A lot of people take issue with hedonic adjustments, substitution threshold, etc - but the specific methdology used is far less important than the fact that they're simply lying about the data.
For one example, the obvious elephant in the room is the shelter category. In the last year, rents are up 12%[1], and home prices have soared nearly 20%[2]
Yet, from your own source, the BLS is claiming 3.2% for the shelter category.
It's not so much that the methodology is wrong (although that argument could be made as well), but that they're flat out lying.
And then you have to contend with the Gell-Mann amnesia effect: if they're lying to your face about the shelter category, what makes you think the data in any of the other categories, for which you have less third party data to corroborate, are not also completely fabricated?
[1] https://www.apartmentlist.com/research/national-rent-data [2] https://fred.stlouisfed.org/series/CSUSHPISA
1) The rent data you cite is for new leases. Since new leases only represent a fraction of total leases, they aren't going to agree with tracking overall rent.
2) When you buy a house, you get two things: shelter and an investment asset. The inflation statistics are only interested in shelter, not investments, so they disaggregate them. They do this by computing the rent for an equivalent dwelling. For this reason, the home price index won't agree with the inflation statistics either.
> 1) The rent data you cite is for new leases. Since new leases only represent a fraction of total leases
The vast, vast plurality of residential leases are for 12 months - and the vast majority are for 6, 12, or 18 months. So no, you're flat out wrong, here. We're talking YoY numbers, so these values reset literally at the end of the timeframe we're discussing.
> 2) When you buy a house, you get two things: shelter and an investment asset.
Cool. Home ownership is at a multi-generational LOW in the United States, with a huge proportion of the HN demographic comprising those currently priced completely out of this market. The OER metric weighting is not updated frequently enough to sufficiently account for this fact.
The reality is that between shelter, fuel, and food, our cost of living is skyrocketing on the order of 12% per year, and the CPI understates this for obvious political reasons. The real yield on a 10y treasury is about -10%, so it's no wonder that obvious scams like AMC, JPEG NFTs, and Shiba Inu (the coin, not the breed) are so popular. You reach for yield any place you can.
The median person does not see a 20% increase in housing prices if they are not currently purchasing a home. I have a 30-year mortgage and my housing costs have not increased since I purchased it. Add in the 65% home ownership rate and it isn't out of question that a large portion of the country isn't seeing as much housing inflation in their actual budgets.
Your source for rentals looks at the cost of new leases. The BLS methodology looks at the most recent rent of a sample of units, regardless of when the rent was set.
We would expect your source to report a higher increase, because its methodology is more responsive to short term changes. That does not show that the BLS is lying.
I wonder if they're able to discount this because the low interest rates mean that monthly payments have not increased 20% for most mortgage borrowers.
What goods, what weights, what substitutions?
The government has done this work and open sourced their calculations. Your unwillingness to accept it isn't proof that it doesn't exist.
To illustrate my point, here is a piece of the "Item replacement and quality adjustment" methodology. I cut it off where the table starts: https://www.bls.gov/opub/hom/cpi/calculation.htm#item-replac...
>Item replacement and quality adjustment
One of the more difficult problems faced in compiling a price index is the accurate measurement and treatment of quality change due to changing product specifications and consumption patterns. The concept of the CPI requires a measurement through time of the cost of purchasing an unchanging, constant-quality set of goods and services. In reality, products disappear, products are replaced with new versions, and new products emerge.
When a data collector finds that he or she can no longer obtain a price for an item in the CPI sample (often because the outlet permanently stops selling it), the data collector uses the CPI item replacement procedure to find a new item. Each priced item stratum in the CPI contains one or more ELIs. CPI commodity analysts have developed checklists that define further subdivisions of each ELI. When seeking a replacement in a retail outlet, the data collector first uses the checklist for the ELI to find the item sold by the outlet that is the closest to the previously priced item. Then the data collector describes the replacement item on the checklist, capturing its important specifications. The CA assigned to the ELI reviews all replacements and selects one of three methods to adjust for quality change and to account for the change in item specifications.
The following example describes the most common type of quality adjustment problem. Assume that a data collector in period t tries to collect the price for item j in its assigned outlet and is not able to do so because the outlet no longer sells this item. (A price for item j was collected in period t–1.) The data collector then finds a replacement item and collects a price for it. This replacement item becomes the new version v+1 of item j. The commodity analyst decides how the CPI treats the replacement. The commodity analyst has the descriptions of the two versions of item j. In addition, he or she has the t–1 price, , for the earlier version v and the period t price, , of the replacement version v+1. The following matrix displays the information available to the commodity analyst:
The BLS isn’t hiding this information, it’s publicly available. The burden of proof would be on you to provide research on why that methodology is incorrect.
https://www.bls.gov/news.release/cpi.nr0.htm
> The CPIs are based on prices of food, clothing, shelter, fuels, transportation, doctors’ and dentists’ services, drugs, and other goods and services that people buy for day-to-day living. Prices are collected each month in 75 urban areas across the country from about 6,000 housing units and approximately 22,000 retail establishments (department stores, supermarkets, hospitals, filling stations, and other types of stores and service establishments). All taxes directly associated with the purchase and use of items are included in the index. Prices of fuels and a few other items are obtained every month in all 75 locations. Prices of most other commodities and services are collected every month in the three largest geographic areas and every other month in other areas. Prices of most goods and services are obtained by personal visits or telephone calls by the Bureau’s trained representatives.