and building size or, on a per-capita basis, for owner-occupied buildings to be larger than 

renter-occupied buildings. These potential biases are offset by the expectation that demographics 

are mostly homogeneous within a tract because a tract contains about 4,000 people. Another 

related assumption is that the algorithm does not distinguish between occupants’ incomes or 

tenures living within the same building. 

Other limitations of these methods are related to data availability. The lack of access to a 

national parcel data set that can be used to identify an individual building’s true characteristics

means that the methods used must rely on FEMA’s HAZUS model (FEMA 2016)—a modeled 

block-level general building stock data set to filter blocks without residential buildings. 

However, HAZUS itself is an estimated model, and could have zoning inconsistencies. 

Additionally, the most-recent version of HAZUS is based on data from the 2000 Decennial 

Census and there might be inconsistencies between residential tracts reported in HAZUS and the 

2011–2015 ACS data for tracts with new building construction. Finally, not having parcel data 

that enables identification of a building’s type (i.e., single-family detached, multi-family 2–4 

units) and the roof size of different building types, means relying on a schema of building type to 

building size. This schema of building type to building size (Table 4) is based on data from the 

2009 Residential Energy Consumption Survey (RECS) (EIA 2013) to sample buildings for each 

building type from a pool of LiDAR building footprints based on the building’s size. Future 

work should attempt to use parcel data—including representation of building and roof age—

which would permit more-precise identification.

2.3.3. Imputing LMI Technical Potential for Tracts Outside of LiDAR Coverage

To determine the technical potential of the remaining 63% of tracts without LiDAR data we 

developed a statistical model, trained on the tracts with observed data,

16 to predict tract-level 

characteristics. The imputation model used largely follows the same methodological framework 

used in Gagnon et al. (2016, Appendix A–D) but is applied at the tract-level and with additional 

processes to disambiguate the number and size of buildings from ACS household totals by number 

of units. This adjusted predictive modeling framework consists of four sub-models, namely:

1. Household-to-Building Model,

2. Small Building Suitability Model,

3. Rooftop Tilt and Azimuth Model, and

4. Rooftop Plane Area Model.

Each model predicts median and upper- and lower-bound estimates for individual components 

defining the suitable rooftop area. The complete model combines the four component models to 

estimate median and confidence intervals for tracts without LiDAR coverage. Appendix B 

details this four-model predictive modeling framework. Coupled with the LiDAR covered tracts 

 

16 Tracts used for the training set were only used if at least 90% of area was covered by the LiDAR raster extent. 

therefore, weighting of tracts by their coverage is not needed for out-of-sample prediction.
12

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from Section 3.2.2, this provides complete coverage of LMI technical potential across all tracts 

in the United States.

There are three main limitations with this imputation approach. First, because the LiDAR area 

estimates for residential buildings are based on the median of sampled runs, the imputation 

estimates out-of-tract technical potential using modeled inputs. Next, as with the estimates for 

LiDAR-covered areas, although counts of households by income at the tract-level were available, 

we do not have direct observations of the cross-tabulation of building income-type-tenure 

combinations by tract. Instead, these counts are simulated based on the observed proportion at 

the county level and using income-tenure relationships. In effect, this means that the model does 

not represent inter-tract differences in proportion of, for example, rental versus owner-occupied 

buildings within the county. Third, imputed results for rural areas likely are biased because most

of the observed LiDAR data is in urban areas. This bias is mitigated, however, by use of urbanrural isolating variables as independent variables (i.e., locale and land use and land cover) in our 

predictive modeling.

2.4. Model Uncertainty and Caution in Interpreting Results

Results presented in this report are based on a series of statistical processes, often with varying 

degrees of certainty. For reasons described below, this report is unable to provide 95% 

confidence intervals for all of the estimates, although the authors acknowledge this 

unintentionally could overstate the certainty of the results. Thus, readers are urged to use caution 

when interpreting results—particularly for policy-planning or regulatory considerations. Two

dimensions of uncertainty within the data are highlighted.

2.4.1 Uncertainty of solar potential for LiDAR-covered and non-covered areas.

The primary data used in this report are a series of LiDAR scans of individual buildings for 128 

metro regions, spanning 40% of the U.S. population. For these regions the LiDAR data enables

the accurate assessment of the total number of buildings in the tract, their area, and the fraction 

of buildings with solar-suitable roofs (solar suitability). Summing over all the buildings gives a 

high degree of confidence of the total residential potential in these tracts. Assigning these 

buildings to the residential sector, estimates of the ratio of the number of buildings per building 

type, and estimates of building size to building type also makes assumptions documented in the 

Methods section. The process uses several sub-models, and the estimates do not have a 

composite confidence internal because the data required to quantify the composite error is 

unavailable. We were able to quantify the error of the LiDAR-covered areas in an indirect way,

however, by rerunning the model 100 times with different random seeds. When doing so, we 

found that the average tract-level estimates had a coefficient of variance of 6.8%. That is, the 

standard deviation of the 100 runs was 6.8% of the mean estimate. For cities, which are 

composed of several tracts, the mean coefficient of variance was 1.1%. Both statistics give 

assurance that the error of the various sampling methods is acceptably low. 

We then trained statistical models to impute the gross solar potential for the building stock in 

each non-LiDAR tract. By its nature, this statistical process is less accurate than having directlyobserved data. The imputation process uses sub-models to individually estimate the ratio of 

households to buildings, the fraction of solar-suitable buildings, the distribution of rooftop tilts 

and azimuths, and the distribution of rooftop plane areas. Because we don’t have direct 
13

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observations of rooftops in the non-LiDAR covered areas we are not able to directly estimate the 

error. However, we did validate the statistical model by training it on 75% of the LiDARcovered areas, to test its accuracy when applied to the remaining 25%. Comparing the gross 

predicted LMI technical potential for the tract to the actual value shows that 68% of the estimates 

are within 10% of the actual values and 90% of the predicted values are within 20% of actual 

values. Tract-level variance of this magnitude could be unacceptable for planning purposes, 

although the accuracy increases when moving to sequentially larger geographic areas (cities, 

counties, states).

2.4.1 Uncertainty of distribution of solar potential by income-tenure-types

A key purpose of this report is to estimate solar technical potential for different income-tenuretype combinations (e.g. 50% to 80% AMI, owner-occupied single-family buildings). The LiDAR 

data does not allow us to directly observe the zoning, income level, tenure, or building type of 

individual buildings. To estimate the frequency of these different combinations for tracts with 

LiDAR coverage, the LiDAR data is intersected with various Census American Community 

Survey data. The justification is that, although the estimate of income-tenure-type for any 

individual building cannot be estimated, the accuracy at a tract or county level improves when 

averaged over thousands of buildings. For tracts with hundreds of buildings or fewer, then this 

source of error could be greater.

Another important note is that the Census does not publish direct observations of income-tenuretype counts at the tract-level. Instead, the estimates herein are compiled from a variety of 

sources. At the tract-level, there is observed data on the count of households by income group. 

Cross-tabulations of tenure by income, however, and tenure by household size by housing units 

in a building all are observed at the county level. To disaggregate to the tract level, we used 

proportions of income at the tract level to proportionally disaggregate the income-tenure counts

from the county. These estimates could be biased whenever a specific tract has substantially 

different proportions of income-tenure-housing type combinations than the county does. For this 

reason, income-tenure tract combinations only are presented in tabular results at the national 

level, where the bias should be minimized by aggregation. In the interest of transparency, 

however, these values are presented at the tract level in the Solar for All web application and the 

REPLICA data set with appropriate disclosure. 
14

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3. Results

This section quantifies the technical potential of roof-mounted solar photovoltaic systems, 

stratified by the income of the building occupants, tenure, and size of the building. In particular,

this report provides two novel contributions. One is that it estimates the intersection of rooftop 

potential with the above building demographics. Another is that the data is aggregated at the U.S. 

tract and county-level, instead of by zip code, which is more congruent with Census Bureau, 

HUD, and other socio-demographic analyses. In Section 3.1 the quantity and spatial distribution 

of rooftop technical potential across various building characteristics is estimated. In 3.2 the 

feasibility of rooftop solar offsetting 33% of residential electricity consumption is explored, with 

a focus on the feasibility of reaching parity in adoption levels across income. Finally, in Section 

3.3, the report diverges from a national analysis to explore the rooftop technical potential in more 

depth in Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C., to

understand the spatial distribution of potential within these cities as well as the potential on nonresidential buildings that serve low-income populations.

3.1. National Residential Rooftop PV Technical Potential

Over all tracts and all residential buildings in the United States, the gross annual residential 

rooftop solar technical potential is nearly 1,000 TWh, or about 75% of residential consumption

(DOE EERE 2017). Significant potential is found in every income group (Table 5), with the 

greatest overall potential in the non-LMI income group, and with 416 TWh for low-to-moderate 

(0% to 80% AMI) households over 25.5 million solar-suitable buildings. The LMI opportunity is 

approximately 42% of the total U.S. residential potential, indicating that it is a non-trivial portion 

of the suitable rooftop space. These estimates should be interpreted as the gross technical 

potential of all residential buildings, i.e. they do not consider existing residential PV; in 2016, 

approximately 1% of residential buildings have installed rooftop PV (EIA 2017).

The gross technical potential by income group was primarily proportional to the number of 

buildings, though there are differences on a per-capita basis between the groups. Nationally, the 

average technical potential was 14,878 kWh per building. However, the per-building technical 

potential is inversely associated with income (17,106 to 13,711 kWh per building for Very Low 

to High) because of the higher rate of occupancy in multi-family buildings among LMI 

households, which tend to have larger, flatter roofs than their single-family counterparts. On a 

per-household basis, however, the technical potential is positively correlated with income 

because the same multi-family buildings have higher household-to-roof area ratios (8,246 to

8,763kWh per household for Very Low to High; 8,553 kWh per household nationally). Taken 

together, these results demonstrate that there is significant per-capita technical potential for all 

income groups, although—as is demonstrated in Section 3.1.2—much of the LMI potential is 

sited on rental-occupied and multi-family buildings.
15

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Table 5. National Residential PV Rooftop Technical Potential by Income Group

Income Group Households

(millions)

Suitable 

Buildings 

(millions)

Suitable 

Module 

Area 

(millions of 

m2)

Capacity 

Potential 

(GWDC)

Annual 

Generation 

Potential 

(TWh/year)

LMI— Very Low

(0%–30% AMI)

19.5 9.4 794.4 127.1 160.8

Low

(30%–50% 

AMI)

11.5 5.7 472.8 75.6 95.3

Moderate

(50%–80% 

AMI)

18.8 10.4 792.0 126.7 159.8

Non-LMI Middle

(80%–120% 

AMI)

21.1 12.3 900.4 144.1 180.8

High

(> 120% AMI)

46.0 29.4 2,003.3 320.5 403.1

All LMI Buildings 49.8 25.5 2,059.2 329.4 415.9

All Residential Buildings 116.9 67.2 4,962.9 794.0 999.8

These estimates are congruent with previous estimates of the total U.S. rooftop technical 

potential, though none of the previous studies explicitly estimate the distribution among income, 

building types, and tenures. Gagnon et al. (2016), for instance, estimated 1,432 TWh of rooftop 

potential across small, medium, and large building types and 926 TWh for small buildings only. 

