DOI: 10.25349/d9xk7f
title: Constructing a Model to Identify Markets for Rooftop Solar on Multifamily Housing

description: As the renewable energy transition accelerates, housing, due to its high
 energy demand, can play a critical role in the clean energy shift.
 Specifically, multifamily housing provides a unique opportunity for solar
 photovoltaic (PV) system adoption, given the existing competing interests
 between landlords and tenants which has historically slowed this
 transition. To address this transition gap, this project identified and
 ranked Metropolitan Statistical Areas (MSAs) in the United States for ZNE
 Capital (the client) to acquire multifamily housing to install solar PV
 systems. The group identified seven criteria to determine favorable
 markets for rooftop solar PV on multifamily housing: landlord policy
 favorability, real estate market potential, CO2 abatement potential,
 electricity generation potential, solar installation internal rate of
 return, climate risk avoidance, and health costs associated with primary
 air pollutants. A total investment favorability score is calculated based
 on criteria importance assigned by the user.  Investment
 favorability scores were investigated for different preferences to
 demonstrate the robustness and generalizability of the framework. The data
 analysis and criteria calculations were conducted using RStudio,
 ultimately to provide reproducible code to be used for future projects.
 The results are presented in a ranked list from best to worst metro areas
 to invest in. Future studies can utilize the reproducible code to inform
 decisions on where to invest in solar PV on multifamily housing anywhere
 in the United States by changing weights within the model depending on
 preferences.
description: Collecting real estate and landlord data for
 metropolitan statistical areas (MSAs) from federal agency databases.
 
 Real estate
 metrics: Six indicator metrics were selected to represent
 areas with growing housing demands. The metrics included were population
 growth, employment growth, average annual occupancy, annual rent change,
 the ratios of median annual rent to median income, and median income to
 median home price. The population estimates and median income data was
 downloaded from the Census Bureau. Median rent data was downloaded from
 HUDuser. Median home price data was downloaded from National Association
 of REALTORS®. Students were provided temporary memberships to Yardi
 Systems Matrix to obtain multifamily occupancy rates, and this data will
 not be redistributed. All the real estate metrics were combined into a
 single dataset using CBSA codes, which each MSA has a unique 5-digit
 identifier. Income-to-home price and rent-to-income ratios were calculated
 in R Studio. Landlord
 data: the minimum security deposit and eviction notice data
 was collected for each state and manually compiled into an Excel. Security
 deposit information was provided as the number of months of rent. States
 with no maximum deposit limit received a score of 1.0, meaning it was the
 most favorable. Two month's rent was scored as 0.5, and one
 month's rent was given a score of 0. 
 Using NREL's REopt web tool
 to 1) model solar PV system on multifamily buildings in various cities and
 2) obtain data to represent energy generation, CO2 abatement potential,
 avoided health costs from emissions, and solar project financial criteria.
 An anchor
 city was identified within each MSA as the city with the highest
 population to input into the REopt tool. Default inputs were changed based
 on information provided by industry experts and changes in federal funding
 programs. Detailed instructions of inputs were created to ensure
 consistency when running the model for each city. The
 four outputs collected from the tool include: annual energy generation
 from renewables (%), lifecycle total CO2 emissions, health costs
 associated with primary air pollutants, and internal rate of return(%).
 The group divided up a list of cities, input the respective data for each
 one, obtained the outputs, then compiled it into a Google
 sheet.  Outputs were checked by other members to
 ensure accuracy. 
 Collecting climate risk data from FEMA's National Risk Index
 Map. 
 Climate risk data was downloaded as a CSV file. The risk score
 was used to represent impacts of climate variability on long-term real
 estate investments. Risk scores were provided at the county level. The
 group identified the county each city resided in, to associate the correct
 score to each city in R Studio 
 Normalizing the data Metrics were
 normalized by subtracting the minimum value for the metric from each value
 and dividing by the difference between the maximum and minimum values.
 This resulted in scores between 0 and 1 that were relative to the MSAs
 included in the analysis. 
 Weighing the data Real Estate
 and Landlord Criteria metrics: these two criteria contained more than one
 metric, so the metrics within these criteria were weighted to produce real
 estate and landlord scores. Weights for each criterion sum to 1, in which
 higher weights indicate greater importance for multifamily real estate
 investments. Each weight was multiplied by the respective metric, then all
 weighted metrics within each criterion were summed to produce the criteria
 score. Investment Favorability Score: seven criteria
 were multiplied by respective weights based on the stakeholder's
 preferences. Weights sum to 1 to ensure consistency throughout the
 project. The sum of the seven weighted criteria is the investment
 favorability score. 
description: RStudio was used to calculate all metrics, criteria scores, and
 the investment favorability score. GitHub Repository:
 https://github.com/gbianch/ZNE_Capital_GP The National Renewable Energy Laboratory (NREL) created a web tool, REopt, to evaluate the economic viability of distributed PV systems in an area given the type of building, utility costs, and net metering policies. In order to replicate this model, future studies will have manually input data for each MSA into REopt. Instructions for the exact inputs are provided in a pdf to follow the same methodology.