Purpose: There is a growing concern in recent years regarding climate change risks to real estate in the developed and developing countries. It is anticipated that the property sector could be affected by variable climate and related extremes as well as by the strategies adopted to combat greenhouse gas (GHG) emissions. This paper aims to analyse the current knowledge regarding future climate changes to understand their possible impacts on the real estate sector of Malaysia with an aim to help stakeholders to adopt necessary responses to reduce negative impacts.Design/methodology/approach: Available literature is reviewed and data related to climatic influences on buildings and structures are analysed to understand the climate change impacts on real estate in Malaysia.Findings: The study reveals that temperature in the Peninsular Malaysia will increase by 1.1 to 3.6°C, rainfall will be more variable and river discharge in some river basins will increase up to 43 per cent during the northeast monsoon season by the end of this century. These changes in turn will pose risks of property damage and increase property lifecycle costs. Furthermore, property prices and the overall growth of the property sector may be affected by the government policy of GHG emission reduction by up to 45 per cent by the year 2030. This study concludes that the property sector of Malaysia will be most affected by the implementation of GHG emission reduction policy in the short term and due to the physical risk posed by variable climate and related extremes in the long term.Originality/value: The study in general will assist in guiding the operational responses of various authorities, especially in terms of those interventions aimed at climate change risk reduction in the property sector of Malaysia.

A genetic programming (GP)-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environmental Prediction reanalysis dataset at 42 grid points surrounding the study area was used to select the predictors. GP models were developed for the downscaling of three extreme rainfall indices: days with larger than or equal to the 90th percentile of rainfall during the north-east monsoon; consecutive wet days; and consecutive dry days in a year. Daily rainfall data for the time periods 1961–1990 and 1991–2000 were used for the calibration and validation of models, respectively. The results are compared with those obtained using the multilayer perceptron neural network (ANN) and linear regression-based statistical downscaling model (SDSM). It was found that models derived using GP can predict both annual and seasonal extreme rainfall indices more accurately compared to ANN and SDSM.

Changes in bioclimatic indicators can provide valuable information on how global warming induced climate change can affect humans, ecology and the environment. Trends in thermal bioclimatic indicators over the diverse climate of Iran were assessed in this study to comprehend their spatio-temporal changes in different climates. The gridded temperature data of Princeton Global Meteorological Forcing with a spatial resolution of 0.25° and temporal extent of 1948–2010 was used for this purpose. Autocorrelation and wavelets analyses were conducted to assess the presence of self-similarity and cycles in the data series. The modified version of the Mann–Kendall (MMK) test was employed to estimate unidirectional trends in 11 thermal bioclimatic indicators through removing the influence of natural cycles on trend significance. A large decrease in the number of grid points showing significant trends was noticed for the MMK in respect to the classical Mann–Kendall (MK) test which indicates that the natural variability of the climate should be taken into consideration in bioclimatic trend analyses in Iran. The unidirectional trends obtained using the MMK test revealed changes in almost all of the bioclimatic indicators in different parts of Iran, which indicates rising temperature have significantly affected the bioclimate of the country. The semi-dry region along the Persian Gulf in the south and mountainous region in the northeast were found to be more affected in terms of the changes in a number of bioclimatic indicators.