Exploration: why are rents and housing prices going through the roof?

One of the recurring themes of the past few months, while we’re waiting for a new cabinet to form (since March 2021, the longest formation in Dutch history), is the rising housing prices. Many people who want to move house simply can’t find a place to live unless they’re willing to pay more than they can afford. Too few houses are for sale compared to the number of people looking for a new home, resulting in people bidding well beyond asking price. Rents are going through the roof, resulting in ridiculous prices in the bigger cities for a ‘house’ that includes a kitchen, a bedroom, a bathroom and living area all on less than 20m2. It’s like living in an Ikea cubicle and you pay half your starter salary for it.

Many reasons for rising prices are given. Professional and amateur real state investors are blamed. They take houses off the market to redo them, break them up in smaller units (rooms even) and then rent them for ridiculous prices. And then there are more and more migrants coming into our country who occupy our homes. Or it’s the ridiculous low interest rate for mortgages, so people can lend more and thus pay more for a house. All these reasons certainly contribute to rising prices. But I also know that no sane 25 year old envisions themselves paying half their salary for a room no bigger than they had in their parents’ home. So why is it that those types of tiny apartments still get tenants? That can only happen when there is a huge shortage on housing. But why are there too few houses built?

I wanted to understand the supply of houses better and turned to the Dutch institute CBS for data. I will write a more polished version of what I found on my other (Dutch) website, so I used Dutch in the graphics. I hope you can forgive me for that.

Here’s a summary.

I first wanted to know how many houses are built every year. There is a dataset available with the total number of houses built and demolished each year, starting in 1920. It also has a total of houses available at the start of a year. I plotted this against the total population.

Green = Total Population, Yellow = Number of houses

When you look at the yellow line, you clearly see a decline during the Second World War. Many houses got destroyed. The 1950’s are known for ‘Woningnood’. People had to take in others when owning a large home and many cheap houses were built to accommodate as many as people as quickly as possible. In this graph you can clearly see that the number of households in the fifties is much higher than the number of houses available.

Green = Total Households, Yellow = Number of houses

That can only mean several households living together under one roof. As you can see the two lines are much closer together in 2020 than in 1950.

Then I got thinking. Who actually needs a house? Adults. Not kids. So I plotted the same graph, but then using the numbers for the adult (20 and older) population since 1950.

Green = Total Adult (20+) Population, Yellow = Number of houses

I saw something interesting happening. Starting in 1966 the number of adults rises more quickly than the number of houses. Why is that? Twenty years earlier WWII ended, resulting in the well-known baby boom. Mid-sixties the first of that group became adults. You can also see that the trend line for growth in the number of adults is slightly steeper than that for growth of house supply. I zoomed in on this further. I calculated year-on-year growth of the population and housing supply.

Yellow = growth percentage houses, Green = growth percentage total population

Based on this graph it seems that growth of newly built houses keeps up with population growth. But adults need homes. Therefore I included the adult population in the graph as well.

Yellow = growth percentage houses, Green = growth percentage total population, Purple = growth percentage adult (20+) population

From this graph you could conclude that enough houses were being built. But you have to remember that the market was already lacking enough homes in 1950, the start date of this graph. Then there was a baby boom and although more houses were being built, it didn’t really make up for the existing shortage. Also notice that since 2007 the number of adults is growing again. The third generation baby boomers (the grand children of the baby boomers) are entering adulthood.

Then there is a totally different trend to add pressure on the housing market. Look at the average number of people living together in a household.

Average size of households in number of people.

The number of singletons living in a house rose quickly since 1980

Yellow = number of houses, Green = single person households, Purple= multiple person households

You can clearly see the added pressure when you look at the year-on-year-growth of single households.

Yellow = growth percentage houses, Green = growth percentage total population, Purple = growth percentage adult (20+) population, Pink = growth percentage single person households

Year-on-year growth of single people looking for a home far exceeds the growth of extra homes on the market.

A shortage of houses to begin with, a baby boom generation, more single households, a new generation of adults looking for a home to start a family in. That seems to be the cocktail that drives prices up right now.

I also wanted to know how big the shortage could be based on the available data. I therefore calculated the difference each year between the number of new homes available and the number of extra people each year.

Yellow = amount of extra houses available, Green = number of population growth, Pink = difference between the two

As you can see most years less houses were built than new people were added to the total population. A rough calculation of the built-up shortage since 1950 using the most recent number of people forming a household (2,14) rounds up to about 776.000 houses.

The actual shortage will be bigger as there already was a shortage before 1950. Recent numbers shared in reports talk of more than 900.000 homes that need to be built in the coming years to make sure homes become affordable and accessible again. My crude calculation comes close to that number and only takes into account the years starting in 1950 and doesn’t project future population growth nor an even further decline of number of people in one household.

I also discovered something interesting. In 2020 almost exactly the same number of people died as were born. There was a steep increase in the number of deaths in 2020 causing this parity earlier than expected. The result of a pandemic.

Yellow = number of living births, Green = number of deceased.

The consequence is that for the first time in history the total population growth in 2020 can be credited to a migration surplus.

Yellow = amount of extra houses available, Green = number of population growth, Pink = difference between the two, Purple = migration balance

But migration surplus is not as big as some politicians want us to believe.

