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Comparable Market Rent and Setting Appropriate Rent for Multifamily Development

Part 1 - Determining an Appropriate Market Area

Part 2 of 4

The 100% Database

The next step is a field survey of all conventional apartment communities within the Effective Market Area (EMA), not just selected comparables. Our research shows that the largest single component of support for an apartment project is tenants already residing in other conventional apartments within the EMA. Typically, an apartment project can expect 45% to 50% of its tenants from other conventional apartments within the EMA. Add support from within the EMA from new household formation, home ownership, or other rental properties, and the total EMA support increases to 60% to 70%, depending on the demographics of the EMA.

The EMA is supportive, rather than competitive. A competitive EMA, including only projects with a similar price or amenity level to the proposed project, only examines a portion of the market at one pricing level.

The 100% Database allows us to measure support and the depth of the market at all levels. Our field surveys identify all conventional apartment developments within the EMA. Each project is surveyed to determine not only rents and vacancies, but also amenity level and curbside appeal. The 100% Database allows analysis of the existing market conditions experienced by those tenants most likely to move into the proposed project, instead of only comparable properties.

The Regression Analysis

Once the 100% field survey is complete, it is not enough simply to determine medians for each unit type and call this median “comparable market rent.” Median rent may not reflect rent movement at existing properties. For an article for Apartment Resources several years ago, we took an existing metropolitan market area field survey of two-bedroom rents and added 500 upper-end units to the base without changing the rents of other existing units. The median rent increased $23 despite the fact that no existing units increased their rents. Adding only 300 units increased the median rent by $18. Median rent can be disproportionately influenced by additions to the high or low end of the market, and therefore does not reflect what tenants are actually paying for units of a moderate amenity level. Therefore, we use regression analysis instead of medians, because of its ability to determine market rent at any amenity level.

Each 100% Database field survey applies a point value for each project’s unit amenities, project amenities, and curbside appeal. These three factors are totaled to determine each project’s Comparability Rating. Next, all rents are converted to net rents Net rent assumes that water, sewer and trash removal services are paid by the owner and that additional utilities (heat, cable television, etc.) are paid by the renter. Making these adjustments allow us to compare all rents on the same scale.

We then create a scatter graph by plotting net rent by Comparability Rating for each project. Next, a regression line (weighted by the total number of units in each project) is established showing average comparable market rent (net) for a project at any Comparability Rating level. We then establish a comparable market rent from this regression line. Projects below the regression line represent a value in the market at their Comparability Rating.

Regression analysis allows us to determine market rent at any amenity level. This is particularly critical because each market sets its own standards based on what renters will pay or what landlords have historically charged. Not every market will reward the same apartment development with the same rents. Regression analysis allows us to identify these differences in standards before development takes place.

Part 3 - The Regression Analysis in Action

 

   

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