Differences between the results presented in this report and those presented in Gagnon et al. 

(2016) can be attributed to differences in scope. Specifically, this report seeks to estimate the 

total residential technical potential, whereas the previous work estimates potential by building 

size class (i.e., small, medium, large). Most small buildings are single-family residential (94%) 

(Gagnon et al. 2016), though not all residential buildings are small; 13% of multi-family 

residential buildings are medium or large (EIA 2013). Thus, the estimate for the residential 

sector (999.8 TWh) is a mixture of the Gagnon et al. (2016) estimates of small (926 TWh) and 

medium and large buildings (506 TWh). In contrast, Denholm and Margolis (2008) estimated 

419 TWh for the residential sector, though this study relied on coarser methods. Additionally, 

these technical potential estimates only include roof-mounted systems on residential buildings; 

for context, previous estimates of the technical potential for ground-mounted systems are vastly 

larger (281,000 TWh) due to the greater area available for deployment (Lopez et al. 2012).

3.1.1. Spatial Trends in LMI Technical Potential

Figure 2 demonstrates the overall quantity of residential potential by U.S. county in gigawatthours. We find that the quantity of technical potential is highly concentrated amid urban and 

other densely-populated areas with more building stock. Many of the areas with high levels of 

potential already have significant levels of residential solar deployment (e.g. Arizona, California, 
16

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Maryland, Massachusetts, New Jersey), although there also are several states with high solar 

potential with currently-low levels of solar deployment (e.g. Illinois, Ohio, Florida, 

Pennsylvania, Texas) (GTM 2017)—indicating the opportunity for future growth.

Figure 2. County residential technical potential (GWh)

Figure 3 shows the county-level technical potential, normalized as the percent of county 

residential generation potential provided by LMI-occupied buildings. Compared to Figure 2—

which demonstrates the areas with the greatest overall LMI potential—Figure 3 shows areas with 

disproportionately high LMI potential after normalizing for population. We find that in 437 

counties (14%) LMI technical potential comprises at least half of the county’s rooftop solar 

potential. Spatial trends in the fraction of LMI potential are substantially different than those of 

the absolute amount. At a high-level, patterns of LMI potential mirror overall income trends in 

the United States. LMI potential percentages are greatest in lower-income counties and are also 

distinctly higher in rural or semi-rural counties. Areas with disproportionately greater fractions 

of LMI potential are seen in the Southeast (i.e., Alabama, Arkansas, Kentucky, Louisiana, 

Mississippi, West Virginia) and portions of Midwest and Mountain West.
17

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Figure 3. County LMI rooftop technical potential as percent of total residential potential

Previous national maps (i.e., Figure 2, Figure 3) are represented at the county-level, though the 

underlying data is tract-level, because tract-level maps are too finely resolved to show 

meaningful spatial trends at a national scale. Tract-level maps, however, are well-suited to 

demonstrate community-level patterns within a city. As a demonstration we mapped solar 

technical potential for LMI households at the tract level for four cities—Chicago, Illinois; San 

Bernardino/Riverside, California; and Washington, D.C. (Figure 4), normalized by the tract’s 

total residential generation potential. These cities were selected for congruence with a 

forthcoming companion analysis of the technical, market, regulatory, and social factors affecting 

LMI solar markets in those three cities. Unsurprisingly, LMI generation is strongly correlated 

with lower-income neighborhoods in each city. Moreover, the results demonstrate that a 

substantial portion of each city’s solar technical potential is in LMI neighborhoods. Therefore, 

lower deployment of solar in lower-income neighborhoods would substantially limit the overall 

deployment potential in these cities.

For additional analysis opportunities at the city-level, readers are invited to explore the 

accompanying web app (https://maps.nrel.gov/solar-for-all), which can be used to explore 

additional dimensions of the underlying data.
18

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Figure 4. Percentage of tract-level rooftop solar potential (MW) on LMI buildings 

3.1.2. Technical Potential by Tenure, Building Type, and Income

This section discusses a first-of-kind estimate of the distribution of rooftop PV technical potential 

by building tenure, housing unit type, and income in the U.S. residential sector. The current U.S. 

residential rooftop PV market largely is concentrated among high-income households and for 

single-family owner-occupied homes (Moezzi et al. 2017; Vaishnav et al. 2017). Solar deployment 

on renter-occupied buildings is low because of principal-agent issues in the motivation for building 

owners to install solar. Namely, because most tenants (88%) directly pay for their utility expenses 

(EIA 2013), there is reduced incentive for building owners to invest in solar or other building 

improvements. Building owners need reasonable confidence that these investments could be 

recouped through higher rent payments, which could be untenable for affordable housing. Similar 

coordination issues exist for multi-family buildings. LMI households face other unique barriers to 

solar adoption, including limited access to financing and available funds (Cook and Bird 2017). If 

unresolved through policy, market, or regulatory measures, these coordination and financing issues 

will inhibit solar deployment on a large fraction of the U.S. building stock.

Figure 5 illustrates the distribution of generation potential by income group and building type. 

We estimate that a majority of the residential potential (683 TWh, 68.4%) is situated on singlefamily buildings, as compared to multi-family buildings (316 TWh, 31.6%) and single-family 

potential exceeded multi-family potential for each income group. Within LMI households,

however, the distribution is more uniform, with multi-family buildings comprising 40.1% of the 

LMI-specific potential. 
19

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Figure 5. Generation potential by income group and building type

Similar trends are seen for the segmentation of potential between owner-occupied and renteroccupied buildings (Figure 6) as there is a strong correlation between occupancy in multi-family 

buildings and rental status. We estimate that 659 TWh (65.9% of the residential potential) is 

situated on owner-occupied buildings, as compared to 341 TWh for renter-occupied buildings 

(34.1%). For LMI households, potential on renter-occupied buildings (212.7 TWh) slightly 

exceeds that of LMI owner-occupied buildings (203.3 TWh).

Figure 6. Generation potential by income group and building tenure

Taken together, Figure 7 shows the intersection of potential by both tenure and building type for 

LMI families. The largest modality of potential is for single-family owner-occupied buildings 

(176.8 TWh), followed closely by multi-family renter-occupied buildings (140.1 TWh). Though 

deployment of rooftop solar historically has been concentrated on single-family, owner-occupied 
20

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buildings, these results indicate that nearly 60% of potential for LMI buildings exists on these 

other underrecognized tenure-type combinations. Table 6 shows the national technical potential 

estimates for all income-tenure-type combinations.

Figure 7. Generation potential (TWh) by building type and tenure
21

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Table 6. National residential PV rooftop technical potential by income group

Income 

Group Tenure Building Type

Suitable 

Buildings 

(millions)

Suitable 

Module Area 

(millions of 

m2)

Capacity 

Potential 

(GWDC)

Annual 

Generation 

Potential 

(TWh/year)

Very Low

(0%–30% 

AMI)

OwnerOccupied

MultiFamily 0.1 38.3 6.1 7.8

SingleFamily 5.1 274.7 43.9 55.7

RenterOccupied

MultiFamily 1.0 311.8 49.9 62.4

SingleFamily 3.1 169.5 27.1 34.9

Low

(30%–

50% AMI)

OwnerOccupied

MultiFamily 0.1 34.2 5.5 6.9

SingleFamily 3.7 198.3 31.7 40.0

RenterOccupied

MultiFamily 0.6 168.0 26.9 33.6

SingleFamily 1.3 72.3 11.6 14.9

Moderate

(50%–

80% AMI)

OwnerOccupied

MultiFamily 0.2 59.6 9.5 11.9

SingleFamily 7.4 402.7 64.4 81.1

RenterOccupied

MultiFamily 0.7 219.9 35.2 44.1

SingleFamily 2.0 109.9 17.6 22.7

Middle

(80%–

120% 

AMI)

OwnerOccupied

MultiFamily 0.3 84.2 13.5 16.7

SingleFamily 9.7 527.6 84.4 105.8

RenterOccupied

MultiFamily 0.6 196.4 31.4 39.3

SingleFamily 1.7 92.2 14.7 19.1

High

(>120% 

AMI)

OwnerOccupied

MultiFamily 0.8 234.2 37.5 46.2

SingleFamily 25.9 1424.7 227.9 286.8

RenterOccupiedMultiFamily 0.8 237.7 38.0 47.8
22

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SingleFamily 1.9 106.8 17.1 22.3

3.2. Feasibility of Parity in Solar Adoption Rates

The U.S. Department of Energy Solar Energy Technology Office recently announced updated 

cost targets for solar energy of $0.05/kWh for residential-scale solar systems by 2030. Using the 

Regional Energy Deployment System (ReEDS) capacity expansion model, NREL estimated that 

achieving these costs could result in 405 GW of PV capacity in 2030, which would provide 17% 

of contiguous U.S. electricity generation (Cole et al. 2017). By 2050, deployment could increase 

to 971 GW, which would provide 33% of generation. These projections do not explicitly 

delineate between utility-scale and distributed-scale solar, nor do they impose constraints of 

minimum regional penetration levels.

This report investigates the technical feasibility of reaching parity in adoption rates of distributed 

solar in the residential sector, regardless of socioeconomic status. Because of the ambiguity of the 

announced goals, the authors interpret this to mean, “Is there sufficient technical potential for 

rooftop solar to offset 33% of low- and moderate-income household annual electrical consumption 

in each U.S. county?” In counties where technical or market barriers prevent reaching the 

consumption-offset goal, we also investigate what types of new business models would be required, 

such as community solar, or virtual net metering (Feldman et al. 2015; Cook and Bird 2018).

To assess the viability of the consumption offset targets we first estimated the total electric 

consumption of low- and moderate-income households aggregated by county. To do so, we start 

with the Low-Income Energy Affordability Data (LEAD) residential energy expenditure data set 

which estimates average annual energy expenditures per Census tract, AMI income group,

17

tenure, and building type (among other breakdowns) (DOE EERE 2017). The LEAD data set is 

based off the 2011–2015 American Community Survey Public Use Microdata Samples (PUMS) 

of self-reported monthly household electricity expenditures. Using this data, the yearly 

household expenditure18 is divided by the average cost of electricity per tract,

19 to get the perhousehold yearly electrical consumption at the tract level. This number then is multiplied by the 

number of households in the tract to derive the total residential energy consumption by tract. Due 

to high variability within the expenditure data at the tract level, we aggregate these results to the 

county level to gain more confidence in the results.

