I hope each of you had a great weekend. At the end of last week, I saw a chart that really confused me. It showed the difference between the CPI Rent Index and the Zillow Rent Index.
These numbers show that Zillow is estimating rents are increasing about 5.5% to 6% annually right now. The CPI Rent Index is showing just under 2% rent growth.
The confusing part of this was that the two metrics, which are supposed to be measuring the exact same thing, are apparently coming in at such different numbers. The Zillow Rent Index is almost 300% higher than the CPI Rent Index. But they weren’t the only ones.
The Apartment Guide rent report published on August 31, 2021 also showed numbers significantly higher than the CPI Rent Index.
The lowest rental increase for the national average was more than 2x the CPI Rent Index number. How could this be?
Well, I started to dig deeper into the data and what I found was honestly shocking. It all starts with the data sets that are being used by the various reports. First, the CPI Rent Index uses the following definition for rent in the CPI:
“The rent estimates used in the CPI are contract rents. They are the payment for all services provided by the landlord to the tenant in exchange for rent. For example, if the landlord provides electricity or other utilities, these would be part of the contract rent. The CPI item expenditure weights also include the full contract rent payment. Rents are calculated as the amounts the tenants pay their landlords, plus any rent reductions tenants receive for performing services on behalf of the landlord, plus any subsidy payment paid to the landlord. Reductions for any other reason are not considered part of the rent.”
So how exactly do they do select which units and/or tenants or landlords to survey? The government uses the 1990 Census data obviously. No, seriously. I had to read this about 50 times before I actually believed it. Here is the exact explanation:
“Using data from the 1990 Decennial Census of Population and Housing, the CPI defined small geographic areas, called segments, within each of the 87 CPI pricing areas. Segments are one or more Census blocks. The Census provided the numbers of renter and owner housing units in each segment and the average rent of the renter units in each segment; BLS estimated the average implicit rent of the owner units in the segment, enabling the CPI to calculate the total spending (rent plus implicit rent) for each segment. The CPI selected a sample of segments in each pricing area using stratified sampling in proportion to total shelter value. CPI agents visited the segments and selected a small number (usually 5) of renter-occupied housing units in each one to represent the segment. For segments that contain largely owner occupied housing units, rental units from segments close to the selected segment to help represent the segment.”
Ok, that seems crazy but maybe we are just overreacting. How many units are included in the national rent index? Must be millions, right? Nope. It is only 32,000 total units. Here is the explanation:
“The CPI Housing survey has about 32,000 renter-occupied housing units. In the 1990 Census, which was the sampling frame for the primary CPI Housing survey, there were about 28.6 million renter-occupied units and 41.3 million owner occupied housing units in the urban United States.”
This is starting to look absolutely ridiculous. But it is about to get even better. If you’re going to report a monthly change in the national rent average, you’re obviously going to survey the units on a monthly basis, right? Nope. The government only surveys the units every 6 months.
“Because rents change rather infrequently, the CPI program collects rent data from each sampled unit every six months. (Price collection is monthly or bimonthly for most other CPI items.) Collecting rent data less frequently allows a much larger sample. The CPI divides each area’s rent sample into six sub-samples called panels. The rents for panel 1 are collected in January and July; panel 2, in February and August, etc.”
Now the government does their best to account for the lack of monthly surveying…by increasing rents by one dollar (lol). I wish that I was making this up. But here is the information directly from the BLS’ fact sheet.
“In addition the CPI adjusts the rent for the effect of aging of the rental units over time. The Housing sample collects the rents from the same housing units every six months. Consequently, each time the CPI observes the rent of a sample unit it is six months older. To account for this aging, an age-bias factor is applied to the current rent; this raises the rent slightly because the older unit is slightly less desirable. For example, a unit with a rent of $900 might have the rent adjusted to $901.”
Alright, in case you weren’t paying attention, here is what we have making up the CPI Rent Index — Approximately 32,000 units across the US that were selected based on the 1990 census data and are each surveyed only twice a year. Sounds like a disaster.
Now what about the Zillow Rent Index? Here is how they describe their methodology:
“Every month, a Rent Zestimate is created for more than 100 million U.S. housing units for which Zillow has sufficient data. The sources of this data include public records (property taxes, transactions), real estate listings and user-generated data. Real estate listing data comes from local Multiple Listings Services and/or direct feeds to Zillow from real estate brokers. User-generated data includes rental listings and for-sale listings posted directly on Zillow, and user corrections to incorrect and/or out-of-date data from listings and public records. Properties enter and leave this ‘universe’ due to many reasons, the most important of which are discussed below.
As the Zillow rental business and our data sources expand, we may become aware of more individual units within a building and add them to our “rental universe.” Periodically, these are retroactively given Rent Zestimate histories using attributes of the unit and the Rent Zestimate model. We take advantage of these retroactive histories in the first calculation of the Zillow Rent Index using our updated methodology.”
Zillow then describes how they don’t take a small sample size of homes within a region, but rather they use every home or unit.
“To create the index, we consider every home within a given region for which we have a Rental Zestimate for that period, then reweight and aggregate those estimates.”
So the government is using 32,000 units that they survey every 6 months and Zillow is using 100 million units that they evaluate every month. You can determine which of these two methodologies you believe are more likely to be accurate :)
This exercise was only done for the rent index, but you can replicate it for pretty much every single CPI number that is presented. The data is bad. The methodology is antiquated. The government is living in the past and refuses to use modern technologies and platforms to capture more accurate data.
It is impossible to make good decisions when you are using bad data. Everything from your understanding of the problems to your belief in the intended outcomes is skewed. This is a very real problem that is provable today.
The Federal Reserve, Treasury, and various politicians are making monetary and fiscal policy decisions on data that is telling them CPI inflation is 5.3%, core inflation is 4%, and the annual change of the rent index is sub 2%. Alternative data sources, which use more robust methodologies and are based on larger data sets, have these numbers at 50% to 300% higher depending on the metric.
Maybe the alternative data sets are accurate. Or maybe they are overestimating to some degree. But what is clear here is that the CPI Rent Index can’t possibly produce an accurate monthly number if they aren’t even surveying the housing units on a monthly basis. Therein lies the problem. Bad data gets you bad decisions. And bad decisions have severe consequences when you are dealing with monetary and fiscal decisions that end up making the rich richer and the poor poorer.
Someone stop the madness. Make it all make sense. Because right now none of this adds up. Hope each of you has a great start to your week. I’ll talk to everyone tomorrow.
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