The Unassuming Economist

A new IMF working paper by Damien Puy, Anil Ari, and Yu Shi:

“We test whether foreign demand matters for local house prices in the US using an identification strategy based on the existence of “home bias abroad” in international real estate markets. Following an extreme political crisis event abroad, a proxy for a strong and exogenous shift in foreign demand, we show that house prices rise disproportionately more in neighbourhoods with a high concentration of population originating from the crisis country. This effect is strong, persistent, and robust to the exclusion of major cities. We also show that areas that were already expensive in the late 1990s have experienced the strongest foreign demand shocks and the biggest drop in affordability between 2000 and 2017. Our findings suggest a non-trivial causal effect of foreign demand shocks on local house prices over the last 20 years, especially in neighbourhoods that were already rather unaffordable for the median household.”

From a new IMF working paper by Adrian Alter and Elizabeth M. Mahoney:

“To identify and quantify downside risks to housing markets, we apply the house price-at-risk methodology to a sample of 37 cities across the United States and Canada using quarterly data from 1983 to 2018. This paper finds that downside risks to housing markets in the United States have seemingly fallen over the past decade, while having increased in Canada. Supply-side drivers, valuation, household debt, and financial conditions jointly play a key role in forecasting house price risks. In addition, capital flows are found to be significantly associated with future downside risks to major housing markets, but the net effect depends on the type of flows and varies across cities and forecast horizons. Using micro-level data, we identify households vulnerable to potential housing shocks and assess the riskiness of household debt.”

 

On the US:

• ‘A Mask for Racial Discrimination.’ How Housing Voucher Programs Can Hurt the Low-Income Families They’re Designed to Help – Time
• Why Does It Cost $750,000 to Build Affordable Housing in San Francisco? – New York Times
• The Airbnb Effect On Housing And Rent – Forbes
• US housing finance is stuck in a complex knot of contradictions – Financial Times
• Housing regulations are getting in the way of fighting homelessness – Washington Post
• The declining elasticity of US housing supply – VOX
• Joe Biden wants tougher standards for real-estate appraisers to help black and Latinx homeowners – MarketWatch
• Special briefing on walkability – American Enterprise Institute
• Does Joe Biden Have a Plan to Stop Gentrification? – Citylab

On other countries:

• [China] History and Outlook of China’s Housing Market – SpringerLink
• [Netherlands] Dutch house price boom continues – Global Property Guide
• [New Zealand] New Zealand’s housing market bounced back strongly – Global Property Guide
• [United Kingdom] Housing insecurity, homelessness, and populism: Evidence from the UK – VOX

Global Housing Watch Newsletter: February 2020

A remarkable new data set constructed by Jean-Charles Bricongne, Alessandro Turrini and Peter Pontuch allows direct comparison of house prices across countries—illustrating big differences in how many years of income it takes to buy a house—and provides suggestive evidence of when house prices may be at risk of correction. Prakash Loungani summarizes the data set and poses a few queries to its creators.

How expensive on average are houses in the United States relative to Australia? And how many years of income would it take the average person to buy the average house in each country?

It would seem that the answers to these questions should not be too difficult to provide. After all there are a number of data sets that give cross-country data on house prices in various countries. But most of the data sets provide price indices, not the actual price of houses. They can be used to compare appreciation in house prices across countries over some time period, but don’t necessarily tell us whether houses in the United States are more affordable than in Australia and how the relative affordability has changed over time.

Figure 1: House price per square meter in 2016 (in euros)

A new data set called HouseLev put together by Jean-Charles Bricongne, Alessandro Turrini and Peter Pontuch fills the gap. It provides the average price per square meter of housing in 40 countries, from as far back in 1970 for most countries to the most recent available, generally 2017 or 2018.

The authors use two methods, top-down and bottom-up, with the latter intended to provide a check on the former or a back-up estimate when the top down method is not feasible due to missing data.

The top-down method computes the average price as the ratio of the total value of dwellings and associated land to the total floor area of dwellings. The total value of dwellings and land (‘the numerator’) is generally taken from the national income accounts of countries; the total floor area (‘the denominator’) is from the census data of countries. Divide one by the other and, presto, you have the average price of a house.

So now we have an answer to the first question posed earlier. As shown in Figure 1, it turns out that the cost of the average house price in the United States is under 1500 euros per square meter and over 5000 euros per square meter in Australia. (For metrically-challenged U.S.-centric readers, that’s about $150 per square foot in the United States.)

Of the countries in the data set, houses in Bulgaria are the cheapest at under 300 euros per square meter and the most expensive houses are in Hong Kong (SAR)—prices there are literally off the charts—nearly 30,000 euros per square meter!—and hence not shown in Figure 1.