Green = total of people died since 1950, Yellow = total of people born since 1950, Purple = total of migrant surplus since 1950

Clearly too few homes were built over a long period to keep up with population growth and declining household size in The Netherlands. When there is more demand for a product than supply, prices will go up. Therefore, investing money in the housing market makes a lot of sense, especially in a situation where having large sums of money (more than €100.000,-) costs money when left on a bank account. And every new inhabitant, either by being born or by moving from another country, adds pressure to the market.

Building, building, building is the only solution. For every ten houses one extra needs to be built, at least. But that’s easier said than done in the complex world of permits, land owners, borders between municipalities and provinces, and a huge shortage of technically skilled personnel to build us those homes (whom, ironically, we already need to ‘import’ from Poland and further to the East). And then I’m not even thinking about future implications of rising sea levels. The majority of inhabitants live below sea level already…

Isn’t there any hope for the under thirties currently longing for a proper place to live and start a family in? There is. Those born just after the war, the baby boom generation are all older than 70. As much as we want our (grand)parents to live forever, they won’t. So be prepared to compromise on where you are going to live for the next decade at least, keep the pressure on politics to reduce carbon emissions AND build more houses (in a sustainable manner of course), be welcoming to migrants who can build houses and take care of your (grand)parents while you are working your ass off to pay your current rent/mortgage. And don’t forget to make babies along the way. They’re a lot of work, but also adorable and great teachers of living in the moment. By the time they become smelly teenagers you’ll be able to afford that big home with a separate floor for them.

The daunting task of getting your mac ready for data science

Today I did something that I have been postponing for some months now: creating an environment on my iMac to be able to do my own data analysis projects.

I learned to use Python and data science packages such as pandas and matplotlib, but all in the safe environment of datacamp. Now that I’m contemplating running my own projects, I had to install Python3 and the packages on my own computer. I started searching online last week, got a bit overwhelmed with all the variations on things other people listed they installed, felt too confused to continue and closed my browser without installing anything.

This afternoon I was ready to try again. I was mentally prepared this time, so I took my time to compare the variations on installing Python3. I quickly discovered that I should refine my search on using Python for data science, as otherwise I would be installing tools geared towards developers.

I first manually installed Python3 and then read at several data science websites about Anaconda, ‘your data science toolkit’ and ‘developed for solo practitioners’. That sounds like me. I installed it, created a new environment using the latest Python (3.10.0) version and ran straight into trouble when installing some packages. Of course the error messages were very human readable (not), but it mentioned lots of version numbers and greater than, equal to, or smaller than signs. I clearly chose the wrong version of Python to work with. I trashed the environment I created, made a new one using the auto-suggested Python version and automagically everything I needed was in there.

As a final step I installed Jupyter Notebook, by clicking on the install button within Anaconda’s GUI, tested whether it worked with a bit of example code one of the helpful instruction sites had and it worked! I now have a fully functioning data science environment waiting for me to do some awesome projects.

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I’m not siloed.

Facebook, Instagram and Whatsapp were offline for six hours and I only know about it because regular news sources started reporting on it. That means I’m doing something good.

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Post academic blues

I had no idea how exhausted I was. September 10 I had my official graduation ceremony party and I wasn’t able to do a lot since. Without the need to study, take care of Daughter during the day, or client work, for the first time since…January (or even longer ago?) I had so many hours to fill during the day. It felt really uncomfortable. The first half of 2021 was mainly about being as efficient as I could be, every minute of the day. This month, without many obligations, I could feel the toll that period took on my body and mind. I felt tired so I often slept in the afternoons. I felt restless, unable to enjoy the quiet hours I longed for in previous months. Nevertheless, I clearly needed a few weeks of idle time. C’est la vie.

Then I started reading again. I started making appointments with people. The Man connected me to some people he knows that work in/on/with data. I had several pleasant conversations. Spent some time in the city centre on my own. Had lunch with a friend. I even wrote a new story for Daughter. Slowly my energy levels went up. And now I’m eager to do tackle some of the projects that I had to postpone. Like finding new professionals to interview for my podcast. Or editing that wedding video. Or landing a new client. I’m back and open for business.

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Read: The Other Afghan Women

Even though headlines try to convince us otherwise, conflict or war is never simple. There is no simple answer to who is right and who is wrong, especially in conflict riddled Afghanistan. I know very little about the country’s history, therefore it was perhaps even more insightful to listen to this article called The Other Afghan Women, written by Anand Gopal. I never knew about those other women. Their stories probably didn’t make headlines, because those stories don’t confirm the Western storyline of being saviours and victors. I’m glad Gopal wrote their stories down for me and you to read.

The Taliban takeover has restored order to the conservative countryside while plunging the comparatively liberal streets of Kabul into fear and hopelessness. This reversal of fates brings to light the unspoken premise of the past two decades: if U.S. troops kept battling the Taliban in the countryside, then life in the cities could blossom. This may have been a sustainable project—the Taliban were unable to capture cities in the face of U.S. airpower. But was it just? Can the rights of one community depend, in perpetuity, on the deprivation of rights in another? In Sangin, whenever I brought up the question of gender, village women reacted with derision. “They are giving rights to Kabul women, and they are killing women here,” Pazaro said. “Is this justice?” Marzia, from Pan Killay, told me, “This is not ‘women’s rights’ when you are killing us, killing our brothers, killing our fathers.” Khalida, from a nearby village, said, “The Americans did not bring us any rights. They just came, fought, killed, and left.”

From: The Other Afghan Women, Anand Gopal, The New Yorker

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