PORTFOLIO MANAGER
In a Nutshell: This tool benchmarks a subject building’s actual energy use against similar buildings, and provides a score on a 1-100 scale (cost, carbon, energy). Because energy bills are used, the influences of building construction as well as occupancy and operations are reflected in the results. Buildings evaluated and certified by Professional Engineers or Registered Architects and score 75 or higher are eligible for Energy Star designation. The tool is applicable anywhere in the U.S. Targets can be specified, and progress towards them tracked. The authorizing professional also warrants that the building meet three important criteria for indoor environmental quality: meets ASHRAE's Standard 62 for ventilation, acceptable thermal comfort conditions per ASHRAE Standard 55, and Adequate Illumination levels and quality per the Illuminating Enegineering Society of North America guidelines.
Access: https://portfoliomanager.energystar.gov/
Inputs: Simple physical and operational characteristics; raw monthly utility bills. Mapping to one of about 80 building types, of which 19 can receive Energy Star ratings.
Outputs: Weather normalized energy use by month; whole-building EUIs (totals and by fuels; site and source); carbon emissions; energy expenditures; charts and tables; input data summary.
How and who to use it: Equipped with the subject property’s utility bills and a handful of other pieces of information about the property, appraisers can “run” this tool themselves. If the building is represented as being energy efficient, the property owner should be encouraged to have the building run through the tool. The tool is web-based.
Where to get previously completed analyses: Ask the building operator or in-house energy manager (if one exists) if the property has already been run through PM. An increasing number of localities are implementing mandatory building benchmarking and bill disclosure laws and ordinances, which provide another avenue for recovering data. Most of these have standardized on Portfolio Manager as the analysis tool. The EPA database is searchable on the EPA website. The GBIG database also catalogs Energy Star buildings.
Data quality and usage considerations: The underlying methodology is developed by the U.S. Environmental Protection Agency and National Laboratories, and for most building types uses the best-available statistically derived survey (CBECS) of the US commercial building stock to constitute peer groups for comparison. Data sources for other building types: Hospitals = Survey from American Society for Healthcare Engineering; Senior Care = Survey from a group of senior care associations; Multifamily = survey from Fannie Mae. Portfolio manager offers a "Data Quality Checker" feature, which identifies irregularities in the information and provides the user with links to help improve the quality of the information provided. Start by selecting a property in your account and then tell us which time period you’d like to inspect.
The tool is well documented and non-proprietary. Utility billing data entered into the tool is generally well metered and subject only to key-entry error. Data entry errors can be eliminated when automated approaches such as Green Button or Portfolio Manager's automated benchmarking services are used to obtain the raw utility data. If an authorized professional performs the analysis (a requirement in order to obtain an official Energy Star rating), they must attest to the accuracy of all inputs (see "Energy Star Data Verification Checklist". To help ensure program quality, EPA performs "Spot Audits" on buildings awarded Energy Star ratings. The quality of recommendations depends highly on the completeness of user inputs and extent of reliance on defaults. The appraiser may wish to request re-analysis if there is heavy reliance on defaults.
Aside from the resultant rating itself, valuation professionals may find utility in the rigor of the energy use and savings estimates furnished by the tool. For example, an engineer-stamped Portfolio Manager study provides a sound basis for
deviating from traditional "default assumptions" for utility cost in a cashflow analysis.
BUILDING ENERGY ASSET SCORE
In a Nutshell: The Asset Score tool predicts energy use of an individual building based on engineering principals, building data, and weather data for the subject property’s location. Actual energy use data are not used. Seventeen building types can be modeled, as well as combinations of individual types. The tool provides recommended energy upgrades with estimates of the energy savings that would be achieved. The tool produces a score relating the buildings energy use compared to those of similar buildings. In contrast to PM and BPD, these estimates assume highly standardized occupancy and operational influences to help isolate the energy implications of the physical asset (insulation levels, types of equipment, etc.). This is similar to standardized fuel-economy (miles per gallon) ratings for cars. Examples of standardization include thermostat settings and schedules, hours of occupancy, plug loads, and ventilation rates, each of which vary by building type. While only the standardized assumptions can be used to calculate the building's score, these assumptions can be over-ridden to obtain a more customized set of recommendations.The tool is applicable anywhere in the U.S.
There are three progressively rigorous levels of data input. While the "Lite" level contains information that could be largely collected by an appraiser, most key variables (e.g., equipment efficiencies and insulation levels) are based on automated default values that may or may not be applicable to the subject property. As a result, much less descriptive info is available to the appraiser than when using the "Full" version, and the energy analysis is less customized. Thus, the developers of the tool do not recommend the Lite level be used for appraisal purposes (Wang et al., 2013).
Access: https://buildingenergyscore.energy.gov/
Inputs: An array of physical features of subject property.
