All data undergoing the human verification process is cross correlated with external sources such as Land registry, The Office for National Statistics and the Council of Mortgage Lenders. This ensures our accuracy standards remain as high as the Government's.

Macro economic factors such as base, LIBOR and UK growth indicators is input into the data. A monetised PESTEL analysis which reduces risk and allows mathematical analysis of risk allocation. For example real wages, non-farm payroll, lending products and property related taxes are all taken into account to determine the likely liquidity of our product. The liquidity in turn gives us cash turnover, a key performance indicator for the Group.
Research includes competitor data, collated, verified and benchmarked against our developments.
Understanding what external factors could do to our risk position within a given development means we get up-to-date guidance on how our investment decisions are shaped. In other words, we have a dynamic investment approach based on statistical analysis and risk allocation systems. 
Competitors guide design decisions. IMA work off a baseline scenario in a given project as to what the bare minimum in terms of design entails, however in order to achieve higher prices and increased liquidity, we need to know and understand the exact requirements and nuances of Our target demographic. Our Research team, collates and updates competitor data on a weekly basis, passing key information onto IMA Design to help shape and mould each development on a perpetual basis. This keeps our developments ahead of and superior to any of those of our immediate competitors. 
Knowing exactly what sells, at what level, how long it takes to sell and who we can rely on to actually sell it - all within a matter of a few minutes is invaluable when appraising certain investment opportunities. IMA receive on average 30 opportunities a month. Those that we pursue take on average 400 man hours to analyse in detail - before purchase. Without the systems we have in place, it would be impossible for IMA to apply so much depth to every deal. 
Data we have can be 'zoomed' down from a postcode, down to street level and further to building level. Within a building, the various floors attract various differences in marketing and actual strike prices. Our data tells us exactly what this variance is, in percentage terms and therefore we can guide our developments accurately and allow our finance teams important data such as, actual expected revenue; and in what timeframe. 

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