20 Table 6 demonstrates key national statistics 

 

17 Note that due differences in the way the AMI incomes are grouped in the LEAD data, we are unable to separately 

identify electrical consumption for the ‘Middle’ (i.e., 80%–120% AMI) and High (i.e., 120%+) income groups, so 

they are reported combined as “Non-LMI” (i.e., 80%+) in this section of the report. 18 A slight mismatch exists when aligning the LEAD data with the data from the present research. This mismatch is due 

to differences in disaggregation methods used for each analysis. Though the mismatch is minimal, there are tract 

instances where LEAD reports no households (of a building type, tenure, and income group) where we report 

household counts. These instances use the state averages of LEAD energy expenditures, per group, to backfill the data. 

19 Average cost of electricity per tract was calculated using the 2016 EIA 861 (EIA 2016b) utility survey reporting 

total kilowatt-hours sold divided by the number of customers served, per utility and state, for bundled utilities only. 

Tracts were geospatially tagged to each utility territory using Ventyx ABB electric utility territory geometries (ABB 

Energy 2017) and ties were broken using NREL’s standard methodology. In cases where no data was reported by 

the EIA 861 for a tract, the state average was used. 20 Validations of LEAD data were performed at the aggregate by comparing national results to RECS estimates. The 

RECS data is insufficiently resolved to permit comparison at the tract or county level. 
23

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of residential energy consumption, where the total residential annual consumption is estimated at 

1,325 TWh and the consumption for LMI (0%–80% AMI) households is estimated at 493 TWh.

Table 7. Annual Residential Electricity Consumption by Income, Building Tenure, and Type

Income Group Building Tenure Building Type

Annual Electricity 

Consumption (TWh)

Very Low 

(0%–30% AMI)

Owner-Occupied Multi-Family 1.1

Other 8.0

Single-Family 76.6

Rental-Occupied Multi-Family 41.7

Other 8.2

Single-Family 52.6

Low (30%–50% AMI) Owner-Occupied Multi-Family 1.6

Other 3.6

Single-Family 51.4

Rental-Occupied Multi-Family 23.5

Other 2.6

Single-Family 21.1

Moderate (50%–80% 

AMI)

Owner-Occupied Multi-Family 3.0

Other 10.3

Single-Family 113.9

Rental-Occupied Multi-Family 32.7

Other 5.3

Single-Family 35.3

Non-LMI (80%+ AMI) Owner-Occupied Multi-Family 19.8

Other 47.9

Single-Family 620.2

Rental-Occupied Multi-Family 67.9

Other 9.3

Single-Family 67.0

Total LMI 492.7

Total Residential 1324.6
24

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Income Group Building Tenure Building Type

Annual Electricity 

Consumption (TWh)

Very Low 

(0%–30% AMI)

Owner-Occupied Multi-Family 1.1

Other 8.0

Single-Family 76.6

Rental-Occupied Multi-Family 41.7

Other 8.2

The method of matching generation potential to household consumption makes three 

assumptions. First, because the LiDAR data does not enable the identification of consumption at 

the individual-building level, the analysis does not explicitly consider mismatches of generation 

potential and consumption at the individual household level, only in aggregate at the county 

level. Multiple factors explain variance in household electrical consumption (e.g., number of 

occupants, building efficiency, fuels used for heating) as well as variance in the building-level 

solar suitability (e.g., unshaded roof area, tilt, azimuth). Next, use of the average cost of 

electricity could underestimate the electrical consumption since some utilities offer belowmarket electricity rates for low-income and elderly subscribers. Despite this potential bias, 

average electrical rates were used because a county-level database of low-income rates was not 

available. Finally, although use of fuels such as natural gas and fuel oil are a significant portion 

of energy expenditures for some households, the present work only considers offsetting existing 

electrical consumption; considering the electrification of these end uses is beyond the scope of 

this study.

Using these techniques, Figure 8 compares estimates of the solar technical potential with 

electrical consumption by income group, for the single-family owner-occupied (SFOO) building 

stock only. We start with this intentionally conservative comparison because, as described 

earlier, there are currently significant barriers to deploying residential solar on renter-occupied 

and multi-family buildings. Under this assumption we find that offsetting at least 33% of LMI 

electrical consumption with rooftop solar only on SFOO buildings is technically feasible on a 

national scale for all income groups except the Very-Low (0% to 30% AMI) group. However, 

this feasibility belies a few practicalities. First, it implies buildout of nearly all SFOO roof 

space—an impractical and unforgiving market challenge. Second, deployment on SFOO 

buildings alone implies that residents of renter-occupied and multi-family buildings would not be 

served, counter to the goal of increasing widespread solar access. Notably, the fraction of 

consumption that can be offset with SFOO buildings is inversely correlated with income group 

because of lower occupancy in SFOO by LMI households. This means that deployment on 

SFOO-only buildings leads to lower consumption-offset levels for the lowest-income 

households. We note that these calculations exclude the effects of roof age or measures needed to 

improve roof structural suitability as these were classified as economic, rather than technical,

factors.
25

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Figure 8. Comparison of fraction of consumption met by generation potential for single-family 

owner-occupied buildings alone, by income group

Next, we estimate the feasibility of solar consumption offset targets when considering the entire 

residential building stock. We find that there is more than sufficient roof space, on a technical 

basis, to offset 33% of LMI electricity consumption, although to do so would require deployment 

on rental-occupied and multi-family buildings. Deployment on these classes of buildings likely 

would require development of new business models or other regulatory changes. Figure 9 

demonstrates the combined national technical potential for the four tenure-type combinations, 

single-family owner-occupied (SFOO), single-family renter-occupied (SFRO), multi-family 

owner-occupied (MFOO), and multi-family renter-occupied (MFRO), as compared to the 

electricity consumed for each group, both on a national basis. For the Very Low, Low, Moderate, 

and Non-LMI income groups the percent of generation potential to electrical consumption is 

85%, 91%, 80%, and 70%, respectively. Due to lower per-capita levels of consumption for lowincome households, the fraction of consumption that can be offset is greater for LMI households 

than for non-LMI households. Note that none of the income groups are projected to have a 

technical potential that exceeds the group’s consumption level.
26

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Figure 9. Technical feasibility of matching residential electrical consumption with rooftop solar, by 

income group

• Though there is sufficient roof space on a national basis to offset 33% of electricity 

consumed for LMI buildings, the roof space is insufficient in all counties. Table 8

demonstrates, on a national basis, the sum of the discrepancy between generation 

potential and a 33% offset for each building type for LMI households. Shortfalls are most 

significant for mobile homes (38 TWh)—whose roofs are typically not structurally 

suitable for solar, followed by single-family (6 TWh) and multi-family (2 TWh) 

buildings. Therefore, many occupants of mobile homes—who are disproportionately lowincome—face significant challenges in gaining access to solar energy.

Table 8. Estimates of National Shortfall in Rooftop Solar Generation Needed to Nationally Offset 

17% and 33% of LMI Consumption

Goal Building Type GWh Needed

17% Multi-Family 789.9

Single-Family 1,224.8

Other21 38,026.8

33% Multi-Family 1,905.2

Single-Family 6,085.5

Other 38,026.8

 

21 “Other” building types primarily include mobile homes, but also include other miscellaneous residential electricity 

uses (e.g., boats, recreation vehicles). 
27

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Figure 10 shows the maximum ratio of LMI electrical consumption that could be offset with 

rooftop solar from all LMI buildings in the county. When allowing deployment on LMI rentaloccupied and multi-family buildings, there are 31 counties with insufficient technical potential to 

offset at least 33% electrical consumption, or about 3 TWh shortfall. When allowing deployment 

only on single-family owner-occupied LMI buildings (Figure 11), however, there are 1,257 

counties (40% of all U.S. counties) with insufficient potential to reach at least 33% consumption 

offset, or about 202 TWh shortfall. Again, note that the statistics in Figure 10 and Figure 11 

represent the average penetration achievable for the entire building stock in the county; there are 

likely to be significant variance in the achievable penetration for individual buildings based on 

the roof characteristics and occupants’ energy use.

Spatial trends in generation penetration largely mirror regional variation in per-capita electricity 

consumed, primarily due to which fuels are used for building heating and cooling loads. For 

instance, per capita electricity consumption is greater in the Southeast because electricity is the 

predominant fuel for heating and cooling loads in that region, whereas natural gas is the 

predominant fuel in California and Southwest, and fuel oil is predominant for the Northeast. In 

effect, this means that more solar generation is needed to offset the 33% of consumption in the 

Southeast, but less is required in California, the Southwest, and the Northeast.

Figure 10. Percent of LMI electrical consumption that can be offset by rooftop solar generation

(county)—all LMI buildings
28

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Figure 11. Percent of LMI electrical consumption that can be offset by rooftop solar generation

(county)—single-family owner-occupied LMI buildings only

From a technical basis alone, there is sufficient rooftop space on LMI buildings for at least 33% 

of their electrical consumption to be met with rooftop solar generation. However, reaching this 

penetration level would require alternative deployment models than commonly found today. 

These models should address coordination issues inherent to rental-occupied and multi-family 

buildings as well as LMI financing and affordability barriers (Cook and Bird 2018). Such models 

should also ensure that rental property owners are incentivized to install solar on their 

buildings—for example, by bundling utility expenses with rent payments as a means of passing 

through savings to the renter. Alternatively, various shared solar models (Feldman et al. 2015), 

including virtual net metering, could be effective in allowing building owners to sell rooftop 

generation directly to their occupants. Similar issues exist for multi-family buildings, where 

shared solar or community solar models could help to address tenant-owner coordination issues. 

Some of the coordination issues, however, might be too intractable to resolve. For this reason, 

this report also includes penetration statistics considering only single-family owner-occupied 

buildings, which essentially identifies the quantity of off-site solar generation (e.g., community 

solar or utility-scale solar) that is needed for the penetration target.

Calculations of the potential for solar generation to offset electrical consumption are based on 

annual generation and consumption levels and do not consider potential mismatches at the hourly 

level. For many buildings, and without energy storage, these mismatches are likely to be 

substantial. For instance, buildings might generate more energy than consumed during midday, 

when irradiance is highest, resulting in excess energy exported to the grid. In contrast, during 
29

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periods of peak demand (e.g., afternoons and evenings), consumption could exceed generation, 

resulting in energy delivered from the grid. There are a few important implications of this 

phenomena for policymakers. One is that retail tariff design and other associated policies 

influence the economic value of distributed solar to LMI households, particularly, the value of 

excess generation exported to the grid. Without careful thought, this could limit the nominal 

purpose of LMI programs—energy burden reduction. Another is that these consumption and 

generation mismatches, sans energy storage, limit the ability for distributed solar to provide 

resiliency benefits, which also could be a policy goal. Finally, LMI solar should be considered 

more broadly in the perspective of transmission and distribution grid-integration issues, such as 

the energy and capacity value of solar energy, potential for solar overgeneration, and the hosting 

capacity of local distribution networks. For more details on some of these issues see Denholm et 

al. (2016) and Palmintier et al. (2016). 

