Data

House Prices and Quality: 1971 vs 2023

Jeremy Horpedahl12 Comments

Last week I did a comparison of “time prices” for several goods and services in 1971 compared with 2024. For almost all goods and services, it took fewer hours of work in 2023 to purchase them. In some cases, huge increases in affordability; air travel is 79% cheaper and milk is 59% cheaper, in terms of how much time an average worker needs to labor to pay for them.

There was one major exception though: housing. Especially the cost of buying a new home. Just using the median sale price of a home, the cost (in terms of hours of work) roughly doubled between 1971 and 2024. That’s not good!

Many who commented on the post mentioned that houses are much bigger today, and I noted that in the post but still claimed this is a worrying trend: “since 1971 you can’t really argue the quality improvements make up for the increase. Yes, houses are much bigger (about double in size), but that’s not clearly driven by consumer demand (more so by zoning and other laws). The 1971 house also had indoor plumbing (but maybe not air conditioning).”

Furthermore, housing is the largest expense for most families, both today and in 1971. In the early 1970s it was 30.8% of consumer spending, and in 2023 it was slightly higher at 32.9%. Given all this, it is worth investigating further.

First, let’s consider the size of a typical house. For most of the 1971 data, I will use this HUD report on new single-family homes. And I will use the similar Characteristics of New Housing report for 2023 (the latest year available) to compare.

Are houses bigger today? Yes, but not nearly enough to account for the decreasing affordability I showed in the previous post. In 1971, the median new home had 1,400 square feet of floor space. In 2023, it was 2,286. That’s a big increase (over 60%), but let’s now do the time-price affordability calculation, which I show in the table below.

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The Price of a Complete [Animal] Protein

Zachary Bartsch2 Comments

I wrote about the protein content of different foods previously. I summarized how much beef versus pea and wheat flour one would need to eat in order to consume the recommended daily intake (RDI) of ‘complete proteins’ – foods that contain all of the essential amino acids that compose protein. These amino acids are called ‘essential’ because, unlike the conditionally essential or non-essential amino acids, your body can’t produce them from other inputs. Here, I want to expand more on complete proteins when eating on a budget.

Step 1: What We Need

To start, there are nine essential amino acids with hard to remember names for non-specialists, so I’ll just use the abbreviations (H, I, L, K, M, F, T, W, V). The presence of all nine essential amino acids is what makes a protein complete. But, having some of each protein is not the same as having enough of each protein. Here, I’ll use the World Health Organization’s (WHO) guidelines for essential amino acid RDI for a 70kg person. See the table below.

Step 2: What We Need to Eat

What foods are considered ‘complete proteins’? There are many, but I will focus on a few animal sources: Eggs, Pork Chops, Ground Beef, Chicken, & Tuna. Non-animal proteins will have to wait for another time. Below are the essential amino acid content per 100 grams expressed as a percent of the RDI for each amino acid. What does that mean? That means, for example, that eating 100 grams of egg provides 85% of the RDI for M, but only 37% of the RDI for H.

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“Time Prices” Today Compared With 1924 and 1971

Jeremy Horpedahl4 Comments

I’ve written before on this blog about “time prices”: the amount of time it takes at a particular wage to buy a specific product. Time prices are especially useful for making historical comparisons of the real price of a good or service. Rather than adjusting historical prices for inflation (which only tells you whether they have increased faster or slower than average prices), time prices give you a real comparison of whether a good has become more or less affordable.

Antony Davies recently did a 100-year comparison of time prices for an average worker in the US. He compared prices in 1924 for several common food items, gasoline, electricity, movie tickets, airline tickets, an automobile, and several measures of housing costs to the best comparable thing in 2024. This following table shows his results:

You will notice a few things here. For the median worker, most things are much more affordable in 2024. Some things are dramatically so! For many items, the median worker in 2024 is similar to someone in the top 1% in 2024. Huge improvements in the standard living.

