Focusing the tiebreaker on credit efficiency will incentivize developers to build projects in the most cost effective locations, which will greatly disadvantage certain localities with dense populations, high market rents, and high median incomes.
If no adjustment is made locations with lower population densities will be advantaged because they won’t need structured parking, cities with lower market rate rents will be advantaged having lower land costs; and counties with lower median incomes will be advantaged due to lower construction costs.
It costs more to develop infill sites because structured parking is needed, more intense construction methods are needed, and tight construction sites are complicated to manage. The closest set of publicly available data I could find to the concept of “infill” was population density in a 1-mile radius. Statistically, population density had a moderate correlation with cost per unit (31%).
It costs more to develop in areas with higher fair market rents because land values are higher and local requirements are more stringent. The closest set of publicly available data I could find to market rents was Small Area Fair Market Rents for a 2-br unit (SAFMRs). Statistically, SAFMRs had a moderate correlation with cost per unit (52%).
It costs more to develop in counties with higher median incomes because construction labor costs are higher. Statistically, county median income had a moderate correlation with cost per unit (48%). However, because median income plays a large part in the amount of supportable conventional debt, the cost coefficient for median income should be discounted by its debt related benefits (if not there would be an incentive to find sites in counties with higher median incomes.
Used together in a multiple linear regression the combined unique effects of each are able to account for 45% of all variances in cost per unit from project to project. See the page Statistical Analysis for more details.
An adjustment module in the denominator would be appropriate to accurately neutralize the cost differences based on location based characteristics.
If a project’s location characteristics are less cumbersome than the state average, its denominator should be increased by the costs it saves to make it comparable to the average. There should likely be a floor or a diminishing effect so projects in extremely convenient locations aren't prohibitively disadvantaged.
If a project’s location characteristics are more cumbersome than the state average, its denominator should be decreased by the costs it bears to make it comparable to the average. There should likely be a ceiling or a diminishing effect so projects in extremely populated locations aren’t guaranteed winners.
Pros: An adjuster for location based cost differences is absolutely necessary for fairness amongst localities.
Cons: State averages will need to be updated once in a while.
Include an adjustment module to the denominator to offset the overall effect population density, Small Area Fair Market Rents, and Area Median Incomes have on costs per unit. The adjustment module should have a ceiling and floor or a diminishing effect at the extremes.