3.2.1. Additional Factors Impacting Building Technical Suitability

Although the LiDAR method for assessing technical potential accounts for several important 

factors that can affect rooftop suitability (e.g., unshaded area, tilt, azimuth), it excludes other 

factors that also might matter. For instance, it is generally not advised to install solar panels on 

older roofs, because it is costly to uninstall solar panels prior to replacing the roof. Thus, 

although roof age is categorized as an economic factor and not a technical factor, it impacts the 

building suitability and overall market potential. Other salient factors that could impact a 

building’s suitability can include electrical code or interconnection issues, unsuitability of the 

roof to support the weight of the panels and racking (e.g. flat roof without enough support or a 

mobile home), and existing violations of building codes that are cost-prohibitive to address in the 

installation. 

To estimate what proportion of LMI buildings might face these types of challenges, we analyzed 

historical data from GRID Alternatives installations, a non-profit provider of solar to low-income 

households. Because the data are collected primarily for single-family homes in California, the 

descriptive statistics are not intended to be representative of other geographies or for multifamily buildings. Nevertheless, these data provide a first-of-kind insight into the frequency of 

additional barriers to solar deployment. Determining the economic feasibility of preparing a roof 

for solar installation often requires physical inspection and is beyond the scope of this report to 

conclusively address. 

Based on a sample of 24,269 potential solar installations (“leads”), we identified 10 reasons why 

a building was determined—either by physical inspection or in conversation—to be unsuitable 

for solar: Old roof (n = 1,474), excessive (unspecified) upgrades required (n = 552), building 

code issues (n = 340), roof shading (n = 314), electrical service issues (n = 187), unsafe roof 

pitch (n = 152), insufficient roof space (n = 137), mobile home (n = 117), roof orientation (n = 

47), and roof type (n = 31). Of these 10 reasons, “roof space,” “roof orientation,” “roof shading,”

and “unsafe roof pitch” are excluded, as these factors already are considered in the LiDAR 

method. “Mobile home” also is excluded because these buildings already were excluded from the 

present technical potential analysis. Additionally, of the 24,269 leads, 10,781 were excluded for 

non-technical reasons (disinterest in solar n = 4,224; ineligibility to receive low-income solar 

grants n = 6,557) and did not receive technical evaluations, so it is undetermined whether these 

leads would have been technically suitable. Thus, it’s estimated that 10.5% to 18.9% of low-
30

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income buildings could face additional techno-economic barriers to solar beyond those 

considered in the LiDAR method alone. Readers are urged to use caution in interpreting this data 

due to representativeness issues. Future work should couple physical inspections with technical 

potential assessments.

3.3. Technical Potential for Select Buildings that Serve LMI 

Households

Next, we investigate the possible role for third-party buildings that serve low-income populations 

to host solar on their roofs. Solar deployed on these third-party buildings could either reduce 

operational costs, thereby helping to expand mission-related activities, or capacity could 

explicitly be designated to offset local electrical consumption via a virtual net metering 

arrangement. The specific building classes investigated are public sites (e.g., libraries, police 

stations, local government), public housing (i.e., federally assisted rental housing), K–12 public 

schools, homeless shelters, and places of worship. These building classes are not intended to be 

comprehensive of all third-party buildings that could deploy solar. Results for this section are 

presented for the three cities analyzed in greater depth in the forthcoming companion report 

(Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C.). Methods for 

collecting the data involved manual effort (see Appendix D), so at this time we are unable to 

present a national assessment of the five building types.

To quantify the technical potential of LMI-serving buildings in the cities, we obtained GIS data 

for each building type and spatially tagged these layers to the LiDAR buildings database. The 

LMI-serving building data is an agglomerate data set from a variety of sources. Because the data 

consists of varied data sets and formats, multiple methods were used to tag each building 

geometry to one or more LiDAR building footprint. Jointly, this enabled identification of the 

total technical potential for each building type in each city. Appendix D provides further detail 

on methods and data sources.

Over the five building classes, schools have the greatest technical potential opportunity by far,

although there are substantial opportunities for all the classes examined (see Table 8). 

Collectively the gross generation potential for the five building classes is estimated to be 

1,012.2 GWh, 311 GWh, and 377.1 GWh for Chicago, San Bernardino/Riverside, and 

Washington, D.C. respectively. These generation estimates are based on the solar suitability 

criteria (i.e., non-North facing) and do not include the possible on-site area available for groundmounted systems. To put these values in context, they represent approximately 13%, 29%, and 

9% of the estimated annual electricity consumption of low- to moderate-income households in 

the city.
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Table 9. Annual Generation Potential for Select Third-Party Buildings in Chicago, 

Washington D.C., and San Bernardino/Riverside (GWh)

Chicago San Bernardino/ Riverside Washington, D.C.

Public Housing 62.8 16.6 31.0

Public Sites 51.9 24.422 41.1

K–12 Schools 306.4 106.2 73.0

Homeless Shelters 5.3 0.6 5.6

Places of Worship 108.1 7.3 32.8

Total (GWh) 1072.3 311.0 377.1

Next, on-site electrical consumption of each of the third-party buildings is estimated to determine 

the feasibility of oversizing a PV system with the purpose of using excess solar generation to 

offset nearby LMI households. To estimate the on-site consumption, each building was matched 

with a comparable representative building in the EIA Residential and Commercial Building 

Energy Consumption Surveys and—to match local climate-related consumption patterns—to the 

nearest Census Division (EIA 2013; EIA 2016a). This allows us to derive the average electrical 

consumption per square foot of building footprint for each building type, which then is 

multiplied by the building footprint of each of the third-party buildings in the LiDAR database. 

When sizing the PV system for each building the same suitability criteria is used (e.g., no 

installation on north-facing roof planes), and it is assumed that each building offsets 100% of its 

on-site consumption before exporting generation to the community. Finally, this method only 

considers generation and consumption on an annual basis and does not consider potential 

mismatches in the hourly profiles. Appendix D describes this process in more detail.

We find that, after accounting for on-site consumption, the five third-party building types 

considered could feasibly oversize PV systems on their roofs such that excess generation 

accounts for 1.3% to 8.7% of LMI consumption in the city (Table 9), and the opportunity 

differed substantially between cities. Due to the greatest solar resource of the three regions, San 

Bernardino/Riverside had the highest generation excess fraction, and it is estimated that thirdparty buildings’ excess could account for 8.7% of LMI consumption. For Washington, D.C.—the 

smallest fraction—it is estimated third-party buildings excess could account for 1.3% of LMI 

consumption.

Schools, followed by Places of Worship were consistently the highest opportunity of the five 

building classes considered. Both buildings typically are in residential neighborhoods. Schools 

usually have large flat roofs and low consumption in the summer, when solar irradiance is 

highest. Places of Worship had low levels of electricity consumption and moderately favorable 

roof characteristics. The results also indicate that Public Housing, Public Sites, and Homeless 

Shelters likely have insufficient rooftop space to offset 100% of their own on-site consumption—

though actual values could vary for individual buildings.

 

22 Due to data limitations, generation potential for San Bernardino public sites was not estimated.
32

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Table 10. Net Excess Rooftop Potential Accounting for Building Self-Consumption by

Building Class and City

Chicago San Bernardino/ Riverside Washington, D.C.

Public Housing (GWh) 0.0 0.0 0.0

Public Sites (GWh) 0.0 0.0 0.0

K-12 Schools (GWh) 19.8 19.6 5.5

Homeless Shelters (GWh) 0.0 0.0 0.0

Places of Worship (GWh) 31.5 3.2 4.6

Total (GWh) 51.4 22.8 10.1

Total (% of LMI 

Consumption in City)

2.1% 8.7% 1.3%
33

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4. Conclusion

This report presents a first-of-kind assessment of the technical potential of rooftop solar for lowand moderate-income households and provides insight on the distribution of solar potential by 

tenure, income, and other building characteristics. The research indicates that a substantial 

fraction (42%) of the national residential rooftop solar potential is located on LMI buildings and, 

for all incomes, a substantial fraction is located on multi-family and renter-occupied buildings. 

We also find that rooftop solar can significantly contribute to meeting U.S. electricity demand. 

Specifically, from a technical basis there is sufficient rooftop space to ensure that at least 33% of 

U.S. residential electrical consumption is offset by rooftop solar—although to do so requires 

deployment on multi-family and renter-occupied buildings. Reaching this level of offset is most 

feasible in Northeast and Southwest portions of the United States, where per-capita electricity 

use is lowest. This report also highlights the importance of ensuring solar access for mobile 

home residents, who are disproportionately low-income yet typically cannot install rooftop solar 

because of structural concerns. 

In cases where LMI households are unable to adopt solar for their own homes, non-profit entities 

might enter agreements to host solar on their own roofs. This study investigated the feasibility 

for five classes of buildings in Chicago, San Bernardino/Riverside, and Washington, D.C., 

finding that the buildings likely could oversize PV systems on their roofs equivalent to offsetting 

1.3% to 8.7% of local LMI consumption. This fraction is significant but is unlikely to be a 

leading mechanism for increasing LMI access to solar. Future research could explore the 

feasibility of ground-mounted systems at strategic locations (i.e., community solar) or 

installation on for-profit buildings.

Traditional deployment models have insufficiently enabled access to solar for low- and 

moderate-income households. In part, this is because there are significant economic and financial 

barriers to LMI solar adoption without policy action. These include poor access to credit, low 

capital availability, housing uncertainty, and many more. Without innovation either in 

regulatory, market, or policy factors, a large fraction of the U.S. potential is unlikely to be met.

Potential electric bill savings from rooftop solar would have the greatest material impact on the 

lives of low-income households as compared to their high-income counterparts and could help 

mitigate the energy burden faced by these households.

4.1. Data for Public Use

This report ultimately seeks to provide objective data for regulators, policymakers, nonprofits, 

and project developers to make informed decisions that are best for their own communities, for 

example to assess the potential for rooftop solar in their jurisdictions and perform policy costbenefit analysis. To this effect, data used in this report is provided freely via NREL’s website in 

two formats. One format is an interactive web application using NREL’s OpenCarto platform,

and was developed to enable user to browse, visualize, and export results 

(https://maps.nrel.gov/solar-for-all). Secondly, the technical potential data used in this report, 

accompanied by several additional techno-economic variables, and aggregated at the tract-level, 

is available for download at (https://data.nrel.gov/submissions/81). The Rooftop Energy Potential 

of Low Income Communities in America (REPLICA) data set includes measures of electricity 
34

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

expenditures ($/month), demographics, utility electric rates, and other important variables 

compiled from a variety of sources and tagged to each Census tract. 

4.2. Future Work

Three topics for future research are suggested to extend and improve this work. First, this work 

uses limited building-level attributes within the analysis, except for rooftop planes obtained via 

LiDAR. Many local governments, however, increasingly make building-level GIS files available 

for free or for a modest fee (i.e., tax rolls). Building-level models would permit substantially 

more-nuanced assessments of the technical, economic, market, and behavioral aspects of rooftop 

solar deployment. 

