My individual square of land, 9 meters on 9

My editorial

And so I am building something with a strategy. Last year, 365 days ago, I was just beginning to play with scientific blogging. Now, I have pretty clear a vision of how I want to grow over the next 365 days. My internal bulldog is sniffing around two juicy bones: putting up a method of and pitching a product, relevant to the teaching of social sciences by participation in the actual doing of research, and, on the other hand, putting together an investment project in the domain of smart cities. In this update, I start developing more specifically on that second one, and I focus on two things. Firstly, I perceive smart cities as both technological and social a change, which develops through diffusion of innovation. Very nearly axiomatically, the phenomenon of diffusion in innovations is represented as a process tending towards saturation. I want to find a method, and, hopefully, the metrics relevant to measuring the compound size of the market in the phenomenon called ‘Smart Cities’, mostly in Europe. In the same time, I want to test the tending-towards-saturation approach in forecasting the size of this market.

The emergence of smart cities, as both an urban concept and a business model, is made of smaller parts. There is investment in the remodelling and rebuilding of infrastructure. On the other hand, there is the issue of energy, both in terms of efficiency in its use, and in terms of its renewable sourcing. Finally, there is the huge field of digital technologies, and, looming somehow at the horizon, the issue of Fintech: the use of digital technologies to create local, flexible monetary systems. I am collecting data, step by step, to acquire a really sharp view of the situation, and so my internal curious ape comes by that report ‘The State of European Cities’ , as well as by that article ‘Smart Cities in Europe’ , and finally it swings to that interesting website: ‘Organicity’ . There seem to be two common denominators to all the reports and websites on this topic: experimentation and teaming up. Cities build their smart cities in consortiums rather than single-handedly. Each project is an experiment, to the extent that ‘established technologies’ are essentially the opposite of what business people expect to invest in when they invest in a smart city.

When I teach my students the fundamentals of business planning, I ask them frequently to look at the business concept from two sides: that of the enthusiastic founder, and that of a conservative investor. The balance sheet of the new business is likely to take shape at the intersection of those two approaches. I am adopting the same approach with my business concept. If I were a truly conservative an investor, I would ask, among other questions, what type of business am I supposed to put capital into, and what is the workable business model. The main types of business that come to my mind, regarding smart cities, are: development of real estate, construction, and technical services. However smart a city is becoming, it is made of architectural structures: buildings, roads, rails etc. Someone owns them before the smart city starts burgeoning, and someone owns them when the smart city is already running. Question: are the someones who own those structures afterwards the same someones who owned them beforehand, or are they different someones? What does the transfer of property in real estate look like in those smart cities? Another thing that I teach my students is ‘When you know nearly nothing about a market or a business, look at the prices and the demographics in the first place. Is the market conflating with people, is it stationary, or is it deflating? Are there any prices, which you can qualify as equilibrium prices, i.e. prices virtually exogenous to the bargaining power of individual market players, and clearly sensitive to other economic variables? If you can spot such prices, what trends do they display?’. Right, so now, I am applying my wisdom for sale to my own plans, and I try to figure out those things for selected cases of smart cities, just to make my hand.

I have recently read a lot about a project of smart city, namely the ‘Confluence’ project in Lyon, France. The place is dear to my heart, as I spent quite a chunk of my youth in Lyon, and I am glad to return there, whenever I can. The Confluence Project is located in the 2nd district of Lyon, at what the locals call ‘Presqu’Ile’, i.e. ‘Nearly an Island’, and the confluence of two rivers: Rhone and Saone. From the bird’s view, it is like an irregular, triangular wedge, with its top pointing South at the exact confluence of the two rivers, and its base resting, more or less, on the Perrache rail station. I am having a look at the local prices of real estate. As I visit the website ‘Meilleurs Agents’  , I can see an almost uninterrupted growth in the price per 1 m2, since 1994. Surprisingly, even the burst of the housing bubble in 2007 – 2009 didn’t curb much that trend. Over the last 10 years, it means almost 31% more in the average price of square meter. I focus on the prices of flats. Right now, the average price in Lyon is 3 690 € per square meter, and that average is expected in a general span from 2 767 € to 5 535 €. Against this general background, I take a few snapshots at different addresses. First, I have a glance at a long street – Cours Charlemagne – which almost makes the longitudinal backbone of the Confluence wedge. The average price per 1 m2 is 3 783 €, in a range from 2 664 € to 5 040 €. That average is slightly higher than the whole city, but the range of prices has slightly lower extremities.

