Where should Britain build 1.5 million homes?
@AmritaKulka .bsky.social l and I analysed 20 billion housing searches + availability to map demand at a hyper-local level.
Map 👉https://t.co/59rGKjgji9
Full + 350 LA reports 👉https://t.co/qGIL5oJ7GN
@cage_warwick
8. The main takeaway - petrol retailers can make "excess profits". But it doesn't happen during oil price spikes when everyone is looking. Full piece here: https://t.co/R8KRsolRvT
There has been a lot of discussion around petrol "price gouging" over the last few days. New
@cage_warwick policy note from myself and Johannes Brinkmann on this claim with some analysis from around the Russian Invasion of Ukrain. https://t.co/rpKiJDUQCJ
The main points:
7. There is substantial heterogeneity in this asymmetry. Some stations exhibit close to no asymmetry, while others have up to 5 times the size of the average.
@AmritaKulka and I are looking to hire a full time predoc to work with us on a project on land use regulation and market power in the housing market. If you're interested in (very) big data and applied econ please do apply! #econra#econtwitter@econ_ra
https://t.co/PjNeEr5w92
📰“Closing the housing gap will require building in the right places, not just building more.” @Nik_Datta and Amrita Kulka discuss their research into tackling the UK’s housing crisis for the Local Government Chronicle.
Any interested stakeholders (policymakers, planners, developers etc) feel free to get in touch. We have also produced 350 LA specific reports accessible here: https://t.co/qGIL5oJ7GN
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Where should Britain build 1.5 million homes?
@AmritaKulka .bsky.social l and I analysed 20 billion housing searches + availability to map demand at a hyper-local level.
Map 👉https://t.co/59rGKjgji9
Full + 350 LA reports 👉https://t.co/qGIL5oJ7GN
@cage_warwick
Adding data on planning restrictiveness, we find that Bexley, Lewisham, and Wandsworth combine high housing gaps with slow, restrictive planning systems. These areas urgently need reform. Conversely, Manchester, Leeds, and Birmingham show high demand and efficient planning. 12/n