Future research also could improve the representation of low-income energy consumption, 

perhaps also at the building level. This analysis does not identify building-level variation in 

consumption, which is significant. We also acknowledge our limitation in not considering hourly 

mismatches between consumption and solar generation, which can be significant. These 

mismatches—and the value of excess generation exported to the electric grid—are an important 

dimension of the economic evaluation. 

Finally, although this analysis does identify differences in technical potential between singlefamily and multi-family buildings, there is room for improvement. Specifically, future research 

could use building-level data sets to more precisely identify the locations of multi-family lowincome buildings. Future work also could improve the representation of tenant-level electrical 

loads versus communal building loads, as well as publish additional data distinguishing how 

income affects electricity consumption patterns.
35

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References

ABB Energy. 2017. Velocity Suite. Data Product. Boulder, CO: ABB Energy.

Borenstein, S. 2017. “Private Net Benefits of Residential Solar PV: The Role of Electricity 

Tariffs, Tax Incentives, and Rebates.” Journal of the Association of Environmental and Resource 

Economists 4(S1), S85–S122.

California Public Utility Commission (CPUC). 2017a. CARE/FERA Programs. 

http://www.cpuc.ca.gov/General.aspx?id=976.

California Public Utility Commission (CPUC). 2017b. “Implementation of AB 693—Solar on 

Multifamily Affordable Housing (SOMAH).” D.17-12-022. Accessed March 5, 2018. 

http://www.cpuc.ca.gov/General.aspx?id=6442454736. 

Chaudhari, Maya, Lisa Frantzis, and Tom E. Hoff. 2004. PV Grid Connected Market Potential 

Under a Cost Breakthrough Scenario. Chicago: Navigant Consulting. 2004-117373.

Cole, W., B. Frew, P. Gagnon, J. Richards, Y. Sun, J. Zuboy, M. Woodhouse, R. Margolis. 2017. 

SunShot 2030 for Photovoltaics (PV): Envisioning a Low-Cost PV Future. Golden, CO: National 

Renewable Energy Laboratory. NREL/TP-6A20-68105.

Cook, J., and L. Bird. 2018. Unlocking Solar for Low- and Moderate-Income Residents: A 

Matrix of Financing Options by Resident, Provider, and Housing Type. Golden, CO: National 

Renewable Energy Laboratory. NREL/TP-6A20-70477.

Denholm, Paul, Kara Clark, and Matt O’Connell. 2016. On the Path to SunShot: Emerging 

Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and 

Transmission System. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-

65800. http://www.nrel.gov/docs/fy16osti/65800.pdf.

Denholm, Paul, and Robert Margolis. 2008. Supply Curves for Rooftop Solar PV-Generated 

Electricity for the United States. Golden, CO: National Renewable Energy Laboratory. 

NREL/TP-6A20-44073.

DOE Better Buildings (2017, December 5). Denver Housing Authority Launches Community 

Solar Garden. https://betterbuildingsinitiative.energy.gov/beat-blog/denver-housing-authoritylaunches-community-solar-garden (accessed March 19, 2018).

Federal Emergency Management Agency (FEMA). 2016. Summary of Databases in Hazus-Multi 

Hazard. http://www.fema.gov/summary-databases-hazus-multi-hazard (accessed March 4, 2016).

Feldman, D., A. Brockway, E. Ulrich, and R. Margolis. 2015. Shared Solar: Current Landscape, 

Market Potential, and the Impact of Federal Securities Regulation. NREL/TP-6A20-63892.

Gagnon, Pieter, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore. 2016. 

Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment. 

NREL/TP-6A20-65298.
36

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Gagnon, P., R. Margolis, J. Melius, C. Phillips, and R. Elmore. 2018. “Estimating Rooftop Solar 

Technical Potential Across the US Using a Combination of GIS-Based Methods, LiDAR Data, 

and Statistical Modeling.” Environmental Research Letters.

GTM Research. 2017. Q4 2017, U.S. Solar Market Insight.

Lopez, A.; B. Roberts, D. Heimiller, N. Blair, and G. Porro. 2012. U.S. Renewable Energy

Technical Potentials: A GIS-Based Analysis. National Renewable Energy Laboratory (NREL), 

Golden, CO. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-51946.

Manson, S., J. Schroeder, D. Van Riper, and S. Ruggles. 2017. IPUMS National Historical 

Geographic Information System: Version 12.0 [Database]. Minneapolis: University of 

Minnesota. http://doi.org/10.18128/D050.V12.0c.

Margolis, R., P. Gagnon, J. Melius, C. Phillips, and R. Elmore. 2017. “Using GIS-Based 

Methods and LiDAR Data to Estimate Rooftop Solar Technical Potential in U.S. Cities.” 

Environmental Research Letters 12(7), 074013.

Moezzi, M., A. Ingle, L. Lutzenhiser, and B. Sigrin. 2017. A Non-Modeling Exploration of 

Residential Solar Photovoltaic (PV) Adoption and Non-Adoption. National Renewable Energy 

Laboratory (NREL). Golden, CO: National Renewable Energy Laboratory. NREL/SR-6A20-

67727.

Multi-Resolution Land Characteristics Consortium (MRLC). 2011. “National Land Cover 

Database 2011.” https://www.mrlc.gov/nlcd11_data.php (accessed December 15, 2017).

National Renewable Energy Laboratory (NREL). December 15, 2017. NREL’s PVWatts 

Calculator. http://pvwatts.nrel.gov/ (accessed March 18, 2018).

Olsson, Ulf. 2005. “Confidence Intervals for the Mean of a Log-Normal Distribution.” Journal 

of Statistics Education 13(1).

Paidipati, J., L. Frantzis, H. Sawyer, and A. Kurrasch. 2008. Rooftop Photovoltaics Market 

Penetration Scenarios. Golden, CO: National Renewable Energy Laboratory (work performed 

by Navigant Consulting). NREL/SR-581-42306.

Palmintier, Bryan, Robert Broderick, Barry Mather, Michael Coddington, Kyri Baker, Fei Ding, 

Matthew Reno, Matthew Lave, and Ashwini Bharatkumar. 2016. On the Path to SunShot: 

Emerging Issues and Challenges in Integrating Solar with the Distribution System. Golden, CO: 

National Renewable Energy Laboratory. NREL/TP-5D00-65331.

http://www.nrel.gov/docs/fy16osti/65331.pdf.

Phillips, Caleb, and Jenny Melius (2016). U.S. PV-Suitable Rooftop Resources. National 

Renewable Energy Laboratory. https://dx.doi.org/10.7799/1258436.

Rosenblatt, Murray. 1956. “Remarks on Some Nonparametric Estimates of a Density Function.”

Annals of Mathematical Statistics. 27, no. 3, 832-837. 
37

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Census Bureau. 2010. “Geographic Terms and Concepts—Census Divisions and Census 

Regions.” 2010 Census. https://www2.census.gov/geo/docs/maps-data/maps/reg_div.txt.

U.S. Census Bureau. 2015a. “B19001: Household Income in the Past 12 Months (in 2015 

Inflation-Adjusted Dollars).” 2011–2015 American Community Survey. Retrieved from website 

https://www.nhgis.org/. 

U.S. Census Bureau. 2015b. “B25118: Tenure by Household Income in the Past 12 Months (in 

2015 Inflation-Adjusted Dollars).” 2011–2015 American Community Survey. 

https://www.nhgis.org/ (accessed April 17, 2017).

U.S. Census Bureau. 2015c. “B25124: Tenure by Household Size by Units in Structure.” 2011–

2015 American Community Survey. https://www.nhgis.org/ (accessed April 17, 2017).

U.S. Census Bureau. 2015d. “B25032: Tenure by Units in Structure.” 2011–2015 American 

Community Survey. https://www.nhgis.org/ (accessed April 17, 2017).

U.S. Department of Energy (DOE), Weatherization and Intergovernmental Office. 

Weatherization Assistance Program. https://www.energy.gov/eere/wipo/weatherizationassistance-program (accessed March 18, 2018).

U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (DOE EERE). 

2017. “Low-Income Energy Affordability Data (LEAD) Tool.” 

https://catalog.data.gov/dataset/clean-energy-for-low-income-communities-accelerator-energydata-profiles-2fffb (accessed December 15, 2017).

U.S. Department of Housing and Urban Development (HUD). 2016. “Income Limits.”

https://www.huduser.gov/portal/datasets/il.html#2016 (accessed March 19, 2018).

U.S. Department of Housing and Urban Development’s Office of Policy and Developed & 

Research (HUD PD&R). 2016. “HUD Income Limits Briefing Material.” 

https://www.huduser.gov/portal/datasets/il//il16/IncomeLimitsBriefingMaterial-FY16.pdf

(accessed December 15, 2017).

U.S. Energy Information Administration (EIA). 2013. “2009 Residential Energy Consumption 

Survey (RECS).” 

http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata (accessed 

December 15, 2017). 

U.S. Energy Information Administration (EIA). 2016a. “2012 Commercial Building Energy 

Consumption Survey (CBECS) Public Use Microdata.” 

https://www.eia.gov/consumption/commercial/data/2012/index.php?view=microdata (accessed 

December 15, 2017).

U.S. Energy Information Administration (EIA). 2016b. “Detailed State Data: Average Price by 

State by Provider (EIA-861) Public Use Microdata.” http://www.eia.gov/electricity/data/eia861/

(accessed December 15, 2017).
38

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Energy Information Administration (EIA). 2017. “What is U.S. electricity generation by 

energy source?” https://www.eia.gov/tools/faqs/faq.php?id=427&t=3%22 (accessed March 7, 

2018). 

U.S. Health and Human Services (HHS). 2017. “About LIHEAP.” 

https://www.acf.hhs.gov/ocs/programs/liheap/about (accessed December 15, 2017).

U.S. National Center for Education Statistics (NCES). 2015. “Locale Boundaries.” Education 

Demographic and Geographic Estimates (EDGE).

https://nces.ed.gov/programs/edge/geographicLocale.aspx (accessed October 1, 2017).

Vaishnav, P.; N. Horner, and I. Azevedo, 2017. “Was It Worthwhile? Where Have the Benefits 

of Rooftop Solar Photovoltaic Generation Exceeded the Cost?” Environmental Research Letters

12(9).

Wilcox, S., and W. Marion. 2008. User’s Manual for TMY3 Data Sets. Golden, CO: National 

Renewable Energy Laboratory. NREL/TP-581-43156.
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A. Allocation of Demographic Attributes

This appendix describes the series of data transformation steps used to identify cross-tabulations 

(i.e., “crosstabs”) of demographic attributes at the Census tract level by income, building type, 

and tenure. This process relies on a set of tables (Table 3) from the 2011–2015 5-year American 

Community Survey (ACS). In general, we rely on four major steps when generating a tract-level 

crosstab of household counts by income, building type, and tenure, namely:

1. Calculate the number of households belonging to each $1,000 income group, at the tract 

level.

