On their own, rents tend to rise more quickly in communities with the most restrictive local land-use regulations. Add rapid population growth to the mix in those same communities and the problem only gets worse.
On their own, rents tend to rise more quickly in communities with the most restrictive local land-use regulations. Add rapid population growth to the mix in those same communities and the problem only gets worse.
Dozens of cities and counties nationwide are struggling to manage growth and keep their local rental markets stable, including places as diverse as tech centers like the San Francisco Bay Area or Seattle and (until recently) oil boom towns in the rural Shale Belt stretching from the Dakotas to Texas. Growth is typically a good problem to have, of course – it usually means a community is inherently desirable or livable, and/or offers abundant job opportunities. But it also tends to lead to age-old supply and demand tensions. The supply of rental housing needs to keep up with rental housing demand from new residents, or else rents can rise uncomfortably fast as new residents vie with long-time residents for those apartments that are available. Often, there is a (sometimes substantial) lag before supply catches up with demand.
But demand is only part of what drives rent up. The pace at which communities are able to add new units to meet population growth is also critical to managing rent growth. Areas where building is relatively easy may be able to manage growth better by quickly adding new housing units as communities grow. But in areas where building new homes is made more difficult by stricter regulation, population growth is a double-edged sword.
Zillow analyzed the relationship between population growth, rent appreciation and the ease or difficulty of building new homes across U.S. counties between 2013 and 2014 (figure 1). The effect of population growth on rent appreciation is 2.5 times stronger in counties with the most residential land-use restrictions compared to counties with the fewest restrictions.
In counties with the fewest residential land-use restrictions, there is no statistically meaningful relationship between population growth and rent appreciation. Some lightly regulated counties with relatively strong population growth, like Charleston County along the South Carolina coast, also experienced strong growth in rents. At the same time, other counties with similar levels of population growth and regulation experienced year-over-year declines in rents, including Saline County just southwest of Little Rock, Arkansas.
But in counties with the most restrictive residential land-use restrictions, there is a strong, significant relationship between population growth and growth in rents. Highly regulated places with strong population growth – like Deschutes County in Oregon, which includes the city of Bend – generally experienced strong rent growth. Over the same period, tightly regulated counties with slower population growth (or population declines), including Washington County in the Maryland panhandle, tended to experience slower or negative rent growth.
The intensity of local residential land-use regulation is often perceived as the work of heavy-handed bureaucrats in city halls or state agencies. But it is equally a phenomenon of local residents seeking a voice in the direction and pace of change in their communities. And the divide between more- and less-strictly regulated communities is not simply a contrast of dense, urban places and rural communities with more room to spread out. Santa Clara County, California – the heart of Silicon Valley – and Yellowstone County, Montana (which includes the city of Billings), have similar levels of regulation, population growth and rent appreciation.
For this analysis, we combined Zillow’s county-level data on annual rent and home value appreciation, Internal Revenue Service (IRS) data on county-to-county migration trends, the Wharton Residential Land Use Regulatory Index (WRLURI), and American Community Survey (ACS) data on rental vacancy rates.
The IRS data cover taxpayers who filed taxes in two consecutive years and who changed their county of residence over that period. Years referenced in the text and charts above correspond to the second year, after the move occurred.
We conducted the analysis at the county level because the IRS data disaggregating inflows and outflows are available only at the county and state levels.
We tested the results presented above regressing annual changes in median rents on the relative net inflow of taxpayers (IRS inflows less outflows relative to the non-migrant population) between 2013 and 2014, county average Wharton Residential Land Use Regulation indices and rental vacancy rates for 2014. The coefficients are all statistically significant with the expected signs, with an adjusted R-squared of 0.084.