Cours Charlemagne connects the posh neighbourhood of Perrache, in the North, to really industrial a place, at the Southern junction of the two rivers. Thus, I take a closer focus, and I target those different environments. Angle of Quai Rambaud and Rue Suchet, a truly posh place in the Northern part of Confluence, displays an average price of 4 725 € per 1 m2, in a range from 3 186 € to 6 207 €. Yes, baby, it just rockets up. Now, I take a little stroll to the South, apparently advancing towards lower prices, and I call by Rue Paul Montrochet. It should be cheaper than up North, and yet it is not: the average price is 5 144 € per square meter, in a range from 3 461 € to 6 483 €.

As usually, observing reality has been of some value. Provisional hypothesis, based on the case of Lyon-Confluence: smart cities grow where the prices of real estate grow. Now, a bit of a bow to reverend Malthus: I check the demographics, with The World Population Review , and I show those numbers in Table 1, below. There has been, and there still is, quite a consistent demographic growth. Basically, if you calculate the annual average growth rates in, respectively, the price of 1 m2 in residential space, and the local population, those two rates look almost like twins: around 3% a year. My provisional hypothesis puts on some ornamentation: smart cities grow where the prices of real estate grow, and where population grows.

Table 1 The population of Lyon, France (urban area)

Year  Population Growth Rate (%) Growth
2030  1 814 000 3,72%  65 000
2025  1 749 000 3,98%  67 000
2020  1 682 000 2,81%  46 000
2017  1 636 000 1,68%  27 000
2015  1 609 000 3,74%  58 000
2010  1 551 000 3,68%  55 000
2005  1 496 000 3,67%  53 000
2000  1 443 000 2,78%  39 000
1995  1 404 000 2,48%  34 000
1990  1 370 000 2,54%  34 000
1985  1 336 000 4,54%  58 000
1980  1 278 000 8,49%  100 000
1975  1 178 000 5,56%  62 000
1970  1 116 000 8,67%  89 000
1965  1 027 000 13,61%  123 000
1960  904 17,71%  136 000
1955  768 5,06%  37 000
1950  731 0,00%  –

source: , last accessed January 11th 2018

The decision makers of the Lyon-Confluence project claim they are in some sort of agreement with two other initiatives: Vienna and Munich. I quickly perform the same check for Vienna as I did for Lyon. In this case, the initiative of smart city seems to be city-wide, and not confined to just one district. As for the prices of apartments, I start with the Global Property Guide . Apparently, the last six years brought a sharp rise in prices (plus 39%), still those prices started curbing down a bit, recently. A quick glance at Numbeo shows an average price of 7 017,18 € per 1 m2 in the city centre, in a range from 4 800 € to 10 000 €, and further out of the centre it makes like 3 613,40 € per square meter on average, comprised between 3 000 € and 5 000 €. On the whole, Vienna looks a shade more expensive than Lyon. Let’s check the demographics, once again with The World Population Review (Table 2, below). Quite similar to Lyon, maybe with a bit more bumps on the way. Interestingly, both initiatives of smart cities started to take shape around 2015, when both cities started to flirt with more or less 1,5 million people in the urban area. Looks like some sort of critical mass, at least for now.

Table 2 The population of Vienna, Austria (urban area)

Year Population Growth Rate (%) Growth
2030 1 548 000 0,98% 15 000
2025 1 533 000 2,06% 31 000
2020 1 502 000 2,32% 34 000
2017 1 468 000 2,09% 30 000
2015 1 438 000 6,28% 85 000
2010 1 353 000 7,89% 99 000
2005 1 254 000 4,33% 52 000
2000 1 202 000 -3,14% (39 000)
1995 1 241 000 1,89% 23 000
1990 1 218 000 -3,87% (49 000)
1985 1 267 000 -2,46% (32 000)
1980 1 299 000 0,23% 3 000
1975 1 296 000 0,15% 2 000
1970 1 294 000 10,13% 119 000
1965 1 175 000 10,85% 115 000
1960 1 060 000 13,25% 124 000
1955 936 12,64% 105 000
1950 831 0,00%

source: , last accessed January 11th, 2018

Good. As my internal curious ape turns and returns those coconuts, ideas start taking shape. At least one type of socio-economic environment, where that curious new species called ‘smart cities’ seem to dwell, is an environment where them growth rates in housing prices, and in population, are like 3% or more. One million and a half people living in a more or less continuous urban area seem to make like a decent size, in terms of feeding grounds for a smart city. Prices of residential real estate, associated with the emergence of smart cities in Europe, seem hitting like 4 500 € or more. This is probably just one type of environment, but one is already better than saying ‘any environment’. The longer I do social sciences, the more I am persuaded that we, humans, are very simple and schematic in our social structures. Theoretically, with the individual flexibility we are capable of, the science we have, and with Twitter, we could form an indefinitely diverse catalogue of social structures. Yet, it is more like in a chess game: there are just a few structures that work, and others just don’t, and we don’t even full comprehend the reasons for them not working at all. When we talk business and investment, there are some contexts that allow the deployment of a business model, whilst it just doesn’t work in other contexts. Same thing here: the type of environment I am casually sketching is the one where smart cities work in terms of business and investment.

