Investors often ask us how scalable our business model is and if we have got plans to open further ranking desks in other cities. The short honest answer is that we are ‘relatively scalable’!
This is often surprising , for a company that bases most of its day to day work analysing properties remotely (click here for our blog entry on the remote analysis of properties and the HITs model). Surely, we are told, you can start analysing properties in Shanghai, Melbourne & Toronto and provide global weekly rankings from some of the world’s leading residential markets from the comfort of your London desk.
There are four main tests that would determine Rankdesk’s analysis of properties in a new city.
1. The quality of the property data provided by the leading estate agents in a city. Our analysts rely on a minimum level of information in order to provide a preliminary assessment of the quality and investment performance of the property. Interestingly, the London market is characterisied by a culture of rich property data with at least 65% of the Agents in the market meeting our minimum property data requirements. These include:
- A floor plan with a clear demarcation of the North orientation
- Photos of each room and the exterior of the building
- A location map
- Tenure details
- Property specific expenses such as service charges
- A property description
The quality of property specific data is particularly relevant in markets where agents are answerable only to vendors.
2. Secondly, we rely on rich information at the city or regional level. We would typically consider residential markets where there is a history of property price movements (based on actual transactions) in at least 2 property cycles. This data would also show us if the residential market in question is ‘built on solid foundations’ or on actual demand (growing city economy, labour market dynamics & household formation) rather than just speculation. This makes Rankdesk particularly suited to more mature residential markets with their equivalent growth rates and yield expectations.
3. Local Knowledge. Because of the very ‘local knowledge’ required to understand the quality and investment performance of a property in a specific neighbourhood, analysing a property from a foreign based ranking desk will provide abstract results. The HITs model would technically allow us to carry out this analysis, but having a centralised hub of analysts who are not fed ‘local’ data from on site observation of neighbourhoods and comparable properties would make Rankdesk less diligent. We are even going a step further and exploring the idea of a premium service where we would carry out an actual site visit of a property if requested by a client.
4. The openess of the market to foreign investment and to foreign ownership of property. Bringing a property on our rankings that would have equal relevance to home purchasers as well as to non-domiciled owners increases our user base exponentially.
We would love to hear of any cities that our users would find relevant in our lists and that meet the criteria above!