It will probably not surprise you that one major exception is housing. For renters, things are not obviously worse, but they are not better, depending on what size of city you are in (renters also have lower incomes, but that would be true in both time periods). However compared to the average home price, things look much worse in 2024. You can reasonably reply that the home is much larger and better quality in 2024 (as late as 1940, barely half of homes had complete indoor plumbing!), and this is all true. Still, an average house today is much better, but also much less affordable.

Despite the high cost of housing, the average worker today is much better off than 1924. It’s hard to deny it.

But what about more recent times? As a recurring meme likes to date it, what about since 1971?

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The Laboratory of the States: Regulatory Reform Edition

James BaileyLeave a comment

The US Federal government has been considering major reforms like the REINS Act, which would require Congressional approval of major regulations proposed by executive branch agencies, or bringing back the “two in one out” rule from the first Trump administration. What would these do?

Right now it’s hard to say much for sure. But similar reforms have already been implemented in the states; as usual, the states provide a laboratory for investigating how policies work and whether they deserve broader adoption. It’s especially valuable to inform the debate over reforms like the REINS act that are still being considered at the federal level. Even for federal reforms that have already happened, it can be easier to evaluate the state version, since states make better control groups for each other than other countries do for the US.

But so far we’ve mostly been ignoring our laboratory results from recent state regulatory reforms. For instance, Broughel, Baugus, and Bose (2022) released a dataset that could be used to evaluate state regulatory reforms, but it has only been cited 3 times. This is why I’m adding this to my ideas page as a good subject for future academic research.  Do state REINS or Red Tape Reduction Acts actually reduce either the stock or flow of regulation? If so, which types of regulations are affected, and does this have any effect on downstream measures like economic growth or new business formation?

Any research along these lines could help inform policy debates in the states, as well as for a new Presidential administration coming in with hopes of boosting economic growth through deregulation.

HT: Adam Millsap

National Survey of Drug Use and Health State-Level Data: Now Cleaned in Excel and Stata

James Bailey1 Comment

I offer a cleaned version of the state-level NSDUH in Stata .dta and Excel .xlsx formats here.

The NSDUH is mostly quite good as government datasets go- they share individual-level data in many formats and with the option to get most years together in a single file. But due to privacy concerns, the individual-level data doesn’t tell you what state people live in, which means it can’t be used to study things like state policy. SAMHSA does offer a state-level version of their data, but it is messy and only available in SAS format. So I offer the 1999-2019 state-level NSDUH Small Area Estimation Dataset in Stata .dta and Excel .xlsx formats here.

If you have Stata I recommend using that version, since the variables are labelled, making it much easier to understand what they represent.

This is the latest addition to my data page, where you can find cleaned/improved versions of other government datasets.

Post-Pandemic Lumber Market

Zachary Bartsch4 Comments

Remember that one time, back when we had a global pandemic, when interest rates fell really low and everyone was borrowing and refinancing? Good times. But they were also times of surging demand for durable goods, supply chain disruptions, and shortages. Specifically, the price of lumber surged by 54% between 2019 and 2022. There were stories of contractors who were unable to do their jobs at their typical prices. Some of them went without work. Others did much less work. Theft of precious lumber was in the news.

As we know, sudden price spikes often make the front pages and the social media rounds. But they peter out and the subsequent decline in prices hardly ever gets coverage in the same way. People used to talk about higher gasoline prices all the time, but never discussed with the same enthusiasm when prices fell. The same is true for lumber. We heard hysterical stories of record high prices, alleged shortages, and the sawmills that lacked adequate capacity to keep up with demand.

What’s going on in the lumber market?

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Did Inflation Make the Median Voter Poorer?

Jeremy Horpedahl4 Comments

A new essay by J. Zachary Mazlish answers the title question in the affirmative: yes, inflation made the median voter poorer. The post is data-heavy, with lots of charts and different ways of slicing the data, which is great! But since I am called out by name (or rather, my evil twin, Jeremy Horpendahl), I want to respond specifically to the claim about my data, but also I’ll make a few broader points.

