The increased use of aggregated data in property risk processing builds on the rapidly evolving economic benefits of automated 'big property' solutions in land and property assessment. This trend shadows the evolution of personal credit files as open banking trends making aggregation of people with property far easier in the 1/10th-second ranges that drive many of today's 'decision in principle' consumer solutions. The organisation of property with known geolocational parameters, unique property identifiers and associated property data attributes has been an accelerating phenomenon over the last decade. Chartered surveyors have not, however, benefitted substantially from these innovations and emerging trends in data organisation and aggregation. Legacy processesin the corporate environment and 'cost of change' in the private client sector have combined to stifle real progress in integrating modern data systems, machine learning (ML) and data capture and retention. Existing and new technologies, data availability, data aggregation, data storage and geospatial referencing should significantly improve reporting and recommendations relating to property condition and risk profiling; however, the nature of legacy systems, the differing requirements of large corporate clients versus private clients and the discontinuities in data are all hampering modernisation of approach. This paper looks at the evolution and convergence of big property data and provides a roadmap for the surveying profession and the sectors instructing clients to benefit from modern data processing and innovation in property risk.
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