How reliable are these estimates? Here’s where the bottom-up approach comes in. The authors have painstakingly gone through the websites of real estate agents and collected the data on sales offers in different locations. These data are then aggregated up to give a country-level average. Reassuringly, for most countries, the top-down approach and the bottom-up approach give similar estimates of house prices—the median difference in only 7 percent and the biggest difference is 12 percent.

Figure 2: Years of income required to purchase a house in 2016

Figure 2 shows that it takes under 4 years of average income to buy a house of average size in the United States and over 16 years in Australia. The median across countries is 10 years.

The creators of the data set also looked into whether high ratios of house prices to incomes can signal a correction in house prices. They find that if the ratio is greater than 10, there is concrete risk of a significant downward correction of house prices in the following three years (see their paper, Bricongne et al. (2019), for the details).

Q&A with the creators:

Q. Is the data set publicly available?

A. The data set that covers forty countries is publicly available with detailed annexes of prices in levels in national currencies, in euros, in PPP (purchasing power parity) and with price to income ratios, in the annex of the European Commission Discussion Paper available at the following link: https://ec.europa.eu/info/sites/info/files/economy-finance/dp101_en_houselev.pdf

Q. You mentioned plans to extend the dataset to the regional level? What is the status of this work?

A. Work is ongoing in the Commission, with first results covering all EU countries expected for Autumn 2020. The Commission, Banque de France and the OECD are cooperating and interacting on these efforts. Initial raw results are available already at a basic regional level for the EU27 countries (the so-called NUTS2 level), with time series broadly spanning the 2010s.

Since national accountants quite rarely publish data at a local (NUTS3) level, the principal source to produce regional house prices in levels is the one based on web scraping, as well as aggregates from administrative data. The main issues that we encountered with the construction of sub-national price levels is comparability, which is why we focus mainly on improving stratification algorithms and refining our data on housing stocks, incorporating also satellite data.

Q. Will the data set be kept updated on a regular basis? By whom?

A. The dataset will be updated at least annually by the European Commission with Banque de France also contributing to possibly expanding the country coverage. The intent is to give access to the dataset to all potential users. The dataset will initially be accessible at the ‘browsable’ website of the REFINE network (real estate finance and economics network: https://www.institutlouisbachelier.org/en/programme/refine-real-estate-finance-and-economics-network-2/) but other solutions are also being considered.

Q. Has there been any reaction to your estimates (e.g. some validation from country authorities or realtors; anyone challenging your estimates)?

A. HouseLev is a database of estimates: these are not official statistics and they have not been validated neither by national statistical authorities or Eurostat nor by realtors. The estimates have been constructed with the objective of reliability, transparency and comparability, keeping in mind that full comparability is a tremendous challenge for non-homogenous objects such as dwellings. To get estimates as comparable as possible, a common concept of floor area is adopted (“useful floor area”). When possible, comparisons are made on the basis of more than one methodology for computing average prices per square meter.

The data have been shared with EU Member States and used by different institutions: central banks, ministries of finance, academics. A number of comments have been received by experts in the fora where preliminary versions of the dataset have been presented (European Commission, ECB, Banque de France, Paris Dauphine, OECD ACPR—the French Supervisor for banks and insurance companies). On a few occasions, the feedback has helped improving the estimates for some countries or to better qualify the results. Such feedback is being continuously used to incrementally improve the data methodology country by country.

From the IMF’s latest report on Croatia:

“Housing prices have begun to accelerate, mainly in the capital and coastal areas. Average housing prices grew 8.0 percent, but 12.2 percent in Zagreb (yoy, September 2019). This increase should be seen in context of higher real wages, better employment prospects, growing consumer confidence, as well as declining interest rates. Tourism is the main driver of real estate price developments in Zagreb and the coast. Investment properties for short-term rentals have grown rapidly. This is facilitated by a favorable flat-tax on short-term rentals compared to higher taxation on long-term rentals. Market observers note that some of these purchases are not loan-financed, but they still assume that the majority is financed by bank loans. The market has also been supported by the government’s housing loan subsidy program for young first-time house buyers introduced in 2017 and the reduction of the real estate transfer tax since 2019. According to the CNB’s housing price index, real estate prices are now beginning to reach pre-crisis levels. Staff recommended that housing prices should be monitored with a holistic approach taking into account mortgage lending, general purpose loans that might be diverted to real estate, as well as government housing subsidies on the demand side. Also, the impacts that the current tourism boom and tourism rental taxation policies have on the supply of housing for purchase need to be taken into consideration. The mission welcomed current research efforts of the CNB to better gauge housing affordability.”