Sterling per square feet per annum or Euros per square meter per month?
Not too complex a question if you have a few properties outside the UK in mainland Europe and you try to compile rental levels to perform a simple comparison between your properties in different markets. Add to that consolidating PnL’s and forecasts from various underlying vehicles from seemingly disparate source systems and service providers.
And how all these operations and processes happen? You answered correctly: manually in Excel!
All the rent rolls are the same – or are they?
I have been reviewing a lot of rent rolls – from Norway, Sweden, Germany, Netherlands, and the UK. What so striking is that basically they all contain the same information – a list of lease agreements, units and tenants (including vacant ones) – but are structured differently every time I encounter a new spreadsheet.
This is compelling especially for cross-border portfolios, where asset managers need to consolidate basic figures from various sources – with different currencies and underlying business conventions, like ”Is the rent expressed as monthly or annual?”, ”Are these square meters or feet?”, ”Is this net or gross lease?”, etc.
This is why Excel has been and still is the number one tool out there in the market. It’s all a manual process.
Furthermore, the case of poor data quality comes up because local service providers have been desperately trying to parse information together to fulfill the client’s Excel template.
Poor data quality leads to frustration and wasted time
I wonder where valuations of bigger portfolios are really based upon. Maybe all the manual flaws and discrepancies evaporate into statistical noise?
Also, no brainer is the simple ruling for quality check. Once, I spoke with an asset manager of a big CRE investment corporation who had been investing a pile of cash to create a system out of scratch for basic data validation.
After running a test once against this validator, he said that he identified 700 errors in his own rent roll data set! And he openly confessed that he thought that his own data was intact. This was quite an eye-opener.
Can you fix a process if the data is broken?
The answer is obvious.
So should we get our act together and have first good data before rushing into big data?
Since it is such a common problem in the industry, we have decided to do something about it. I am a true believer of data consolidation tools. I also encompass Standard APIs to make the data seamlessly flow between systems and apps – not to mention the substantial saving of time and manual effort!
