_Why do some countries receive more inbound real estate investment than others?

The world of real estate is globalising. The volume of cross-border transactions has grown by 80% over the past five years, but that increase has been heavily concentrated within a limited number of locations. In fact, the top five countries by capital inflows have typically accounted for well over 60% of total cross-border investment over the decade.

The UK has been the top destination for cross-border capital for six of the past ten years. So why do relatively small, medium growth countries such as the UK attract more inbound capital than larger or faster growing rivals? The answers are well rehearsed: they benefit from a significant market size, with large and high quality assets, good levels of transparency, consistency in the rule of law, to name just a few.

Of course there are also softer factors too, such as familiarity, and these often play an equally valid role in the choice of investment location, particularly for many firsttime overseas investors.

But is there a danger that accepted notions of attractive investment markets overshadow more quantifiable data on factors such as demographic trends or income growth prospects? If we were to account for these factors, would we expect some markets to receive more inbound investment than they do at present?

Gravitational pull

In order to put the question to the test, we have developed our own version of a gravity model to analyse cross-border real estate investment inflows. The aim was to identify markets that currently receive less inbound capital than their demographic, economic and business environment rankings might suggest.

Gravity models are common in the field of international trade, helping to predict the flows between location based on mainly economic factors, yet there has been little application  of the technique to real estate investment.

By testing the model with a large number of different inputs, we were able to refine it to the point where it explains 80% of the variation in annual investment flows between countries. As a sense check, investment volumes for the US and UK - arguably the most developed cross-border markets – were predicted to within a few per cent of actual levels.

We explored around 40 variables for this model, carefully analysing the importance of each while bearing in mind factors such as the likely high level of correlation between the variables.

For this version of the model we focused on identifying the key factors that explained the most variation in direct real estate investment flows.

Many of the factors that we initially identified were discarded due to high correlation with other key factors such as GDP. Variables that were theoretically promising such as the country’s GINI coefficient (which shows wealth distribution), or using volume of flight routes between countries as opposed to a pure physical distance separation, were excluded as they were found to add no improvement to the existing model.

Interestingly, the income tax rate and the number of days it takes to complete a property deal were positively correlated with deal flow. These variables are likely to be acting as proxies for other factors such as a country’s economic health or level of development in the eyes of an investor.

Future hotspots

Comparing the volume of inflows predicted by the model with the actual volume of transactions seen in 2017 gives an idea of the theoretical potential for additional investment.

The results showed that there are a number of countries and regions that might reasonably be expected to see significantly higher levels of cross-border inflows each year, based on the factors that typically drive inbound investment. In the graphic on the previous page, we have grouped these countries by region and indicated their combined potential for additional annual investment.

Of course, some markets have well-understood reasons for seeing a lower-than-expected volume of inbound investment. In Canada, for example, the high number of sophisticated, locally based global investors can effectively “crowd out” demand from foreign investors. Other countries operate constraints such as ownership restrictions, which preclude investment from non-domestic sources.

Fundamentally, however, the model accords with our outlook for global capital flows. We do not envisage the demand for real estate in the established European market slowing in the short term, but we recognise that the hunt for returns is causing investors to give greater consideration to emerging markets. Our model demonstrates that there is a quantifiable logic to these trends.

Few of the hurdles to inbound investment are insurmountable in the longer term. We predict that the fixed or binary factors currently identified as drivers of investment, such as location, language and colonial ties, will become less important over time. Instead, variable factors such as transparency, economic growth and market liquidity will play a stronger role in determining the volumes of capital inflows to real estate.

The model in detail

Past studies have tended to focus on analysing the total amount of direct real estate flows into countries, rather than on the flow of direct real estate investment between an/the origin country and destination country. We have used a spatial interaction model, also called a gravity model, to analyse these flows.

Such models have long been used to predict levels of cross-border investment and trade. They start from the premise that there are certain factors that can generate investment in one country, and certain factors that can draw that investment towards a specific destination. In addition, there is a cost, usually physical distance or a monetary cost, which grows with the measurable separation between origin and destination country.

For this particular research, which is part of a wider study, we have focused on the destination aspect of direct real estate investment flows by using an origin-constrained spatial interaction model.

We were interested in identifying which factors best predict the flow of direct real estate investment as well as using the resulting model to analyse countries that were over- or underinvested in according to the model. We can then look at these countries to see if there are obvious reasons that explain the findings.

Data used in the model was sourced from the World Bank, The Heritage Foundation, MSCIand the CEPII research project.

The model produced a highly satisfactory outcome in that it explained over 80% of the variation in investment flows. The final model found that shared ethnographic elements such as common official language, religion and a colonial history increased the amount of direct real estate investment between countries.

Not surprisingly, a country’s economic productivity and wealth were found to be linked to the volume of investment that was likely to be received by that country.

We found that the greater the dispersion between the origin and destination countries’ scores in the Index of Economic Freedom, the lower the investment between those countries.

Currency fluctuations also played an important role in predicting the flows between countries. Countries that share a border with each other were more likely to see greater investment levels, which ties in with the finding that as the physical distance grew between two countries the flow of real estate investment fell.