The District Department of Transportation (DDOT) and the Office of Planning (OP) led a research effort starting in February 2014 to understand how parking utilization in multi-family residential buildings is related to neighborhood and building characteristics. The primary goal of the research was to provide a tool to estimate parking utilization on a dynamic website to support and guide parking supply decisions. The intent is that a transparent, data driven process for parking supply decisions may help relieve problems associated with over- or under-supply of parking.

The tool relies on local information reflecting residential development and auto ownership patterns in the District. Supported in part by a grant from the Metropolitan Washington Council of Governments, DDOT assembled information about multi-family residential parking use at over 115 buildings covering nearly 18,000 dwelling units in the District during the winter and spring of 2014 and 2015.

Parking utilization was recorded on typical weekdays between midnight and 5 a.m. in all residential spaces identified by property managers in each multi-family development studied. Interviews with property managers focused on building characteristics, enabling development of a model that links building characteristics (such as rental price, parking cost, unit size), and neighborhood characteristics (access to transit, density, pedestrian friendliness) to help better match parking demand and supply.

What We Learned

The entire research process, including coordination with the development community, provided valuable insight to DDOT and OP on factors driving parking supply decisions.

  • On average, in the over 115 developments researched, only 60% of parking stalls are being used.
  • Parking supply was found to be the variable that correlates most with parking utilization accounting with 66% of the variation in observed parking utilization. Other building variables were found to be statistically significant as well, including parking price, average rent, and unit size.
  • The most significant neighborhood variable was a combination of walkability (measured by block size) and frequency of transit service within walking distance. As walkability and transit frequency increased, parking utilization decreased.
  • The model achieved an R-square of 0.82 – indicating that the variables used in the model on average predict about 82 percent of the variance in parking utilization (leaving 18 percent unexplained). This is a very strong model given the complexity of the relationship being researched.