Here’s the Tweet of mine that he links to:

https://twitter.com/jmhorp/status/1854548669317455894

Regular readers will recognize the chart in that Tweet comes from an EWED post from April 2024. Mazlich says that my chart and others like it are “misleading for understanding the election because a) they compare wages now versus January 2020, rather than January 2021.”

Fair enough, but if you read my Tweet you will see that I am specifically responding to an NPR story which said, “if you look at the difference between what… groceries cost in 2019 and what it costs today, and what wages looked like in 2019 and today, the gap is really gigantic.” So, they are specifically using 2019 as a baseline in that story, and my chart specifically used that as the baseline too! That’s why I thought that chart was relevant.

It’s true, of course, that if you want to understand median voter sentiment about the Biden administration, you should probably start the data at the beginning of the Biden administration. But I was responding to the more general claim people make, that they are worse off than in 2019.

With that clarification out of the way, what does Mazlich’s broader post say?

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Protein, Protein Everywhere

Zachary Bartsch8 Comments

If you’ve ever been vegetarian or if you have ever spoken to a vegetarian about their diet, then you have probably heard or asked “How do you get enough protein?”.  While it’s important for health and economic achievement to get adequate protein, not too long after comes the questions about types and sources of protein. This question is relevant for vegetarians and vegans, but also people with meat allergies and people with religious dietary guidelines that prohibit meat always or seasonally. Let’s break it down.

Some omnivores are incredulous that vegetarianism can provide adequate protein or protein quality. But protein itself is relatively easy to get and any judgmental attitudes on both sides are mostly just vibes. Legumes and nuts tend to have a lot of protein. But relative to what?

The World Health Organization recommends that an 80-kilogram (176 lb) adult should get 66.4 grams of protein per day (0.83g per kg). That’s the protein content of about a 9oz of peanuts. Protein is super important and it’s luckily not that hard to get if you eat a variety of foods. Even if you’re trying to consume double the WHO recommended daily intake (RDI), it’s an easy feat.

Below is a table of some popular protein sources. The table includes the grams of protein per 100 grams of food, which makes the protein content a percent. The table also includes the number of grams needed in order to achieve the WHO protein RDI of 66.4 grams. The last column is for our American readers who need the serving to be in ounces.

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What Markets Expect From A Trump Presidency

James Bailey3 Comments

Last week I laid out my own expectations for what economic policy would look like in a Trump or Harris presidency. Now after yesterday’s market reaction, we can infer what market participants as a whole expect by roughly doubling the size of yesterday’s market moves. Prediction markets had a 50-60% change of Trump winning as of Tuesday morning’s market close, which moved to a 99+% chance by Wednesday morning. Look at how other markets moved over the same time, multiply it by 2-2.5x, and you get the expected effect of a Trump presidency relative to a Harris presidency. So what do we see?

Stocks Up Overall: S&P 500 up 2%, Dow up 3%, Russell 2000 (small caps) up 6%. My guess this is mostly about avoiding tax increases- the odds that most of the Tax Cuts and Jobs Act gets renewed when it expires in 2025 just went way up. Lower corporate taxes boost corporate earnings directly, while lower taxes on households mean that they have more money to spend on their stocks and their products. Lower regulation and looser antitrust rules are also likely to boost corporate earnings.

Bond Prices Down (Yields Up): 10yr Treasury yields rose from 4.29% to 4.4%. This is the flip side of the tax cuts- they need to be paid for, and markets expect they will be paid for through deficits rather than cutting spending. The government will issue more bonds to borrow the money, lowering the value of existing bonds.

Dollar Up: The US dollar is up 2% against a basket of foreign currencies. I think this is mostly about the expected tariffs. People like the sound of the phrase “strong dollar” but it isn’t necessarily a good thing; it makes it cheaper to vacation abroad, but makes it harder to export, even before we consider potential retaliatory tariffs.

Crypto Way Up: Bitcoin went up 7% overnight, Ethereum is now 15% up since Tuesday. Crypto exchange Coinbase was up 31%. Markets anticipate friendlier regulation of crypto, along with a potential ‘strategic Bitcoin reserve’.