2. Disaggregate from county data to estimate the number of households, per $1,000 income 

group, per tenure, at the tract level.

3. Disaggregate from county data to estimate the number of households, per $1,000 income 

group, per tenure, per dwelling type, per household size. 

4. Aggregate tract estimates by AMI income group. Create final data set of number of 

households by AMI income group, by building type, and by tenure, per tract.

Each of these steps involves a series of data transformations and relies on a set of statistical 

sampling assumptions. Described below are these methods and assumptions for each step in 

more detail.

A.1. Calculate the Number of Households Belonging to Each 

$1,000 Income Group, at the Tract Level

In the first step of the ACS data processing methodology, we use the tract level “Household 

Income in the Past 12 Months (in 2015 Inflation-Adjusted Dollars)” ACS table, which provides 

tract-level household counts classified according to ACS income bins.

23 We take this table and 

deconstruct the ACS predefined income brackets into $1,000 increments. To do so, we randomly 

sample $1,000 increments for each bin assuming that income is uniformly distributed within the 

bin. We also assign a maximum income cap of $1,000,000/year because no identification in our 

results is needed at this resolution.

Disaggregating these ACS defined income bins into $1,000 increments serves two purposes. 

First, it allows us to reconcile ACS tables with different income binning (e.g. $35,000–$39,000 

versus $35,000–$49,000 reported in the county data set). Second, it allows us to more precisely 

allocate incomes to different AMI income groups.

 

23 ACS tract-level incomes are classified into 14 bins, namely: $0–$10,000, $10,000–$14,000, $15,000–$19,000, 

$20,000–$24,000, $25,000–$29,000, $30,000–$34,000, $35,000–$39,000, $40,000–$44,000, $45,000–$49,000, 

$50,000–$59,000, $60,000–$74,000, $75,000–$99,000, $100,000–$124,000, $125,000–$149,000, $150,000–

$199,000, and $200,000 or more.
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A.2. Disaggregate from County Data to Estimate the Number of 

Households, per $1,000 Income Group, per Tenure, at the Tract Level

Next, we disaggregate county demographic crosstabs to the tract level, using the tract household 

counts as the disaggregating factor. We start with the “Tenure by Household Income in the Past 

12 Months (in 2015 Inflation-Adjusted Dollars)” ACS table at the county level. To disaggregate 

this county data to get the estimates at the tract level, we rely on a random-weighted sampling 

method, weighted by the total household counts per income bin. Because we do not have the 

desired income by tenure data at the tract level, we rely on these random-weighted methods to 

help us disaggregate. In doing so, we do not directly assume that the county-to-tract counts are 

proportional. Rather, we assume that there is some variability across the component tracts, but 

this variability is inevitably weighted by tract household counts per income. This approach 

allows us to obtain tract-level estimates of the number of households, per $1,000 income group, 

per tenure.

A.3. Disaggregate from County Data to Estimate the Number of 

Households, per $1,000 Income Group, per Tenure, per Dwelling Type, 

per Household Size 

This next process is similar to that described in A.2 above. Here we rely on two ACS countylevel tables, namely, “Tenure by Units in Structure” and “Tenure by Household Size by Units in 

Structure.” We first combine these two tables using a proportional allocation method, joining 

based on the shared tenure field. Doing so gives us a county cross-tabulation of tenure, by units 

in structure, by household size. We use this county crosstab to then disaggregate to the tract 

level. Using the data set created in the previous step (A.2), we use a random-weighted sampling 

methodology to disaggregate the county crosstab. Similar to A.2, we do not assume that the 

county-to-tract disaggregation is directly proportional to the total household counts. Instead, we 

know there is variability across tract components, therefore, we use the random-weighted 

methodology to allow for variation, but ultimately, weighted by total counts.

A.4. Aggregate Tract Estimates by AMI Income Group

Using this large tract-level crosstab described above (Section A.1 though Section A.3), we assign 

the AMI income group break limits to classify the data according to our five AMI income 

groups: Very Low Income (0% to 30% AMI), Low Income (30% to 50% AMI), Moderate 

Income (50% to 80% AMI), Middle Income (80% to 120% AMI), and High Income (>120% 

AMI). To do so, we join the “2016 HUD Income Limits” (HUD 2016) table to each row in our 

data set based on the Census Tract location, household size, and the $1,000-increment binned 

yearly household income. With the data categorized into our five income groups, we remove the 

household size category and aggregate the final data set by the income group, building type, and 

tenure.
41

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Appendix B. Validation of Monte Carlo Simulations for 

Building Sampling

This analysis uses a stochastic process to sample residential buildings in LiDAR covered Census 

Tracts (Section 2.3.2) to derive tract-level estimates. As such, we rely on a Monte-Carlo 

methodology to generate probability distributions of tract-level technical potential, per building 

type. The workflow is run 100 times, each using a different random seed, and the median value 

over the 100 runs is used as the final estimate to obtain a more accurate estimate of the technical 

potential in each tract. 

To validate our sampling methodology and better understand the variability of the technical 

potential estimates, we calculate the coefficients of variance across Monte Carlo runs for tracts 

and cities; city coefficients of variance are detailed in Table B-1. For each run, we calculate the 

tract and city technical potential averages. Doing so, we find that both the average tract and city 

errors are low, 6.8% and 1.1% for tracts and cities, respectively. When plotting these error ratios 

at the tract level (Figure B-1), we find that the distribution is lognormally distributed, indicating 

most tracts have relatively high levels of certainty (low levels of variability) across sampled runs. 

The low coefficient of variance helps to validate the results and give confidence that the 

sampling methodology estimates technical potential of residential buildings per tract and 

building type with minimal error.

Figure B-1. Density function of coefficient of variance in the tract-level technical potential, for all 

tracts with LiDAR coverage24

 

24 This graph plots the distribution of the coefficient of variance of technical potential for each tract with LiDAR 

coverage. Each point in the plot represents the coefficient of variance for a single tract across the 100 runs. The data 

is graphed using a Kernel Density Estimation (KDE) plot, a non-parametric technique to estimate the probability 

density function and visualize the underlying distribution of a continuous variable (Rosenblatt 1956). Notably, 

because of smoothing used by the plotting library, the x-axis is not constrained to the minimum 0 bound.
42

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Table B-1 demonstrates the mean technical potential over the 100 Monte Carlo runs for each city 

with LiDAR coverage and the standard deviation. Notably, there is more uncertainty in smaller 

cities (e.g., Frankfort, KY) with fewer Census Tracts than the larger metropolitan cities (e.g.,

New York, NY). This result is intuitive because model error is the greatest at the tract level and 

reduced when tract-level estimates are aggregated over larger areas.

Table B-1. Assumptions for PV Performance Simulations Summary Statistics for City-Level 

Technical Potential25

LiDAR City LiDAR State Mean (GWh) STD (GWh) Coefficient of 

Variance

Albany NY 1.9 0.015 0.008

Albuquerque NM 3.2 0.022 0.007

Allentown PA 2.0 0.018 0.009

Amarillo TX 2.2 0.025 0.012

Anaheim CA 3.9 0.060 0.015

Atlanta GA 1.0 0.029 0.028

Augusta GA 2.7 0.029 0.011

Augusta ME 1.8 0.078 0.043

Austin TX 2.2 0.024 0.011

Bakersfield CA 3.0 0.014 0.005

Baltimore MD 1.4 0.009 0.007

Baton Rouge LA 3.7 0.040 0.011

Birmingham AL 1.6 0.029 0.017

Bismarck ND 2.3 0.036 0.016

Boise ID 3.3 0.025 0.008

Boulder CO 1.8 0.032 0.018

Bridgeport CT 1.8 0.023 0.013

Buffalo NY 2.0 0.013 0.007

Carson City NV 2.3 0.091 0.039

Charleston SC 1.5 0.024 0.016

Charleston WV 1.9 0.022 0.012

Charlotte NC 2.4 0.026 0.011

Cheyenne WY 2.3 0.040 0.018

Chicago IL 1.3 0.006 0.004

 

25 The LiDAR cities listed in Table B-1 do not necessarily align with the legal footprints of city or metropolitan area 

boundaries. Rather they refer to the focal city covered in the LiDAR raster extent. A single LiDAR city extent can 

span multiple cities and states (e.g. Washington D.C. covers much of Northern Virginia and southern Maryland 

cities in addition to the District of Columbia) or they can only span partial cities (e.g. Richmond, VA, is only 55% 

covered by its LiDAR raster extent). 
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LiDAR City LiDAR State Mean (GWh) STD (GWh) Coefficient of 

Variance

Cincinnati OH 1.5 0.011 0.008

Cleveland OH 1.2 0.008 0.006

Colorado Springs CO 2.5 0.014 0.006

Columbia SC 2.2 0.031 0.014

Columbus GA 2.7 0.031 0.011

Columbus OH 2.2 0.016 0.007

Concord NH 1.8 0.070 0.039

Corpus Christi TX 2.4 0.052 0.022

Dayton OH 2.2 0.019 0.009

Denver CO 2.0 0.010 0.005

Des Moines IA 2.2 0.031 0.014

Detroit MI 1.2 0.007 0.006

Dover DE 2.6 0.075 0.029

El Paso TX 3.4 0.028 0.008

Flint MI 2.1 0.019 0.009

Fort Wayne IN 1.6 0.027 0.017

Frankfort KY 3.0 0.083 0.028

Fresno CA 2.4 0.014 0.006

Fort Belvoir DC 1.7 0.044 0.026

Grand Rapids MI 3.8 0.051 0.013

Greensboro NC 2.4 0.025 0.011

Harrisburg PA 2.9 0.023 0.008

Hartford CT 1.6 0.014 0.009

Helena MT 2.2 0.045 0.020

Houston TX 3.0 0.017 0.006

Huntsville AL 3.9 0.026 0.007

Indianapolis IN 2.6 0.018 0.007

Jackson MS 1.9 0.024 0.013

Jacksonville FL 2.5 0.026 0.011

Jefferson City MO 4.2 0.106 0.025

Kansas City MO 1.7 0.012 0.007

Lancaster PA 2.9 0.038 0.013

Lansing MI 2.3 0.036 0.016

Las Vegas NV 1.8 0.011 0.006

Lexington KY 2.5 0.024 0.010
44

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LiDAR City LiDAR State Mean (GWh) STD (GWh) Coefficient of 

