= Construction and development data include the costs of land, the costs to develop the
structures, and the basic assumptions of types of units, size of units, and unit amenities.
«= Revenue and expenditure data includes prevailing rent rates (both market rate and income
controlled), prevailing sales prices, and operation costs for rental housing. Operation cost data
points include direct operations (i.e., maintenance, marketing) and indirect costs (ie., real
estate taxes).
« Financial and investment data include prevailing lending rates, debt/equity requirements,
capitalization rates, and discount rates.
RKG used several tools to gather this information, with a preference to gather locally relevant
information specific to the City of Nashua. In areas where local data was not available or not
appropriate, RKG relied on regional data (i.e., Nashua Metro). The primary data collection method
was capturing primary and secondary data about the Nashua housing market. RKG gathered current
rent rates (per month) and sales prices (by unit type) for owner and renter housing within the city to
determine potential revenues. RKG gathered sales data from the city to understand current pricing.
RKG also interviewed several for-profit and non-profit residential developers, and commercial
lending bank professionals to garner greater understanding of the local marketplace. Finally, RKG
used nationally recognized secondary data sources, such as Marshall & Swift Valuation Services, to
verify data provided by the local real estate community. The results of this effort were used to create
the baseline market assumptions for the financial feasibility model.
The following section provides details on the results of the data collection and provides the underlying
performance metrics used to test the financial impacts of inclusionary zoning on specific development
examples.
Components of the Model
As mentioned, the model functions on a traditional proforma analysis platform, measuring the
potential revenue of a real estate investment and comparing it to the costs and expenditures to
construct, operate, and sell the asset. The modeling efforts compared the financial performance of
eight distinct residential development scenarios without inclusionary zoning against the financial
performance of those same scenarios under inclusionary zoning. The eight development scenarios
reflect various small, medium, and large-scale ownership and rental development projects that may
occur within Nashua. The results were compared to understand the impact of inclusionary zoning on
the financial feasibility of each scenario. Table 1 identifies each of the scenarios modeled.