Single Stock Moves: Private prison stocks are up 30%+. Tesla is up 15%, mostly due to Elon Musk’s ties to Trump, but also due to tariffs. Foreign car companies were way down on the expectation of tariffs- Mercedes-Benz down 8%, BMW down 10%, Honda down 8%.

Sector Moves: Steel stocks are up on the expectation of tariffs, while solar stocks (which can’t catch a break, doing poorly under Biden despite big subsidies and big revenue increases) were down 12% in the expectation of falling subsidies. Bank stocks did especially well, with one bank ETF up 12%. This gives us one hint on what to me is now the biggest question about the second Trump administration- who will staff it? I could see Trump appointing free-market types, or wall-streeters in the mold of Steve Mnuchin, or dirigiste nationalist conservatives in the JD Vance / Heritage Foundation mold, or an eclectic mix of political backers like Elon Musk and RFK Jr, or a combination of all of the above. The fact that bank stocks are way up tells me that markets expect the free-marketers and/or the Wall-Street types to mostly win out.

Just Ask Prediction Markets: If you want to know what markets expect from a Presidency, you can do what I just did, look at moves the big traditional markets like stocks and bonds and try to guess what is driving them. But increasingly you can skip this step and just ask prediction markets directly- the same markets that just had a very good election night. Kalshi now has markets on both who Trump will nominate to cabinet posts, as well as the fate of specific policies like ‘no tax on tips‘

Big Win for Prediction Markets

Jeremy Horpedahl1 Comment

Last night was a big win for Trump, but it was also a big win for prediction markets. In January 2024, I suggested that one of the best ways to follow the election was by following prediction markets. That prediction turned out to be correct!

Before any polls had closed, prediction markets had Trump with about 60% odds of winning. That’s far from a sure thing, but it’s much better than many prediction models, which all had the race as basically a 50-50 toss-up with a very slight edge to Harris (though one simple model that I wrote about two weeks ago had Harris slightly losing the popular vote, a good call in hindsight). So going into the election results, you would have been more confident that a Trump win was a real possibility if you watched predictions markets

Last night after the results started coming in, the average over five different prediction markets from Election Betting Odds put Trump at over 90% odds by 11:00pm Eastern Time. By about 12:45am, he was already over 95%. These aren’t absolutely certain odds, but if you were watching the election night news coverage, they were still treating this as essentially a toss-up in the battleground states.

The Associated Press hadn’t even called Georgia, the second of the battleground states, by the time prediction markets were over 95% for the overall race! Decision Desk HQ, which is a very good source for calling races in real time, didn’t declare Trump the winner until 1:21am, when they called Pennsylvania (they also have a nice explanation of how they made the call). The AP didn’t declare Trump the winner until 5:34am, when they called Wisconsin.

Polymarket is the largest of the five markets in the Election Betting Odds average, and they are also a good source because they have markets for all of the battleground states (here’s the market for Michigan, which still hasn’t been called as of 11:30am on Wednesday by most news sources!). This table shows when the 90% and 95% thresholds were permanently crossed on Polymarket odds for each of the 5 early battleground states, in comparison with the DDHQ and the AP.

Notice that the 90% threshold consistently beats DDHQ by at least an hour (the one exception is North Carolina, where DDHQ called it very early — they are very good at what they do!). And the 90% threshold is consistently beating the AP by at least 3 hours.

None of this should be read as a criticism of the Associated Press. They should be cautious about predictions! But if you want to know things fast (or, before your bedtime in this case), prediction markets are clearly worth following.

How can prediction markets be so far ahead of media sources? Because there is a strong incentive to be right early: that’s how you make money in these markets! How exactly this is done is unclear, since the traders are all anonymous and we generally can’t ask them. But likely they are doing a similar analysis of counties results compared to the 2020 election, as DDHQ told us they did after the fact, just quicker (indeed, if you were watching news coverage, they were doing the same thing, just in an ad hoc way, and much more slowly).