How to calculate purchasing power on postcode level?

Question

Does anyone know of an algorithm to calculate purchasing power on a postcode level? What source data is needed? Purchasing power on admin. level, population?

Answer 1

You need census data, the spatial files for postal code geography (census tacts, dissemination areas ..... some form of spatial parameter) and you should read up on the Cluster Analysis Tool in ArcGIS. The database containing the the census data includes household income, family size, etc, etc (most of the data you will need). The cluster analysis will factor in all relevant variables in the analysis (which you will need to determine!!). I have only done this with a statistical package called SPSS for Windows; never "just" in ESRI. That said, I don't know how accurate this technique would be in the ESRI environment. SPSS is a VERY powerful tool. Statisical analysis is an iterative process; be mindful of this. You need to carefully choose your variables, run the analysis, determine which variables should be removed, and re-run the technique. You are looking for an R-squared value of at least 80% to achieve any sort of accuracy in the analysis. You will need to perform a table join following the analysis to add the output to the attribute table in your map (if you go the SPSS route). That said, you will need to maintain a commin identifier in the output table (ie - census tract ID#).

As far as I know, census data are free in the USA, but cost money in Canada if it's being used "for profit" (unless it's for a school project - then your Geography Department will have access). You will need a solid understanding of Census Geography, and the statistical techniques as well. You might want to read up on Principle Components Analysis as well. This will produce "components" as opposed to "variables", which can augment you analysis a great deal. If you've never done anything like this (stats/quantitative methods) you may find it difficult to grasp without the benefit of formal schooling in this area. I don't know what your background is, and I don't know what kind of a budget you have. What I can tell you, is that purchasing an older version of the software online will save you a lot of money, and a version that's a few years old will do everything you need. Do some reading, and if you have any questions, or need clarity, feel free to ask. Statistic/quantitative methods was one of my strengths in University (I love that kind of math!!). I also did a number of courses in demographics. You're working with some really fun and interesting data!!

Hope this helps -- cheers

Answer 2

I am sorry I dont know of any specific algorithm, but I could find some links which might be helpful.

Purchasing Power in Europe

A New Method for Classifying Customer Purchasing Power

Doubly Truncated ARMA-GARCH Model and MCMC Algorithms

Hope it helps...
Cheers!

Answer 3

You'd need something like an experian data cd, which are insanely expensive, about demographics, which you'd then have to query into the data you required.

As I said, it is very expensive data. I used to design drive time polygons which would be used to query this data, in order shops could be better located according to who they were after tapping into.

I'd call someone like Pitney Bowes MapInfo who would be able to supply the current data, or at least give you a supplier name.

Answer 4

Depending on which country you are in there may be census microdata available (e.g. PUMS in US, SARs in UK, Canada has some too I think). You may need to find some finance data too and do a statistical merge to get the variables you need. Then you can look at microsimulation methods to estimate the population at post code level (see http://jhi.sagepub.com/content/12/1/65.short for example).

Answer 5

To construct a purchasing power variable, call it PPP_zip, for USA zipcode areas, the key variables could be as follows:

• CPI_Metro, Consumer Price Index by Metro area, source BLS
• Income_USA, Median income USA, source Census
• Income_zip, Median income by zipcode, source Census
• Home_Zip, Zipcode home property values
• Home_Metro, Metro area home property values

FORMULA:

PPP_zip = Income_zip / Income_USA * 1/ ( CPI_Metro * Home_Zip / Home_Metro )

Since CPI is probably not reported at the zipcode level, I think finding a good zipcode local cost adjustment for CPI_Metro is the challenge. I am using Home_Zip/Home_Metro above, but there might be better data available to represent the living cost differences between zipcodes of a metro area.