My business plan for investing in smart cities has certainly one cornerstone, namely that of gains in the market value of real estate involved. One cornerstone is not bad at all, and now I am thinking about putting some stones under the remaining three corners. In that report which I mentioned earlier, namely report ‘The State of European Cities’ , I have already spotted two interesting pieces of information. Firstly, the sustainable density of population for a smart city is generally the same as for sustainable public transport: 3000 people per km2 or more. Secondly, the dominant trend in the European urbanisation is the growth of suburbs and towns, rather than cities strictly spoken. It pertains to my home country, Poland, as well. Thus, what we have as market, is a network of urban units moderate in size, but big in connections with other similar units. Two classes of business prospects emerge, then, regarding the investment in smart cities. Following my maths classes at school, I call those prospects, respectively, the necessary context, and the favourable context. The necessary is based on the density of population: the more we are per square kilometre, the more fun we are having, and the special kind of fun we can have in a smart city requires at least 3000 people per km2, or, in other words, each individual person having for their personal use no more than a square of 18 meters on 18. The favourable is made of real estate prices, and demographic growth, the former hitting above 4 500 € per 1 m2, and growing at 3% per annum, on average; the latter needs to make the same 3% a year.

By the way, I made a quick calculation for my family and our house. We live in a terraced house, located on a plot of land of 250 m2. We are three, which makes 83,6 m2 per capita, which, in turn, means that each capita has an individual square of land the size of 9,14 meters on 9,14. We are double the density of population required for a smart city. There is no other way: I have to go for it.

Educational: microeconomics and management, the market and the business model

My editorial

This time, in the educational stream of my blog, I am addressing the students of 1st year undergraduate. This update is about microeconomic and management. Regarding your overall educational curriculum, these two courses are very much complementary. I am introducing you now into the theory of markets, and, in the same time, into the managerial concept of business model. We are going to consider a business of vital importance for our everyday life, although very much unnoticed: energy, and, more specifically, electricity. We are going to have a look at the energy business from two points of view: that of the consumer, and that of the supplier. If you have a look at your energy bill, you can basically see two lines: a fixed amount you pay to your supplier of energy, just for being connected to the grid, and a variable amount, which is, roughly speaking, the mathematical product: [Price of 1 kWh * Quantity of kWh consumed]. Of course, ‘kWh’ stands for kilowatt-hour. On the whole, your expenditure on electricity is computed as:

E = Fixed price for connection to grid + [Price of 1 kWh * Quantity of kWh consumed]

                             P1                                                                 P2                                 Q   

From the point of view of the supplier of energy, their market is made of N consumers of energy. We can represent this market as a set made of N elements, for example as N = {k1, k2, …, kn}, where each i-th consumer ki pays the same fixed price P1 for the connection to the grid, the same price P2 for each kWh consumed, and consumes an individually specific amount Q(ki) of energy measured in kWh. In that set of N = {k1, k2, …, kn} consumers, the total volume Q of the market is computed as:

Q = Q(k1) + Q(k2) + …+ Q(kn) [kWh]

…whilst the total value of the market is more complex a construct, and you compute it as:

Value of the market = N*P1 + Q*P2

  Most consumers have a more or less fixed budget to spend on electricity. If you take 1000 people and you check their housing expenses every month, you will see that their expenditures on electric power are pretty constant, unless some of them are building spaceships in their basements. So we introduce in our model of the market a budget on electricity, or Be, specific to each individual customer ki. Hence, that budget can be noted as Be(ki). Actually, that budget is the same as what we have introduced earlier as expenditure E, so:

Be(ki) = E = P1 + P2*Q(ki)

This mathematical construct allows reverse engineering of individual power consumption. Each consumer uses the amount Q(ki) of kilowatt-hours, which satisfies the condition:

Q(ki) = [Be(ki) – P1] / P2

In other words, each of us has a budget to spend on electricity bills, from this budget we subtract the fixed amount of money P1, to pay for being connected to the power grid, and we use the remaining sum so as to buy as many kilowatt-hours as possible, given the price P2. This condition is a first approach to what is called the demand function, on the part of the consumers. Although this function is still pretty sketchy, we can notice one pattern. The total amount of electricity Q(ki) that I consume depends on three parameters: my budget Be(ki), and the two prices P1 and P2. In economics, we call this an elasticity. We say that the quantity Q(ki) is elastic on: Be(ki), P1, P2. How elastic is it? We can calculate it, if we now the magnitudes of change in particular factors. If I know that my consumption of electricity has changed from like 40 000 kWh a year to 42 000 kWh, and I know that in the meantime the price P2 of one kilowatt-hour has moved from 0,1 euro to 0,12 euro, I can calculate something called deltas:

delta [Q(ki)] = ∆ Q(ki) = 42 000 40 000 = 2 000 kWh

delta (P2) = ∆P2 = €0,12 €0,1 = €0,02

The local (i.e. specific to this precise situation) elasticity of my consumption Q(ki) to the price P2 can be estimated, in a first approximation, as

e = ∆ Q(ki) / ∆P2 = 100 000 kWh per €1

The first thing to notice about this elasticity is that it is exactly contrary to what you see in my lectures, and what you can read in textbooks, about the demand function. The basic law of demand says something like: the greater the price, the lower the consumers’ willingness to buy. Here, we have something contrary to that law: greater consumption of energy is associated with a higher price, through a positive elasticity. I am behaving contrarily to the law of demand. In science, we call such a situation a paradox. Yet, notice that it is a local paradox: I cannot keep on increasing my personal consumption of electricity ad infinitum, even in the presence of a constant price. At some point, I have to start saving energy and increase my consumption just as much, as the prices possibly fall. So, generally, as opposed to locally, I am likely to behave in conformity with the law of demand. Still, keep in mind that in real life, paradoxes abound. It is not obvious at all to peg down a market equilibrium exactly as shown in textbooks. Most real-life markets are imperfect markets.

Now, if you look at this demand function, you can find it a bit distant from how you consume electricity. I mean, personally I don’t purposefully maximize the quantity of kilowatt-hours consumed. I just buy stuff powered by electricity, like a computer or a refrigerator, I plug it in, I turn it on, and I use it. Sometimes, I vaguely practice energy saving, like turning off the light in rooms where I am not currently staying. Anyway, my consumption of electricity Q(ki) is determined by the technology T I have at my disposal, which, in turn, consists of a set M = {g1, g2, …, gm} of goods powered by electricity: fridge, computer, TV set etc. We say that each j-th good gj, in the set M, is a complementary good to electricity. I can more or less accurately assume that an average refrigerator consumes x1(fridge) kWh, whilst an average set of house lighting burns x2(lighting) kWh. We can slice subsets out of the set N of consumers: N1 people with fridges, N2 people with air conditioners etc. With Q(gj) standing for the consumption of electricity in a given item powered with it, I can write:

 Q(ki) = N1*Q(g1) + N2*Q(g2) + …+ Nm*Q(gm) = [Be(ki) – P1] / P2

It means that, besides being elastic on my budget and the prices of electricity, my individual demand for a given amount of kilowatt-hours is elastic on the range of electricity-powered items I possess, and this, in turn, means that it is elastic on the budget I spend on those pieces of equipment, as well as on the prices of those goods (with a given budget to spend on houseware, I am more likely to buy a cheaper fridge rather than a more expensive one).

Now, business planning and management. Imagine that you are an entrepreneur, and you want to build a solar farm, and sell electricity to the people living around it. Your market works as shown above. You know that whatever you want to do, your organisation will have to satisfy the needs of those N customers, with their individual budgets and their individual elasticities in expenditures. The size of your organization, and its structure, will be significantly determined by the necessity to maintain profitable relations with N customers. Two questions emerge: what such organizational structure (i.e. the one serving to build and maintain those customer relations) would look like, and how could it be connected to other functional structures in the business, like building the solar farm, maintaining it in good technical state, purchasing components for construction and maintenance, hiring and firing people etc. You certainly know one thing: you have a given value of the market = N*P1 + Q*P2 and you have to adapt your costs (e.g. the sum total of salaries paid to your people) to this value of the market. Thus, you know that:

Average salary in my business = [(N*P1 + Q*P2) – The profit I want – Other costs] / the number of employees

In other words, the size of my business, e.g. in terms of the number of people employed, as well as my profit and the wages I can pay, will be determined by the value of my market. Now, let’s go along a path at the frontier of economics and management. I want to know how much capital I should invest in my business. I posit a condition: that capital should return to me, in the form of profits from business, in 7 years. Thus, I know that:

My initial investment = 7* My annual profit = 7*(N*P1 + Q*P2 – Current costs) = N*Be(ki) current costs = N*E current costs = N*[P1 + P2*Q(ki)] current costs

This is how the size of my business, both in terms of capital invested, and in terms of the number of people employed, is determined by, or is elastic on, the prices I can practice with my customers, the sheer number of those customers, as well as on their individual budgets.