Abstract

PurposeThe purpose of this paper is to compute an aggregate misalignment index using a multiple indicator approach to identify under- or over-valuation of house prices in Malta based on fundamentals.Design/methodology/approachA total of six indicators are used that capture households, investors and system-wide factors: the house price-to-Retail Price Index ratio, the price-to-hypothetical borrowing volume ratio, price-to-construction costs ratio, price-to-rent ratio, dwelling investment-to-GDP ratio and the loan bearing capacity. The weights are derived using principal component analysis. The analysis is performed using both the house price indices of the National Statistics Office (NSO) and the Central Bank of Malta (CBM), which are based on contract and advertised prices, respectively.FindingsHouse prices in Malta were overvalued by around 20 to 25 per cent in the pre-crisis boom. This disequilibrium started to be corrected following the decline in house prices, with the CBM and NSO house price cycles reaching a trough in 2013 and 2014, respectively. At the trough, house prices were undervalued by around 10 to 15 per cent. Since then, house prices started to recover although the recovery in advertised prices was more pronounced compared to that based on contract prices. In mid-2017, advertised house prices were slightly overvalued, while contract prices still have to reach their equilibrium level. The dynamics from the misalignment index, including its peaks and troughs, are remarkably similar to the range derived from statistical filters.Practical implicationsEstimates of house price misalignment have both economic and financial stability implications.Originality/valueThis paper allows for a decomposition of the house price cycle, tailored for the particular characteristics of the Maltese housing market. It also takes into account the relationship between house prices and private sector rents, which in recent years have been buoyed, among other factors, by the high inflow of foreign workers and changing patterns in the tourism industry.

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