Chicagoland Single-Family Housing Characterization

In this project, the PARR team identifies housing characteristics and energy use for fifteen single-family housing types (groups) in the Chicagoland region and specifies measure packages that provide an optimum level of energy savings based on a BEopt analysis. In its research, the team used property assessor data and actual energy consumption of 432,605 houses representing approximately 30% of the single-family home population. The optimum package of energy efficiency measures developed is based on a target of cost effectiveness at the measure level and 30% source energy savings. Based on the BEopt result, the project identifies the three housing groups that provide the maximum energy savings potential as defined by annual source energy savings multiplied by the total number of houses in the sample population. The three groups based on construction characteristics (structural, vintage and size) and identified as having the greatest potential as a result of this analysis are:

1. Wood frame, pre-1942 construction, 1 to 1.5 stories
2. Brick (double brick), pre-1942 construction, 1 to 1.5 stories
3. Wood frame, 1942-1978 construction, 1 to 1.5 stories.

These three housing groups will be used in the next step in the project to evaluate the measure packages. The PARR team will work with installing contractors in the field to document the upgrades that have been done to the houses since they were built, determine the cost of performing the proposed set of upgrades, and identify techniques that can be used for low-cost implementation on a community scale.

Homeowners and efficiency program implementers require meaningful peer groups and benchmark comparisons in order to make informed upgrade decisions, and to use realistic savings estimates based on performance of a large population of similar homes of comparable size, vintage, and construction.

The segmentation methodology presented in this report is intended to be replicable in other parts of the country. Each region has varying yet distinct home characteristics (construction type, vintage and size), weather and energy regulatory environments that influence how much energy a home consumes, and upgrades that can offer the greatest energy savings. Coupling housing segmentation with measured energy data analysis for a large population of homes reduces the effect of outliers, and can significantly reduce program costs by eliminating the need for modeling each individual similarly constructed home. This replicable approach can dramatically impact how broad-scale retrofit implementation programs are developed, implemented, and brought to scale.