A lot of the anlaysis here deals with correlations between two variables; for example, between price per square meter and square meters above. In some of these cases we can make an appeal to why we might think that this correlation is causation – that is, we can say that it would make sense for a larger house to lead to a lower price per square meter because of the efficiency savings (and so on). In many cases though, we cannot (although that does not stop me trying) and In all cases we cannot prove anything, only suggest.

The significant factor, I think, in all these types of discussion is the housing stock. I love the village in which I live. I also like the idea of a smaller home (big homes collect clutter), in an energy efficient home and in one close to some green spaces. The other day I was walking with my son and I thought we could play a game – how many houses fulfil just two of my criteria: close to green space and small-ish. At best, I think there are ten properties in the village, ten! Say we are lucky and one of these becomes available and say we are lucky and we are able to put our house on the market in order to purchase this…I am not sure that any of them will have terribly good energy efficiency.

Importantly, although energy efficiency is an important part in my decision making process, there are many important parts and the market cannot provide me with the perfect house. When we are constrained by the housing stock, the supply of housing, then very much correlations might not be causation. All houses are unique.

The housing market is not like the market for, say, new cars. Here one can add on all different modifications that appeal to them. Car dealerships must know how much people value picking the colour of their car, having those mirrors that swing in when the engine is switched off, cruise control…in houses we do not get this choice, and thus one cannot say with any confidence what forces are driving what.

The results here are still fun though!