Identifying Next Investment Destination
This is a business problem everyone faces. In fact, all investment decisions are futuristic and one of the biggest challenge to the investor is to decide where to invest for a better return on investment. Believe me, it is not an easy task. Till a few years back, one of the most followed path to decide on this was simple gut based decision. Please do not think that gut based decisions were complete failures. No, those were not. There is a critical reason behind why gut based decisions did not fail completely. The Indian market had lot more open space for investors and marketeers to explore. Prime reason was limited competition, limited product, limited exposures of the consumers and finally, limited purchasing power. The consumers have changed to a large extent, especially the middle income group. With added affordability, easy of access to consumer finance has added a booster for acceleration to consumption propensity. Demonstration effects in the consumption market allowed more power to sellers to exploit the sentiments of the consumers. Overall, the consumption space has opened up and the spread has increased significantly. While in the known big markets, tooth and nail completion is visibly evident, companies are targeting vast unexplored markets, including rural market, to drive business growth. Everyone looks for fast mover advantage today. With this, location decision for investment has emerged as one of the key strategic need for everyone.
It always reminds me an example from the road. I am sure all of you have noticed that the cars on road follow the ones in front of them quite blindly. If the car in front is driving at 60 km per hour, the next car follows him on the same speed. And, unfortunately, the Indian drivers do not maintain the minimum distance to be kept between two cars while driving. And, we see the consequence of the same, at least one accident every day, because of this process. In case the front car needs to apply an emergency brake, the car following it invariably bumps onto it, unless maintains the safe distance and the driver is not concentrating on road. Exactly the same happens in case of business also. Let me share a fact that I came across in several meetings with clients from different sectors. When discussing about likely research on where to invest next, I generally ask how they decided in the past. The most common answer was “following the competitors”. Perhaps it will be easier for you to connect with certain examples. Think of banks in your location. You will find that in many of the urban areas, which are generally known as relatively affluent, different banks have opened up their branches next to each other. The same is true for automobile dealership, quick service restaurants like Pizza Huts, Dominos, McDonalds etc. The thought process behind the same is if my competitors are there, there must have potential for business. Let me follow them. The same principle is followed by most of the drivers on road. If the car in front is driving at 60 km, the road must be clear. I can also relax and drive at the same speed. And, that causes the unwanted incidents, both on road and in business!
This can be addressed to a large extent with the help of spatial economic analysis. We can visualize such solutions through a simple example. Let’s take an example of hospitality sector. Assume a hotel chain is planning to open 30 new mid-range (3 star or 4 star) hotels in India. They already have about 20 hotels in Delhi, Mumbai, Bangalore Pune, Chennai and Hyderabad. Their plan is to expand the chain to the locations where they are not yet present. And, they want to capture the markets that are relatively unexplored by most of the other renowned hotel chains. Also, they need to cover up the investment in 3 years because of cost of capital. These two constraints pose a challenge to the decision makers. Based on common sense and general knowledge one can think of large number of locations, especially tourist destinations, as identified locations for new hotel locations. But, these may not serve the purpose of recovering the investment within three years in each of these locations, which is a mandatory condition as mentioned above. Spatial economics can help solving this problem with the help of scientific approach.
To understand the process of using spatial economics for the purpose, we can think how a state level analysis can be done for this problem. To keep it simple, what are the key information areas one needs to focus on to crack a solution. A few examples are given below:
- Who are the likely customers for these hotels? – The target group to focus on. This requires quite a few minute information areas. For example, who are overnight visitors to the state, their purpose of visit, their likely duration of stay, likely spending pattern on accommodation and F&B (Food), do they travel as a group, or family or individual backpackers, probability of repeat visits etc.
- What are the likely income group of these visitors, what are the state of origin, how that income group is going to change over a period of time, how the economy of the origin states’ are going to change in future?
- What are the profile of the states in terms of competitions’ presence, which are the competition to be targeted to seize customers from and the similar questions
- What are the supporting infrastructure of the state to run business, state policies towards hospitality sector development, accessibility, crime situation and the similar factors.
If we take a stock of all these factors and try to understand those from a quantitative perspective, it can be listed as 30 to 40 factors, including current and future perspective, that need to be evaluated to understand potential of each state as locations for new hotels of this chain. Some of those variables are extremely relevant and some may be less relevant. At times, it is not that easy to understand the degree of relevance of a variable from common sense understanding. We will come to that later. Before that, let us see what key problem we face, when we have a large number of variables to consider for a phenomenon. Let us take a small example of similar nature that involves multiple variables. Assume there are 5 students in a class. We need to pick up the best student for a project which require sharp knowledge in Physics, Maths and English. Following are the marks obtained by the students in last examination and we need to choose the best student based on this marks.
As we see from the above table, each student scores differently in different subjects. The top ranking student in Physics is poor in Maths and English, the top ranking one in Maths is poor in Physics and English and the top ranking student in English is poor in Physics and Maths. Therefore, we cannot judge the best student who is good in all three subjects looking at each subject separately. Thus, we add up numbers of all three subjects and see who has scored the highest. In case of this example, the student with name “Z”, ranked 2nd in both Physics and Mathematics and 3rd in English. But his aggregate score is way above the rests’. Therefore, Z is the best person for the project since he has proficiency in all three subjects. Technically what we have done in the above example is combining three variables linearly assuming the variables are equally important.
When we face the given problem of the hotel chain, we come across exactly the same constraints when we need to consider large number of variables that reflect best potential locations for new hotels. We follow the similar approach. We construct a composite index combining the relevant variables that reflect potential for hospitality market. However, in real life situation, most of the time the importance of each variable chosen will vary from the point of view of reflecting potential. Therefore, one may require to know what will be the appropriate importance level or weight to assign for each variable according. Many a times subjective weights, based on gut feeling of the researcher, are used. However, it is avoidable since this may involve individual research’s bias. There are econometric and statistical tools to arrive at appropriate weights. With these weights one can combine these variables and arrive at the composite index. Based on the value of the composite index, that locations/states can be prioritized for opening new hotels.
This is a more scientific and robust methodology one can use while facing a problem like this. This not only ensures that one selects the right locations for better returns on investment, but also ascertains that the investment decision is not a loss making proposition. If one can choose the parameter/variables properly, considering the current as well as future envisaged changes, this is the right guiding principle for “where do I invest next”. One word of caution. Research of this sort is highly data and researcher’s skill dependent. It is like cooking a special dish, say Biriyani. Biriyani cannot be cooked unless you have all ingredients. But, every cook cannot produce the same taste of Biriyani even if being provided with exactly same ingredients in same quantity. Therefore, the quality of the cook is also extremely critical to bring that mouth-watering taste in the biriyani that you look for. The same principle applies to researches like this too.