Variance

Lincoln NE 1.8 0.022 0.012

Little Rock AR 2.0 0.026 0.013

Los Angeles CA 2.3 0.007 0.003

Louisville KY 1.8 0.045 0.025

Lubbock TX 2.8 0.030 0.011

Madison WI 2.2 0.022 0.010

McAllen TX 5.6 0.038 0.007

Miami FL 3.1 0.009 0.003

Milwaukee WI 1.7 0.013 0.008

Minneapolis MN 1.7 0.011 0.007

Mission Viejo CA 3.5 0.024 0.007

Mobile AL 1.1 0.021 0.019

Modesto CA 3.0 0.037 0.013

Montgomery AL 3.0 0.037 0.012

Montpelier VT 1.8 0.058 0.032

New Haven CT 2.1 0.021 0.010

New Orleans LA 2.3 0.008 0.004

New York NY 0.7 0.003 0.004

Norfolk VA 2.0 0.010 0.005

Oklahoma City OK 2.7 0.014 0.005

Olympia WA 3.1 0.032 0.010

Omaha NE 2.1 0.013 0.006

Orlando FL 3.2 0.032 0.010

Oxnard CA 3.4 0.028 0.008

Palm Bay FL 5.9 0.029 0.005

Pensacola FL 3.4 0.037 0.011

Philadelphia PA 1.6 0.007 0.004

Pittsburgh PA 1.3 0.008 0.006

Portland OR 2.1 0.011 0.005

Poughkeepsie NY 2.0 0.016 0.008

Providence RI 1.6 0.014 0.009

Raleigh-Durham NC 1.9 0.019 0.010

Reno NV 2.0 0.027 0.013

Richmond VA 0.8 0.019 0.024

Rochester NY 1.4 0.019 0.013
45

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LiDAR City LiDAR State Mean (GWh) STD (GWh) Coefficient of 

Variance

Sacramento CA 2.2 0.013 0.006

Salem OR 3.1 0.029 0.009

Salt Lake City UT 2.1 0.018 0.009

San Antonio TX 3.4 0.014 0.004

San Bernardino–

Riverside

CA 2.9 0.016 0.006

San Diego CA 2.1 0.013 0.006

San Francisco CA 2.5 0.009 0.004

Santa Fe NM 3.2 0.043 0.013

Sarasota FL 7.3 0.039 0.005

Scranton PA 1.4 0.018 0.013

Seattle WA 1.5 0.012 0.008

Shreveport LA 2.6 0.028 0.011

Spokane WA 1.5 0.014 0.010

Springfield IL 2.1 0.023 0.011

Springfield MA 1.1 0.018 0.016

St Louis MO 2.4 0.011 0.005

Stockton CA 2.4 0.026 0.011

Syracuse NY 1.4 0.021 0.015

Tallahassee FL 2.2 0.032 0.015

Tampa FL 2.4 0.023 0.010

Toledo OH 1.0 0.014 0.014

Topeka KS 2.3 0.028 0.012

Trenton NJ 1.5 0.027 0.018

Tucson AZ 3.2 0.022 0.007

Tulsa OK 2.7 0.020 0.007

Washington DC 1.7 0.006 0.003

Wichita KS 1.9 0.020 0.011

Winston-Salem NC 2.2 0.030 0.014

Worcester MA 1.5 0.021 0.014

Youngstown OH 2.2 0.024 0.011
46

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Appendix C. Predictive Modeling Framework for 

Imputing Solar Suitability

This appendix describes the predictive modeling framework used in this analysis to impute solar 

suitability for the 63% of Census Tracts without LiDAR coverage. The overall framework builds 

off the original methodology developed by Gagnon et al. (2016) to impute solar suitability for 

small, medium, and large buildings in zip codes without LiDAR data. The approach here extends 

this work by increasing the spatial fidelity of the statistical model used for imputation (i.e., tracts 

versus zip codes) and adding an additional submodel to aid in predicting suitability across 

residential building types, per income, building type, and tenure. In total, there are four 

submodels used for the imputation.

1. Household-to-Building Model

2. Small Building Suitability Model

3. Rooftop Tilt and Azimuth Model

4. Rooftop Plane Area Model

The following sections explore each submodel in greater detail. 

C.1. Household-to-Building Model

The household-to-building model is a composite model that estimates the number and size of 

buildings (i.e., small, medium, large) for each tract outside of the LiDAR covered extents. The 

model is comprised of two submodels: (1) the household-to-building-count model, and (2) the 

building-type-to-building-size model. Both submodels rely on a distribution sampling approach 

for imputing values from simulated LiDAR estimates.

C.1.1. Household-to-Building-Count Ratios

The household-to-building-count submodel uses the ACS-derived tract-level crosstab of 

household counts to estimate number of buildings per building type for an imputed tract based on 

the number of households in the tract. Ratio distributions are created for multi-family buildings 

only. For single-family buildings, we assume a one-to-one ratio of number of households to 

number of buildings. This study does not account for vacant or seasonal housing. For multifamily buildings, we calculate the mean household-to-building-count ratio for 2–4-unit, 5–19 

unit, and 20+ unit multi-family buildings in LiDAR covered Census Tracts. These mean ratios 

(Table C-1) are used to impute tracts outside of the LiDAR coverage and translate household 

counts to building counts. 

Table C-1. National Mean Household-to-Building Ratio

Building Type Ratio

Multi-Family 2–4 Units 3.28

Multi-Family 5–19 Units 9.14

Multi-Family 20+ Units 80.0
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C.1.2. Building-Type-to-Building-Size Ratios

The building-type-to-building-size submodel translates the building counts from Table C-1 into 

counts by building size (e.g. small, medium, large). Categorizing building counts by size aligns 

our data set for national imputation using the methods developed by Gagnon et al. (2016). Like

that shown above, this model is performed on multi-family buildings only; for single-family 

detached, we assume all buildings are small. For multi-family buildings, we compute national 

means of the smallest size ratios for each multi-family building type and use these to sample 

sizes in Census Tracts without LiDAR data. For 2–4 unit and 5–19-unit multi-family buildings, 

we take the mean of the percent of the buildings that are small (compared to those that are 

medium sized), and for 20+ unit buildings, we take the mean of the percent that are medium 

(compared to those that are large sized). These national averages of building size ratios per 

building type are depicted in Table C-2. 

Table C-2. National Mean Building Type to Building Size Ratio for Census Tracts

Building Type Ratio Type Ratio

Multi-Family 2 to 4 Units Small to Medium 68%

Multi-Family 5 to 19 Units Small to Medium 60%

Multi-Family 20+ Units Medium to Large 73%

C.2. Small Building Suitability Model

Gagnon et al. (2016) found that medium and large buildings are almost unanimously suitable, 

because they are overwhelmingly flat with at least one 10m2 plane, and small buildings 

(<= 5,000 ft2) suitability was more variable. For these reasons, we apply a predictive modeling 

approach for estimating small buildings’ suitability. For medium and large buildings, we assume 

a percent suitability of 100%. The following describes the small building suitability model.

The small building suitability model is a multiple regression model for describing percent 

suitability of small buildings (Gagnon et al. 2016). It relies on data from the U.S. National Land 

Cover Database (NLCD) (MRLC 2011), the National Center for Education Statistics (NCES 

2015), and the U.S. Census Bureau (U.S. Census 2010). Predictive variables include the 

northing, locale, census division, and percent land cover for water, developed open space, 

developed high-intensity, and all forest-type land uses. The model specification is shown in (1).

Percent_Suitable = Locale*Census_Region + Northing + Water + Forest +

Developed_Open_Space + Developed_High_Intensity (1)

Though the model originally was applied to zip codes, it was designed to be applied using any 

geographic area. This analysis utilizes this model and applies it to the Census Tract. As such, we 

used tract-specific data (e.g., assigning the locale of a tract based on the tract centroid rather than 

the centroid of the larger zip code area) and updated data vintages for both the NLCD and the 

NCES.

The Analysis of variance (ANOVA) results for the predictive modeling are detailed in Table C3. We applied this model to 75% of the LiDAR-derived tracts and assessed the predictive 
48

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accuracy using the remaining 25% of the LiDAR data. When comparing the predicted values to 

the actual values, we find that 68% of the estimates are within 10% of the actual values and 90% 

of the predicted values are within 20% of actual values. We note that these numbers are 8% to 

10%greater (68% versus 60% and 90% versus 80%) than the original zip code assessment 

(Gagnon et al. 2016). The increase in predictability is likely a consequence of geospatiallyresolved tract-level assessment, the increased number of tract observations, and the higher 

resolutions of the newer vintage data.

Table C-3. ANOVA of Small Building Suitability OLS Model

Source DF SS MS F P-Value

Locale Description 3 0.29 0.10 8.87 0.0000

Census Division 8 23.93 2.99 272.41 0.0000

Developed—Open Space 1 5.38 5.38 489.62 0.0000

Developed—High Intensity 1 0.09 0.09 8.03 0.0046

Forest 1 0.67 0.67 60.96 0.0000

Water 1 0.07 0.07 6.50 0.0108

Northing 1 5.53 5.53 503.15 0.0000

Locale Description * Census Division 24 3.71 0.15 14.09 0.0000

Residuals 11,509 130.43 0.01 — —

C.3. Rooftop Plane Area Model

The third model in the series is a predictive model used to predict the number and size of 

developable planes on a building. This predictive model follows a statistical distribution fitting 

approach. The number of developable surfaces is defined as the mean of a fitted exponential 

distribution for each building size class (i.e., small, medium, large). The number of suitable 

planes was calculated using a random sample from a national distribution of building suitable 

plane counts, per building size class (Table C-4).
49

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Table C-4. National Distributions of Building Plane Count Probabilities by Building Size Class

Number of Planes Small Medium Large

1 50% 21% 14%

2 28% 14% 7%

3 14% 13% 6%

4 5% 12% 5%

5 2% 10% 5%

6 1% 8% 5%

7 — 6% 5%

8 — 5% 4%

9 — 3% 4%

10 — 2% 4%

11 — 2% 3%

12 — 2% 3%

13 — 2% 3%

14 — 2% 3%

15–16 — — 3%

17–20 — — 2%

21–32 — — 1%

Source: Gagnon et al. (2016).

Using a per building size class random sample, Gagnon et al. (2016) used the mean of the fitted 

probability density function to calculate the size of each developable plane. Because buildings 

with multiple planes were found to have a single plane larger than the remaining planes, the first 

plane in a building’s sequence of multiple planes are fitted differently than the subsequent 

planes. Additionally, in in their assessments Gagnon et al. (2016) found that buildings with only 

one plane generally have a larger single plane. Therefore, to account for this, they built

exponential fits for one-plane buildings separately from multi-plane buildings. These distribution 

fitting assessments were used here to inform the imputation of developable plane size. Table C-5 

details the fitted areas assigned to each plane given the plane’s sequential index and the 

building’s size class. Confidence intervals for each plane’s size were computed using the Gagnon 

et al. (2016) methodology for the 95th percentile range.
50

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Table C-5. Fitted Areas for Each ith Plane, per Building Size Class

Building Size i

th Plane Fitted Area

Small 1 33.81 m2

2–6 24.08 m2

Medium 1 800.00 m2

2-14 31.74 m2

Large 1 4,000.00 m2

2-32 181.81 m2

Source: Gagnon et al. (2016).

