RSMEANS METHODOLOGY

To conduct the analysis RSMeans applied its proprietary data and estimating expertise to estimate the labor hours required in a typical week to maintain the portfolio. Drawing upon the standards for auditing and performing interior and exterior maintenance duties in RSMeans Facilities Repair and Cost Maintenance Data, RSMeans consultants applied regression analysis to reliably make projections to a larger universe based on a limited number of cases. On site auditing of properties within the portfolio served to assess the general condition of the buildings as well as to define building characteristics to be used in the analysis. In subsequent site visits, actual building measurements were taken. The following building characteristics were determined to influence activities involved with maintaining building areas: Exterior Area Building size in square feet Lot size in square feet Area differential in square feet Trash Area Sidewalk Area Distance from trash area to curb Distance for recyclable area to curb Number of trashcans Courtyard(s) in square feet Entrance dimensions that include steps to entry, landing size, front door, sidelights transom, foyer door Interior Area Foyer Area in square feet Main Hall area in square feet Stair areas in square feet Elevator cab 2nd floor to top floor areas in square feet Hall areas in square feet and stair areas in square feet Lighting areas including entrance, halls, basement, storage and stair areas/landings Dimensions of boiler room Dimensions of basement RSMeans applied regression analysis to plot building size vs. estimated minutes/unit/week for prescribed maintenance activities for the interior and exterior of the building. This data provided a "curve of best fit" and the labor requirements for each building size to predict cleaning times for up to 83 units.

CLIENT IMPACT AND RESULTS

For the inventory of 1-2 apartment unit buildings, the data reported how many minutes/per unit/per week for the non-extrapolated weighted average custodial time. Extrapolation was then used for the entire inventory lowering the weighted average by less than half a minute. For the portfolio of 4+ apartment unit buildings, the extrapolated and nonextrapoloated weighted average was also determined for custodial time in 4-83 units. The data illustrated that the greater the number of units in a building the less maintenance time is spent on each unit. This is attributable to the fact that there is less common space per unit as the size of the building increases. There were several conclusions drawn from the study: There was a correlation of the data between the curve of best fit and the non-extrapolated data that indicated insignificant outliers in the data. The correlation suggests that the data fell within an acceptance deviation from the norm The data also indicated that the time and labor hours calculated for each size building represented a normative average for that size building The weighted average for the entire portfolio is appropriate to the range within the 40+ minutes/week averages that were determined by the regression analysis