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I would like to create a model which predicts the amount of energy used in an area, dependent on the number of properties in 5 categories (detached, semi, flats, bungalow and terrace). I have daily data, giving the total daily energy consumption, with the number of properties in each of the 5 categories.

My question, would it be better to build a multiple linear regression model using total energy consumption as the dependent variable, with property type as the explanatory variables (a coefficient for each of the 5 groups). Or, would it be better to create 5 simple regression models, using energy as the dependent, and each property type as different explanatory variables.

To be clear, I have some daily data which only contains readings from areas with one property type (daily energy readings for an area with only detached properties, for example), and some data which is from areas with a combination of properties (for example, daily energy readings for an area with semi-detached and flats).

What would be the difference between the methods, and are there any benefits/caveats to doing either way?

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