C.4. Rooftop Tilt and Azimuth Model

The rooftop tilt and azimuth model was developed by Gagnon et al. (2016) to predict the percent 

of rooftop planes with a particular tilt and azimuth class. This predictive model follows a 

statistical distribution fitting approach to predict the probability of plane having a particular tilt 

and azimuth given the buildings size and the number of planes on the building. For small 

buildings, per locale–type national average values are used from a lognormal fit of data in each 

of the azimuth-tilt classes. For medium and large buildings, we use national average values for 

each azimuth-tilt class. Table C-6 shows the national average tilt and azimuth distribution for 

building planes by building type used to sample a plane’s orientation. 
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Table C-6. National Average Tilt/Azimuth Distribution for Building Planes, by Building Size Class

Building 

Size Tilt

Azimuth

Flat E SE S SW W

Small 0 ̊ 0.26

15 ̊ 0.03 0.02 0.06 0.02 0.03

28 ̊ 0.09 0.06 0.18 0.06 0.09

41 ̊ 0.02 0.02 0.03 0.01 0.02

54 ̊

Medium 0 ̊ 0.74

15 ̊ 0.02 0.01 0.04 0.01 0.02

28 ̊ 0.03 0.02 0.05 0.02 0.03

41 ̊ 0.01

54 ̊

Large 0 ̊ 0.93

15 ̊ 0.01 0.01 0.01

28 ̊ 0.01 0.01 0.01

41 ̊

54 ̊

Source: Gagnon et al. (2016).
52

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Appendix D. Quantifying the Technical Potential and 

On-Site Consumption of LMI-Serving Buildings in 

Three Case Study Cities 

We investigated the possible role for third-party buildings that serve low-income populations to 

host solar on their roofs. The specific building classes investigated are: Public sites (e.g.,

libraries, police stations, local government), Public housing (i.e., federally assisted rental 

housing), K–12 public schools, Homeless shelters, and Places of Worship. These building classes 

were chosen based on a combination of nonprofit status, a priori expectation of magnitude of 

potential, and feasibility in collecting GIS data. These building classes are not intended to be 

comprehensive of all third-party buildings that could deploy solar, and future analysis could 

examine potential for multiple other building classes, including on for-profit commercial 

buildings. Because of data limitations, estimates for these building classes are restricted to the 

cities of Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C.

D.1. Quantifying the Technical Potential of Third-Party Buildings

To quantify the technical potential of LMI-serving buildings in the case study cities, we obtained 

GIS data for each building type and spatially tagged these layers to our LiDAR buildings data. 

This allowed us to identify the total technical potential for each building type in each city. 

Described in this appendix are the data sources and methods used to spatially join varied GIS 

layers of building locations to LiDAR building footprints.

The LMI-serving building data is an agglomerate data set sourced from a variety of locations, 

including both open and proprietary sources. Table D-1 lists all the data used to identify LMIserving building footprints and then estimate the technical potential for each building class in our 

case study cities. Because the data consists of varied data sets and formats, we inevitably used 

varied methods to tag each building geometry to one or more LiDAR building footprints. 

Discussed below are the case-by-case methods used for each building type.
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Table D-1. Core GIS Data Sources for LMI-serving Buildings in Case Study Cities

Building Type City Source

K–12 Public 

Schools

All The National Center for Education Statistics’ (NCES) Core Common 

Data (CCD), 2015–2016. 

Homeless 

Shelters

Chicago Chicago’s Department of Family and Support Services, Homeless 

Services Map. 

Downloaded from the Chicago Data Portal, 2017. 

Homeless 

Shelters

Washington, 

D.C.

DC Department of Human Services’, Homeless Shelter Locations.

Downloaded from Open Data DC, 2017.

Homeless 

Shelters

San 

Bernardino; 

Riverside

Google Maps, 2017.

Places of 

Worship

All Homeland Security Infrastructure Program (HSIP) Gold, 2013.

Public 

Housing

All The National Housing Preservation Database (NHPD), 2016 Active 

Properties.

Public Sites Washington, 

D.C.

2016 District Government Land (Owned, Operated, and or 

managed), from DC Government.

Downloaded from Open Data DC.

Public Sites Riverside Riverside County, 2017.

Public Sites Chicago Libraries: Chicago Public Library

Police Stations: City of Chicago

Fire Stations: City of Chicago

Senior Centers: City of Chicago; Family and Support Services

Community Centers: City of Chicago; Family and Support Services

Workforce Centers: City of Chicago; Family and Support Services

County Government Administrative Facilities: Cook County 

Department of Facilities Management

All data downloaded from the Chicago Data Portal, 2017 except for 

the county administration data, which was downloaded from Cook 

County Government Open Data, 2017.
54

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K–12 Public Schools

Two data sets were used to geotag public schools with LiDAR building footprints: (1) the 

geocoded addresses from the National Center for Education Statistics’ Common Core of Data 

(CCD), K12 Pubic data set (NCES CCD 2015-2016), and (2) campus boundaries from 

OpenStreetMap (OSM).

26

Because each point in the CCD data set represents a single school’s address location, we used the 

OSM school boundaries to help tag a single point to the set of buildings within the school 

campus. In the cases where an OSM boundary did not exist, we tagged the CCD school point to 

the nearest LiDAR building, using a maximum search radius of 500 meters. We ensured that no 

double-counting was occurring in the cases where multiple schools share the same building(s). 

Finally, technical potential was calculated from the aggregate of all school buildings, per city. 

Homeless Shelters

Data for homeless shelters is an agglomerate data set. Where possible, we collected from 

individual city GIS repositories (i.e., Chicago and Washington, D.C.). For Chicago, we had to 

filter the Homeless Services data layer to include only homeless shelters. Where these 

authoritative government data sets did not exist (i.e., San Bernardino and Riverside), we 

collected coordinate data from Google Maps searches of homeless shelters in these cities. The 

resulting data set is an agglomeration point layer of homeless-shelter locations. We then tagged 

these homeless-shelter points to LiDAR buildings based on a nearest neighbor join, applying a 

maximum search radius of 500 meters. Finally, technical potential was calculated from the 

aggregate of homeless shelter buildings, per city. 

Places of Worship

The places of worship layer was obtained from a single proprietary data source, the Homeland 

Security Infrastructure Program (HSIP) Gold (HSIP Gold 2013). Using this layer, we tagged the 

point locations to the LiDAR buildings polygons using a nearest neighbor method, applying a 

maximum search radius of 500 meters. We ensured that no buildings were being double-counted 

in the cases where more than one place of worship occupied the same building. Finally, technical 

potential was calculated from the aggregate of places of worship buildings, per city. 

Public Housing

The public housing layer was obtained from a single data source, the 2016 Active Properties 

from the National Housing Preservation Database (NHPD 2016). Because the NHPD data set 

represents a single point location for multiple buildings that could span multiple parcels, we used 

the ownership attribution and parcel data to identify all the buildings belonging to a single 

NHPD property point. Parcel data was collected from individual government authorities (see

Table D-2) and partial fuzzy join methods27 were used to join parcel ownership attributes to 

NHPD property-owner attributes and identify all the parcels associated with each NHPD 

 

26 School boundaries were identified from a 2017 OSM United States extract, where “school” was tagged in the

building and amenity keys. 27 We set the partial fuzzy match cutoff to 80%. This was determined to be a conservative cutoff resulting in only a 

handful of false negatives and very few false positives.
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property. We then tagged these parcels to the LiDAR building data to identify all buildings

associated with a NHPD property. Care was taken to ensure that no two properties overlapped,

and that double-counting was not occurring. The final technical potential was calculated from the 

aggregate of all public housing buildings, per city.

Table D-2. Parcel Data Used for Appropriating Buildings to Public Housing Properties 

City Source

Chicago Parcel 2015, Cook County Clerk Map Department. Downloaded from Cook 

County Government Open Data.

Riverside Parcels layer from the Assessor’s County GIS geodatabase. Downloaded from 

Riverside County Open Data, 2017.

San Bernardino County of San Bernardino’s Geographic Information Services, Parcel Basemap. 

Retrieved from FTP Services, 2017.

Washington, D.C. Common Ownership Lots, from the District's Vector Property Mapping Database. 

Downloaded from Open DC Data, 2017.

Importantly, Chicago’s parcel data layer did not obtain ownership attribution. As a result, we 

were unable to tag NHPD points to all parcels associated with the property. Instead, we simply 

tagged the NHPD property points to the single intersecting parcel. Therefore, we are likely 

underestimating the total technical potential of public housing units in Chicago.

Public Sites

The public sites layer also is an agglomerate layer collected from a variety of sources. The layer 

consists of public site locations for Chicago, Riverside, and Washington, D.C. For both 

Washington D.C. and Riverside, we obtained the public sites data directly from the city or 

county data repositories. For Chicago, we collected the data in a piecemeal fashion from the city 

and county open data repositories. Filters were applied on these layers to include only sites 

pertaining to government administration buildings, rec centers, senior centers, community 

centers, workforce centers, libraries, police stations, and fire stations. For each public site, a 

LiDAR building were tagged based on a nearest neighbor algorithm, applying a maximum search 

radius of 500 meters. Care was taken to ensure no double counting of buildings in the cases 

where two public sites occupy the same building. Finally, technical potential was calculated 

based on the aggregate of the tagged buildings, per case study city.

We note two important qualifications regarding the public sites estimates. First, we were unable 

to identify public sites technical potential for San Bernardino, California, due to data limitations. 

Despite numerous requests, we were unable to obtain a public sites layer from the San 

Bernardino County. Second, this method assumes one building per site, and therefore, estimates 

of technical potential could be underestimated, especially for government administrative 

buildings which likely have multiple buildings per site.

D.2. Estimating the On-Site Consumption of Third-Party Buildings

To estimate the on-site consumption of LMI-serving third-party buildings in our case study 

cities, we used representative building loads from the 2009 Residential Energy Consumption 

Survey (RECS) or the 2012 Commercial Building Energy Consumption Survey (CBECS) (EIA 
56

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2013; EIA 2016a). Each building type was tagged to the most representative building in RECS or 

CBECS and was given the building consumption (kWh/year/ft2) of that building type per Census 

Division. For each representative building, we calculated the average annual building 

consumption per roof area (kWh/year/ft2), where roof areas were estimated from the number of 

floors and the total building area. Table D-3 describes the specific criteria used to tag each 

building type with its most appropriate building representative.

Table D-3. Representative Building Types Used for Third-Party Building Energy Consumption 

Estimates

Building Type Energy Consumption Survey Query Applied

K–12 Public Schools CBECS 2012 Filter for education buildings identified as 

elementary, middle, or high schools.

Homeless Shelters CBECS 2012 Filter for buildings with primary building 

activity of dormitory.

Places of Worship CBECS 2012 Filter for religious worship buildings 

identified as places of worship. 

Public Housing RECS 2009 Filtered for buildings under the Public 

Housing Authority.

Public Sites CBECS 2012 Filtered for state or local governmentowned buildings where the primary 

building activity is one of the following.

Administrative/professional office

Government office

Fire station/police station

Other public order and safety

Library

Recreation

Social/meeting

Other public assembly

Courthouse/probation office