Abstract
Housing affordability is an issue of increasing importance and interest, particularly in the United States. Much of this interest is due to skyrocketing rents in coastal cities with tight housing markets. Shrinking cities, in contrast, are often characterized as rich in low-cost housing, providing an affordable alternative to superstar cities. This paper compares income and rent dynamics in cities with growing versus shrinking populations. While costs may be lower in shrinking cities, falling incomes have likely rendered housing unaffordable for many residents. We employ multiple lines of evidence to test for different dynamics between growing and shrinking cities. Matching is used to explore changes in income and rent between 1980 and 2017 in shrinking and the most similar non-shrinking cities. After controlling for baseline conditions, shrinking cities exhibit faster falling incomes and growing cities exhibit faster rising rents, while rent burden increases at a very similar rate in both groups. We also use a fixed effects regression model to test for differences between growing and shrinking cities in sensitivity of rent burden to changes in income and rent. Rent burden has considerably increased across US cities since 1980, yet growing and shrinking cities exhibit clearly different pathways toward that end. Shrinking cities are more sensitive to identical changes in income and rent, likely because a greater share of their residents live near the edge of affordability.
| Original language | English |
|---|---|
| Article number | 102302 |
| Journal | Applied Geography |
| Volume | 125 |
| DOIs | |
| State | Published - Dec 2020 |
Funding
We would like to thank Maxwell Hartt for comments on an earlier draft, Dylan Connor and the attendees of our session at the 2019 American Association of Geographers conference, and the anonymous reviewers for their comments. All errors remain ours alone. Seymour and Franklin were supported by the National Science Foundation of the United States, award #1561060. Seymour and Franklin were supported by the National Science Foundation of the United States , award # 1561060 .
| Funder number |
|---|
| 1561060 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
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