Environmental risks like air pollution and indoor mould and dampness are reported to cause asthma-type illness and death for 11 million people, or 44% of the worlds population. In turn, primary prevention opportunities are focussed on housing and community awareness. However, this work is premised on the successful identification of occupational or in-the-home asthmagens. To this end, we have collected data from one hundred Melbourne homes to quantify the levels of viable airborne fungi. Indoor air quality (IAQ) and mould inspections are increasingly requested by owner occupiers, tenants, landlords, property managers and insurers. Usually this is to provide environmental health type services to quantify hygiene and risk caused by hidden, suspect or overt water damage and/or mould. The aim of this study was to investigate the use of text cloud processing to uncover those dominant Genus and Species of fungi that are statistically associated with each indoor living environment suspected of having a mould problem. Air quality in each home was analysed using a microbial air sampler onto potato dextrose agar across all rooms. Fundamental or outlier signatures in the indoor microbiome are increasingly attracting attention for use as microbiological indicators of human behaviour within the built environment. Potentially, they can be used for forensics apart from their utility for standard air quality assessment. We exploit word clouds as a data visualisation method for extracting the dominant syntax in a data set and display this in a visually semantic form that is easy to interpret. We saw that across different homes and room-types, that there was excellent correlation of Taxa against previous studies that used non-viable spore traps. The data showed how word clouds summarised the biogeographic frequency distribution revealing the most common Genera from: Aspergillus, Penicillium, Cladosporium. This work is important for analysing statistical trends in IAQ data in ‘before’ and ‘after’ mould remediation situations, especially with regard to accuracy and for minimising potential fraud points in water damage insurance claims.