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Real Estate

Single-Family Rent Growth Exceeded Pre-Pandemic Rate in November

Source: https://www.corelogic.com/blog/2021/1/single-family-rent-growth-exceeded-pre-pandemic-rate-in-november.aspx

  • Rent growth of lower-tier rentals still lags behind pre-pandemic rates.
  • Boston saw the largest decrease in rent prices, falling 4.6% from a year earlier.

U.S. single-family rent growth strengthened in November, increasing 3.7% year over year, showing solid improvement from the low of 1.4% reported for June 2020, and up from the 2.8% rate recorded for November 2019, according to the CoreLogic Single-Family Rent Index (SFRI). The index measures rent changes among single-family rental homes, including condominiums, using a repeat-rent analysis to measure the same rental properties over time.

Figure 1: National Single-Family Rent Index Year-Over-Year Percent Change By Price Tier

Rent growth for low-priced rentals was slower than that of high-priced rentals in November for the first time since 2014 (Figure 1). Unlike the high-priced tier, rent growth in the low-priced tier remains below year-pre-pandemic levels as the recession has disproportionately affect lower-wages workers. Rent prices for the low-end tier, defined as properties with rent prices less than 75% of the regional median, increased 3.3% year over year in November 2020, down from 3.6% in November 2019. Meanwhile, higher-priced rentals, defined as properties with rent prices greater than 125% of a region’s median rent, increased 3.7% in November 2020, up from a gain of 2.5% in November 2019.

Figure 2: Single-Family Rent Index Year-Over-Year Percent Change in 20 Markets

Figure 2 shows the year-over-year change in the rental index for 20 large metropolitan areas in November 2020. Among the 20 metro areas shown, Phoenix had the highest year-over-year rent growth this November as it has since late 2018, with an increase of 9.9%, followed by Tucson (+8.9%) and Las Vegas (+6.2%). Three metro areas experienced annual declines in rent prices: Boston (-4.6%), Chicago (-0.5%) and Honolulu (-0.1%).

While 8 of the 20 metros shown in Figure 2 had lower rent growth than a year ago, Boston had the largest deceleration in rent growth, showing annual rent growth of 7 percentage points lower than in November 2019. The weakness might be attributed to a large number of students choosing to not return to Boston — a city that’s home to 35 colleges and universities — but instead opting to continue virtual learning in their hometowns.

U.S. unemployment rates remain elevated and the nation had 6% fewer jobs in November 2020 than a year earlier. However, some areas of the country are continuing to experience higher rates of job loss — adversely impacting rental demand and slowing rent price growth. For example, Honolulu posted an employment decrease of 13.3%, compared to November 2019, which has contributed to a 0.1% decline in single-family rent prices. Meanwhile, employment declines in Phoenix (-2.6%) and Tucson (-3.7%) were relatively small amongst the 20 metros covered in the report. With the continued resurgence of COVID-19 cases across the country, we may expect to see further disruption of local rental markets.

To learn more about the data behind this article and what CoreLogic has to offer, visit https://www.corelogic.com/.

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ForwardKeys is one of the leading travel analytics firms that provides investment firms with alternative and predictive data on the travel industry. Data is offered both on the demand and the offer-side of the aviation market.

Actual Air Reservations is an aggregation of flight bookings made via the Global Distribution Systems. Exclusive agreements make that global travel data is available, including regions that are generally difficult to track. This data contains valuable insights into the profile, behaviour, and impact of events on past, present and future air travellers.

Seat Capacity provides insights into the scheduled flights from 99% of global commercial airlines.

Financial institutions use this data for artificial intelligence and predictive models to forecast the performances of businesses. Combined, the datasets can provide insights into the financial health of businesses operating in the travel and tourism industry. This list contains companies like airlines, airplane leasing companies, oil companies, hotels and travel agencies.