Comparing different methods for the Maltese property price index

Abstract

This paper illustrates the computation of property price indices for the Maltese properties, using different property price index methodologies including: hedonic regressions, median, average, weighted average and weighted median methods. According to literature, the hedonic regressions are the preferred methodology as they cater for housing quality. The data used for the purpose of this dissertation is based on quarterly advertised prices compiled by the Central Bank of Malta on the basis of information published in the ‘Times of Malta’. The use of advertised prices is one of the limitation of this study as transaction prices should be used. The dataset permits the computation of property price indices for the years 2010 to 2013. The computation of the Property price index will consider several aggregation methods such as Laspeyres, Paasche and Fisher index as well as a chain –linked indices of these aggregation methods. The results from this study were compared to the property price index of the Central Bank of Malta since both are based on advertised data. The results show that all indices in general seems to move in the same trend however each method seems to have different turning points. The Central Bank of Malta index also moved similar in the same direction to the other indices and was also coherent to the weighted median method computed by the author. The double hedonic imputation method and the central bank of Malta index seemed to fluctuate less than the other methods albeit still moving in the same trend, with the least volatile index being the double hedonic imputation method. This index possessed ideal properties for an index as when calculating changes in inflation had the least price ambiguity among the other methods and also took into account changes in housing quality. The three year period growth rates of the different indices differed substantially and were not in line with the Central Bank of Malta index as the majority of the indices had negative growth rates whilst the Central Bank of Malta had a positive growth rate. The great discrepancies in the growth rates, shows the difficulty to interpret trends in property price indices. Therefore it is important that these indices have desirable properties such as low volatility, adjusting for housing quality and intensive data to explain changes in housing quality as these indices are used as tools for example analyzing financial stability. The use of advertised data gives an inaccurate short term pictures of the housing market and may not be a good indicator for likely future developments. The implications of different measures of trends in policy prices for policy making and decision making with regards to property price indices, may give different interpretations of property prices causing ambiguity among policy making and decision making.