Mike SimpsonMike Simpson

Mike Simpson has completed a Bachelor of Commerce (Honours) in Economics and a Bachelor of Science in Statistics and Applied Mathematics at the University of Auckland.

Prior to working for Covec, Mike completed an internship at New Zealand Treasury, and has also been employed by the University of Auckland in several research positions. Mike has recently worked on a number of modelling projects, including fuel consumption modelling and an econometric study into house prices. He is a highly talented researcher with excellent empirical skills.

Contact Details

Phone: 
+64 9 916 2014
Email: 
mike [dot] simpson [at] covec [dot] co [dot] nz
Qualifications & Experience: 
BCom Hons (economics), University of Auckland
BSc, University of Auckland

Expertise

local government
aviation & surface transport
economic modelling & analysis
forecasting & projections

Recent Work

Economic impact of non-casino gaming machines (aka pokies)

Each territorial authority is required to adopt a policy on non-casino gambling machines (pokies), which must be reviewed every three years. We recently assisted a local authority with its first review, by estimating the economic impacts of this activity (relative to those that may have occurred if funds were not gambled). This report is now being used to refine Council’s policy.

Forecasts of fuel consumption in New Zealand

UBS New Zealand

Covec produced forecasts of fuel consumption in New Zealand as part of the Shell New Zealand sale process.  Annual forecasts to 2030 were generated for the retail, commercial, aviation and marine sectors, and for various sub-markets within each of these sectors.

Hedonic pricing analysis of property attributes

Hedonic pricing analysis of property attributes. This project used local sales data for 38,000 residential dwellings to examine the impact of various factors on sales prices. These include structural factors (the size of each house and section), neighbourhood factors (local incomes and ethnicity) and locational (access to the CBD, major parks etc). It was used to identify premium suburbs, where sales prices were significantly higher (taking account of all these factors).