Growth in rental rates was slower in 2019 than in the previous year, although it remained elevated from a historical perspective, according to research conducted by economists at the Central Bank of Malta.
In a statement published on Friday it was explained how in 2020, slower growth in rental rates was amplified by the effects of COVID-19, which led to an 11% drop during the second quarter.
COVID-19 also led the annual growth of rental rates to turn negative during the first quarter of 2020, where advertised rental rates were between 3% and 3.5% lower than they had been during the corresponding quarter of 2019.
A more pronounced drop in rental rates was observed in the second quarter of 2020, with rental rates decreasing by 11%. This period started to reflect more fully the impact of containment measures worldwide, including Malta, to address the spread of the COVID-19 virus, with the drop in rates driven by a combination of reduced demand for and increased supply of rental units on the market.
The Bank’s annual Research Bulletin includes a detailed article explaining the development of a quality-adjusted residential real estate rent index, as well as the linkages between house prices, inflows of foreign workers and domestic consumption. It also includes a comparison of the results of Malta’s Household Finance and Consumption Survey with those of Euro Area Member States and other participating EU countries, and an extension of one of the macroeconometric models used by the Bank, known as STREAM.
The Central Bank said that its approach to analysing the residential real estate rental market rests on the use of ‘Big Data’ from publicly available sources so as to get a better understanding of the evolution of private-sector rents after taking into account quality adjustments. The database now comprises 21,883 listings of advertised rental rates, and takes into account details concerning the type of property, location, size and other attributes that may have an impact on rental rates.
It was explained how until the last quarter of 2018, the database consisted of solely two housing types; apartments and maisonettes, along with information about the locality in which the property is located and the number of bedrooms.
Starting in 2019, this information was supplemented by the collection of data about penthouses, additional localities that were previously not incorporated and other observable property characteristics. Information on attributes such as the availability of a garage, garden or pool facilities and instances where a property is advertised as being on or close to a seafront or enjoying some view, also started to be collected.
This approach can be used both for estimation of over/under-valuation of rental rates and to construct the growth rates of quality-adjusted private sector rental rates over time.