Commercial Property Price Dynamics

This is the second part to the earlier post.

In summary, the framework returns the following predictions regarding the sensitivity of house prices to different drivers: 

• prices should be higher when households’ disposable income and population growth are higher;
• lower when interest rates are higher; and 
• higher when credit conditions loosen and the footprint of international investors is higher.

At the heart of the model is the observation that market forces should adjust to make households indifferent between buying and renting. There is a ”user cost relationship” linking house prices, rents and interest rates. According to this relationship, market forces should equate the cost of renting – the rent/price ratio – to the cost of home ownership – the interest income forgone upon purchasing a
property net of expected house price appreciation

A second relationship represents demand for housing, and states that rents are higher when the stock of housing is lower, but also that rents increase when households have more income to spend on accommodation services and when population increases. 

The model is completed by a third relationship capturing housing supply, which assumes that new housing is built when house prices increase relative to the marginal cost of building new dwellings. The framework implies that house prices increase when interest rates fall; when disposable income and/or the size of population increase; and when building costs rise.

Since housing purchases are typically financed through mortgage loans, and many potential homeowners are constrained in the amount of credit they can obtain, credit supply ought to impact house prices. Pressures on the housing market may also arise from abroad – for example, because foreign investors with abundant liquidity and a high willingness to pay acquire property in local residential markets

An increase in credit availability and a loosening of borrowing constraints, for example, is likely to reduce the interest rates faced by households and push prices up.

The sensitivity of house prices to drivers varies across jurisdictions depending on the structural characteristics of each economy. For example, the interest rate sensitivity of prices ought to be greater in countries where mortgages are predominantly adjustable rate rather than fixed rate. More broadly, changes in housing demand drivers should have a larger impact on house prices in countries where the price elasticity of supply is low. Price sensitivities to demand drivers could also be expected to be greater in jurisdictions with tax incentives for home ownership such as tax deductibility of mortgage interest payments.

How about Commercial Real Estate?

The framework for thinking about commercial real estate markets – the user cost model – is the same as that for residential, with the exception that the drivers are somewhat different because most commercial real estate is purchased for the purpose of generating rental income instead of for the consumption of housing services. 

The framework links commercial property prices, the supply of commercial space and commercial rents (endogenous variables) to each other, as well as to returns on alternative investments (including interest rates), GDP growth (demand drivers) and building costs (supply drivers). Concretely, in the commercial real estate case, market forces should make investors indifferent between purchasing commercial properties and acquiring other assets. In other words, commercial real estate prices should equate the returns on holding commercial property – capitalisation rates – to the risk-adjusted returns on alternative investments (eg bonds and stocks), which tend to increase with interest rates. In addition to this user cost relationship, the framework includes a “commercial space demand” relationship linking rental income to the stock of available commercial space and economic growth: as the economy expands, businesses increase demand for space and rental income increases. The model is completed by a supply relationship postulating that new commercial space is built when prices increase relative to the marginal cost of building. It implies that commercial real estate prices increase when

• interest rates fall; 
• when increases in economic activity push up rental income; and 
• when building costs rise.

The framework can be adapted to characterise the sensitivity of commercial property prices to changes in global factors. Suppose that capital inflows into commercial property markets were large enough to make the “marginal” buyer of property a foreign rather than a domestic investor. Assume also that the returns on alternative investments were relatively lower for foreign investors. Then the relevant “user cost” underpinning price adjustments would fall, and commercial property prices would have to rise for foreign investors to still be indifferent between domestic commercial real estate property and other assets.

To sum up, the framework returns the following predictions about the sensitivity of commercial real estate prices to different drivers: prices should be higher when GDP growth is higher; lower when interest rates and returns on alternative investments are higher; and higher when the footprint of international investors is larger.

The sensitivity of commercial real estate prices to drivers is thought to vary across jurisdictions depending on the structural characteristics of each economy. In particular, changes in demand drivers should have a larger impact on commercial real estate prices in countries where strict zoning regulations dampen the elasticity of supply.

For more details please refer to the paper here. What are your views on the drivers, and how will your view influence your property investment decisions?