Housing is considered affordable if it costs less than 30% of a household budget. Transportation is the second largest expense for families, but few consider these costs when choosing a place to live. Center for Neighborhood Technology
We’ve run a few posts lately discussing the topic of housing affordability and transportation costs. Of course you can’t separate the concept of housing affordability from transportation costs, since most people have to travel to work to pay for their housing.
The posts were based on recently published research from the University of Otago, by Kerry Mattingly and John Morrissey titled, “Housing and transport expenditure: Socio-spatial indicators of affordability in Auckland”. Their work included a series of maps that showed a significant transportation premium associated with living in the distant suburbs. While the maps they published did not assume everyone would be working in the cbd, the results of the research showed that households furthest away from the centre generally travel greater distances for work and therefore spend more of their income on housing AND transportation.
There was a robust discussion in the comments about how the data was not so useful since everyone’s situation is so different. This is where Alex Raichev and Saeid Adli come in. Using the methodology of Mattingly and Morrissey as a starting point, they developed a dynamic website called Affordable New Zealand that shows housing (rent) costs + transportation costs and allows users to identify housing and travel costs based on specific location of work.
They have included a number of factors which contribute to the overall cost of living such as parking and car ownership. The car ownership cost reflects the sunk cost of the car, even if the mode of travel chosen is public transport (or walking, cycling). Further travel costs for the car are determined by distance traveled to work. The public transport costs are estimated using the formula from Mattingly and Morrissey.
Here is a look at the results using Albany as the job location.
Another way to interrogate the data is to consider the pin drop as a housing choice and then the data reveals the relative affordability of accessing employment areas. Here is a comparative fictitious example of a young family with one parent working trying to decide between living in Titirangi or Freemans Bay. The Titirangi family has 2 cars and use 1 car to go to work paying $15 for parking. The Freemans Bay family has 1 car, but uses PT to get to work.
The site is still under development. One idea is to expand the search criteria to suit a couple/household scenario with two unique job locations. Another improvement will be refining the data to provide a more accurate cost of public transport. Wellington and Canterbury versions are coming soon. The project is open source (Github), and if you have any comments about the project leave them below or contact Alex from the notes page.