Nationwide sources of local rental information can be broken down into two categories: public surveys (specifically, the American Community Survey, or ACS) and proprietary real estate data. The ACS provides more reliable information about rents for large areas, which is how we know is the nation is facing a shortage of affordable rental housing.

But the ACS surveys only a few people in each neighborhood, which means its neighborhood-level information can be very uncertain. Moreover, the Census Bureau must combine five years of data to create viable neighborhood estimates. This creates a delay in the publication of survey results and obfuscates shorter-term trends because the latest data represent a five-year average from 2013 to 2017.

On the other hand, rent estimates from proprietary real estate sources are likely biased. These firms often collect online rental listings opportunistically or survey only large, multifamily buildings. The samples are typically biased toward more expensive listings and larger complexes, so they miss informal rentals, smaller complexes, and units that may be affordable for low income families. Although this “big data” approach solves the ACS’s problem of small sample sizes, not all rental listings are equally likely to be posted online, so we cannot be confident these data are representative of all renters.

Better rent data would help create affordable rental housing
Without high-quality local rent data, we cannot identify, predict, or effectively respond to rapid increases in rents. Local rent data would help increase the supply of affordable housing for three reasons.

First, these data would allow policymakers and advocates to identify gentrifying neighborhoods in real time. Although residents of gentrifying neighborhoods know from lived experience that their homes are becoming less affordable, articulating the scale of the problem can be difficult without data. Additionally, when we can identify which neighborhoods are currently facing displacement pressures, we can begin predicting where gentrification will happen in the future.

Second, because the effects of housing policies may vary between neighborhoods, local data are needed to evaluate interventions. When measuring how housing policies affect the rental market, economists typically use the ACS or private real estate data. But as mentioned, these have shortcomings in timeliness, geographic specificity, or representation.

Third, because of resource constraints and efforts to fight segregation, social programs providing housing assistance use estimates of local rental prices to allocate funds. If these programs make inaccurate estimates of rents in gentrifying neighborhoods, they will not provide residents with the assistance they need.

How can we get better local rental data?
So how do we develop accurate, neighborhood-level data on rental trends? Three strategies could help:

  1. Research and quantify sample bias among private rental data sources.
  2. Explore the feasibility of using tax data to estimate rental prices.
  3. Catalog local sources of rental data.

Read the full article about the data we need about housing prices by Joshua Feldman and Graham MacDonald at Urban Institute.