What factors go into UBS’s Global Real Estate Bubble Index (GREBI)?

Question

UBS publishes an annual Global Real Estate Bubble Index, which categorizes real estate prices in various cities as either “undervalued”, “fair valued”, “overvalued” or “bubble risk”.

I am currently wondering how to read/interpret this index. Obviously, I guess that owning real estate in a city classified as “bubble risk” means that UBS’s analysts believe my property might decline in value in the foreseeable future.

However, I am wondering what factors go into this index:

• Increase in real estate prices (higher increase over time is worse, though this might also be brought about by more people moving into the city, or trying to move into a city that is trying to cap its growth)?
• Percentage of objects inhabited by their owners (the lower, the bigger the risk, although this is also heavily influenced by cultural factors, as different countries have different general opinions on buying vs. renting)?
• Supply/demand on the rent market (a lot of vacant rental properties drive up the index)?
• Others?

Answer 1

From the 2022 Global Real Estate Bubble Index:

The index score is a weighted average of the following five standardized city sub-indices: price-to-income and price-to-rent (fundamental valuation), change in mortgage-to-GDP ratio and change in construction-to-GDP ratio (economic distortion), and relative price-city-to-country indicator.

The price-city-to-country indicator in Singapore, Hong Kong, and Dubai is replaced by an inflation-adjusted price index.

The approach cannot fully account for the complexity of the bubble phenomenon. We cannot predict if or when a correction will happen. Hence, “bubble risk” refers to the prevalence of a high risk of a large price correction.

Answer 2

Doing some more research I came across a few indicators that might influence the index. Bloomberg and CNBChas an article which directly refers to the UBS Global Real Estate Bubble Index (GREBI). Another CNBC article predates the COVID-19 crisis and focuses primarily on the Eurozone. Visual Capitalist discusses some general indicators. Unless indicated, I am not sure to what extent the GREBI considers these factors.

1. Government support measures for homeowners and/or buyers, even more so when these measures are expected to come to an end in the foreseeable future. According to Bloomberg, the GREBI considers that. They specifically mention support of personal income and property markets in the wake of the COVID-19 crisis, along with suspended foreclosures. These would allow prices to rise further. This would explain how Munich further extended its lead in 2020.

2. Low interest rates (which can also be a form of government support, especially if interest rates are being kept artificially low). Interest rates have declined sharply in the Eurozone and loans are available at less than half the interest rate of eight years ago. If interest rates were to rise again, houses would become less affordable (curbing demand) and some homeowners might be forced to sell (generating additional supply). Both tend to drive prices down. That, too, is a factor that would have affected Munich for a number of years, but also other cities in the EU (including Milan, which is considered fairly valued). On the other hand, as long as low interest rates are in place, they can offset increases in house prices (relative to rent, income or inflation).

3. House price to rent ratio, expressed as how many monthly rents it takes in order for a property to pay for itself. If that figure rises (either because of rising house prices or falling rents), renting out property becomes less profitable/sustainable, and buying becomes less attractive compared to renting (again, reduced demand and increased supply). According to CNBC this went into the GREBI and would affect Munich, where house prices have roughly tripled over the last 15 years, whereas rents are about 1.5 times what they were then—meaning the number of rents to pay for a house has approximately doubled.

4. Government support measures for tenants which penalize landlords, most importantly an impending cap on rents. If the government limits the rent that can be earned on a property but prices keep increasing, the house price to rent ratio rises.

5. House price to income ratio. If house prices increase faster than incomes, houses become less affordable (also considered in the GREBI). Again, in Munich development of house prices has outpaced the development of incomes.

6. Inflation-adjusted development of house prices. If house prices outpace inflation, that is a potential bubble sign. Inflation in the Eurozone has been low, but house prices in Munich have roughly doubled since 2012, whereas they have remained fairly stable in Milan (which is considered fairly valued).

7. Consumer debt to GDP ratio. If consumer debt exceeds GDP, it can be a sign that this lending is unsustainable and there is an increased risk of homeowners defaulting on mortgages. Excessive lending is another factor in the GREBI. This is not a problem in most of the Eurozone (notable exceptions being France and Belgium), where debt relative to GDB has been declining.

8. Construction boom. More new properties mean more supply, driving down prices especially if there is no demand to counter it (also a factor in the GREBI). However, in the Eurozone the percentage of GDP invested in construction of residential property has significantly declined since 2008. In Germany the decline was less pronounced but still present, hence this factor likely did not affect Munich.

9. Demographic trends, also a GREBI factor. These are numerous and include:

   1. Economic situation of large employment sectors in the area, causing people to either move into the area or away from it. I would expect Munich to benefit somewhat from such trends.

   2. Trends we have seen as a result of the COVID-19 restrictions: a move away from densely-populated areas and small apartments into homes with a garden. For 2020 this could have turned against Munich to some extent, though I would expect it to be among the less severely affected ones.