New Real-Time Rental Price Feeds & Trading Markets: A Unique Opportunity to Trade the Gap Between Home Prices and Rental Markets Nationwide
New Real-Time Rental Price Feeds & Trading Markets
Parcl recently launched a new product, rental price feeds. Rentals are a very large part of the real estate market and increasingly important given the rise of home prices and institutional ownership across the world. Rental markets add a new dynamic to Parcl’s current exchange and unlocks another completely new asset class with utility for many market participants including renters, physical real estate investors, hedgers, and speculators.
While the methodology behind the rental market price feeds are extensively covered in our white paper, we are going to click down into the three that are available to trade today.
Those three include:
These three were carefully selected out of dozens of candidates. They exhibited differing characteristics regarding seasonality, had strong relationships with OER, a core component of housing costs in CPI, and geographic dispersion across the country.
All 3 rental price feed series had greater than 0.5 correlation coefficient with OER input to CPI, including the entire aggregated series used by the FED from BLS. Denver had the strongest relationship, 0.9 correlation coefficient with the Denver Metro OER input to National OER.
United States of America
The US Rental price feed is the stalwart among the three, representing a real time barometer on US rental costs. It went through a pronounced period of volatility at the onset of the Pandemic, followed by moderate growth in the midst of enormous home price appreciation.
We specifically analyzed the relationship with home prices across a spectrum of -2 years to +2 years to identify where the relationship with home prices was strongest.
When rentals are lagged 361 days, the relationship with US home prices is strongest. Thus, the US price feed typically leads rental price feed behavior by approximately 1 year.
There are clear seasonal effects associated with these relationships at 6 and 12 month intervals.
We can see this more clearly when we shift the actual rental series back 361 days. We observe similar up and down swings, with nearly identical magnitudes of appreciation or depreciation ultimately reached.
Chicago City
Chicago City is denoted by harsh winters. This plays a role in both home and rental prices during seasonal months. Both home prices and rental prices show strong seasonal shifts.
The relationship among the series is also strongest at approximately a year, or 349 days. Once again, home prices lead the rental market.
When shifting the rental price feed back by this value of 349 days, the cyclical pattern becomes more obvious.
Denver City
Denver City has less obvious seasonal patterns than Chicago City. Both rentals and home prices have followed a relatively similar trajectory since `20.
What’s unique to this relationship in Denver relative to the other two markets is the decline in the relationship when shifting home values vs. rents. Rents become less correlated with home values if trying to use them as a forward looking indicator for where home prices may be going.
Again, home prices lead rents by 327 days with a correlation coefficient of 0.86.
When shifting rents 327 days back, this relationship becomes apparent. Similar up and down cycles, and magnitudes of change.
Across all 3 markets, home prices typically have the strongest relationship with rents about a year out, all with correlation coefficients >0.8.
When you think about a renter, they observe home prices throughout the year. They are typically locked into 12 month terms. As home prices decline, the calculus for ownership vs. rent shifts and they have a full year to consider it. The opportunity to purchase a home becomes more appealing, removing renters from the rental demand category and putting them into the home ownership demand category.
As home prices increase, less renters are able to consider the possibility of home ownership. This likely shifts demand out of the home ownership category into the rental category, driving up rental prices.
This analysis is open sourced here. You can reproduce it via the Parcl Labs API by acquiring an API key here.
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