Outputs: Summary of major assumptions. Whole-building EUI (site kBTU/kWh). Score (1-10 scale, in half-point increments, with 10 being the most efficient). Charts showing EUI by fuel and major end use (interior lighting, heating, cooling, and hot water; current building and with upgrades. The tool also provides system evaluations for the building envelope (roof, walls, windows, floor), lighting, HVAC, and service hot water systems. Each system is ranked as “Fair”, “Good”, or “Superior” based on results of energy modeling described. Upgrade recommendations and potentially improved score. Qualitative indication of energy savings and cost to achieve those savings. These recommendations are not intended to replace a detailed engineering analysis or to determine decisions to purchase specific equipment or materials.
How and who to use it: The Asset Score is a sophisticated simulation model, intended for technicians familiar with energy analysis. Unless skilled in this area, appraisers should look to other experts to run the tool for them. If the building is represented as being energy efficient, the property owner should be encouraged to have the building run through the tool. The tool is web-based.
Where to get previously completed analyses: Ask the building operator or in-house energy manager (if one exists) if the property has already been run through the Asset Score.
Data quality and usage considerations: The Asset Score is based on state-of-the-art building-energy simulation models developed by the U.S. Department of Energy and National Laboratories. The tool is well documented and non-proprietary. The relevance of results will depend in part on how much information about the subject building is entered into the tool. Only some input are required. Excessive reliance on defaults will result in results that less accurately represent the subject building. For example, the efficiencies of heating and cooling equipment are not required inputs and thus, any baseline energy estimates or upgrade recommendations will be driven by default values stipulated in the absence of actual input data. No economic analysis is provided in the reports, and because commodity values (e.g., annual kilowatt-hours of electricity consumed) are not provided in the reports a conversion to bills cannot be made. The tool developers offer an "Input Priority Map" to help users know which inputs are most important, depending on building type and location. The quality of recommendations depends highly on the completeness of user inputs and extent of reliance on defaults. The appraiser may wish to request re-analysis if there is heavy reliance on defaults. The appraiser should keep in mind that the analysis embeds strict standardized assumptions about occupancy and operational factors. By comparing this result to an analysis where these assumptions are tuned to reflect the subject property, it may be possible to determine whether the operations and/or the physical features are energy-efficient. For example, if the building looks "better" through the Asset Score assessment (lower EUI) than through Portfolio Manager (higher EUI), inefficient operations are indicated.
BUILDING PERFORMANCE DATABASE
In a Nutshell: BPD allows the user to see the distribution of various energy use metrics for a peer group. Various filters can be adjusted to explore how sensitive the results are to factors such as geographical area included, operating hours, inclusion/exclusion of property subtypes (e.g. motels versus hotels). As an inference-based proxy for energy savings potential, two different sets of peer-group buildings can be compared to find the anticipated difference in energy use when one variable differs (e.g., type of heating/cooling system, lighting choices, etc.). The tool is applicable anywhere in the U.S.
Access: http://bpd.lbl.gov
Inputs: Optional selection of a wide range of filters (building type [83 choices], climate, equipment, operation, etc.). User can manually compare their own energy use to that of the peer group, but energy use for a specific building is not a formal tool input. Portfolio Manager can be used to obtain the energy use intensity (EUI) for this purpose. If Portfolio Manager analysis is not available, the estimation of EUI can be done manually using the same method.
Outputs: Charts and tables showing distribution of buildings (utility billing data) matching the filter criteria and metrics chosen by user. Metrics include whole-building EUIs. Median, inter-quartile values, and standard deviations are shown for any distribution generated by the user. The probabilities of different levels of energy savings can also be explored. Output metrics are strictly in energy terms, and do not include costs.
How and who to use it: The BPD is readily usable by appraisers with minimal training. If the building is represented as being energy efficient, the property owner should be encouraged to have the building run through the tool. No data entry is required; only the setting of filters in order to isolate a peer group. The tool is web-based.
Where to get previously completed analyses: Ask the building operator or in-house energy manager (if one exists) if BPD has already been “run” using a relevant peer group.
Data quality and usage considerations: The underlying methodology is developed by the U.S. Department of Energy and National Laboratories and represents the largest sampling of buildings from across the US. Centralized efforts are made to vet and "clean" input building data that are added to the database. Note that the underlying samples are not statistically representative nationally or regionally, but provided the best-available fine-grain data for localities. The tool is well documented and non-proprietary. Selection of peer group is critical to achieving a representative comparison. Regions with sparse data or particularly strict filtering selections will result in small comparison cohorts. EUIs can be generated for fuel and electricity can be converted manually (outside the tool) to cost units if the appraiser is prepared to stipulate an energy price. Electricity and fuel costs thus maintained cannot be simply combined to get a total value, as sample sizes will differ (all buildings meeting a certain filter combination will use electricity, but only a subset will use fuel).
A potential shortcoming of this tool for appraisers is that the data cannot be filtered by its vintage. In other words, data could reflect conditions across a wide timeframe, during which market conditions have been changing.
References
U.S. Department of Energy, Commercial Buildings End Use Survey. http://www.eia.gov/consumption/reports.cfm#/T88
Wang, N., S. Goel, and A Makhmalbaf. 2013. “Commercial Building Energy Asset Score: Program Overview and Technical Protocol (version 1.1)” PNNL-22045 Rev. 1.1 180pp